Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- other
+task_ids:
+- other-other-disambiguation
+---
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** []()
+- **Repository:** [https://github.com/BruceWen120/medal]()
+- **Paper:** [https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/]()
+- **Dataset (Kaggle):** [https://www.kaggle.com/xhlulu/medal-emnlp]()
+- **Dataset (Zenodo):** [https://zenodo.org/record/4265632]()
+- **Pretrained model:** [https://huggingface.co/xhlu/electra-medal]()
+- **Leaderboard:** []()
+- **Point of Contact:** []()
+
+### Dataset Summary
+
+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
+
+### Supported Tasks and Leaderboards
+
+Medical abbreviation disambiguation
+
+### Languages
+
+English (en)
+
+## Dataset Structure
+
+[More Information Needed]
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{wen-etal-2020-medal,
+    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
+    author = "Wen, Zhi  and
+      Lu, Xing Han  and
+      Reddy, Siva",
+    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
+    pages = "130--135",
+    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.",
+}
+```

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}}

dummy/1.0.0/dummy_data.zip

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+oid sha256:5d4a921d222c4bbe5efd7ee2ce77bf13e0dbe7d5a848206327ff44d679109026
+size 3772

medal.py

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+# coding=utf-8
+# Copyright 2020 the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import logging
+
+import datasets
+
+
+logger = logging.getLogger(__name__)
+
+
+_CITATION = """\
+@inproceedings{wen-etal-2020-medal,
+    title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
+    author = "Wen, Zhi  and
+      Lu, Xing Han  and
+      Reddy, Siva",
+    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
+    pages = "130--135",
+    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.",
+}"""
+
+_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
+"""
+
+_URL = "https://zenodo.org/record/4276178/files/"
+_URLS = {
+    "train": _URL + "train.csv",
+    "test": _URL + "test.csv",
+    "valid": _URL + "valid.csv",
+    "full": _URL + "full_data.csv",
+}
+
+
+class Medal(datasets.GeneratorBasedBuilder):
+    """Medal: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "abstract_id": datasets.Value("int32"),
+                    "text": datasets.Value("string"),
+                    "location": datasets.Sequence(datasets.Value("int32")),
+                    "label": datasets.Sequence(datasets.Value("string")),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/BruceWen120/medal",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        urls_to_dl = _URLS
+        try:
+            dl_dir = dl_manager.download_and_extract(urls_to_dl)
+        except Exception:
+            logger.warning(
+                "This dataset is downloaded through Zenodo which is flaky. If this download failed try a few times before reporting an issue"
+            )
+            raise
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["test"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["valid"], "split": "val"},
+            ),
+            datasets.SplitGenerator(
+                name="full",
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": dl_dir["full"], "split": "full"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.reader(f)
+            # Skip header
+            next(data)
+            # print(split, filepath, next(data))
+            if split == "full":
+                id_ = 0
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "abstract_id": -1,
+                        "text": row[0],
+                        "location": [int(location) for location in row[1].split("|")],
+                        "label": row[2].split("|"),
+                    }
+            else:
+                for id_, row in enumerate(data):
+                    yield id_, {
+                        "abstract_id": int(row[0]),
+                        "text": row[1],
+                        "location": [int(row[2])],
+                        "label": [row[3]],
+                    }