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Update files from the datasets library (from 1.2.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.2.0
- .gitattributes +27 -0
- README.md +161 -0
- dataset_infos.json +1 -0
- dummy/1.0.0/dummy_data.zip +3 -0
- medal.py +146 -0
.gitattributes
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README.md
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---
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annotations_creators:
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- expert-generated
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language_creators:
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- expert-generated
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languages:
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- en
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licenses:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- n<1K
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source_datasets:
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- original
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task_categories:
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- other
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task_ids:
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- other-other-disambiguation
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---
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# Dataset Card Creation Guide
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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- **Homepage:** []()
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- **Repository:** [https://github.com/BruceWen120/medal]()
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- **Paper:** [https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/]()
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- **Dataset (Kaggle):** [https://www.kaggle.com/xhlulu/medal-emnlp]()
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- **Dataset (Zenodo):** [https://zenodo.org/record/4265632]()
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- **Pretrained model:** [https://huggingface.co/xhlu/electra-medal]()
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- **Leaderboard:** []()
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- **Point of Contact:** []()
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### Dataset Summary
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A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
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### Supported Tasks and Leaderboards
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Medical abbreviation disambiguation
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### Languages
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English (en)
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## Dataset Structure
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[More Information Needed]
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### Data Instances
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[More Information Needed]
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### Data Fields
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[More Information Needed]
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### Data Splits
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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[More Information Needed]
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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[More Information Needed]
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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[More Information Needed]
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### Licensing Information
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[More Information Needed]
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### Citation Information
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```
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@inproceedings{wen-etal-2020-medal,
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title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
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author = "Wen, Zhi and
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Lu, Xing Han and
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Reddy, Siva",
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booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
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month = nov,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
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pages = "130--135",
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abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
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}
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```
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dataset_infos.json
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{"default": {"description": "A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate\n", "citation": "@inproceedings{wen-etal-2020-medal,\n title = \"{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining\",\n author = \"Wen, Zhi and\n Lu, Xing Han and\n Reddy, Siva\",\n booktitle = \"Proceedings of the 3rd Clinical Natural Language Processing Workshop\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.clinicalnlp-1.15\",\n pages = \"130--135\",\n abstract = \"One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.\",\n}", "homepage": "https://github.com/BruceWen120/medal", "license": "", "features": {"abstract_id": {"dtype": "int32", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "location": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "label": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "medal", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3573399948, "num_examples": 3000000, "dataset_name": "medal"}, "test": {"name": "test", "num_bytes": 1190766821, "num_examples": 1000000, "dataset_name": "medal"}, "validation": {"name": "validation", "num_bytes": 1191410723, "num_examples": 1000000, "dataset_name": "medal"}, "full": {"name": "full", "num_bytes": 15536883723, "num_examples": 14393619, "dataset_name": "medal"}}, "download_checksums": {"https://zenodo.org/record/4276178/files/train.csv": {"num_bytes": 3541556520, "checksum": "c5fef2feebd1ecd35b4fe7a0aec266b631c0ac511d4d6b685835328b1ffbf32d"}, "https://zenodo.org/record/4276178/files/test.csv": {"num_bytes": 1180152075, "checksum": "ad391a63449c2bbbdbdf8d1827da4c053607a8586f4162174ba4ccf13efd8f86"}, "https://zenodo.org/record/4276178/files/valid.csv": {"num_bytes": 1180795804, "checksum": "08a0a6c2ee40747744ec15675ab5dc1e2b04491ca951b14c15d8d7bf9d33694d"}, "https://zenodo.org/record/4276178/files/full_data.csv": {"num_bytes": 15158424679, "checksum": "70f1ad891bdf98a42395a8907b48284457ae36d17fcc5a0a9c65c0b6b45ecf8d"}}, "download_size": 21060929078, "post_processing_size": null, "dataset_size": 21492461215, "size_in_bytes": 42553390293}}
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dummy/1.0.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d4a921d222c4bbe5efd7ee2ce77bf13e0dbe7d5a848206327ff44d679109026
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size 3772
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medal.py
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# coding=utf-8
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# Copyright 2020 the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
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from __future__ import absolute_import, division, print_function
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import csv
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import logging
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import datasets
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logger = logging.getLogger(__name__)
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_CITATION = """\
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@inproceedings{wen-etal-2020-medal,
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title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
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author = "Wen, Zhi and
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34 |
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Lu, Xing Han and
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35 |
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Reddy, Siva",
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booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
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month = nov,
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38 |
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year = "2020",
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39 |
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
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pages = "130--135",
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abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
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}"""
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_DESCRIPTION = """\
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A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
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"""
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_URL = "https://zenodo.org/record/4276178/files/"
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_URLS = {
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"train": _URL + "train.csv",
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"test": _URL + "test.csv",
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"valid": _URL + "valid.csv",
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"full": _URL + "full_data.csv",
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}
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class Medal(datasets.GeneratorBasedBuilder):
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"""Medal: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# datasets.features.FeatureConnectors
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features=datasets.Features(
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{
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"abstract_id": datasets.Value("int32"),
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"text": datasets.Value("string"),
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"location": datasets.Sequence(datasets.Value("int32")),
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"label": datasets.Sequence(datasets.Value("string")),
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# These are the features of your dataset like images, labels ...
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}
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),
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# If there's a common (input, target) tuple from the features,
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# specify them here. They'll be used if as_supervised=True in
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# builder.as_dataset.
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage="https://github.com/BruceWen120/medal",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# dl_manager is a datasets.download.DownloadManager that can be used to
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# download and extract URLs
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urls_to_dl = _URLS
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try:
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dl_dir = dl_manager.download_and_extract(urls_to_dl)
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except Exception:
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logger.warning(
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"This dataset is downloaded through Zenodo which is flaky. If this download failed try a few times before reporting an issue"
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)
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raise
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["valid"], "split": "val"},
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),
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datasets.SplitGenerator(
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name="full",
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": dl_dir["full"], "split": "full"},
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),
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]
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def _generate_examples(self, filepath, split):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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data = csv.reader(f)
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# Skip header
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next(data)
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# print(split, filepath, next(data))
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if split == "full":
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id_ = 0
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for id_, row in enumerate(data):
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yield id_, {
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"abstract_id": -1,
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"text": row[0],
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"location": [int(location) for location in row[1].split("|")],
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"label": row[2].split("|"),
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}
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else:
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for id_, row in enumerate(data):
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yield id_, {
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"abstract_id": int(row[0]),
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"text": row[1],
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"location": [int(row[2])],
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"label": [row[3]],
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}
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