diff --git a/app.py b/app.py index 6bedf5c52776f1696744a3686b2d5277e54f11d5..0da0319a5b670dce5025888fde58916b96f19869 100644 --- a/app.py +++ b/app.py @@ -39,6 +39,7 @@ def respond( response += token yield response + """ For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface """ @@ -60,4 +61,4 @@ demo = gr.ChatInterface( if __name__ == "__main__": - demo.launch() \ No newline at end of file + demo.launch() diff --git a/llm_toolkit/translation_utils.py b/llm_toolkit/translation_utils.py index 4b85598e4915b6334d19a0a09287212db14fdb13..f1f82d1bd3c0f669bb82749aed1ff8c76b20b889 100644 --- a/llm_toolkit/translation_utils.py +++ b/llm_toolkit/translation_utils.py @@ -230,9 +230,9 @@ def get_metrics(df, max_output_tokens=2048): metrics_df["ews_score"] = ews_score metrics_df["repetition_score"] = repetition_score metrics_df["total_repetitions"] = total_repetitions - metrics_df["num_entries_with_max_output_tokens"] = ( - num_entries_with_max_output_tokens - ) + metrics_df[ + "num_entries_with_max_output_tokens" + ] = num_entries_with_max_output_tokens return metrics_df diff --git a/notebooks/00_Data Analysis.ipynb b/notebooks/00_Data Analysis.ipynb index 64d5705955db6f7e18768c17677d7a783d56cb08..ac56a07f5d409282a0245c793ff9c82091382cc6 100644 --- a/notebooks/00_Data Analysis.ipynb +++ b/notebooks/00_Data Analysis.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":210,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":211,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n"," workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":212,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":212,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":213,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results.csv\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":214,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 9.65 ms, sys: 19.5 ms, total: 29.1 ms\n","Wall time: 1.87 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":215,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":216,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 55 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 16 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 18 Qwen/Qwen2-72B-Instruct/rpp-1.02 1133 non-null object\n"," 19 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 21 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 22 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 23 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 25 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 26 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 27 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 28 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 29 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 30 Qwen/Qwen2-72B-Instruct/rpp-1.04 1133 non-null object\n"," 31 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 32 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 33 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 34 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 35 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 36 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 38 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 39 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 41 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 43 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 45 Qwen/Qwen2-72B-Instruct/rpp-1.06 1133 non-null object\n"," 46 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 49 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 51 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 52 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 53 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 54 Qwen/Qwen2-72B-Instruct/rpp-1.08 1133 non-null object\n","dtypes: object(55)\n","memory usage: 487.0+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":217,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":217,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":218,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":219,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.3931693232556192, 'bleu_scores': {'bleu': 0.12273151341458781, 'precisions': [0.4199273774494459, 0.16226917210268393, 0.07941374663072777, 0.04192938209331652], 'brevity_penalty': 1.0, 'length_ratio': 1.0581649552832064, 'translation_length': 31946, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4422424373632814, 'rouge2': 0.19255208879947344, 'rougeL': 0.38436072285817197, 'rougeLsum': 0.384629860342585}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.02: {'meteor': 0.3925672197170406, 'bleu_scores': {'bleu': 0.12421056155279153, 'precisions': [0.4254972181364712, 0.16363093460734549, 0.08028819635962493, 0.042581432056249105], 'brevity_penalty': 1.0, 'length_ratio': 1.0359059291156012, 'translation_length': 31274, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4427056080159416, 'rouge2': 0.19219660001604671, 'rougeL': 0.38353574009053226, 'rougeLsum': 0.38400128515398857}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.04: {'meteor': 0.39235866930301305, 'bleu_scores': {'bleu': 0.12402693297052149, 'precisions': [0.4284005689164727, 0.16380901251551858, 0.07997907220090687, 0.04215992446800784], 'brevity_penalty': 1.0, 'length_ratio': 1.0247101689301092, 'translation_length': 30936, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4426208198446371, 'rouge2': 0.19199681779764224, 'rougeL': 0.3839514694136028, 'rougeLsum': 0.3841982412661236}, 'accuracy': 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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.3636360
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.3459840
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.3565750
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.3565750
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.3459840
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.2639010
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.2639010
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.2550750
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.2497790
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.2418360
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.2506620
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.2506620
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.2506620
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.2850840
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.2753750
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.2859660
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.2056490
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.1791700
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.2135920
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.2197710
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.2127101
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.3706970
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.3398060
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.3601060
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.3309800
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.3556930
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.3186230
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.3389230
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.3601060
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.3759930
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.3830540
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.3830540
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.4033540
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.4880850
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.3495150
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.2974400
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.2806710
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.1721090
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.1862310
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.8464251
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.8367171
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.8420121
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.3009710
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.4466020
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.2771400
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.2824360
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.1562220
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.1562220
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.1535750
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.1006180
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.2356580
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.0847310
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.1253310
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"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 0 \n","17 0 \n","18 0 \n","19 0 \n","20 1 \n","21 0 \n","22 0 \n","23 0 \n","24 0 \n","25 0 \n","26 0 \n","27 0 \n","28 0 \n","29 0 \n","30 0 \n","31 0 \n","32 0 \n","33 0 \n","34 0 \n","35 0 \n","36 0 \n","37 0 \n","38 0 \n","39 1 \n","40 1 \n","41 1 \n","42 0 \n","43 0 \n","44 0 \n","45 0 \n","46 0 \n","47 0 \n","48 0 \n","49 0 \n","50 0 \n","51 0 \n","52 0 "]},"execution_count":219,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":220,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(lambda x: x[\"meteor\"] / math.log10( 10 + x[\"total_repetitions\"]), axis=1)"]},{"cell_type":"code","execution_count":221,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.36363600.387164
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.34598400.386853
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.35657500.386478
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.35657500.385133
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.34598400.386278
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.12533100.312547
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.387164 \n","1 0 0.386853 \n","2 0 0.386478 \n","3 0 0.385133 \n","4 0 0.386278 \n","5 0 0.373093 \n","6 0 0.373454 \n","7 0 0.373729 \n","8 0 0.373352 \n","9 0 0.370858 \n","10 0 0.368729 \n","11 0 0.367036 \n","12 0 0.364101 \n","13 0 0.362961 \n","14 0 0.359723 \n","15 0 0.355384 \n","16 0 0.354276 \n","17 0 0.350735 \n","18 0 0.344795 \n","19 0 0.340595 \n","20 1 0.337439 \n","21 0 0.352132 \n","22 0 0.352985 \n","23 0 0.351197 \n","24 0 0.351146 \n","25 0 0.349324 \n","26 0 0.348124 \n","27 0 0.346480 \n","28 0 0.346720 \n","29 0 0.344995 \n","30 0 0.343295 \n","31 0 0.342157 \n","32 0 0.340244 \n","33 0 0.337120 \n","34 0 0.337607 \n","35 0 0.336064 \n","36 0 0.335319 \n","37 0 0.323207 \n","38 0 0.322975 \n","39 1 0.271615 \n","40 1 0.271447 \n","41 1 0.271328 \n","42 0 0.321824 \n","43 0 0.318475 \n","44 0 0.318923 \n","45 0 0.318833 \n","46 0 0.319488 \n","47 0 0.318472 \n","48 0 0.316997 \n","49 0 0.317564 \n","50 0 0.314726 \n","51 0 0.314323 \n","52 0 0.312547 "]},"execution_count":221,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":223,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":223,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":224,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n"," )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":234,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":225,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":226,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":227,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-72B-Instruct/rpp-1.02Qwen/Qwen2-72B-Instruct/rpp-1.04Qwen/Qwen2-72B-Instruct/rpp-1.06Qwen/Qwen2-72B-Instruct/rpp-1.08Qwen/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04...shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30ews_scorerepetition_scoretotal_repetitionsoutput_tokens
193“有…… 没有…… 有…… 没有……'Yes . . . no . . . yes . . . no . . .\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 59 columns

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"],"text/plain":[" chinese english \\\n","193 “有…… 没有…… 有…… 没有…… 'Yes . . . no . . . yes . . . no . . . \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.02 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.04 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.06 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.08 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","193 \"Yes... No... Yes... No...\" \"Yes... No... Yes... No...\" \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 ... \\\n","193 \"Yes... No... Yes... No...\" ... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score \\\n","193 Yes... No... Yes... No... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","193 6496 6496 2049 \n","\n","[1 rows x 59 columns]"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":228,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":230,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":231,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 159-3407: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":231,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":232,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ews_scorerepetition_scoretotal_repetitionsoutput_tokens
count1133.01133.0000001133.0000001133.000000
mean0.05.8464255.84642533.958517
std0.0192.990061192.99006163.822891
min0.00.0000000.0000003.000000
25%0.00.0000000.00000017.000000
50%0.00.0000000.00000027.000000
75%0.00.0000000.00000042.000000
max0.06496.0000006496.0000002049.000000
\n","
"],"text/plain":[" ews_score repetition_score total_repetitions output_tokens\n","count 1133.0 1133.000000 1133.000000 1133.000000\n","mean 0.0 5.846425 5.846425 33.958517\n","std 0.0 192.990061 192.990061 63.822891\n","min 0.0 0.000000 0.000000 3.000000\n","25% 0.0 0.000000 0.000000 17.000000\n","50% 0.0 0.000000 0.000000 27.000000\n","75% 0.0 0.000000 0.000000 42.000000\n","max 0.0 6496.000000 6496.000000 2049.000000"]},"execution_count":232,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"code","execution_count":210,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":211,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n"," workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":212,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":212,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":213,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results.csv\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":214,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 9.65 ms, sys: 19.5 ms, total: 29.1 ms\n","Wall time: 1.87 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":215,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":216,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 55 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 16 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 18 Qwen/Qwen2-72B-Instruct/rpp-1.02 1133 non-null object\n"," 19 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 21 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 22 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 23 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 25 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 26 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 27 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 28 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 29 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06 1133 non-null object\n"," 30 Qwen/Qwen2-72B-Instruct/rpp-1.04 1133 non-null object\n"," 31 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 32 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08 1133 non-null object\n"," 33 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 34 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10 1133 non-null object\n"," 35 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 36 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12 1133 non-null object\n"," 37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 38 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14 1133 non-null object\n"," 39 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16 1133 non-null object\n"," 40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 41 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18 1133 non-null object\n"," 42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 43 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20 1133 non-null object\n"," 44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 45 Qwen/Qwen2-72B-Instruct/rpp-1.06 1133 non-null object\n"," 46 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22 1133 non-null object\n"," 47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 49 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24 1133 non-null object\n"," 50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 51 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26 1133 non-null object\n"," 52 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28 1133 non-null object\n"," 53 shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30 1133 non-null object\n"," 54 Qwen/Qwen2-72B-Instruct/rpp-1.08 1133 non-null object\n","dtypes: object(55)\n","memory usage: 487.0+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":217,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat/rpp-1.30',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.06',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.08',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.10',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.12',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.14',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.16',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.18',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30']"]},"execution_count":217,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":218,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":219,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.3931693232556192, 'bleu_scores': {'bleu': 0.12273151341458781, 'precisions': [0.4199273774494459, 0.16226917210268393, 0.07941374663072777, 0.04192938209331652], 'brevity_penalty': 1.0, 'length_ratio': 1.0581649552832064, 'translation_length': 31946, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4422424373632814, 'rouge2': 0.19255208879947344, 'rougeL': 0.38436072285817197, 'rougeLsum': 0.384629860342585}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.02: {'meteor': 0.3925672197170406, 'bleu_scores': {'bleu': 0.12421056155279153, 'precisions': [0.4254972181364712, 0.16363093460734549, 0.08028819635962493, 0.042581432056249105], 'brevity_penalty': 1.0, 'length_ratio': 1.0359059291156012, 'translation_length': 31274, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4427056080159416, 'rouge2': 0.19219660001604671, 'rougeL': 0.38353574009053226, 'rougeLsum': 0.38400128515398857}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.04: {'meteor': 0.39235866930301305, 'bleu_scores': {'bleu': 0.12402693297052149, 'precisions': [0.4284005689164727, 0.16380901251551858, 0.07997907220090687, 0.04215992446800784], 'brevity_penalty': 1.0, 'length_ratio': 1.0247101689301092, 'translation_length': 30936, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4426208198446371, 'rouge2': 0.19199681779764224, 'rougeL': 0.3839514694136028, 'rougeLsum': 0.3841982412661236}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.06: {'meteor': 0.39099278006036825, 'bleu_scores': {'bleu': 0.1232450878300488, 'precisions': [0.4272606426093441, 0.16253786603837092, 0.07929176289453425, 0.04189893248806791], 'brevity_penalty': 1.0, 'length_ratio': 1.0216296787015569, 'translation_length': 30843, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44144425317154196, 'rouge2': 0.19133012055570608, 'rougeL': 0.38314456527389706, 'rougeLsum': 0.3834154006635245}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-72B-Instruct/rpp-1.08: {'meteor': 0.3919843215003691, 'bleu_scores': {'bleu': 0.12201600208223494, 'precisions': [0.4260587376277787, 0.16168047975203828, 0.07821366024518389, 0.04113935592107663], 'brevity_penalty': 1.0, 'length_ratio': 1.0207022192779065, 'translation_length': 30815, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44234072135783076, 'rouge2': 0.19220259288979116, 'rougeL': 0.3836061734813752, 'rougeLsum': 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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.3636360
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.3459840
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.3565750
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.3565750
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.3459840
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.2639010
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.2639010
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.2550750
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.2497790
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.2418360
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.2506620
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.2506620
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.2506620
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.2850840
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.2753750
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.2859660
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.2056490
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.1791700
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.2135920
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.2197710
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.2127101
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.3706970
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.3398060
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.3601060
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.3309800
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.3556930
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.3186230
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.3389230
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.3601060
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.3759930
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.3830540
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.3830540
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.4033540
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.4880850
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.3495150
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.2974400
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.2806710
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.1721090
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.1862310
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.8464251
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.8367171
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.8420121
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.3009710
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.4466020
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.2771400
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.2824360
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.1562220
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.1562220
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.1535750
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.1006180
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.2356580
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.0847310
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.1253310
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"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 0 \n","17 0 \n","18 0 \n","19 0 \n","20 1 \n","21 0 \n","22 0 \n","23 0 \n","24 0 \n","25 0 \n","26 0 \n","27 0 \n","28 0 \n","29 0 \n","30 0 \n","31 0 \n","32 0 \n","33 0 \n","34 0 \n","35 0 \n","36 0 \n","37 0 \n","38 0 \n","39 1 \n","40 1 \n","41 1 \n","42 0 \n","43 0 \n","44 0 \n","45 0 \n","46 0 \n","47 0 \n","48 0 \n","49 0 \n","50 0 \n","51 0 \n","52 0 "]},"execution_count":219,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":220,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n"," lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":221,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3931690.1227320.3843610.00.3636360.36363600.387164
1Qwen/Qwen2-72B-Instruct1.020.3925670.1242110.3835360.00.3459840.34598400.386853
2Qwen/Qwen2-72B-Instruct1.040.3923590.1240270.3839510.00.3565750.35657500.386478
3Qwen/Qwen2-72B-Instruct1.060.3909930.1232450.3831450.00.3565750.35657500.385133
4Qwen/Qwen2-72B-Instruct1.080.3919840.1220160.3836060.00.3459840.34598400.386278
5Qwen/Qwen2-7B-Instruct1.000.3773140.1174820.3687770.00.2639010.26390100.373093
6Qwen/Qwen2-7B-Instruct1.020.3776790.1164320.3693180.00.2639010.26390100.373454
7Qwen/Qwen2-7B-Instruct1.040.3778170.1154580.3689630.00.2550750.25507500.373729
8Qwen/Qwen2-7B-Instruct1.060.3773530.1150990.3671350.00.2497790.24977900.373352
9Qwen/Qwen2-7B-Instruct1.080.3747070.1116490.3641090.00.2418360.24183600.370858
10Qwen/Qwen2-7B-Instruct1.100.3726930.1092540.3601940.00.2506620.25066200.368729
11Qwen/Qwen2-7B-Instruct1.120.3709820.1064750.3585560.00.2506620.25066200.367036
12Qwen/Qwen2-7B-Instruct1.140.3680160.1043740.3564450.00.2506620.25066200.364101
13Qwen/Qwen2-7B-Instruct1.160.3673920.1020630.3542770.00.2850840.28508400.362961
14Qwen/Qwen2-7B-Instruct1.180.3639670.0987850.3500970.00.2753750.27537500.359723
15Qwen/Qwen2-7B-Instruct1.200.3597350.0951480.3464210.00.2859660.28596600.355384
16Qwen/Qwen2-7B-Instruct1.220.3574080.0919950.3451150.00.2056490.20564900.354276
17Qwen/Qwen2-7B-Instruct1.240.3534400.0864390.3394490.00.1791700.17917000.350735
18Qwen/Qwen2-7B-Instruct1.260.3479600.0814270.3347510.00.2135920.21359200.344795
19Qwen/Qwen2-7B-Instruct1.280.3438110.0734160.3301920.00.2197710.21977100.340595
20Qwen/Qwen2-7B-Instruct1.300.3405230.0723060.3268720.00.2127100.21271010.337439
21shenzhi-wang/Llama3.1-8B-Chinese-Chat1.000.3576980.1015240.3456620.00.3706970.37069700.352132
22shenzhi-wang/Llama3.1-8B-Chinese-Chat1.020.3581070.1010700.3454840.00.3398060.33980600.352985
23shenzhi-wang/Llama3.1-8B-Chinese-Chat1.040.3565930.1007710.3448640.00.3601060.36010600.351197
24shenzhi-wang/Llama3.1-8B-Chinese-Chat1.060.3561110.0993820.3432290.00.3309800.33098000.351146
25shenzhi-wang/Llama3.1-8B-Chinese-Chat1.080.3546270.0969250.3429150.00.3556930.35569300.349324
26shenzhi-wang/Llama3.1-8B-Chinese-Chat1.100.3528660.0967160.3412880.00.3186230.31862300.348124
27shenzhi-wang/Llama3.1-8B-Chinese-Chat1.120.3514960.0947580.3406650.00.3389230.33892300.346480
28shenzhi-wang/Llama3.1-8B-Chinese-Chat1.140.3520470.0946390.3400980.00.3601060.36010600.346720
29shenzhi-wang/Llama3.1-8B-Chinese-Chat1.160.3505260.0935260.3384690.00.3759930.37599300.344995
30shenzhi-wang/Llama3.1-8B-Chinese-Chat1.180.3489000.0928940.3376210.00.3830540.38305400.343295
31shenzhi-wang/Llama3.1-8B-Chinese-Chat1.200.3477430.0913330.3357230.00.3830540.38305400.342157
32shenzhi-wang/Llama3.1-8B-Chinese-Chat1.220.3460870.0902440.3346420.00.4033540.40335400.340244
33shenzhi-wang/Llama3.1-8B-Chinese-Chat1.240.3440970.0832330.3325460.00.4880850.48808500.337120
34shenzhi-wang/Llama3.1-8B-Chinese-Chat1.260.3426440.0851370.3306300.00.3495150.34951500.337607
35shenzhi-wang/Llama3.1-8B-Chinese-Chat1.280.3403420.0837870.3281820.00.2974400.29744000.336064
36shenzhi-wang/Llama3.1-8B-Chinese-Chat1.300.3393500.0819880.3272860.00.2806710.28067100.335319
37shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.000.3256020.0834010.3158220.00.1721090.17210900.323207
38shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.020.3255640.0839780.3158770.00.1862310.18623100.322975
39shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.040.3259200.0808490.3166050.05.8464255.84642510.271615
40shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.060.3256450.0811270.3150510.05.8367175.83671710.271447
41shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.080.3255420.0817670.3149460.05.8420125.84201210.271328
42shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.100.3259690.0855630.3152080.00.3009710.30097100.321824
43shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.120.3245180.0852720.3139130.00.4466020.44660200.318475
44shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.140.3227100.0845800.3135310.00.2771400.27714000.318923
45shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.160.3226900.0832600.3131360.00.2824360.28243600.318833
46shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.180.3216390.0817910.3111010.00.1562220.15622200.319488
47shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.200.3206160.0803960.3104100.00.1562220.15622200.318472
48shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.220.3190950.0793960.3080240.00.1535750.15357500.316997
49shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.240.3189440.0796320.3072170.00.1006180.10061800.317564
50shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.260.3179100.0772310.3069260.00.2356580.23565800.314726
51shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.280.3154750.0753160.3047500.00.0847310.08473100.314323
52shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat1.300.3142370.0745430.3034690.00.1253310.12533100.312547
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.393169 0.122732 \n","1 Qwen/Qwen2-72B-Instruct 1.02 0.392567 0.124211 \n","2 Qwen/Qwen2-72B-Instruct 1.04 0.392359 0.124027 \n","3 Qwen/Qwen2-72B-Instruct 1.06 0.390993 0.123245 \n","4 Qwen/Qwen2-72B-Instruct 1.08 0.391984 0.122016 \n","5 Qwen/Qwen2-7B-Instruct 1.00 0.377314 0.117482 \n","6 Qwen/Qwen2-7B-Instruct 1.02 0.377679 0.116432 \n","7 Qwen/Qwen2-7B-Instruct 1.04 0.377817 0.115458 \n","8 Qwen/Qwen2-7B-Instruct 1.06 0.377353 0.115099 \n","9 Qwen/Qwen2-7B-Instruct 1.08 0.374707 0.111649 \n","10 Qwen/Qwen2-7B-Instruct 1.10 0.372693 0.109254 \n","11 Qwen/Qwen2-7B-Instruct 1.12 0.370982 0.106475 \n","12 Qwen/Qwen2-7B-Instruct 1.14 0.368016 0.104374 \n","13 Qwen/Qwen2-7B-Instruct 1.16 0.367392 0.102063 \n","14 Qwen/Qwen2-7B-Instruct 1.18 0.363967 0.098785 \n","15 Qwen/Qwen2-7B-Instruct 1.20 0.359735 0.095148 \n","16 Qwen/Qwen2-7B-Instruct 1.22 0.357408 0.091995 \n","17 Qwen/Qwen2-7B-Instruct 1.24 0.353440 0.086439 \n","18 Qwen/Qwen2-7B-Instruct 1.26 0.347960 0.081427 \n","19 Qwen/Qwen2-7B-Instruct 1.28 0.343811 0.073416 \n","20 Qwen/Qwen2-7B-Instruct 1.30 0.340523 0.072306 \n","21 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.00 0.357698 0.101524 \n","22 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.02 0.358107 0.101070 \n","23 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.04 0.356593 0.100771 \n","24 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.06 0.356111 0.099382 \n","25 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.08 0.354627 0.096925 \n","26 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.10 0.352866 0.096716 \n","27 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.12 0.351496 0.094758 \n","28 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.14 0.352047 0.094639 \n","29 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.16 0.350526 0.093526 \n","30 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.18 0.348900 0.092894 \n","31 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.20 0.347743 0.091333 \n","32 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.22 0.346087 0.090244 \n","33 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.24 0.344097 0.083233 \n","34 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.26 0.342644 0.085137 \n","35 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.28 0.340342 0.083787 \n","36 shenzhi-wang/Llama3.1-8B-Chinese-Chat 1.30 0.339350 0.081988 \n","37 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.00 0.325602 0.083401 \n","38 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.02 0.325564 0.083978 \n","39 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.04 0.325920 0.080849 \n","40 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.06 0.325645 0.081127 \n","41 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.08 0.325542 0.081767 \n","42 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.10 0.325969 0.085563 \n","43 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.12 0.324518 0.085272 \n","44 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.14 0.322710 0.084580 \n","45 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.16 0.322690 0.083260 \n","46 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.18 0.321639 0.081791 \n","47 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.20 0.320616 0.080396 \n","48 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.22 0.319095 0.079396 \n","49 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.24 0.318944 0.079632 \n","50 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.26 0.317910 0.077231 \n","51 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.28 0.315475 0.075316 \n","52 shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat 1.30 0.314237 0.074543 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.384361 0.0 0.363636 0.363636 \n","1 0.383536 0.0 0.345984 0.345984 \n","2 0.383951 0.0 0.356575 0.356575 \n","3 0.383145 0.0 0.356575 0.356575 \n","4 0.383606 0.0 0.345984 0.345984 \n","5 0.368777 0.0 0.263901 0.263901 \n","6 0.369318 0.0 0.263901 0.263901 \n","7 0.368963 0.0 0.255075 0.255075 \n","8 0.367135 0.0 0.249779 0.249779 \n","9 0.364109 0.0 0.241836 0.241836 \n","10 0.360194 0.0 0.250662 0.250662 \n","11 0.358556 0.0 0.250662 0.250662 \n","12 0.356445 0.0 0.250662 0.250662 \n","13 0.354277 0.0 0.285084 0.285084 \n","14 0.350097 0.0 0.275375 0.275375 \n","15 0.346421 0.0 0.285966 0.285966 \n","16 0.345115 0.0 0.205649 0.205649 \n","17 0.339449 0.0 0.179170 0.179170 \n","18 0.334751 0.0 0.213592 0.213592 \n","19 0.330192 0.0 0.219771 0.219771 \n","20 0.326872 0.0 0.212710 0.212710 \n","21 0.345662 0.0 0.370697 0.370697 \n","22 0.345484 0.0 0.339806 0.339806 \n","23 0.344864 0.0 0.360106 0.360106 \n","24 0.343229 0.0 0.330980 0.330980 \n","25 0.342915 0.0 0.355693 0.355693 \n","26 0.341288 0.0 0.318623 0.318623 \n","27 0.340665 0.0 0.338923 0.338923 \n","28 0.340098 0.0 0.360106 0.360106 \n","29 0.338469 0.0 0.375993 0.375993 \n","30 0.337621 0.0 0.383054 0.383054 \n","31 0.335723 0.0 0.383054 0.383054 \n","32 0.334642 0.0 0.403354 0.403354 \n","33 0.332546 0.0 0.488085 0.488085 \n","34 0.330630 0.0 0.349515 0.349515 \n","35 0.328182 0.0 0.297440 0.297440 \n","36 0.327286 0.0 0.280671 0.280671 \n","37 0.315822 0.0 0.172109 0.172109 \n","38 0.315877 0.0 0.186231 0.186231 \n","39 0.316605 0.0 5.846425 5.846425 \n","40 0.315051 0.0 5.836717 5.836717 \n","41 0.314946 0.0 5.842012 5.842012 \n","42 0.315208 0.0 0.300971 0.300971 \n","43 0.313913 0.0 0.446602 0.446602 \n","44 0.313531 0.0 0.277140 0.277140 \n","45 0.313136 0.0 0.282436 0.282436 \n","46 0.311101 0.0 0.156222 0.156222 \n","47 0.310410 0.0 0.156222 0.156222 \n","48 0.308024 0.0 0.153575 0.153575 \n","49 0.307217 0.0 0.100618 0.100618 \n","50 0.306926 0.0 0.235658 0.235658 \n","51 0.304750 0.0 0.084731 0.084731 \n","52 0.303469 0.0 0.125331 0.125331 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.387164 \n","1 0 0.386853 \n","2 0 0.386478 \n","3 0 0.385133 \n","4 0 0.386278 \n","5 0 0.373093 \n","6 0 0.373454 \n","7 0 0.373729 \n","8 0 0.373352 \n","9 0 0.370858 \n","10 0 0.368729 \n","11 0 0.367036 \n","12 0 0.364101 \n","13 0 0.362961 \n","14 0 0.359723 \n","15 0 0.355384 \n","16 0 0.354276 \n","17 0 0.350735 \n","18 0 0.344795 \n","19 0 0.340595 \n","20 1 0.337439 \n","21 0 0.352132 \n","22 0 0.352985 \n","23 0 0.351197 \n","24 0 0.351146 \n","25 0 0.349324 \n","26 0 0.348124 \n","27 0 0.346480 \n","28 0 0.346720 \n","29 0 0.344995 \n","30 0 0.343295 \n","31 0 0.342157 \n","32 0 0.340244 \n","33 0 0.337120 \n","34 0 0.337607 \n","35 0 0.336064 \n","36 0 0.335319 \n","37 0 0.323207 \n","38 0 0.322975 \n","39 1 0.271615 \n","40 1 0.271447 \n","41 1 0.271328 \n","42 0 0.321824 \n","43 0 0.318475 \n","44 0 0.318923 \n","45 0 0.318833 \n","46 0 0.319488 \n","47 0 0.318472 \n","48 0 0.316997 \n","49 0 0.317564 \n","50 0 0.314726 \n","51 0 0.314323 \n","52 0 0.312547 "]},"execution_count":221,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":222,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":223,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'shenzhi-wang/Llama3.1-8B-Chinese-Chat',\n"," 'shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat'], dtype=object)"]},"execution_count":223,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":224,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n"," )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":234,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":225,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":226,"metadata":{},"outputs":[],"source":["col = \"shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.04\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":227,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-72B-Instruct/rpp-1.02Qwen/Qwen2-72B-Instruct/rpp-1.04Qwen/Qwen2-72B-Instruct/rpp-1.06Qwen/Qwen2-72B-Instruct/rpp-1.08Qwen/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04...shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30ews_scorerepetition_scoretotal_repetitionsoutput_tokens
193“有…… 没有…… 有…… 没有……'Yes . . . no . . . yes . . . no . . .\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"There is... There isn't... There is... There ...\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"\"Yes... No... Yes... No...\"...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...Yes... No... Yes... No...0649664962049
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1 rows × 59 columns

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"],"text/plain":[" chinese english \\\n","193 “有…… 没有…… 有…… 没有…… 'Yes . . . no . . . yes . . . no . . . \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.02 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.04 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.06 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.08 \\\n","193 \"There is... There isn't... There is... There ... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","193 \"Yes... No... Yes... No...\" \"Yes... No... Yes... No...\" \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 ... \\\n","193 \"Yes... No... Yes... No...\" ... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.20 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.22 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.24 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.26 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.28 \\\n","193 Yes... No... Yes... No... \n","\n"," shenzhi-wang/Mistral-7B-v0.3-Chinese-Chat/rpp-1.30 ews_score \\\n","193 Yes... No... Yes... No... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","193 6496 6496 2049 \n","\n","[1 rows x 59 columns]"]},"execution_count":227,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":228,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":229,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["“有…… 没有…… 有…… 没有……\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":230,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["'Yes . . . no . . . yes . . . no . . .\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":231,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Yes, I can help you with that! Here's the translation:\n","\n","\"Yes, I can help you with that! Here's the translation:\n","\n","有 - Yes\n","没有 - No\n","\n","So, the translated content is:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 159-3407: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 2 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","Group 3 found at 3407-6655: `:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation:\n","\n","Yes, I can help you with that! Here's the translation`\n","(0, 6496, 6496)\n"]},{"data":{"text/plain":["(0, 6496, 6496)"]},"execution_count":231,"metadata":{},"output_type":"execute_result"}],"source":["output = row[col]\n","print(output)\n","detect_repetitions(output, debug=True)"]},{"cell_type":"code","execution_count":232,"metadata":{},"outputs":[{"data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
ews_scorerepetition_scoretotal_repetitionsoutput_tokens
count1133.01133.0000001133.0000001133.000000
mean0.05.8464255.84642533.958517
std0.0192.990061192.99006163.822891
min0.00.0000000.0000003.000000
25%0.00.0000000.00000017.000000
50%0.00.0000000.00000027.000000
75%0.00.0000000.00000042.000000
max0.06496.0000006496.0000002049.000000
\n","
"],"text/plain":[" ews_score repetition_score total_repetitions output_tokens\n","count 1133.0 1133.000000 1133.000000 1133.000000\n","mean 0.0 5.846425 5.846425 33.958517\n","std 0.0 192.990061 192.990061 63.822891\n","min 0.0 0.000000 0.000000 3.000000\n","25% 0.0 0.000000 0.000000 17.000000\n","50% 0.0 0.000000 0.000000 27.000000\n","75% 0.0 0.000000 0.000000 42.000000\n","max 0.0 6496.000000 6496.000000 2049.000000"]},"execution_count":232,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"mostRecentlyExecutedCommandWithImplicitDF":{"commandId":-1,"dataframes":["_sqldf"]},"pythonIndentUnit":4},"notebookName":"10_eval-lf-medium-py3.11","widgets":{}},"colab":{"gpuType":"L4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0} diff --git a/notebooks/00a_Data Analysis_greedy_decoding.ipynb b/notebooks/00a_Data Analysis_greedy_decoding.ipynb index 3ddc4ab070f7fb158494693cbb2945d692d196bd..20bc6185d08c96992562cb37a9b0c07044150f22 100644 --- a/notebooks/00a_Data Analysis_greedy_decoding.ipynb +++ b/notebooks/00a_Data Analysis_greedy_decoding.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":476,"status":"ok","timestamp":1720679526275,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"uWKRSV6eZsCn"},"outputs":[{"name":"stdout","output_type":"stream","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n"]}],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":25,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"6d394937-6c99-4a7c-9d32-7600a280032f","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"G5pNu3zgZBrL","outputId":"160a554f-fb08-4aa0-bc00-0422fb7c1fac"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/code/engd/papers/rapget-translation\n"]}],"source":["import os\n","import sys\n","from pathlib import Path\n","\n","# check if workding_dir is in local variables\n","if \"workding_dir\" not in locals():\n"," workding_dir = str(Path.cwd().parent)\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":26,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"hPCC-6m7ZBrM","outputId":"c7aa2c96-5e99-440a-c148-201d79465ff9"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/code/engd/papers/rapget-translation/.env\n"]},{"data":{"text/plain":["True"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n"," found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":27,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"f1597656-8042-4878-9d3b-9ebfb8dd86dc","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720679529345,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"1M3IraVtZBrM","outputId":"29ab35f6-2970-4ade-d85d-3174acf8cda0"},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-7B-Instruct None False datasets/mac/mac.tsv results/mac-results_greedy_decoding.csv\n"]}],"source":["import os\n","\n","model_name = os.getenv(\"MODEL_NAME\")\n","adapter_name_or_path = os.getenv(\"ADAPTER_NAME_OR_PATH\")\n","load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n","data_path = os.getenv(\"DATA_PATH\")\n","results_path = \"results/mac-results_greedy_decoding.csv\" # os.getenv(\"RESULTS_PATH\")\n","use_english_datasets = os.getenv(\"USE_ENGLISH_DATASETS\") == \"true\"\n","\n","print(model_name, adapter_name_or_path, load_in_4bit, data_path, results_path)"]},{"cell_type":"code","execution_count":28,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{"byteLimit":2048000,"rowLimit":10000},"inputWidgets":{},"nuid":"b2a43943-9324-4839-9a47-cfa72de2244b","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":564,"status":"ok","timestamp":1720679529907,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"UgMvt6dIZBrM","outputId":"ce37581c-fd26-46c2-ad87-d933d99f68f7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Python 3.11.9\n","Name: torch\n","Version: 2.4.0\n","Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n","Home-page: https://pytorch.org/\n","Author: PyTorch Team\n","Author-email: packages@pytorch.org\n","License: BSD-3\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions\n","Required-by: accelerate, peft, torchaudio, torchvision\n","---\n","Name: transformers\n","Version: 4.43.3\n","Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n","Home-page: https://github.com/huggingface/transformers\n","Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n","Author-email: transformers@huggingface.co\n","License: Apache 2.0 License\n","Location: /Users/inflaton/anaconda3/envs/rapget/lib/python3.11/site-packages\n","Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n","Required-by: peft\n","CPU times: user 8.7 ms, sys: 12.4 ms, total: 21.1 ms\n","Wall time: 1.9 s\n"]}],"source":["%%time\n","os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n","\n","!python --version\n","!pip show torch transformers"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1685,"status":"ok","timestamp":1720679531591,"user":{"displayName":"HUANG DONGHAO _","userId":"00977795705617022768"},"user_tz":-480},"id":"ZuS_FsLyZBrN","outputId":"2cba0105-c505-4395-afbd-2f2fee6581d0"},"outputs":[{"name":"stdout","output_type":"stream","text":["MPS is available\n"]}],"source":["from llm_toolkit.llm_utils import *\n","from llm_toolkit.translation_utils import *\n","\n","device = check_gpu()"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 25 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 16 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 18 internlm/internlm2_5-7b-chat-1m/rpp-1.00 1133 non-null object\n"," 19 internlm/internlm2_5-7b-chat-1m/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 21 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 22 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 23 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06 1133 non-null object\n","dtypes: object(25)\n","memory usage: 221.4+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat-1m/rpp-1.00',\n"," 'internlm/internlm2_5-7b-chat-1m/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06']"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.39496912014495184, 'bleu_scores': {'bleu': 0.12294894050451377, 'precisions': [0.42391407360606537, 0.1626695498329074, 0.079349416448331, 0.041761041902604754], 'brevity_penalty': 1.0, 'length_ratio': 1.048526001987413, 'translation_length': 31655, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44242617045618315, 'rouge2': 0.19166824249542752, 'rougeL': 0.3835643396648639, 'rougeLsum': 0.3844919778233326}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-7B-Instruct/rpp-1.00: {'meteor': 0.3757937058055942, 'bleu_scores': {'bleu': 0.11257687997946404, 'precisions': [0.4221057489451477, 0.15152552819915763, 0.07046669041681511, 0.03563738956121464], 'brevity_penalty': 1.0, 'length_ratio': 1.004836038423319, 'translation_length': 30336, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4241235957669323, 'rouge2': 0.17433830983598061, 'rougeL': 0.3642501836106533, 'rougeLsum': 0.364584190239183}, 'accuracy': 0.00088261253309797, 'correct_ids': [364]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.02: {'meteor': 0.3768162203335968, 'bleu_scores': {'bleu': 0.11553860771639841, 'precisions': [0.421923611570795, 0.15446511467968776, 0.07288535852297123, 0.03751491646778043], 'brevity_penalty': 1.0, 'length_ratio': 1.0007949652202717, 'translation_length': 30214, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4273577053163239, 'rouge2': 0.1800744214940156, 'rougeL': 0.3695393969769755, 'rougeLsum': 0.36955057550298287}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.04: {'meteor': 0.3715147429622351, 'bleu_scores': {'bleu': 0.11311605625702598, 'precisions': [0.41758205508824014, 0.15180590775135358, 0.07144639737602053, 0.036148159155923766], 'brevity_penalty': 1.0, 'length_ratio': 1.0041404438555812, 'translation_length': 30315, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41870140882606066, 'rouge2': 0.17377238271646123, 'rougeL': 0.3637109748338643, 'rougeLsum': 0.3636218000079854}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 533]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.06: {'meteor': 0.3721614566005243, 'bleu_scores': {'bleu': 0.10986034422062402, 'precisions': [0.41770767752410615, 0.14848860428286167, 0.06846272346218608, 0.03435399551904406], 'brevity_penalty': 0.9996355745538857, 'length_ratio': 0.9996356409407089, 'translation_length': 30179, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41850128527699093, 'rouge2': 0.17078364572425722, 'rougeL': 0.36087822210596066, 'rougeLsum': 0.36118431102497384}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.08: {'meteor': 0.3712966405354824, 'bleu_scores': {'bleu': 0.10809530671609749, 'precisions': [0.41541684679591634, 0.14717672264842077, 0.06768566804531559, 0.033518296340731855], 'brevity_penalty': 0.9960505187431468, 'length_ratio': 0.9960582974494866, 'translation_length': 30071, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4182834195898706, 'rouge2': 0.17246572493226453, 'rougeL': 0.3594012849048782, 'rougeLsum': 0.35954397088231455}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.10: {'meteor': 0.3713527017404089, 'bleu_scores': {'bleu': 0.10809698094017595, 'precisions': [0.4147023571713943, 0.145728817077812, 0.06795102628736047, 0.03393775575327552], 'brevity_penalty': 0.9948859408394681, 'length_ratio': 0.9948989731699238, 'translation_length': 30036, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41773046267654856, 'rouge2': 0.17260745480056372, 'rougeL': 0.3594686692074592, 'rougeLsum': 0.35936406339125093}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 533, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.12: {'meteor': 0.36820419885143935, 'bleu_scores': {'bleu': 0.10505573355971856, 'precisions': [0.4098240955857949, 0.14277339035072595, 0.06492248062015504, 0.03232202311922487], 'brevity_penalty': 0.9980106107363413, 'length_ratio': 0.9980125869493209, 'translation_length': 30130, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41529417030158233, 'rouge2': 0.16878067248558315, 'rougeL': 0.3583796005764026, 'rougeLsum': 0.3583877478177061}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.14: {'meteor': 0.36307746488229864, 'bleu_scores': {'bleu': 0.10051614663163566, 'precisions': [0.4013952416992991, 0.13692917692097348, 0.06165771788216051, 0.030122267506483884], 'brevity_penalty': 1.0, 'length_ratio': 1.0065915866180855, 'translation_length': 30389, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40811191571085215, 'rouge2': 0.16172547308011448, 'rougeL': 0.34960574280699774, 'rougeLsum': 0.3496392100850815}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 658]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.16: {'meteor': 0.36060381551154586, 'bleu_scores': {'bleu': 0.09572351387840275, 'precisions': [0.3943648240226187, 0.13195897159052566, 0.05795474478161726, 0.027838667251205613], 'brevity_penalty': 1.0, 'length_ratio': 1.019244783040742, 'translation_length': 30771, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40695948021161266, 'rouge2': 0.16078136792998138, 'rougeL': 0.35054008230260014, 'rougeLsum': 0.35063402472045585}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 533]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.18: {'meteor': 0.36078545841521914, 'bleu_scores': {'bleu': 0.09571300097111912, 'precisions': [0.3949360480292352, 0.13088260206674573, 0.05813543795363258, 0.027927630371756763], 'brevity_penalty': 1.0, 'length_ratio': 1.0151705862868499, 'translation_length': 30648, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.404989374187655, 'rouge2': 0.15814652869870766, 'rougeL': 0.34417758327892045, 'rougeLsum': 0.3446171215887235}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.20: {'meteor': 0.3567548354175595, 'bleu_scores': {'bleu': 0.0912485469982839, 'precisions': [0.3872236189002772, 0.12631719800622218, 0.05570236439499304, 0.025445200521210368], 'brevity_penalty': 1.0, 'length_ratio': 1.0276912885061278, 'translation_length': 31026, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40377684729404284, 'rouge2': 0.1571965940862049, 'rougeL': 0.34423642973720203, 'rougeLsum': 0.3445297239478309}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 658]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.22: {'meteor': 0.3510044718361491, 'bleu_scores': {'bleu': 0.08350689777294566, 'precisions': [0.3702997530709843, 0.11766040181464679, 0.050021865644027316, 0.02231237322515213], 'brevity_penalty': 1.0, 'length_ratio': 1.059721762172905, 'translation_length': 31993, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3959486722874449, 'rouge2': 0.1528521180014643, 'rougeL': 0.3366921385756027, 'rougeLsum': 0.3373639725516262}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.24: {'meteor': 0.3465600044661264, 'bleu_scores': {'bleu': 0.07954262823239741, 'precisions': [0.3656348982343902, 0.11231059374390323, 0.04652104925559569, 0.020954720954720955], 'brevity_penalty': 1.0, 'length_ratio': 1.056210665783372, 'translation_length': 31887, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3936141020749143, 'rouge2': 0.14925954755118478, 'rougeL': 0.3330877244705648, 'rougeLsum': 0.333560266399453}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.26: {'meteor': 0.3435165661403993, 'bleu_scores': {'bleu': 0.07858780987337025, 'precisions': [0.35780525502318394, 0.1090751833936637, 0.04563887780880202, 0.02141475545730865], 'brevity_penalty': 1.0, 'length_ratio': 1.0715468698244452, 'translation_length': 32350, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39019940101936945, 'rouge2': 0.1481484713673767, 'rougeL': 0.3302144332821232, 'rougeLsum': 0.33045625596891903}, 'accuracy': 0.00353045013239188, 'correct_ids': [240, 364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.28: {'meteor': 0.34053363547339577, 'bleu_scores': {'bleu': 0.07203840378380885, 'precisions': [0.3451020592757862, 0.10142348754448399, 0.0418756674541277, 0.018374202216996975], 'brevity_penalty': 1.0, 'length_ratio': 1.0986088108645247, 'translation_length': 33167, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38636745171321535, 'rouge2': 0.1431996521188323, 'rougeL': 0.3260958081203139, 'rougeLsum': 0.3272219000106166}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.30: {'meteor': 0.33446931317267503, 'bleu_scores': {'bleu': 0.062148408497464926, 'precisions': [0.3152004454342984, 0.08905625664759824, 0.035419266654781005, 0.015004765858008178], 'brevity_penalty': 1.0, 'length_ratio': 1.1897979463398476, 'translation_length': 35920, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38245854011919, 'rouge2': 0.1427848839089301, 'rougeL': 0.3218688387965617, 'rougeLsum': 0.322593189811201}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","internlm/internlm2_5-7b-chat-1m/rpp-1.00: {'meteor': 0.3715346402699926, 'bleu_scores': {'bleu': 0.1059772684959813, 'precisions': [0.39683339104158144, 0.1431975453714584, 0.06656950140663662, 0.03334508283397956], 'brevity_penalty': 1.0, 'length_ratio': 1.0523020867837032, 'translation_length': 31769, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41974392291556095, 'rouge2': 0.17056433452207728, 'rougeL': 0.36313270123673597, 'rougeLsum': 0.3632694308153429}, 'accuracy': 0.0, 'correct_ids': []}\n","internlm/internlm2_5-7b-chat-1m/rpp-1.02: {'meteor': 0.352901317633597, 'bleu_scores': {'bleu': 0.08697903417673139, 'precisions': [0.3666595931730682, 0.11979657185910718, 0.05260074213918365, 0.024771882392700235], 'brevity_penalty': 1.0, 'length_ratio': 1.0926465717124876, 'translation_length': 32987, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39945641283002464, 'rouge2': 0.1518373201584628, 'rougeL': 0.33999857134039985, 'rougeLsum': 0.34085417765557335}, 'accuracy': 0.00088261253309797, 'correct_ids': [511]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00: {'meteor': 0.38168584246814397, 'bleu_scores': {'bleu': 0.11518296996672078, 'precisions': [0.42672642762284196, 0.15593196950357058, 0.07280560043080236, 0.036672529281892005], 'brevity_penalty': 0.9976786612989592, 'length_ratio': 0.9976813514408744, 'translation_length': 30120, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42528503521639993, 'rouge2': 0.17637973566560697, 'rougeL': 0.3705723503547834, 'rougeLsum': 0.37026767128935023}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02: {'meteor': 0.381084663579427, 'bleu_scores': {'bleu': 0.11434064727385712, 'precisions': [0.42645298576938423, 0.15516705248246554, 0.07212973283952392, 0.03635818433974287], 'brevity_penalty': 0.996216776830359, 'length_ratio': 0.9962239152037098, 'translation_length': 30076, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4245877414493061, 'rouge2': 0.17555464152945213, 'rougeL': 0.3698762430021683, 'rougeLsum': 0.3695464753833268}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04: {'meteor': 0.38019108433175514, 'bleu_scores': {'bleu': 0.11353152954579881, 'precisions': [0.42572246637368494, 0.15441303670899215, 0.0716574844262, 0.03599984984421337], 'brevity_penalty': 0.9948859408394681, 'length_ratio': 0.9948989731699238, 'translation_length': 30036, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4238319064139158, 'rouge2': 0.17523970074952674, 'rougeL': 0.3693886253078722, 'rougeLsum': 0.36906425269244736}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06: {'meteor': 0.37862157681270814, 'bleu_scores': {'bleu': 0.11220469680226439, 'precisions': [0.42524207011686144, 0.15293056182114723, 0.07094094274878093, 0.03547621737656762], 'brevity_penalty': 0.9920186657513808, 'length_ratio': 0.9920503477972838, 'translation_length': 29950, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42330817492734973, 'rouge2': 0.1739818636031837, 'rougeL': 0.3689343348685089, 'rougeLsum': 0.36845353949593573}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n"]},{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3949690.1229490.3835640.0000000.3609890.3609890
1Qwen/Qwen2-7B-Instruct1.000.3757940.1125770.3642500.0000000.2656660.2656660
2Qwen/Qwen2-7B-Instruct1.020.3768160.1155390.3695390.0000000.2559580.2559580
3Qwen/Qwen2-7B-Instruct1.040.3715150.1131160.3637110.0000000.2683140.2683140
4Qwen/Qwen2-7B-Instruct1.060.3721610.1098600.3608780.0000000.2815530.2815530
5Qwen/Qwen2-7B-Instruct1.080.3712970.1080950.3594010.0000000.2118270.2118270
6Qwen/Qwen2-7B-Instruct1.100.3713530.1080970.3594690.0000000.2533100.2533100
7Qwen/Qwen2-7B-Instruct1.120.3682040.1050560.3583800.0000000.4404240.4404240
8Qwen/Qwen2-7B-Instruct1.140.3630770.1005160.3496060.0000000.2806710.2806710
9Qwen/Qwen2-7B-Instruct1.160.3606040.0957240.3505400.0000000.2656660.2656660
10Qwen/Qwen2-7B-Instruct1.180.3607850.0957130.3441780.0000000.2859660.2859660
11Qwen/Qwen2-7B-Instruct1.200.3567550.0912490.3442360.0000000.2912620.2912620
12Qwen/Qwen2-7B-Instruct1.220.3510040.0835070.3366920.0000000.2691970.2691970
13Qwen/Qwen2-7B-Instruct1.240.3465600.0795430.3330880.0000000.3009710.3009710
14Qwen/Qwen2-7B-Instruct1.260.3435170.0785880.3302140.0000000.2665490.2665490
15Qwen/Qwen2-7B-Instruct1.280.3405340.0720380.3260960.0000000.1844660.1844660
16Qwen/Qwen2-7B-Instruct1.300.3344690.0621480.3218690.0052960.3256840.3309801
17internlm/internlm2_5-7b-chat-1m1.000.3715350.1059770.3631330.0000005.5401595.5401591
18internlm/internlm2_5-7b-chat-1m1.020.3529010.0869790.3399990.0000000.3071490.3071490
19shenzhi-wang/Llama3.1-70B-Chinese-Chat1.000.3816860.1151830.3705720.0000000.4068840.4068840
20shenzhi-wang/Llama3.1-70B-Chinese-Chat1.020.3810850.1143410.3698760.0000000.4333630.4333630
21shenzhi-wang/Llama3.1-70B-Chinese-Chat1.040.3801910.1135320.3693890.0000000.4236540.4236540
22shenzhi-wang/Llama3.1-70B-Chinese-Chat1.060.3786220.1122050.3689340.0000000.4236540.4236540
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.394969 0.122949 \n","1 Qwen/Qwen2-7B-Instruct 1.00 0.375794 0.112577 \n","2 Qwen/Qwen2-7B-Instruct 1.02 0.376816 0.115539 \n","3 Qwen/Qwen2-7B-Instruct 1.04 0.371515 0.113116 \n","4 Qwen/Qwen2-7B-Instruct 1.06 0.372161 0.109860 \n","5 Qwen/Qwen2-7B-Instruct 1.08 0.371297 0.108095 \n","6 Qwen/Qwen2-7B-Instruct 1.10 0.371353 0.108097 \n","7 Qwen/Qwen2-7B-Instruct 1.12 0.368204 0.105056 \n","8 Qwen/Qwen2-7B-Instruct 1.14 0.363077 0.100516 \n","9 Qwen/Qwen2-7B-Instruct 1.16 0.360604 0.095724 \n","10 Qwen/Qwen2-7B-Instruct 1.18 0.360785 0.095713 \n","11 Qwen/Qwen2-7B-Instruct 1.20 0.356755 0.091249 \n","12 Qwen/Qwen2-7B-Instruct 1.22 0.351004 0.083507 \n","13 Qwen/Qwen2-7B-Instruct 1.24 0.346560 0.079543 \n","14 Qwen/Qwen2-7B-Instruct 1.26 0.343517 0.078588 \n","15 Qwen/Qwen2-7B-Instruct 1.28 0.340534 0.072038 \n","16 Qwen/Qwen2-7B-Instruct 1.30 0.334469 0.062148 \n","17 internlm/internlm2_5-7b-chat-1m 1.00 0.371535 0.105977 \n","18 internlm/internlm2_5-7b-chat-1m 1.02 0.352901 0.086979 \n","19 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.00 0.381686 0.115183 \n","20 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.02 0.381085 0.114341 \n","21 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.04 0.380191 0.113532 \n","22 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.06 0.378622 0.112205 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.383564 0.000000 0.360989 0.360989 \n","1 0.364250 0.000000 0.265666 0.265666 \n","2 0.369539 0.000000 0.255958 0.255958 \n","3 0.363711 0.000000 0.268314 0.268314 \n","4 0.360878 0.000000 0.281553 0.281553 \n","5 0.359401 0.000000 0.211827 0.211827 \n","6 0.359469 0.000000 0.253310 0.253310 \n","7 0.358380 0.000000 0.440424 0.440424 \n","8 0.349606 0.000000 0.280671 0.280671 \n","9 0.350540 0.000000 0.265666 0.265666 \n","10 0.344178 0.000000 0.285966 0.285966 \n","11 0.344236 0.000000 0.291262 0.291262 \n","12 0.336692 0.000000 0.269197 0.269197 \n","13 0.333088 0.000000 0.300971 0.300971 \n","14 0.330214 0.000000 0.266549 0.266549 \n","15 0.326096 0.000000 0.184466 0.184466 \n","16 0.321869 0.005296 0.325684 0.330980 \n","17 0.363133 0.000000 5.540159 5.540159 \n","18 0.339999 0.000000 0.307149 0.307149 \n","19 0.370572 0.000000 0.406884 0.406884 \n","20 0.369876 0.000000 0.433363 0.433363 \n","21 0.369389 0.000000 0.423654 0.423654 \n","22 0.368934 0.000000 0.423654 0.423654 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 1 \n","17 1 \n","18 0 \n","19 0 \n","20 0 \n","21 0 \n","22 0 "]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(lambda x: x[\"meteor\"] / math.log10( 10 + x[\"total_repetitions\"]), axis=1)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3949690.1229490.3835640.0000000.3609890.36098900.388978
1Qwen/Qwen2-7B-Instruct1.000.3757940.1125770.3642500.0000000.2656660.26566600.371563
2Qwen/Qwen2-7B-Instruct1.020.3768160.1155390.3695390.0000000.2559580.25595800.372725
3Qwen/Qwen2-7B-Instruct1.040.3715150.1131160.3637110.0000000.2683140.26831400.367291
4Qwen/Qwen2-7B-Instruct1.060.3721610.1098600.3608780.0000000.2815530.28155300.367727
5Qwen/Qwen2-7B-Instruct1.080.3712970.1080950.3594010.0000000.2118270.21182700.367947
6Qwen/Qwen2-7B-Instruct1.100.3713530.1080970.3594690.0000000.2533100.25331000.367362
7Qwen/Qwen2-7B-Instruct1.120.3682040.1050560.3583800.0000000.4404240.44042400.361439
8Qwen/Qwen2-7B-Instruct1.140.3630770.1005160.3496060.0000000.2806710.28067100.358765
9Qwen/Qwen2-7B-Instruct1.160.3606040.0957240.3505400.0000000.2656660.26566600.356544
10Qwen/Qwen2-7B-Instruct1.180.3607850.0957130.3441780.0000000.2859660.28596600.356421
11Qwen/Qwen2-7B-Instruct1.200.3567550.0912490.3442360.0000000.2912620.29126200.352361
12Qwen/Qwen2-7B-Instruct1.220.3510040.0835070.3366920.0000000.2691970.26919700.347001
13Qwen/Qwen2-7B-Instruct1.240.3465600.0795430.3330880.0000000.3009710.30097100.342154
14Qwen/Qwen2-7B-Instruct1.260.3435170.0785880.3302140.0000000.2665490.26654900.339636
15Qwen/Qwen2-7B-Instruct1.280.3405340.0720380.3260960.0000000.1844660.18446600.337852
16Qwen/Qwen2-7B-Instruct1.300.3344690.0621480.3218690.0052960.3256840.33098010.329805
17internlm/internlm2_5-7b-chat-1m1.000.3715350.1059770.3631330.0000005.5401595.54015910.311833
18internlm/internlm2_5-7b-chat-1m1.020.3529010.0869790.3399990.0000000.3071490.30714900.348325
19shenzhi-wang/Llama3.1-70B-Chinese-Chat1.000.3816860.1151830.3705720.0000000.4068840.40688400.375187
20shenzhi-wang/Llama3.1-70B-Chinese-Chat1.020.3810850.1143410.3698760.0000000.4333630.43336300.374190
21shenzhi-wang/Llama3.1-70B-Chinese-Chat1.040.3801910.1135320.3693890.0000000.4236540.42365400.373461
22shenzhi-wang/Llama3.1-70B-Chinese-Chat1.060.3786220.1122050.3689340.0000000.4236540.42365400.371920
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.394969 0.122949 \n","1 Qwen/Qwen2-7B-Instruct 1.00 0.375794 0.112577 \n","2 Qwen/Qwen2-7B-Instruct 1.02 0.376816 0.115539 \n","3 Qwen/Qwen2-7B-Instruct 1.04 0.371515 0.113116 \n","4 Qwen/Qwen2-7B-Instruct 1.06 0.372161 0.109860 \n","5 Qwen/Qwen2-7B-Instruct 1.08 0.371297 0.108095 \n","6 Qwen/Qwen2-7B-Instruct 1.10 0.371353 0.108097 \n","7 Qwen/Qwen2-7B-Instruct 1.12 0.368204 0.105056 \n","8 Qwen/Qwen2-7B-Instruct 1.14 0.363077 0.100516 \n","9 Qwen/Qwen2-7B-Instruct 1.16 0.360604 0.095724 \n","10 Qwen/Qwen2-7B-Instruct 1.18 0.360785 0.095713 \n","11 Qwen/Qwen2-7B-Instruct 1.20 0.356755 0.091249 \n","12 Qwen/Qwen2-7B-Instruct 1.22 0.351004 0.083507 \n","13 Qwen/Qwen2-7B-Instruct 1.24 0.346560 0.079543 \n","14 Qwen/Qwen2-7B-Instruct 1.26 0.343517 0.078588 \n","15 Qwen/Qwen2-7B-Instruct 1.28 0.340534 0.072038 \n","16 Qwen/Qwen2-7B-Instruct 1.30 0.334469 0.062148 \n","17 internlm/internlm2_5-7b-chat-1m 1.00 0.371535 0.105977 \n","18 internlm/internlm2_5-7b-chat-1m 1.02 0.352901 0.086979 \n","19 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.00 0.381686 0.115183 \n","20 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.02 0.381085 0.114341 \n","21 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.04 0.380191 0.113532 \n","22 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.06 0.378622 0.112205 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.383564 0.000000 0.360989 0.360989 \n","1 0.364250 0.000000 0.265666 0.265666 \n","2 0.369539 0.000000 0.255958 0.255958 \n","3 0.363711 0.000000 0.268314 0.268314 \n","4 0.360878 0.000000 0.281553 0.281553 \n","5 0.359401 0.000000 0.211827 0.211827 \n","6 0.359469 0.000000 0.253310 0.253310 \n","7 0.358380 0.000000 0.440424 0.440424 \n","8 0.349606 0.000000 0.280671 0.280671 \n","9 0.350540 0.000000 0.265666 0.265666 \n","10 0.344178 0.000000 0.285966 0.285966 \n","11 0.344236 0.000000 0.291262 0.291262 \n","12 0.336692 0.000000 0.269197 0.269197 \n","13 0.333088 0.000000 0.300971 0.300971 \n","14 0.330214 0.000000 0.266549 0.266549 \n","15 0.326096 0.000000 0.184466 0.184466 \n","16 0.321869 0.005296 0.325684 0.330980 \n","17 0.363133 0.000000 5.540159 5.540159 \n","18 0.339999 0.000000 0.307149 0.307149 \n","19 0.370572 0.000000 0.406884 0.406884 \n","20 0.369876 0.000000 0.433363 0.433363 \n","21 0.369389 0.000000 0.423654 0.423654 \n","22 0.368934 0.000000 0.423654 0.423654 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.388978 \n","1 0 0.371563 \n","2 0 0.372725 \n","3 0 0.367291 \n","4 0 0.367727 \n","5 0 0.367947 \n","6 0 0.367362 \n","7 0 0.361439 \n","8 0 0.358765 \n","9 0 0.356544 \n","10 0 0.356421 \n","11 0 0.352361 \n","12 0 0.347001 \n","13 0 0.342154 \n","14 0 0.339636 \n","15 0 0.337852 \n","16 1 0.329805 \n","17 1 0.311833 \n","18 0 0.348325 \n","19 0 0.375187 \n","20 0 0.374190 \n","21 0 0.373461 \n","22 0 0.371920 "]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'internlm/internlm2_5-7b-chat-1m',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat'], dtype=object)"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n"," )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[],"source":["col = \"internlm/internlm2_5-7b-chat-1m/rpp-1.00\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04Qwen/Qwen2-7B-Instruct/rpp-1.06Qwen/Qwen2-7B-Instruct/rpp-1.08Qwen/Qwen2-7B-Instruct/rpp-1.10Qwen/Qwen2-7B-Instruct/rpp-1.12...internlm/internlm2_5-7b-chat-1m/rpp-1.00internlm/internlm2_5-7b-chat-1m/rpp-1.02shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06ews_scorerepetition_scoretotal_repetitionsoutput_tokens
503青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟——A blue sky yo – a sapphire sky yo – a painted ...Blue sky oh—clear sky oh—colorful sky oh—my de...Blue sky oh - blue heaven oh - colorful sky oh...Blue heaven—oh, blue sky—oh, colorful sky—stic...Blue heaven—oh, blue sky—oh, colorful sky—stup...Blue sky oh - blue heaven oh - colorful sky oh...Blue sky—oh, blue heaven—colorful sky—stupid f...Blue sky - oh blue heaven - colorful sky - you...Blue sky - oh blue sky - colorful sky - you've......Oh, the blue sky, the blue sky, the sky with i...Oh blue sky - oh green sky - oh colorful sky -...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...0611261122049
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1 rows × 29 columns

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"],"text/plain":[" chinese \\\n","503 青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟—— \n","\n"," english \\\n","503 A blue sky yo – a sapphire sky yo – a painted ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","503 Blue sky oh—clear sky oh—colorful sky oh—my de... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 \\\n","503 Blue sky oh - blue heaven oh - colorful sky oh... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","503 Blue heaven—oh, blue sky—oh, colorful sky—stic... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 \\\n","503 Blue heaven—oh, blue sky—oh, colorful sky—stup... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.06 \\\n","503 Blue sky oh - blue heaven oh - colorful sky oh... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.08 \\\n","503 Blue sky—oh, blue heaven—colorful sky—stupid f... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.10 \\\n","503 Blue sky - oh blue heaven - colorful sky - you... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.12 ... \\\n","503 Blue sky - oh blue sky - colorful sky - you've... ... \n","\n"," internlm/internlm2_5-7b-chat-1m/rpp-1.00 \\\n","503 Oh, the blue sky, the blue sky, the sky with i... \n","\n"," internlm/internlm2_5-7b-chat-1m/rpp-1.02 \\\n","503 Oh blue sky - oh green sky - oh colorful sky -... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06 ews_score \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","503 6112 6112 2049 \n","\n","[1 rows x 29 columns]"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":45,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟——\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["A blue sky yo – a sapphire sky yo – a painted sky yo – a mighty cudgel yo – dear elder brother yo – death has claimed you – you have brought down little sister's sky yo –.\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Oh, the blue sky, the blue sky, the sky with its colorful hues, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the 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sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 2-16: `, the blue sky`\n","Group 2 found at 16-30: `, the blue sky`\n","Group 3 found at 16-30: `, the blue sky`\n","\n","RangeIndex: 1133 entries, 0 to 1132\n","Data columns (total 25 columns):\n"," # Column Non-Null Count Dtype \n","--- ------ -------------- ----- \n"," 0 chinese 1133 non-null object\n"," 1 english 1133 non-null object\n"," 2 Qwen/Qwen2-7B-Instruct/rpp-1.00 1133 non-null object\n"," 3 Qwen/Qwen2-7B-Instruct/rpp-1.02 1133 non-null object\n"," 4 Qwen/Qwen2-7B-Instruct/rpp-1.04 1133 non-null object\n"," 5 Qwen/Qwen2-7B-Instruct/rpp-1.06 1133 non-null object\n"," 6 Qwen/Qwen2-7B-Instruct/rpp-1.08 1133 non-null object\n"," 7 Qwen/Qwen2-7B-Instruct/rpp-1.10 1133 non-null object\n"," 8 Qwen/Qwen2-7B-Instruct/rpp-1.12 1133 non-null object\n"," 9 Qwen/Qwen2-7B-Instruct/rpp-1.14 1133 non-null object\n"," 10 Qwen/Qwen2-7B-Instruct/rpp-1.16 1133 non-null object\n"," 11 Qwen/Qwen2-7B-Instruct/rpp-1.18 1133 non-null object\n"," 12 Qwen/Qwen2-7B-Instruct/rpp-1.20 1133 non-null object\n"," 13 Qwen/Qwen2-7B-Instruct/rpp-1.22 1133 non-null object\n"," 14 Qwen/Qwen2-7B-Instruct/rpp-1.24 1133 non-null object\n"," 15 Qwen/Qwen2-7B-Instruct/rpp-1.26 1133 non-null object\n"," 16 Qwen/Qwen2-7B-Instruct/rpp-1.28 1133 non-null object\n"," 17 Qwen/Qwen2-7B-Instruct/rpp-1.30 1133 non-null object\n"," 18 internlm/internlm2_5-7b-chat-1m/rpp-1.00 1133 non-null object\n"," 19 internlm/internlm2_5-7b-chat-1m/rpp-1.02 1133 non-null object\n"," 20 Qwen/Qwen2-72B-Instruct/rpp-1.00 1133 non-null object\n"," 21 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00 1133 non-null object\n"," 22 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02 1133 non-null object\n"," 23 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04 1133 non-null object\n"," 24 shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06 1133 non-null object\n","dtypes: object(25)\n","memory usage: 221.4+ KB\n"]}],"source":["import pandas as pd\n","\n","df = pd.read_csv(results_path)\n","df.info()"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"data":{"text/plain":["['chinese',\n"," 'english',\n"," 'Qwen/Qwen2-72B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.00',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.02',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.04',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.06',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.08',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.10',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.12',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.14',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.16',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.18',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.20',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.22',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.24',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.26',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.28',\n"," 'Qwen/Qwen2-7B-Instruct/rpp-1.30',\n"," 'internlm/internlm2_5-7b-chat-1m/rpp-1.00',\n"," 'internlm/internlm2_5-7b-chat-1m/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06']"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["columns = df.columns[2:].to_list()\n","columns.sort()\n","columns = df.columns[:2].to_list() + columns\n","columns"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["df = df[columns]"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Qwen/Qwen2-72B-Instruct/rpp-1.00: {'meteor': 0.39496912014495184, 'bleu_scores': {'bleu': 0.12294894050451377, 'precisions': [0.42391407360606537, 0.1626695498329074, 0.079349416448331, 0.041761041902604754], 'brevity_penalty': 1.0, 'length_ratio': 1.048526001987413, 'translation_length': 31655, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.44242617045618315, 'rouge2': 0.19166824249542752, 'rougeL': 0.3835643396648639, 'rougeLsum': 0.3844919778233326}, 'accuracy': 0.0, 'correct_ids': []}\n","Qwen/Qwen2-7B-Instruct/rpp-1.00: {'meteor': 0.3757937058055942, 'bleu_scores': {'bleu': 0.11257687997946404, 'precisions': [0.4221057489451477, 0.15152552819915763, 0.07046669041681511, 0.03563738956121464], 'brevity_penalty': 1.0, 'length_ratio': 1.004836038423319, 'translation_length': 30336, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4241235957669323, 'rouge2': 0.17433830983598061, 'rougeL': 0.3642501836106533, 'rougeLsum': 0.364584190239183}, 'accuracy': 0.00088261253309797, 'correct_ids': [364]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.02: {'meteor': 0.3768162203335968, 'bleu_scores': {'bleu': 0.11553860771639841, 'precisions': [0.421923611570795, 0.15446511467968776, 0.07288535852297123, 0.03751491646778043], 'brevity_penalty': 1.0, 'length_ratio': 1.0007949652202717, 'translation_length': 30214, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4273577053163239, 'rouge2': 0.1800744214940156, 'rougeL': 0.3695393969769755, 'rougeLsum': 0.36955057550298287}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.04: {'meteor': 0.3715147429622351, 'bleu_scores': {'bleu': 0.11311605625702598, 'precisions': [0.41758205508824014, 0.15180590775135358, 0.07144639737602053, 0.036148159155923766], 'brevity_penalty': 1.0, 'length_ratio': 1.0041404438555812, 'translation_length': 30315, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41870140882606066, 'rouge2': 0.17377238271646123, 'rougeL': 0.3637109748338643, 'rougeLsum': 0.3636218000079854}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 533]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.06: {'meteor': 0.3721614566005243, 'bleu_scores': {'bleu': 0.10986034422062402, 'precisions': [0.41770767752410615, 0.14848860428286167, 0.06846272346218608, 0.03435399551904406], 'brevity_penalty': 0.9996355745538857, 'length_ratio': 0.9996356409407089, 'translation_length': 30179, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41850128527699093, 'rouge2': 0.17078364572425722, 'rougeL': 0.36087822210596066, 'rougeLsum': 0.36118431102497384}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.08: {'meteor': 0.3712966405354824, 'bleu_scores': {'bleu': 0.10809530671609749, 'precisions': [0.41541684679591634, 0.14717672264842077, 0.06768566804531559, 0.033518296340731855], 'brevity_penalty': 0.9960505187431468, 'length_ratio': 0.9960582974494866, 'translation_length': 30071, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4182834195898706, 'rouge2': 0.17246572493226453, 'rougeL': 0.3594012849048782, 'rougeLsum': 0.35954397088231455}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.10: {'meteor': 0.3713527017404089, 'bleu_scores': {'bleu': 0.10809698094017595, 'precisions': [0.4147023571713943, 0.145728817077812, 0.06795102628736047, 0.03393775575327552], 'brevity_penalty': 0.9948859408394681, 'length_ratio': 0.9948989731699238, 'translation_length': 30036, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41773046267654856, 'rouge2': 0.17260745480056372, 'rougeL': 0.3594686692074592, 'rougeLsum': 0.35936406339125093}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 533, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.12: {'meteor': 0.36820419885143935, 'bleu_scores': {'bleu': 0.10505573355971856, 'precisions': [0.4098240955857949, 0.14277339035072595, 0.06492248062015504, 0.03232202311922487], 'brevity_penalty': 0.9980106107363413, 'length_ratio': 0.9980125869493209, 'translation_length': 30130, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41529417030158233, 'rouge2': 0.16878067248558315, 'rougeL': 0.3583796005764026, 'rougeLsum': 0.3583877478177061}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.14: {'meteor': 0.36307746488229864, 'bleu_scores': {'bleu': 0.10051614663163566, 'precisions': [0.4013952416992991, 0.13692917692097348, 0.06165771788216051, 0.030122267506483884], 'brevity_penalty': 1.0, 'length_ratio': 1.0065915866180855, 'translation_length': 30389, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40811191571085215, 'rouge2': 0.16172547308011448, 'rougeL': 0.34960574280699774, 'rougeLsum': 0.3496392100850815}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 658]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.16: {'meteor': 0.36060381551154586, 'bleu_scores': {'bleu': 0.09572351387840275, 'precisions': [0.3943648240226187, 0.13195897159052566, 0.05795474478161726, 0.027838667251205613], 'brevity_penalty': 1.0, 'length_ratio': 1.019244783040742, 'translation_length': 30771, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40695948021161266, 'rouge2': 0.16078136792998138, 'rougeL': 0.35054008230260014, 'rougeLsum': 0.35063402472045585}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 533]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.18: {'meteor': 0.36078545841521914, 'bleu_scores': {'bleu': 0.09571300097111912, 'precisions': [0.3949360480292352, 0.13088260206674573, 0.05813543795363258, 0.027927630371756763], 'brevity_penalty': 1.0, 'length_ratio': 1.0151705862868499, 'translation_length': 30648, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.404989374187655, 'rouge2': 0.15814652869870766, 'rougeL': 0.34417758327892045, 'rougeLsum': 0.3446171215887235}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.20: {'meteor': 0.3567548354175595, 'bleu_scores': {'bleu': 0.0912485469982839, 'precisions': [0.3872236189002772, 0.12631719800622218, 0.05570236439499304, 0.025445200521210368], 'brevity_penalty': 1.0, 'length_ratio': 1.0276912885061278, 'translation_length': 31026, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.40377684729404284, 'rouge2': 0.1571965940862049, 'rougeL': 0.34423642973720203, 'rougeLsum': 0.3445297239478309}, 'accuracy': 0.00176522506619594, 'correct_ids': [364, 658]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.22: {'meteor': 0.3510044718361491, 'bleu_scores': {'bleu': 0.08350689777294566, 'precisions': [0.3702997530709843, 0.11766040181464679, 0.050021865644027316, 0.02231237322515213], 'brevity_penalty': 1.0, 'length_ratio': 1.059721762172905, 'translation_length': 31993, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3959486722874449, 'rouge2': 0.1528521180014643, 'rougeL': 0.3366921385756027, 'rougeLsum': 0.3373639725516262}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.24: {'meteor': 0.3465600044661264, 'bleu_scores': {'bleu': 0.07954262823239741, 'precisions': [0.3656348982343902, 0.11231059374390323, 0.04652104925559569, 0.020954720954720955], 'brevity_penalty': 1.0, 'length_ratio': 1.056210665783372, 'translation_length': 31887, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.3936141020749143, 'rouge2': 0.14925954755118478, 'rougeL': 0.3330877244705648, 'rougeLsum': 0.333560266399453}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.26: {'meteor': 0.3435165661403993, 'bleu_scores': {'bleu': 0.07858780987337025, 'precisions': [0.35780525502318394, 0.1090751833936637, 0.04563887780880202, 0.02141475545730865], 'brevity_penalty': 1.0, 'length_ratio': 1.0715468698244452, 'translation_length': 32350, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39019940101936945, 'rouge2': 0.1481484713673767, 'rougeL': 0.3302144332821232, 'rougeLsum': 0.33045625596891903}, 'accuracy': 0.00353045013239188, 'correct_ids': [240, 364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.28: {'meteor': 0.34053363547339577, 'bleu_scores': {'bleu': 0.07203840378380885, 'precisions': [0.3451020592757862, 0.10142348754448399, 0.0418756674541277, 0.018374202216996975], 'brevity_penalty': 1.0, 'length_ratio': 1.0986088108645247, 'translation_length': 33167, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38636745171321535, 'rouge2': 0.1431996521188323, 'rougeL': 0.3260958081203139, 'rougeLsum': 0.3272219000106166}, 'accuracy': 0.00264783759929391, 'correct_ids': [364, 658, 659]}\n","Qwen/Qwen2-7B-Instruct/rpp-1.30: {'meteor': 0.33446931317267503, 'bleu_scores': {'bleu': 0.062148408497464926, 'precisions': [0.3152004454342984, 0.08905625664759824, 0.035419266654781005, 0.015004765858008178], 'brevity_penalty': 1.0, 'length_ratio': 1.1897979463398476, 'translation_length': 35920, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.38245854011919, 'rouge2': 0.1427848839089301, 'rougeL': 0.3218688387965617, 'rougeLsum': 0.322593189811201}, 'accuracy': 0.00264783759929391, 'correct_ids': [240, 364, 659]}\n","internlm/internlm2_5-7b-chat-1m/rpp-1.00: {'meteor': 0.3715346402699926, 'bleu_scores': {'bleu': 0.1059772684959813, 'precisions': [0.39683339104158144, 0.1431975453714584, 0.06656950140663662, 0.03334508283397956], 'brevity_penalty': 1.0, 'length_ratio': 1.0523020867837032, 'translation_length': 31769, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.41974392291556095, 'rouge2': 0.17056433452207728, 'rougeL': 0.36313270123673597, 'rougeLsum': 0.3632694308153429}, 'accuracy': 0.0, 'correct_ids': []}\n","internlm/internlm2_5-7b-chat-1m/rpp-1.02: {'meteor': 0.352901317633597, 'bleu_scores': {'bleu': 0.08697903417673139, 'precisions': [0.3666595931730682, 0.11979657185910718, 0.05260074213918365, 0.024771882392700235], 'brevity_penalty': 1.0, 'length_ratio': 1.0926465717124876, 'translation_length': 32987, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.39945641283002464, 'rouge2': 0.1518373201584628, 'rougeL': 0.33999857134039985, 'rougeLsum': 0.34085417765557335}, 'accuracy': 0.00088261253309797, 'correct_ids': [511]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00: {'meteor': 0.38168584246814397, 'bleu_scores': {'bleu': 0.11518296996672078, 'precisions': [0.42672642762284196, 0.15593196950357058, 0.07280560043080236, 0.036672529281892005], 'brevity_penalty': 0.9976786612989592, 'length_ratio': 0.9976813514408744, 'translation_length': 30120, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42528503521639993, 'rouge2': 0.17637973566560697, 'rougeL': 0.3705723503547834, 'rougeLsum': 0.37026767128935023}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02: {'meteor': 0.381084663579427, 'bleu_scores': {'bleu': 0.11434064727385712, 'precisions': [0.42645298576938423, 0.15516705248246554, 0.07212973283952392, 0.03635818433974287], 'brevity_penalty': 0.996216776830359, 'length_ratio': 0.9962239152037098, 'translation_length': 30076, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4245877414493061, 'rouge2': 0.17555464152945213, 'rougeL': 0.3698762430021683, 'rougeLsum': 0.3695464753833268}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04: {'meteor': 0.38019108433175514, 'bleu_scores': {'bleu': 0.11353152954579881, 'precisions': [0.42572246637368494, 0.15441303670899215, 0.0716574844262, 0.03599984984421337], 'brevity_penalty': 0.9948859408394681, 'length_ratio': 0.9948989731699238, 'translation_length': 30036, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.4238319064139158, 'rouge2': 0.17523970074952674, 'rougeL': 0.3693886253078722, 'rougeLsum': 0.36906425269244736}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n","shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06: {'meteor': 0.37862157681270814, 'bleu_scores': {'bleu': 0.11220469680226439, 'precisions': [0.42524207011686144, 0.15293056182114723, 0.07094094274878093, 0.03547621737656762], 'brevity_penalty': 0.9920186657513808, 'length_ratio': 0.9920503477972838, 'translation_length': 29950, 'reference_length': 30190}, 'rouge_scores': {'rouge1': 0.42330817492734973, 'rouge2': 0.1739818636031837, 'rougeL': 0.3689343348685089, 'rougeLsum': 0.36845353949593573}, 'accuracy': 0.00176522506619594, 'correct_ids': [77, 531]}\n"]},{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokens
0Qwen/Qwen2-72B-Instruct1.000.3949690.1229490.3835640.0000000.3609890.3609890
1Qwen/Qwen2-7B-Instruct1.000.3757940.1125770.3642500.0000000.2656660.2656660
2Qwen/Qwen2-7B-Instruct1.020.3768160.1155390.3695390.0000000.2559580.2559580
3Qwen/Qwen2-7B-Instruct1.040.3715150.1131160.3637110.0000000.2683140.2683140
4Qwen/Qwen2-7B-Instruct1.060.3721610.1098600.3608780.0000000.2815530.2815530
5Qwen/Qwen2-7B-Instruct1.080.3712970.1080950.3594010.0000000.2118270.2118270
6Qwen/Qwen2-7B-Instruct1.100.3713530.1080970.3594690.0000000.2533100.2533100
7Qwen/Qwen2-7B-Instruct1.120.3682040.1050560.3583800.0000000.4404240.4404240
8Qwen/Qwen2-7B-Instruct1.140.3630770.1005160.3496060.0000000.2806710.2806710
9Qwen/Qwen2-7B-Instruct1.160.3606040.0957240.3505400.0000000.2656660.2656660
10Qwen/Qwen2-7B-Instruct1.180.3607850.0957130.3441780.0000000.2859660.2859660
11Qwen/Qwen2-7B-Instruct1.200.3567550.0912490.3442360.0000000.2912620.2912620
12Qwen/Qwen2-7B-Instruct1.220.3510040.0835070.3366920.0000000.2691970.2691970
13Qwen/Qwen2-7B-Instruct1.240.3465600.0795430.3330880.0000000.3009710.3009710
14Qwen/Qwen2-7B-Instruct1.260.3435170.0785880.3302140.0000000.2665490.2665490
15Qwen/Qwen2-7B-Instruct1.280.3405340.0720380.3260960.0000000.1844660.1844660
16Qwen/Qwen2-7B-Instruct1.300.3344690.0621480.3218690.0052960.3256840.3309801
17internlm/internlm2_5-7b-chat-1m1.000.3715350.1059770.3631330.0000005.5401595.5401591
18internlm/internlm2_5-7b-chat-1m1.020.3529010.0869790.3399990.0000000.3071490.3071490
19shenzhi-wang/Llama3.1-70B-Chinese-Chat1.000.3816860.1151830.3705720.0000000.4068840.4068840
20shenzhi-wang/Llama3.1-70B-Chinese-Chat1.020.3810850.1143410.3698760.0000000.4333630.4333630
21shenzhi-wang/Llama3.1-70B-Chinese-Chat1.040.3801910.1135320.3693890.0000000.4236540.4236540
22shenzhi-wang/Llama3.1-70B-Chinese-Chat1.060.3786220.1122050.3689340.0000000.4236540.4236540
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.394969 0.122949 \n","1 Qwen/Qwen2-7B-Instruct 1.00 0.375794 0.112577 \n","2 Qwen/Qwen2-7B-Instruct 1.02 0.376816 0.115539 \n","3 Qwen/Qwen2-7B-Instruct 1.04 0.371515 0.113116 \n","4 Qwen/Qwen2-7B-Instruct 1.06 0.372161 0.109860 \n","5 Qwen/Qwen2-7B-Instruct 1.08 0.371297 0.108095 \n","6 Qwen/Qwen2-7B-Instruct 1.10 0.371353 0.108097 \n","7 Qwen/Qwen2-7B-Instruct 1.12 0.368204 0.105056 \n","8 Qwen/Qwen2-7B-Instruct 1.14 0.363077 0.100516 \n","9 Qwen/Qwen2-7B-Instruct 1.16 0.360604 0.095724 \n","10 Qwen/Qwen2-7B-Instruct 1.18 0.360785 0.095713 \n","11 Qwen/Qwen2-7B-Instruct 1.20 0.356755 0.091249 \n","12 Qwen/Qwen2-7B-Instruct 1.22 0.351004 0.083507 \n","13 Qwen/Qwen2-7B-Instruct 1.24 0.346560 0.079543 \n","14 Qwen/Qwen2-7B-Instruct 1.26 0.343517 0.078588 \n","15 Qwen/Qwen2-7B-Instruct 1.28 0.340534 0.072038 \n","16 Qwen/Qwen2-7B-Instruct 1.30 0.334469 0.062148 \n","17 internlm/internlm2_5-7b-chat-1m 1.00 0.371535 0.105977 \n","18 internlm/internlm2_5-7b-chat-1m 1.02 0.352901 0.086979 \n","19 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.00 0.381686 0.115183 \n","20 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.02 0.381085 0.114341 \n","21 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.04 0.380191 0.113532 \n","22 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.06 0.378622 0.112205 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.383564 0.000000 0.360989 0.360989 \n","1 0.364250 0.000000 0.265666 0.265666 \n","2 0.369539 0.000000 0.255958 0.255958 \n","3 0.363711 0.000000 0.268314 0.268314 \n","4 0.360878 0.000000 0.281553 0.281553 \n","5 0.359401 0.000000 0.211827 0.211827 \n","6 0.359469 0.000000 0.253310 0.253310 \n","7 0.358380 0.000000 0.440424 0.440424 \n","8 0.349606 0.000000 0.280671 0.280671 \n","9 0.350540 0.000000 0.265666 0.265666 \n","10 0.344178 0.000000 0.285966 0.285966 \n","11 0.344236 0.000000 0.291262 0.291262 \n","12 0.336692 0.000000 0.269197 0.269197 \n","13 0.333088 0.000000 0.300971 0.300971 \n","14 0.330214 0.000000 0.266549 0.266549 \n","15 0.326096 0.000000 0.184466 0.184466 \n","16 0.321869 0.005296 0.325684 0.330980 \n","17 0.363133 0.000000 5.540159 5.540159 \n","18 0.339999 0.000000 0.307149 0.307149 \n","19 0.370572 0.000000 0.406884 0.406884 \n","20 0.369876 0.000000 0.433363 0.433363 \n","21 0.369389 0.000000 0.423654 0.423654 \n","22 0.368934 0.000000 0.423654 0.423654 \n","\n"," num_entries_with_max_output_tokens \n","0 0 \n","1 0 \n","2 0 \n","3 0 \n","4 0 \n","5 0 \n","6 0 \n","7 0 \n","8 0 \n","9 0 \n","10 0 \n","11 0 \n","12 0 \n","13 0 \n","14 0 \n","15 0 \n","16 1 \n","17 1 \n","18 0 \n","19 0 \n","20 0 \n","21 0 \n","22 0 "]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df = get_metrics(df)\n","metrics_df"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[],"source":["metrics_df[\"rap\"] = metrics_df.apply(\n"," lambda x: x[\"meteor\"] / math.log10(10 + x[\"total_repetitions\"]), axis=1\n",")"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"data":{"text/html":["
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modelrppmeteorbleu_1rouge_lews_scorerepetition_scoretotal_repetitionsnum_entries_with_max_output_tokensrap
0Qwen/Qwen2-72B-Instruct1.000.3949690.1229490.3835640.0000000.3609890.36098900.388978
1Qwen/Qwen2-7B-Instruct1.000.3757940.1125770.3642500.0000000.2656660.26566600.371563
2Qwen/Qwen2-7B-Instruct1.020.3768160.1155390.3695390.0000000.2559580.25595800.372725
3Qwen/Qwen2-7B-Instruct1.040.3715150.1131160.3637110.0000000.2683140.26831400.367291
4Qwen/Qwen2-7B-Instruct1.060.3721610.1098600.3608780.0000000.2815530.28155300.367727
5Qwen/Qwen2-7B-Instruct1.080.3712970.1080950.3594010.0000000.2118270.21182700.367947
6Qwen/Qwen2-7B-Instruct1.100.3713530.1080970.3594690.0000000.2533100.25331000.367362
7Qwen/Qwen2-7B-Instruct1.120.3682040.1050560.3583800.0000000.4404240.44042400.361439
8Qwen/Qwen2-7B-Instruct1.140.3630770.1005160.3496060.0000000.2806710.28067100.358765
9Qwen/Qwen2-7B-Instruct1.160.3606040.0957240.3505400.0000000.2656660.26566600.356544
10Qwen/Qwen2-7B-Instruct1.180.3607850.0957130.3441780.0000000.2859660.28596600.356421
11Qwen/Qwen2-7B-Instruct1.200.3567550.0912490.3442360.0000000.2912620.29126200.352361
12Qwen/Qwen2-7B-Instruct1.220.3510040.0835070.3366920.0000000.2691970.26919700.347001
13Qwen/Qwen2-7B-Instruct1.240.3465600.0795430.3330880.0000000.3009710.30097100.342154
14Qwen/Qwen2-7B-Instruct1.260.3435170.0785880.3302140.0000000.2665490.26654900.339636
15Qwen/Qwen2-7B-Instruct1.280.3405340.0720380.3260960.0000000.1844660.18446600.337852
16Qwen/Qwen2-7B-Instruct1.300.3344690.0621480.3218690.0052960.3256840.33098010.329805
17internlm/internlm2_5-7b-chat-1m1.000.3715350.1059770.3631330.0000005.5401595.54015910.311833
18internlm/internlm2_5-7b-chat-1m1.020.3529010.0869790.3399990.0000000.3071490.30714900.348325
19shenzhi-wang/Llama3.1-70B-Chinese-Chat1.000.3816860.1151830.3705720.0000000.4068840.40688400.375187
20shenzhi-wang/Llama3.1-70B-Chinese-Chat1.020.3810850.1143410.3698760.0000000.4333630.43336300.374190
21shenzhi-wang/Llama3.1-70B-Chinese-Chat1.040.3801910.1135320.3693890.0000000.4236540.42365400.373461
22shenzhi-wang/Llama3.1-70B-Chinese-Chat1.060.3786220.1122050.3689340.0000000.4236540.42365400.371920
\n","
"],"text/plain":[" model rpp meteor bleu_1 \\\n","0 Qwen/Qwen2-72B-Instruct 1.00 0.394969 0.122949 \n","1 Qwen/Qwen2-7B-Instruct 1.00 0.375794 0.112577 \n","2 Qwen/Qwen2-7B-Instruct 1.02 0.376816 0.115539 \n","3 Qwen/Qwen2-7B-Instruct 1.04 0.371515 0.113116 \n","4 Qwen/Qwen2-7B-Instruct 1.06 0.372161 0.109860 \n","5 Qwen/Qwen2-7B-Instruct 1.08 0.371297 0.108095 \n","6 Qwen/Qwen2-7B-Instruct 1.10 0.371353 0.108097 \n","7 Qwen/Qwen2-7B-Instruct 1.12 0.368204 0.105056 \n","8 Qwen/Qwen2-7B-Instruct 1.14 0.363077 0.100516 \n","9 Qwen/Qwen2-7B-Instruct 1.16 0.360604 0.095724 \n","10 Qwen/Qwen2-7B-Instruct 1.18 0.360785 0.095713 \n","11 Qwen/Qwen2-7B-Instruct 1.20 0.356755 0.091249 \n","12 Qwen/Qwen2-7B-Instruct 1.22 0.351004 0.083507 \n","13 Qwen/Qwen2-7B-Instruct 1.24 0.346560 0.079543 \n","14 Qwen/Qwen2-7B-Instruct 1.26 0.343517 0.078588 \n","15 Qwen/Qwen2-7B-Instruct 1.28 0.340534 0.072038 \n","16 Qwen/Qwen2-7B-Instruct 1.30 0.334469 0.062148 \n","17 internlm/internlm2_5-7b-chat-1m 1.00 0.371535 0.105977 \n","18 internlm/internlm2_5-7b-chat-1m 1.02 0.352901 0.086979 \n","19 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.00 0.381686 0.115183 \n","20 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.02 0.381085 0.114341 \n","21 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.04 0.380191 0.113532 \n","22 shenzhi-wang/Llama3.1-70B-Chinese-Chat 1.06 0.378622 0.112205 \n","\n"," rouge_l ews_score repetition_score total_repetitions \\\n","0 0.383564 0.000000 0.360989 0.360989 \n","1 0.364250 0.000000 0.265666 0.265666 \n","2 0.369539 0.000000 0.255958 0.255958 \n","3 0.363711 0.000000 0.268314 0.268314 \n","4 0.360878 0.000000 0.281553 0.281553 \n","5 0.359401 0.000000 0.211827 0.211827 \n","6 0.359469 0.000000 0.253310 0.253310 \n","7 0.358380 0.000000 0.440424 0.440424 \n","8 0.349606 0.000000 0.280671 0.280671 \n","9 0.350540 0.000000 0.265666 0.265666 \n","10 0.344178 0.000000 0.285966 0.285966 \n","11 0.344236 0.000000 0.291262 0.291262 \n","12 0.336692 0.000000 0.269197 0.269197 \n","13 0.333088 0.000000 0.300971 0.300971 \n","14 0.330214 0.000000 0.266549 0.266549 \n","15 0.326096 0.000000 0.184466 0.184466 \n","16 0.321869 0.005296 0.325684 0.330980 \n","17 0.363133 0.000000 5.540159 5.540159 \n","18 0.339999 0.000000 0.307149 0.307149 \n","19 0.370572 0.000000 0.406884 0.406884 \n","20 0.369876 0.000000 0.433363 0.433363 \n","21 0.369389 0.000000 0.423654 0.423654 \n","22 0.368934 0.000000 0.423654 0.423654 \n","\n"," num_entries_with_max_output_tokens rap \n","0 0 0.388978 \n","1 0 0.371563 \n","2 0 0.372725 \n","3 0 0.367291 \n","4 0 0.367727 \n","5 0 0.367947 \n","6 0 0.367362 \n","7 0 0.361439 \n","8 0 0.358765 \n","9 0 0.356544 \n","10 0 0.356421 \n","11 0 0.352361 \n","12 0 0.347001 \n","13 0 0.342154 \n","14 0 0.339636 \n","15 0 0.337852 \n","16 1 0.329805 \n","17 1 0.311833 \n","18 0 0.348325 \n","19 0 0.375187 \n","20 0 0.374190 \n","21 0 0.373461 \n","22 0 0.371920 "]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["metrics_df"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["models = metrics_df[\"model\"].unique()"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"text/plain":["array(['Qwen/Qwen2-72B-Instruct', 'Qwen/Qwen2-7B-Instruct',\n"," 'internlm/internlm2_5-7b-chat-1m',\n"," 'shenzhi-wang/Llama3.1-70B-Chinese-Chat'], dtype=object)"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["models"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot meteor vs rpp\n","\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"meteor\"], label=model + \" (METEOR)\")\n"," ax.plot(\n"," model_df[\"rpp\"], model_df[\"rap\"], label=model + \" (RAP-METEOR)\", linestyle=\"--\"\n"," )\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"METEOR & RAP-METEOR\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.5))\n","plt.show()"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["# plot mtr vs rpp\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10, 6))\n","# set grid\n","ax.grid(True)\n","ax.set_axisbelow(True)\n","ax.minorticks_on()\n","ax.grid(which=\"major\", linestyle=\"-\", linewidth=\"0.5\", color=\"red\")\n","# ax.grid(which=\"minor\", linestyle=\":\", linewidth=\"0.5\", color=\"black\")\n","\n","for model in models:\n"," model_df = metrics_df[metrics_df[\"model\"] == model]\n"," ax.plot(model_df[\"rpp\"], model_df[\"total_repetitions\"], label=model)\n","\n","\n","ax.set_xlabel(\"Repetition Penalty Parameter\")\n","ax.set_ylabel(\"Mean Total Repetitions (MTR)\")\n","ax.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.3))\n","plt.show()"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[],"source":["tokenizers = {model: load_tokenizer(model) for model in models}"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[],"source":["col = \"internlm/internlm2_5-7b-chat-1m/rpp-1.00\"\n","df[[\"ews_score\", \"repetition_score\", \"total_repetitions\"]] = df[col].apply(\n"," detect_scores\n",")\n","df[\"output_tokens\"] = df[col].apply(\n"," lambda x: len(tokenizers[col.split(\"/rpp\")[0]](x)[\"input_ids\"])\n",")"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"data":{"text/html":["
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chineseenglishQwen/Qwen2-72B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.00Qwen/Qwen2-7B-Instruct/rpp-1.02Qwen/Qwen2-7B-Instruct/rpp-1.04Qwen/Qwen2-7B-Instruct/rpp-1.06Qwen/Qwen2-7B-Instruct/rpp-1.08Qwen/Qwen2-7B-Instruct/rpp-1.10Qwen/Qwen2-7B-Instruct/rpp-1.12...internlm/internlm2_5-7b-chat-1m/rpp-1.00internlm/internlm2_5-7b-chat-1m/rpp-1.02shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06ews_scorerepetition_scoretotal_repetitionsoutput_tokens
503青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟——A blue sky yo – a sapphire sky yo – a painted ...Blue sky oh—clear sky oh—colorful sky oh—my de...Blue sky oh - blue heaven oh - colorful sky oh...Blue heaven—oh, blue sky—oh, colorful sky—stic...Blue heaven—oh, blue sky—oh, colorful sky—stup...Blue sky oh - blue heaven oh - colorful sky oh...Blue sky—oh, blue heaven—colorful sky—stupid f...Blue sky - oh blue heaven - colorful sky - you...Blue sky - oh blue sky - colorful sky - you've......Oh, the blue sky, the blue sky, the sky with i...Oh blue sky - oh green sky - oh colorful sky -...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...Blue sky, oh — Green sky, oh — Colorful sky, o...0611261122049
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"],"text/plain":[" chinese \\\n","503 青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟—— \n","\n"," english \\\n","503 A blue sky yo – a sapphire sky yo – a painted ... \n","\n"," Qwen/Qwen2-72B-Instruct/rpp-1.00 \\\n","503 Blue sky oh—clear sky oh—colorful sky oh—my de... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.00 \\\n","503 Blue sky oh - blue heaven oh - colorful sky oh... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.02 \\\n","503 Blue heaven—oh, blue sky—oh, colorful sky—stic... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.04 \\\n","503 Blue heaven—oh, blue sky—oh, colorful sky—stup... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.06 \\\n","503 Blue sky oh - blue heaven oh - colorful sky oh... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.08 \\\n","503 Blue sky—oh, blue heaven—colorful sky—stupid f... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.10 \\\n","503 Blue sky - oh blue heaven - colorful sky - you... \n","\n"," Qwen/Qwen2-7B-Instruct/rpp-1.12 ... \\\n","503 Blue sky - oh blue sky - colorful sky - you've... ... \n","\n"," internlm/internlm2_5-7b-chat-1m/rpp-1.00 \\\n","503 Oh, the blue sky, the blue sky, the sky with i... \n","\n"," internlm/internlm2_5-7b-chat-1m/rpp-1.02 \\\n","503 Oh blue sky - oh green sky - oh colorful sky -... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.00 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.02 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.04 \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... \n","\n"," shenzhi-wang/Llama3.1-70B-Chinese-Chat/rpp-1.06 ews_score \\\n","503 Blue sky, oh — Green sky, oh — Colorful sky, o... 0 \n","\n"," repetition_score total_repetitions output_tokens \n","503 6112 6112 2049 \n","\n","[1 rows x 29 columns]"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["rows = df.query(\"total_repetitions > 1000\")\n","rows"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[],"source":["row = rows.iloc[0]"]},{"cell_type":"code","execution_count":45,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["青天哟——蓝天哟——花花绿绿的天哟——棒槌哟亲哥哟你死了——可就塌了妹妹的天哟——\n"]}],"source":["print(row[\"chinese\"])"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["A blue sky yo – a sapphire sky yo – a painted sky yo – a mighty cudgel yo – dear elder brother yo – death has claimed you – you have brought down little sister's sky yo –.\n"]}],"source":["print(row[\"english\"])"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Oh, the blue sky, the blue sky, the sky with its colorful hues, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the 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sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky, the sky\n","----detect excessive whitespaces----\n","----detect text repetitions----\n","\n","Group 1 found at 2-16: `, the blue sky`\n","Group 2 found at 16-30: `, the blue sky`\n","Group 3 found at 16-30: `, the blue sky`\n","