llm-compression.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

import datasets
import pandas as pd
from datasets import DatasetInfo, DownloadManager


_CITATION = """\
"""

_DESCRIPTION = """\
"""


_LICENSE = ""

_URL = r"https://huggingface.co/datasets/hkust-nlp/llm-compression/resolve/main/data.zip"


Task_list = [
    "python",
    "cc",
    "arxiv_math",
]


class LlmCompressionConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)


class LlmCompression(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        LlmCompressionConfig(
            name=task_name,
        )
        for task_name in Task_list
    ]

    def _info(self):
        features = datasets.Features(
            {
                "content": datasets.Value("string"),
                "subset": datasets.Value("string"),
                "meta": datasets.features.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager: DownloadManager):
        data_dir = dl_manager.download_and_extract(_URL)
        task_name = self.config.name
        return [
            datasets.SplitGenerator(name=datasets.Split.TEST,
                                    gen_kwargs={"filepath": os.path.join(data_dir, f"{task_name}.jsonl")})
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath,encoding="utf-8") as f:
            for id_, row in enumerate(f):
                cur_data = json.loads(row)
                yield id_, {
                    "content": cur_data["content"],
                    "subset": cur_data["subset"],
                    "meta": str(cur_data["meta"]),
                }