rename to train.jsonl.gz

personalized_passkey_retrieval.py

@@ -1,91 +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.
-"""personalized_passkey_retrieval: a synthetic dataset to evaluate long-context embeddings"""
-
-
-import json
-import gzip
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@inproceedings{Wang2023ImprovingTE,
-  title={Improving Text Embeddings with Large Language Models},
-  author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
-  year={2023},
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-This dataset contains synthetic data for personalized passkey retrieval.
-It is only intended for evaluation purposes, you should not use it for training.
-"""
-
-_URLS = {
-    "train": "personalized_passkey_retrieval.jsonl.gz"
-}
-
-
-class Query2docMsmarco(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("0.1.0")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name='plain_text', version=VERSION, description='plain text')
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "query": datasets.Value("string"),
-                "candidates": datasets.features.Sequence(datasets.Value("string")),
-                "label": datasets.Value("int32"),
-                "context_length": datasets.Value("int32"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download(_URLS)
-        print(downloaded_files)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": downloaded_files["train"],
-                    "split": "train",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        id_ = 0
-        with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
-            for line in f:
-                if line:
-                    example = json.loads(line)
-                    yield id_, {
-                        "query": example["query"],
-                        "candidates": example["candidates"],
-                        "label": example["label"],
-                        "context_length": example["context_length"],
-                    }
-                    id_ += 1