Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,1257 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:9020
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/all-MiniLM-L6-v2
+widget:
+- source_sentence: python multiprocessing show cpu count
+  sentences:
+  - "def unique(seq):\n    \"\"\"Return the unique elements of a collection even if\
+    \ those elements are\n       unhashable and unsortable, like dicts and sets\"\"\
+    \"\n    cleaned = []\n    for each in seq:\n        if each not in cleaned:\n\
+    \            cleaned.append(each)\n    return cleaned"
+  - "def is_in(self, point_x, point_y):\n        \"\"\" Test if a point is within\
+    \ this polygonal region \"\"\"\n\n        point_array = array(((point_x, point_y),))\n\
+    \        vertices = array(self.points)\n        winding = self.inside_rule ==\
+    \ \"winding\"\n        result = points_in_polygon(point_array, vertices, winding)\n\
+    \        return result[0]"
+  - "def machine_info():\n    \"\"\"Retrieve core and memory information for the current\
+    \ machine.\n    \"\"\"\n    import psutil\n    BYTES_IN_GIG = 1073741824.0\n \
+    \   free_bytes = psutil.virtual_memory().total\n    return [{\"memory\": float(\"\
+    %.1f\" % (free_bytes / BYTES_IN_GIG)), \"cores\": multiprocessing.cpu_count(),\n\
+    \             \"name\": socket.gethostname()}]"
+- source_sentence: python subplot set the whole title
+  sentences:
+  - "def set_title(self, title, **kwargs):\n        \"\"\"Sets the title on the underlying\
+    \ matplotlib AxesSubplot.\"\"\"\n        ax = self.get_axes()\n        ax.set_title(title,\
+    \ **kwargs)"
+  - "def moving_average(array, n=3):\n    \"\"\"\n    Calculates the moving average\
+    \ of an array.\n\n    Parameters\n    ----------\n    array : array\n        The\
+    \ array to have the moving average taken of\n    n : int\n        The number of\
+    \ points of moving average to take\n    \n    Returns\n    -------\n    MovingAverageArray\
+    \ : array\n        The n-point moving average of the input array\n    \"\"\"\n\
+    \    ret = _np.cumsum(array, dtype=float)\n    ret[n:] = ret[n:] - ret[:-n]\n\
+    \    return ret[n - 1:] / n"
+  - "def to_query_parameters(parameters):\n    \"\"\"Converts DB-API parameter values\
+    \ into query parameters.\n\n    :type parameters: Mapping[str, Any] or Sequence[Any]\n\
+    \    :param parameters: A dictionary or sequence of query parameter values.\n\n\
+    \    :rtype: List[google.cloud.bigquery.query._AbstractQueryParameter]\n    :returns:\
+    \ A list of query parameters.\n    \"\"\"\n    if parameters is None:\n      \
+    \  return []\n\n    if isinstance(parameters, collections_abc.Mapping):\n    \
+    \    return to_query_parameters_dict(parameters)\n\n    return to_query_parameters_list(parameters)"
+- source_sentence: python merge two set to dict
+  sentences:
+  - "def make_regex(separator):\n    \"\"\"Utility function to create regexp for matching\
+    \ escaped separators\n    in strings.\n\n    \"\"\"\n    return re.compile(r'(?:'\
+    \ + re.escape(separator) + r')?((?:[^' +\n                      re.escape(separator)\
+    \ + r'\\\\]|\\\\.)+)')"
+  - "def csvtolist(inputstr):\n    \"\"\" converts a csv string into a list \"\"\"\
+    \n    reader = csv.reader([inputstr], skipinitialspace=True)\n    output = []\n\
+    \    for r in reader:\n        output += r\n    return output"
+  - "def dict_merge(set1, set2):\n    \"\"\"Joins two dictionaries.\"\"\"\n    return\
+    \ dict(list(set1.items()) + list(set2.items()))"
+- source_sentence: python string % substitution float
+  sentences:
+  - "def _configure_logger():\n    \"\"\"Configure the logging module.\"\"\"\n   \
+    \ if not app.debug:\n        _configure_logger_for_production(logging.getLogger())\n\
+    \    elif not app.testing:\n        _configure_logger_for_debugging(logging.getLogger())"
+  - "def __set__(self, instance, value):\n        \"\"\" Set a related object for\
+    \ an instance. \"\"\"\n\n        self.map[id(instance)] = (weakref.ref(instance),\
+    \ value)"
+  - "def format_float(value): # not used\n    \"\"\"Modified form of the 'g' format\
+    \ specifier.\n    \"\"\"\n    string = \"{:g}\".format(value).replace(\"e+\",\
+    \ \"e\")\n    string = re.sub(\"e(-?)0*(\\d+)\", r\"e\\1\\2\", string)\n    return\
+    \ string"
+- source_sentence: bottom 5 rows in python
+  sentences:
+  - "def refresh(self, document):\n\t\t\"\"\" Load a new copy of a document from the\
+    \ database.  does not\n\t\t\treplace the old one \"\"\"\n\t\ttry:\n\t\t\told_cache_size\
+    \ = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\
+    \t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\
+    \t\treturn obj"
+  - "def table_top_abs(self):\n        \"\"\"Returns the absolute position of table\
+    \ top\"\"\"\n        table_height = np.array([0, 0, self.table_full_size[2]])\n\
+    \        return string_to_array(self.floor.get(\"pos\")) + table_height"
+  - "def get_dimension_array(array):\n    \"\"\"\n    Get dimension of an array getting\
+    \ the number of rows and the max num of\n    columns.\n    \"\"\"\n    if all(isinstance(el,\
+    \ list) for el in array):\n        result = [len(array), len(max([x for x in array],\
+    \ key=len,))]\n\n    # elif array and isinstance(array, list):\n    else:\n  \
+    \      result = [len(array), 1]\n\n    return result"
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
+- **Maximum Sequence Length:** 256 tokens
+- **Output Dimensionality:** 384 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Devy1/MiniLM-cosqa-32")
+# Run inference
+sentences = [
+    'bottom 5 rows in python',
+    'def table_top_abs(self):\n        """Returns the absolute position of table top"""\n        table_height = np.array([0, 0, self.table_full_size[2]])\n        return string_to_array(self.floor.get("pos")) + table_height',
+    'def refresh(self, document):\n\t\t""" Load a new copy of a document from the database.  does not\n\t\t\treplace the old one """\n\t\ttry:\n\t\t\told_cache_size = self.cache_size\n\t\t\tself.cache_size = 0\n\t\t\tobj = self.query(type(document)).filter_by(mongo_id=document.mongo_id).one()\n\t\tfinally:\n\t\t\tself.cache_size = old_cache_size\n\t\tself.cache_write(obj)\n\t\treturn obj',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 384]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[ 1.0000,  0.4728, -0.0350],
+#         [ 0.4728,  1.0000, -0.0494],
+#         [-0.0350, -0.0494,  1.0000]])
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 9,020 training samples
+* Columns: <code>anchor</code> and <code>positive</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                           | positive                                                                            |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.67 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 40 tokens</li><li>mean: 86.17 tokens</li><li>max: 256 tokens</li></ul> |
+* Samples:
+  | anchor                                                  | positive                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>1d array in char datatype in python</code>        | <code>def _convert_to_array(array_like, dtype):<br>        """<br>        Convert Matrix attributes which are array-like or buffer to array.<br>        """<br>        if isinstance(array_like, bytes):<br>            return np.frombuffer(array_like, dtype=dtype)<br>        return np.asarray(array_like, dtype=dtype)</code>                                                                                            |
+  | <code>python condition non none</code>                  | <code>def _not(condition=None, **kwargs):<br>    """<br>    Return the opposite of input condition.<br><br>    :param condition: condition to process.<br><br>    :result: not condition.<br>    :rtype: bool<br>    """<br><br>    result = True<br><br>    if condition is not None:<br>        result = not run(condition, **kwargs)<br><br>    return result</code>                                                       |
+  | <code>accessing a column from a matrix in python</code> | <code>def get_column(self, X, column):<br>        """Return a column of the given matrix.<br><br>        Args:<br>            X: `numpy.ndarray` or `pandas.DataFrame`.<br>            column: `int` or `str`.<br><br>        Returns:<br>            np.ndarray: Selected column.<br>        """<br>        if isinstance(X, pd.DataFrame):<br>            return X[column].values<br><br>        return X[:, column]</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 32
+- `fp16`: True
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+<details><summary>Click to expand</summary>
+
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0035 | 1    | 0.5705        |
+| 0.0071 | 2    | 0.1217        |
+| 0.0106 | 3    | 0.1985        |
+| 0.0142 | 4    | 0.2742        |
+| 0.0177 | 5    | 0.0782        |
+| 0.0213 | 6    | 0.1748        |
+| 0.0248 | 7    | 0.1914        |
+| 0.0284 | 8    | 0.0911        |
+| 0.0319 | 9    | 0.0368        |
+| 0.0355 | 10   | 0.219         |
+| 0.0390 | 11   | 0.1571        |
+| 0.0426 | 12   | 0.081         |
+| 0.0461 | 13   | 0.1152        |
+| 0.0496 | 14   | 0.0556        |
+| 0.0532 | 15   | 0.1375        |
+| 0.0567 | 16   | 0.1844        |
+| 0.0603 | 17   | 0.3164        |
+| 0.0638 | 18   | 0.2312        |
+| 0.0674 | 19   | 0.1767        |
+| 0.0709 | 20   | 0.0975        |
+| 0.0745 | 21   | 0.2848        |
+| 0.0780 | 22   | 0.0972        |
+| 0.0816 | 23   | 0.3153        |
+| 0.0851 | 24   | 0.1087        |
+| 0.0887 | 25   | 0.1673        |
+| 0.0922 | 26   | 0.2074        |
+| 0.0957 | 27   | 0.2197        |
+| 0.0993 | 28   | 0.2571        |
+| 0.1028 | 29   | 0.1873        |
+| 0.1064 | 30   | 0.0657        |
+| 0.1099 | 31   | 0.0675        |
+| 0.1135 | 32   | 0.0749        |
+| 0.1170 | 33   | 0.0948        |
+| 0.1206 | 34   | 0.0849        |
+| 0.1241 | 35   | 0.0882        |
+| 0.1277 | 36   | 0.0436        |
+| 0.1312 | 37   | 0.1173        |
+| 0.1348 | 38   | 0.1512        |
+| 0.1383 | 39   | 0.1062        |
+| 0.1418 | 40   | 0.0384        |
+| 0.1454 | 41   | 0.148         |
+| 0.1489 | 42   | 0.0432        |
+| 0.1525 | 43   | 0.1027        |
+| 0.1560 | 44   | 0.4193        |
+| 0.1596 | 45   | 0.1003        |
+| 0.1631 | 46   | 0.113         |
+| 0.1667 | 47   | 0.0846        |
+| 0.1702 | 48   | 0.0899        |
+| 0.1738 | 49   | 0.0952        |
+| 0.1773 | 50   | 0.0553        |
+| 0.1809 | 51   | 0.11          |
+| 0.1844 | 52   | 0.1955        |
+| 0.1879 | 53   | 0.1103        |
+| 0.1915 | 54   | 0.0738        |
+| 0.1950 | 55   | 0.1217        |
+| 0.1986 | 56   | 0.274         |
+| 0.2021 | 57   | 0.1471        |
+| 0.2057 | 58   | 0.0727        |
+| 0.2092 | 59   | 0.0438        |
+| 0.2128 | 60   | 0.1521        |
+| 0.2163 | 61   | 0.1359        |
+| 0.2199 | 62   | 0.1217        |
+| 0.2234 | 63   | 0.2226        |
+| 0.2270 | 64   | 0.2676        |
+| 0.2305 | 65   | 0.1649        |
+| 0.2340 | 66   | 0.1675        |
+| 0.2376 | 67   | 0.1278        |
+| 0.2411 | 68   | 0.1627        |
+| 0.2447 | 69   | 0.108         |
+| 0.2482 | 70   | 0.1327        |
+| 0.2518 | 71   | 0.1762        |
+| 0.2553 | 72   | 0.41          |
+| 0.2589 | 73   | 0.1551        |
+| 0.2624 | 74   | 0.1893        |
+| 0.2660 | 75   | 0.0847        |
+| 0.2695 | 76   | 0.0949        |
+| 0.2730 | 77   | 0.2214        |
+| 0.2766 | 78   | 0.0439        |
+| 0.2801 | 79   | 0.1355        |
+| 0.2837 | 80   | 0.1951        |
+| 0.2872 | 81   | 0.068         |
+| 0.2908 | 82   | 0.1032        |
+| 0.2943 | 83   | 0.1131        |
+| 0.2979 | 84   | 0.2245        |
+| 0.3014 | 85   | 0.2323        |
+| 0.3050 | 86   | 0.1512        |
+| 0.3085 | 87   | 0.1686        |
+| 0.3121 | 88   | 0.0797        |
+| 0.3156 | 89   | 0.2182        |
+| 0.3191 | 90   | 0.2181        |
+| 0.3227 | 91   | 0.0944        |
+| 0.3262 | 92   | 0.083         |
+| 0.3298 | 93   | 0.1554        |
+| 0.3333 | 94   | 0.0999        |
+| 0.3369 | 95   | 0.1948        |
+| 0.3404 | 96   | 0.1446        |
+| 0.3440 | 97   | 0.2856        |
+| 0.3475 | 98   | 0.0786        |
+| 0.3511 | 99   | 0.1112        |
+| 0.3546 | 100  | 0.0571        |
+| 0.3582 | 101  | 0.2553        |
+| 0.3617 | 102  | 0.0546        |
+| 0.3652 | 103  | 0.1948        |
+| 0.3688 | 104  | 0.0945        |
+| 0.3723 | 105  | 0.0973        |
+| 0.3759 | 106  | 0.0478        |
+| 0.3794 | 107  | 0.3652        |
+| 0.3830 | 108  | 0.2676        |
+| 0.3865 | 109  | 0.1216        |
+| 0.3901 | 110  | 0.0701        |
+| 0.3936 | 111  | 0.0918        |
+| 0.3972 | 112  | 0.1813        |
+| 0.4007 | 113  | 0.1243        |
+| 0.4043 | 114  | 0.2819        |
+| 0.4078 | 115  | 0.0103        |
+| 0.4113 | 116  | 0.2099        |
+| 0.4149 | 117  | 0.0879        |
+| 0.4184 | 118  | 0.1614        |
+| 0.4220 | 119  | 0.0869        |
+| 0.4255 | 120  | 0.0942        |
+| 0.4291 | 121  | 0.0592        |
+| 0.4326 | 122  | 0.1387        |
+| 0.4362 | 123  | 0.0805        |
+| 0.4397 | 124  | 0.1844        |
+| 0.4433 | 125  | 0.0292        |
+| 0.4468 | 126  | 0.3999        |
+| 0.4504 | 127  | 0.1031        |
+| 0.4539 | 128  | 0.3445        |
+| 0.4574 | 129  | 0.2309        |
+| 0.4610 | 130  | 0.1887        |
+| 0.4645 | 131  | 0.2472        |
+| 0.4681 | 132  | 0.1128        |
+| 0.4716 | 133  | 0.1276        |
+| 0.4752 | 134  | 0.1141        |
+| 0.4787 | 135  | 0.1117        |
+| 0.4823 | 136  | 0.1593        |
+| 0.4858 | 137  | 0.0363        |
+| 0.4894 | 138  | 0.1564        |
+| 0.4929 | 139  | 0.21          |
+| 0.4965 | 140  | 0.2024        |
+| 0.5    | 141  | 0.1785        |
+| 0.5035 | 142  | 0.1456        |
+| 0.5071 | 143  | 0.0986        |
+| 0.5106 | 144  | 0.1947        |
+| 0.5142 | 145  | 0.1733        |
+| 0.5177 | 146  | 0.1656        |
+| 0.5213 | 147  | 0.0951        |
+| 0.5248 | 148  | 0.1216        |
+| 0.5284 | 149  | 0.0875        |
+| 0.5319 | 150  | 0.1284        |
+| 0.5355 | 151  | 0.1066        |
+| 0.5390 | 152  | 0.0692        |
+| 0.5426 | 153  | 0.2287        |
+| 0.5461 | 154  | 0.233         |
+| 0.5496 | 155  | 0.1066        |
+| 0.5532 | 156  | 0.0862        |
+| 0.5567 | 157  | 0.0877        |
+| 0.5603 | 158  | 0.3095        |
+| 0.5638 | 159  | 0.1237        |
+| 0.5674 | 160  | 0.0682        |
+| 0.5709 | 161  | 0.0741        |
+| 0.5745 | 162  | 0.2003        |
+| 0.5780 | 163  | 0.1392        |
+| 0.5816 | 164  | 0.0493        |
+| 0.5851 | 165  | 0.3129        |
+| 0.5887 | 166  | 0.1186        |
+| 0.5922 | 167  | 0.0369        |
+| 0.5957 | 168  | 0.1224        |
+| 0.5993 | 169  | 0.2212        |
+| 0.6028 | 170  | 0.0809        |
+| 0.6064 | 171  | 0.116         |
+| 0.6099 | 172  | 0.2251        |
+| 0.6135 | 173  | 0.0195        |
+| 0.6170 | 174  | 0.0476        |
+| 0.6206 | 175  | 0.0818        |
+| 0.6241 | 176  | 0.0313        |
+| 0.6277 | 177  | 0.188         |
+| 0.6312 | 178  | 0.2736        |
+| 0.6348 | 179  | 0.1444        |
+| 0.6383 | 180  | 0.0924        |
+| 0.6418 | 181  | 0.0895        |
+| 0.6454 | 182  | 0.2116        |
+| 0.6489 | 183  | 0.3288        |
+| 0.6525 | 184  | 0.1659        |
+| 0.6560 | 185  | 0.1367        |
+| 0.6596 | 186  | 0.1834        |
+| 0.6631 | 187  | 0.0822        |
+| 0.6667 | 188  | 0.1384        |
+| 0.6702 | 189  | 0.1602        |
+| 0.6738 | 190  | 0.1325        |
+| 0.6773 | 191  | 0.1033        |
+| 0.6809 | 192  | 0.1102        |
+| 0.6844 | 193  | 0.0786        |
+| 0.6879 | 194  | 0.1158        |
+| 0.6915 | 195  | 0.0639        |
+| 0.6950 | 196  | 0.18          |
+| 0.6986 | 197  | 0.0512        |
+| 0.7021 | 198  | 0.1271        |
+| 0.7057 | 199  | 0.0839        |
+| 0.7092 | 200  | 0.0838        |
+| 0.7128 | 201  | 0.0691        |
+| 0.7163 | 202  | 0.1457        |
+| 0.7199 | 203  | 0.1363        |
+| 0.7234 | 204  | 0.1059        |
+| 0.7270 | 205  | 0.1051        |
+| 0.7305 | 206  | 0.0541        |
+| 0.7340 | 207  | 0.1409        |
+| 0.7376 | 208  | 0.0911        |
+| 0.7411 | 209  | 0.2823        |
+| 0.7447 | 210  | 0.156         |
+| 0.7482 | 211  | 0.394         |
+| 0.7518 | 212  | 0.1946        |
+| 0.7553 | 213  | 0.0282        |
+| 0.7589 | 214  | 0.1497        |
+| 0.7624 | 215  | 0.1643        |
+| 0.7660 | 216  | 0.0236        |
+| 0.7695 | 217  | 0.0654        |
+| 0.7730 | 218  | 0.0537        |
+| 0.7766 | 219  | 0.1068        |
+| 0.7801 | 220  | 0.051         |
+| 0.7837 | 221  | 0.072         |
+| 0.7872 | 222  | 0.0413        |
+| 0.7908 | 223  | 0.0918        |
+| 0.7943 | 224  | 0.1308        |
+| 0.7979 | 225  | 0.0694        |
+| 0.8014 | 226  | 0.0852        |
+| 0.8050 | 227  | 0.0321        |
+| 0.8085 | 228  | 0.1497        |
+| 0.8121 | 229  | 0.0959        |
+| 0.8156 | 230  | 0.226         |
+| 0.8191 | 231  | 0.1129        |
+| 0.8227 | 232  | 0.0831        |
+| 0.8262 | 233  | 0.2181        |
+| 0.8298 | 234  | 0.1054        |
+| 0.8333 | 235  | 0.1812        |
+| 0.8369 | 236  | 0.0455        |
+| 0.8404 | 237  | 0.1413        |
+| 0.8440 | 238  | 0.0801        |
+| 0.8475 | 239  | 0.0301        |
+| 0.8511 | 240  | 0.0846        |
+| 0.8546 | 241  | 0.1862        |
+| 0.8582 | 242  | 0.1015        |
+| 0.8617 | 243  | 0.0459        |
+| 0.8652 | 244  | 0.0774        |
+| 0.8688 | 245  | 0.1444        |
+| 0.8723 | 246  | 0.2849        |
+| 0.8759 | 247  | 0.3935        |
+| 0.8794 | 248  | 0.2126        |
+| 0.8830 | 249  | 0.0845        |
+| 0.8865 | 250  | 0.1429        |
+| 0.8901 | 251  | 0.0107        |
+| 0.8936 | 252  | 0.0599        |
+| 0.8972 | 253  | 0.1192        |
+| 0.9007 | 254  | 0.1369        |
+| 0.9043 | 255  | 0.1246        |
+| 0.9078 | 256  | 0.0163        |
+| 0.9113 | 257  | 0.1844        |
+| 0.9149 | 258  | 0.1017        |
+| 0.9184 | 259  | 0.0415        |
+| 0.9220 | 260  | 0.1658        |
+| 0.9255 | 261  | 0.0755        |
+| 0.9291 | 262  | 0.086         |
+| 0.9326 | 263  | 0.081         |
+| 0.9362 | 264  | 0.2776        |
+| 0.9397 | 265  | 0.1284        |
+| 0.9433 | 266  | 0.1591        |
+| 0.9468 | 267  | 0.1397        |
+| 0.9504 | 268  | 0.0334        |
+| 0.9539 | 269  | 0.0449        |
+| 0.9574 | 270  | 0.1382        |
+| 0.9610 | 271  | 0.1736        |
+| 0.9645 | 272  | 0.236         |
+| 0.9681 | 273  | 0.225         |
+| 0.9716 | 274  | 0.2444        |
+| 0.9752 | 275  | 0.0497        |
+| 0.9787 | 276  | 0.1212        |
+| 0.9823 | 277  | 0.1405        |
+| 0.9858 | 278  | 0.1116        |
+| 0.9894 | 279  | 0.0369        |
+| 0.9929 | 280  | 0.0321        |
+| 0.9965 | 281  | 0.1481        |
+| 1.0    | 282  | 0.1046        |
+| 1.0035 | 283  | 0.0673        |
+| 1.0071 | 284  | 0.078         |
+| 1.0106 | 285  | 0.0723        |
+| 1.0142 | 286  | 0.1328        |
+| 1.0177 | 287  | 0.1399        |
+| 1.0213 | 288  | 0.186         |
+| 1.0248 | 289  | 0.0747        |
+| 1.0284 | 290  | 0.0291        |
+| 1.0319 | 291  | 0.0427        |
+| 1.0355 | 292  | 0.0288        |
+| 1.0390 | 293  | 0.1552        |
+| 1.0426 | 294  | 0.0123        |
+| 1.0461 | 295  | 0.0617        |
+| 1.0496 | 296  | 0.0646        |
+| 1.0532 | 297  | 0.2001        |
+| 1.0567 | 298  | 0.068         |
+| 1.0603 | 299  | 0.0108        |
+| 1.0638 | 300  | 0.0776        |
+| 1.0674 | 301  | 0.1037        |
+| 1.0709 | 302  | 0.0087        |
+| 1.0745 | 303  | 0.1564        |
+| 1.0780 | 304  | 0.0665        |
+| 1.0816 | 305  | 0.0246        |
+| 1.0851 | 306  | 0.061         |
+| 1.0887 | 307  | 0.038         |
+| 1.0922 | 308  | 0.1016        |
+| 1.0957 | 309  | 0.0434        |
+| 1.0993 | 310  | 0.1178        |
+| 1.1028 | 311  | 0.1235        |
+| 1.1064 | 312  | 0.0164        |
+| 1.1099 | 313  | 0.0838        |
+| 1.1135 | 314  | 0.0516        |
+| 1.1170 | 315  | 0.1195        |
+| 1.1206 | 316  | 0.1026        |
+| 1.1241 | 317  | 0.0387        |
+| 1.1277 | 318  | 0.1057        |
+| 1.1312 | 319  | 0.0332        |
+| 1.1348 | 320  | 0.033         |
+| 1.1383 | 321  | 0.0648        |
+| 1.1418 | 322  | 0.0067        |
+| 1.1454 | 323  | 0.0402        |
+| 1.1489 | 324  | 0.1376        |
+| 1.1525 | 325  | 0.0852        |
+| 1.1560 | 326  | 0.0245        |
+| 1.1596 | 327  | 0.087         |
+| 1.1631 | 328  | 0.0403        |
+| 1.1667 | 329  | 0.0998        |
+| 1.1702 | 330  | 0.0634        |
+| 1.1738 | 331  | 0.0218        |
+| 1.1773 | 332  | 0.1244        |
+| 1.1809 | 333  | 0.1178        |
+| 1.1844 | 334  | 0.1135        |
+| 1.1879 | 335  | 0.0721        |
+| 1.1915 | 336  | 0.0427        |
+| 1.1950 | 337  | 0.0314        |
+| 1.1986 | 338  | 0.0577        |
+| 1.2021 | 339  | 0.0337        |
+| 1.2057 | 340  | 0.0312        |
+| 1.2092 | 341  | 0.0336        |
+| 1.2128 | 342  | 0.0289        |
+| 1.2163 | 343  | 0.0946        |
+| 1.2199 | 344  | 0.2581        |
+| 1.2234 | 345  | 0.1359        |
+| 1.2270 | 346  | 0.0223        |
+| 1.2305 | 347  | 0.055         |
+| 1.2340 | 348  | 0.0591        |
+| 1.2376 | 349  | 0.0286        |
+| 1.2411 | 350  | 0.0128        |
+| 1.2447 | 351  | 0.0676        |
+| 1.2482 | 352  | 0.0744        |
+| 1.2518 | 353  | 0.0208        |
+| 1.2553 | 354  | 0.0877        |
+| 1.2589 | 355  | 0.0759        |
+| 1.2624 | 356  | 0.052         |
+| 1.2660 | 357  | 0.2666        |
+| 1.2695 | 358  | 0.0455        |
+| 1.2730 | 359  | 0.0893        |
+| 1.2766 | 360  | 0.1706        |
+| 1.2801 | 361  | 0.059         |
+| 1.2837 | 362  | 0.049         |
+| 1.2872 | 363  | 0.1249        |
+| 1.2908 | 364  | 0.0229        |
+| 1.2943 | 365  | 0.1088        |
+| 1.2979 | 366  | 0.198         |
+| 1.3014 | 367  | 0.2119        |
+| 1.3050 | 368  | 0.0397        |
+| 1.3085 | 369  | 0.1772        |
+| 1.3121 | 370  | 0.1251        |
+| 1.3156 | 371  | 0.0286        |
+| 1.3191 | 372  | 0.0273        |
+| 1.3227 | 373  | 0.1161        |
+| 1.3262 | 374  | 0.1128        |
+| 1.3298 | 375  | 0.1323        |
+| 1.3333 | 376  | 0.0245        |
+| 1.3369 | 377  | 0.0342        |
+| 1.3404 | 378  | 0.1177        |
+| 1.3440 | 379  | 0.0584        |
+| 1.3475 | 380  | 0.0164        |
+| 1.3511 | 381  | 0.1174        |
+| 1.3546 | 382  | 0.043         |
+| 1.3582 | 383  | 0.0706        |
+| 1.3617 | 384  | 0.0862        |
+| 1.3652 | 385  | 0.1093        |
+| 1.3688 | 386  | 0.0849        |
+| 1.3723 | 387  | 0.0252        |
+| 1.3759 | 388  | 0.0517        |
+| 1.3794 | 389  | 0.0634        |
+| 1.3830 | 390  | 0.0526        |
+| 1.3865 | 391  | 0.1388        |
+| 1.3901 | 392  | 0.0747        |
+| 1.3936 | 393  | 0.0362        |
+| 1.3972 | 394  | 0.1148        |
+| 1.4007 | 395  | 0.0208        |
+| 1.4043 | 396  | 0.1426        |
+| 1.4078 | 397  | 0.1611        |
+| 1.4113 | 398  | 0.0302        |
+| 1.4149 | 399  | 0.0446        |
+| 1.4184 | 400  | 0.0182        |
+| 1.4220 | 401  | 0.089         |
+| 1.4255 | 402  | 0.1423        |
+| 1.4291 | 403  | 0.1599        |
+| 1.4326 | 404  | 0.0438        |
+| 1.4362 | 405  | 0.0103        |
+| 1.4397 | 406  | 0.083         |
+| 1.4433 | 407  | 0.0914        |
+| 1.4468 | 408  | 0.0436        |
+| 1.4504 | 409  | 0.124         |
+| 1.4539 | 410  | 0.0896        |
+| 1.4574 | 411  | 0.256         |
+| 1.4610 | 412  | 0.0061        |
+| 1.4645 | 413  | 0.0529        |
+| 1.4681 | 414  | 0.0851        |
+| 1.4716 | 415  | 0.08          |
+| 1.4752 | 416  | 0.0115        |
+| 1.4787 | 417  | 0.0784        |
+| 1.4823 | 418  | 0.0321        |
+| 1.4858 | 419  | 0.0976        |
+| 1.4894 | 420  | 0.0725        |
+| 1.4929 | 421  | 0.0834        |
+| 1.4965 | 422  | 0.122         |
+| 1.5    | 423  | 0.1294        |
+| 1.5035 | 424  | 0.2754        |
+| 1.5071 | 425  | 0.0884        |
+| 1.5106 | 426  | 0.076         |
+| 1.5142 | 427  | 0.0799        |
+| 1.5177 | 428  | 0.0439        |
+| 1.5213 | 429  | 0.0943        |
+| 1.5248 | 430  | 0.077         |
+| 1.5284 | 431  | 0.0696        |
+| 1.5319 | 432  | 0.0251        |
+| 1.5355 | 433  | 0.1715        |
+| 1.5390 | 434  | 0.0913        |
+| 1.5426 | 435  | 0.0251        |
+| 1.5461 | 436  | 0.0642        |
+| 1.5496 | 437  | 0.0375        |
+| 1.5532 | 438  | 0.0381        |
+| 1.5567 | 439  | 0.0628        |
+| 1.5603 | 440  | 0.095         |
+| 1.5638 | 441  | 0.0441        |
+| 1.5674 | 442  | 0.0496        |
+| 1.5709 | 443  | 0.0531        |
+| 1.5745 | 444  | 0.0304        |
+| 1.5780 | 445  | 0.2032        |
+| 1.5816 | 446  | 0.109         |
+| 1.5851 | 447  | 0.1481        |
+| 1.5887 | 448  | 0.0706        |
+| 1.5922 | 449  | 0.0346        |
+| 1.5957 | 450  | 0.0364        |
+| 1.5993 | 451  | 0.0513        |
+| 1.6028 | 452  | 0.3153        |
+| 1.6064 | 453  | 0.1135        |
+| 1.6099 | 454  | 0.1034        |
+| 1.6135 | 455  | 0.0566        |
+| 1.6170 | 456  | 0.0707        |
+| 1.6206 | 457  | 0.1564        |
+| 1.6241 | 458  | 0.1602        |
+| 1.6277 | 459  | 0.0149        |
+| 1.6312 | 460  | 0.1243        |
+| 1.6348 | 461  | 0.0579        |
+| 1.6383 | 462  | 0.1693        |
+| 1.6418 | 463  | 0.0911        |
+| 1.6454 | 464  | 0.0278        |
+| 1.6489 | 465  | 0.0315        |
+| 1.6525 | 466  | 0.0176        |
+| 1.6560 | 467  | 0.1197        |
+| 1.6596 | 468  | 0.0162        |
+| 1.6631 | 469  | 0.0492        |
+| 1.6667 | 470  | 0.0495        |
+| 1.6702 | 471  | 0.0318        |
+| 1.6738 | 472  | 0.0703        |
+| 1.6773 | 473  | 0.0175        |
+| 1.6809 | 474  | 0.1457        |
+| 1.6844 | 475  | 0.026         |
+| 1.6879 | 476  | 0.067         |
+| 1.6915 | 477  | 0.0657        |
+| 1.6950 | 478  | 0.1421        |
+| 1.6986 | 479  | 0.0341        |
+| 1.7021 | 480  | 0.022         |
+| 1.7057 | 481  | 0.0641        |
+| 1.7092 | 482  | 0.1315        |
+| 1.7128 | 483  | 0.0328        |
+| 1.7163 | 484  | 0.0489        |
+| 1.7199 | 485  | 0.0199        |
+| 1.7234 | 486  | 0.0475        |
+| 1.7270 | 487  | 0.0662        |
+| 1.7305 | 488  | 0.0133        |
+| 1.7340 | 489  | 0.0081        |
+| 1.7376 | 490  | 0.0356        |
+| 1.7411 | 491  | 0.092         |
+| 1.7447 | 492  | 0.0653        |
+| 1.7482 | 493  | 0.0457        |
+| 1.7518 | 494  | 0.0949        |
+| 1.7553 | 495  | 0.0108        |
+| 1.7589 | 496  | 0.0287        |
+| 1.7624 | 497  | 0.1043        |
+| 1.7660 | 498  | 0.0166        |
+| 1.7695 | 499  | 0.0068        |
+| 1.7730 | 500  | 0.1521        |
+| 1.7766 | 501  | 0.0356        |
+| 1.7801 | 502  | 0.0083        |
+| 1.7837 | 503  | 0.1221        |
+| 1.7872 | 504  | 0.046         |
+| 1.7908 | 505  | 0.0339        |
+| 1.7943 | 506  | 0.021         |
+| 1.7979 | 507  | 0.1706        |
+| 1.8014 | 508  | 0.0176        |
+| 1.8050 | 509  | 0.0275        |
+| 1.8085 | 510  | 0.0521        |
+| 1.8121 | 511  | 0.1083        |
+| 1.8156 | 512  | 0.098         |
+| 1.8191 | 513  | 0.0746        |
+| 1.8227 | 514  | 0.0944        |
+| 1.8262 | 515  | 0.075         |
+| 1.8298 | 516  | 0.0997        |
+| 1.8333 | 517  | 0.0416        |
+| 1.8369 | 518  | 0.154         |
+| 1.8404 | 519  | 0.1534        |
+| 1.8440 | 520  | 0.0387        |
+| 1.8475 | 521  | 0.0957        |
+| 1.8511 | 522  | 0.0136        |
+| 1.8546 | 523  | 0.0426        |
+| 1.8582 | 524  | 0.1499        |
+| 1.8617 | 525  | 0.0111        |
+| 1.8652 | 526  | 0.122         |
+| 1.8688 | 527  | 0.2204        |
+| 1.8723 | 528  | 0.1677        |
+| 1.8759 | 529  | 0.0298        |
+| 1.8794 | 530  | 0.0873        |
+| 1.8830 | 531  | 0.0747        |
+| 1.8865 | 532  | 0.0849        |
+| 1.8901 | 533  | 0.0525        |
+| 1.8936 | 534  | 0.0233        |
+| 1.8972 | 535  | 0.0805        |
+| 1.9007 | 536  | 0.0236        |
+| 1.9043 | 537  | 0.142         |
+| 1.9078 | 538  | 0.0585        |
+| 1.9113 | 539  | 0.0271        |
+| 1.9149 | 540  | 0.1606        |
+| 1.9184 | 541  | 0.2148        |
+| 1.9220 | 542  | 0.0568        |
+| 1.9255 | 543  | 0.0248        |
+| 1.9291 | 544  | 0.0878        |
+| 1.9326 | 545  | 0.0044        |
+| 1.9362 | 546  | 0.0354        |
+| 1.9397 | 547  | 0.0638        |
+| 1.9433 | 548  | 0.1875        |
+| 1.9468 | 549  | 0.031         |
+| 1.9504 | 550  | 0.0547        |
+| 1.9539 | 551  | 0.1292        |
+| 1.9574 | 552  | 0.23          |
+| 1.9610 | 553  | 0.0913        |
+| 1.9645 | 554  | 0.0561        |
+| 1.9681 | 555  | 0.0189        |
+| 1.9716 | 556  | 0.0177        |
+| 1.9752 | 557  | 0.0195        |
+| 1.9787 | 558  | 0.1032        |
+| 1.9823 | 559  | 0.1502        |
+| 1.9858 | 560  | 0.0457        |
+| 1.9894 | 561  | 0.0577        |
+| 1.9929 | 562  | 0.1172        |
+| 1.9965 | 563  | 0.0504        |
+| 2.0    | 564  | 0.0374        |
+| 2.0035 | 565  | 0.1079        |
+| 2.0071 | 566  | 0.0609        |
+| 2.0106 | 567  | 0.0366        |
+| 2.0142 | 568  | 0.0674        |
+| 2.0177 | 569  | 0.1084        |
+| 2.0213 | 570  | 0.066         |
+| 2.0248 | 571  | 0.0102        |
+| 2.0284 | 572  | 0.0876        |
+| 2.0319 | 573  | 0.0407        |
+| 2.0355 | 574  | 0.0581        |
+| 2.0390 | 575  | 0.1215        |
+| 2.0426 | 576  | 0.0068        |
+| 2.0461 | 577  | 0.1015        |
+| 2.0496 | 578  | 0.0047        |
+| 2.0532 | 579  | 0.0925        |
+| 2.0567 | 580  | 0.0836        |
+| 2.0603 | 581  | 0.021         |
+| 2.0638 | 582  | 0.0209        |
+| 2.0674 | 583  | 0.0702        |
+| 2.0709 | 584  | 0.0117        |
+| 2.0745 | 585  | 0.0517        |
+| 2.0780 | 586  | 0.061         |
+| 2.0816 | 587  | 0.0207        |
+| 2.0851 | 588  | 0.034         |
+| 2.0887 | 589  | 0.1045        |
+| 2.0922 | 590  | 0.03          |
+| 2.0957 | 591  | 0.0081        |
+| 2.0993 | 592  | 0.0234        |
+| 2.1028 | 593  | 0.073         |
+| 2.1064 | 594  | 0.0074        |
+| 2.1099 | 595  | 0.0655        |
+| 2.1135 | 596  | 0.079         |
+| 2.1170 | 597  | 0.0358        |
+| 2.1206 | 598  | 0.1006        |
+| 2.1241 | 599  | 0.0624        |
+| 2.1277 | 600  | 0.0479        |
+| 2.1312 | 601  | 0.0105        |
+| 2.1348 | 602  | 0.0448        |
+| 2.1383 | 603  | 0.0305        |
+| 2.1418 | 604  | 0.0432        |
+| 2.1454 | 605  | 0.0771        |
+| 2.1489 | 606  | 0.0545        |
+| 2.1525 | 607  | 0.0299        |
+| 2.1560 | 608  | 0.0712        |
+| 2.1596 | 609  | 0.1006        |
+| 2.1631 | 610  | 0.0117        |
+| 2.1667 | 611  | 0.0462        |
+| 2.1702 | 612  | 0.0576        |
+| 2.1738 | 613  | 0.0696        |
+| 2.1773 | 614  | 0.0685        |
+| 2.1809 | 615  | 0.0596        |
+| 2.1844 | 616  | 0.0127        |
+| 2.1879 | 617  | 0.0089        |
+| 2.1915 | 618  | 0.0135        |
+| 2.1950 | 619  | 0.2405        |
+| 2.1986 | 620  | 0.0212        |
+| 2.2021 | 621  | 0.0637        |
+| 2.2057 | 622  | 0.1356        |
+| 2.2092 | 623  | 0.0943        |
+| 2.2128 | 624  | 0.0147        |
+| 2.2163 | 625  | 0.0038        |
+| 2.2199 | 626  | 0.0624        |
+| 2.2234 | 627  | 0.016         |
+| 2.2270 | 628  | 0.032         |
+| 2.2305 | 629  | 0.0154        |
+| 2.2340 | 630  | 0.0724        |
+| 2.2376 | 631  | 0.008         |
+| 2.2411 | 632  | 0.0877        |
+| 2.2447 | 633  | 0.0228        |
+| 2.2482 | 634  | 0.1929        |
+| 2.2518 | 635  | 0.026         |
+| 2.2553 | 636  | 0.0117        |
+| 2.2589 | 637  | 0.0325        |
+| 2.2624 | 638  | 0.0127        |
+| 2.2660 | 639  | 0.0054        |
+| 2.2695 | 640  | 0.0909        |
+| 2.2730 | 641  | 0.0326        |
+| 2.2766 | 642  | 0.0291        |
+| 2.2801 | 643  | 0.0499        |
+| 2.2837 | 644  | 0.0394        |
+| 2.2872 | 645  | 0.0422        |
+| 2.2908 | 646  | 0.0156        |
+| 2.2943 | 647  | 0.0626        |
+| 2.2979 | 648  | 0.0143        |
+| 2.3014 | 649  | 0.0707        |
+| 2.3050 | 650  | 0.0474        |
+| 2.3085 | 651  | 0.0387        |
+| 2.3121 | 652  | 0.104         |
+| 2.3156 | 653  | 0.0981        |
+| 2.3191 | 654  | 0.0284        |
+| 2.3227 | 655  | 0.0123        |
+| 2.3262 | 656  | 0.1346        |
+| 2.3298 | 657  | 0.0157        |
+| 2.3333 | 658  | 0.1276        |
+| 2.3369 | 659  | 0.0634        |
+| 2.3404 | 660  | 0.0327        |
+| 2.3440 | 661  | 0.0633        |
+| 2.3475 | 662  | 0.0618        |
+| 2.3511 | 663  | 0.0171        |
+| 2.3546 | 664  | 0.141         |
+| 2.3582 | 665  | 0.0626        |
+| 2.3617 | 666  | 0.0149        |
+| 2.3652 | 667  | 0.0455        |
+| 2.3688 | 668  | 0.0507        |
+| 2.3723 | 669  | 0.0492        |
+| 2.3759 | 670  | 0.1528        |
+| 2.3794 | 671  | 0.0484        |
+| 2.3830 | 672  | 0.0826        |
+| 2.3865 | 673  | 0.044         |
+| 2.3901 | 674  | 0.2045        |
+| 2.3936 | 675  | 0.0083        |
+| 2.3972 | 676  | 0.0109        |
+| 2.4007 | 677  | 0.0262        |
+| 2.4043 | 678  | 0.0965        |
+| 2.4078 | 679  | 0.1926        |
+| 2.4113 | 680  | 0.0494        |
+| 2.4149 | 681  | 0.1212        |
+| 2.4184 | 682  | 0.0467        |
+| 2.4220 | 683  | 0.0093        |
+| 2.4255 | 684  | 0.0662        |
+| 2.4291 | 685  | 0.0487        |
+| 2.4326 | 686  | 0.1391        |
+| 2.4362 | 687  | 0.1416        |
+| 2.4397 | 688  | 0.1691        |
+| 2.4433 | 689  | 0.0936        |
+| 2.4468 | 690  | 0.1812        |
+| 2.4504 | 691  | 0.0327        |
+| 2.4539 | 692  | 0.1146        |
+| 2.4574 | 693  | 0.0711        |
+| 2.4610 | 694  | 0.0947        |
+| 2.4645 | 695  | 0.0525        |
+| 2.4681 | 696  | 0.0223        |
+| 2.4716 | 697  | 0.0266        |
+| 2.4752 | 698  | 0.206         |
+| 2.4787 | 699  | 0.0669        |
+| 2.4823 | 700  | 0.0421        |
+| 2.4858 | 701  | 0.0198        |
+| 2.4894 | 702  | 0.0255        |
+| 2.4929 | 703  | 0.008         |
+| 2.4965 | 704  | 0.0183        |
+| 2.5    | 705  | 0.0498        |
+| 2.5035 | 706  | 0.0839        |
+| 2.5071 | 707  | 0.0219        |
+| 2.5106 | 708  | 0.0977        |
+| 2.5142 | 709  | 0.0206        |
+| 2.5177 | 710  | 0.0051        |
+| 2.5213 | 711  | 0.0199        |
+| 2.5248 | 712  | 0.0366        |
+| 2.5284 | 713  | 0.01          |
+| 2.5319 | 714  | 0.1622        |
+| 2.5355 | 715  | 0.0452        |
+| 2.5390 | 716  | 0.0681        |
+| 2.5426 | 717  | 0.0103        |
+| 2.5461 | 718  | 0.0059        |
+| 2.5496 | 719  | 0.0493        |
+| 2.5532 | 720  | 0.016         |
+| 2.5567 | 721  | 0.134         |
+| 2.5603 | 722  | 0.0119        |
+| 2.5638 | 723  | 0.1173        |
+| 2.5674 | 724  | 0.2206        |
+| 2.5709 | 725  | 0.0368        |
+| 2.5745 | 726  | 0.0176        |
+| 2.5780 | 727  | 0.0599        |
+| 2.5816 | 728  | 0.123         |
+| 2.5851 | 729  | 0.0764        |
+| 2.5887 | 730  | 0.0695        |
+| 2.5922 | 731  | 0.0405        |
+| 2.5957 | 732  | 0.012         |
+| 2.5993 | 733  | 0.0469        |
+| 2.6028 | 734  | 0.0142        |
+| 2.6064 | 735  | 0.1236        |
+| 2.6099 | 736  | 0.0194        |
+| 2.6135 | 737  | 0.115         |
+| 2.6170 | 738  | 0.105         |
+| 2.6206 | 739  | 0.0937        |
+| 2.6241 | 740  | 0.1916        |
+| 2.6277 | 741  | 0.0903        |
+| 2.6312 | 742  | 0.1579        |
+| 2.6348 | 743  | 0.0902        |
+| 2.6383 | 744  | 0.0304        |
+| 2.6418 | 745  | 0.0881        |
+| 2.6454 | 746  | 0.0646        |
+| 2.6489 | 747  | 0.0941        |
+| 2.6525 | 748  | 0.0204        |
+| 2.6560 | 749  | 0.1679        |
+| 2.6596 | 750  | 0.028         |
+| 2.6631 | 751  | 0.0205        |
+| 2.6667 | 752  | 0.0307        |
+| 2.6702 | 753  | 0.0365        |
+| 2.6738 | 754  | 0.0141        |
+| 2.6773 | 755  | 0.0212        |
+| 2.6809 | 756  | 0.0447        |
+| 2.6844 | 757  | 0.1072        |
+| 2.6879 | 758  | 0.0332        |
+| 2.6915 | 759  | 0.0513        |
+| 2.6950 | 760  | 0.062         |
+| 2.6986 | 761  | 0.0941        |
+| 2.7021 | 762  | 0.0201        |
+| 2.7057 | 763  | 0.2132        |
+| 2.7092 | 764  | 0.0323        |
+| 2.7128 | 765  | 0.0654        |
+| 2.7163 | 766  | 0.059         |
+| 2.7199 | 767  | 0.1027        |
+| 2.7234 | 768  | 0.0091        |
+| 2.7270 | 769  | 0.0585        |
+| 2.7305 | 770  | 0.0102        |
+| 2.7340 | 771  | 0.0265        |
+| 2.7376 | 772  | 0.0403        |
+| 2.7411 | 773  | 0.0913        |
+| 2.7447 | 774  | 0.0212        |
+| 2.7482 | 775  | 0.0423        |
+| 2.7518 | 776  | 0.083         |
+| 2.7553 | 777  | 0.0073        |
+| 2.7589 | 778  | 0.0815        |
+| 2.7624 | 779  | 0.0786        |
+| 2.7660 | 780  | 0.1079        |
+| 2.7695 | 781  | 0.0477        |
+| 2.7730 | 782  | 0.116         |
+| 2.7766 | 783  | 0.0523        |
+| 2.7801 | 784  | 0.049         |
+| 2.7837 | 785  | 0.0153        |
+| 2.7872 | 786  | 0.0173        |
+| 2.7908 | 787  | 0.0656        |
+| 2.7943 | 788  | 0.0094        |
+| 2.7979 | 789  | 0.0757        |
+| 2.8014 | 790  | 0.0924        |
+| 2.8050 | 791  | 0.0717        |
+| 2.8085 | 792  | 0.011         |
+| 2.8121 | 793  | 0.0312        |
+| 2.8156 | 794  | 0.0188        |
+| 2.8191 | 795  | 0.0244        |
+| 2.8227 | 796  | 0.0138        |
+| 2.8262 | 797  | 0.0956        |
+| 2.8298 | 798  | 0.0125        |
+| 2.8333 | 799  | 0.0196        |
+| 2.8369 | 800  | 0.0766        |
+| 2.8404 | 801  | 0.0105        |
+| 2.8440 | 802  | 0.0347        |
+| 2.8475 | 803  | 0.1152        |
+| 2.8511 | 804  | 0.0745        |
+| 2.8546 | 805  | 0.0275        |
+| 2.8582 | 806  | 0.1096        |
+| 2.8617 | 807  | 0.0571        |
+| 2.8652 | 808  | 0.008         |
+| 2.8688 | 809  | 0.0428        |
+| 2.8723 | 810  | 0.0639        |
+| 2.8759 | 811  | 0.1364        |
+| 2.8794 | 812  | 0.062         |
+| 2.8830 | 813  | 0.0782        |
+| 2.8865 | 814  | 0.0311        |
+| 2.8901 | 815  | 0.1234        |
+| 2.8936 | 816  | 0.0302        |
+| 2.8972 | 817  | 0.0984        |
+| 2.9007 | 818  | 0.0141        |
+| 2.9043 | 819  | 0.1342        |
+| 2.9078 | 820  | 0.0115        |
+| 2.9113 | 821  | 0.0608        |
+| 2.9149 | 822  | 0.0246        |
+| 2.9184 | 823  | 0.0388        |
+| 2.9220 | 824  | 0.0557        |
+| 2.9255 | 825  | 0.011         |
+| 2.9291 | 826  | 0.0262        |
+| 2.9326 | 827  | 0.0655        |
+| 2.9362 | 828  | 0.0843        |
+| 2.9397 | 829  | 0.0549        |
+| 2.9433 | 830  | 0.0791        |
+| 2.9468 | 831  | 0.0254        |
+| 2.9504 | 832  | 0.1365        |
+| 2.9539 | 833  | 0.2078        |
+| 2.9574 | 834  | 0.0485        |
+| 2.9610 | 835  | 0.0309        |
+| 2.9645 | 836  | 0.0974        |
+| 2.9681 | 837  | 0.004         |
+| 2.9716 | 838  | 0.1136        |
+| 2.9752 | 839  | 0.0227        |
+| 2.9787 | 840  | 0.0458        |
+| 2.9823 | 841  | 0.016         |
+| 2.9858 | 842  | 0.1003        |
+| 2.9894 | 843  | 0.0289        |
+| 2.9929 | 844  | 0.0702        |
+| 2.9965 | 845  | 0.055         |
+| 3.0    | 846  | 0.2404        |
+
+</details>
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 5.1.1
+- Transformers: 4.56.2
+- PyTorch: 2.8.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.1",
+    "transformers": "4.56.2",
+    "pytorch": "2.8.0+cu128"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e9f78ed22994f22ff7deb08500f2080bc93c9a3b7bbbed842a0af4908f8edbd
+size 90864192

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}