Delete llm-compression.py

llm-compression.py

@@ -1,85 +0,0 @@
-# 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"]),
-                }