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- Host data files (7ec7ce7e729bbda330a72ffe9d8e189314918b21)
- Update loading script (6110dd4115ece376c5aa842c42904785990ba1b2)
- Update dataset card (0da59a4d3dc11ffef79598e65c2eacf1a30e74ba)
- Delete legacy metadata JSON file (d99aa99aec91e8dae2738018ec53603a5b0638da)

README.md CHANGED
@@ -74,14 +74,14 @@ dataset_info:
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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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86
  ### Dataset Summary
87
 
 
74
 
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  ## Dataset Description
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+ - **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+ - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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86
  ### Dataset Summary
87
 
data/full_data.csv.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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data/pretrain_subset.zip ADDED
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dataset_infos.json DELETED
@@ -1 +0,0 @@
1
- {"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, "task_templates": null, "builder_name": "medal", "config_name": "default", "version": {"version_str": "4.0.0", "description": null, "major": 4, "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/4482922/files/train.csv": {"num_bytes": 3541556520, "checksum": "c5fef2feebd1ecd35b4fe7a0aec266b631c0ac511d4d6b685835328b1ffbf32d"}, "https://zenodo.org/record/4482922/files/test.csv": {"num_bytes": 1180152075, "checksum": "ad391a63449c2bbbdbdf8d1827da4c053607a8586f4162174ba4ccf13efd8f86"}, "https://zenodo.org/record/4482922/files/valid.csv": {"num_bytes": 1180795804, "checksum": "08a0a6c2ee40747744ec15675ab5dc1e2b04491ca951b14c15d8d7bf9d33694d"}, "https://zenodo.org/record/4482922/files/full_data.csv": {"num_bytes": 15158424679, "checksum": "70f1ad891bdf98a42395a8907b48284457ae36d17fcc5a0a9c65c0b6b45ecf8d"}}, "download_size": 21060929078, "post_processing_size": null, "dataset_size": 21492461215, "size_in_bytes": 42553390293}}
 
 
medal.py CHANGED
@@ -18,13 +18,11 @@
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19
 
20
  import csv
 
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  import datasets
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- logger = datasets.logging.get_logger(__name__)
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-
27
-
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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",
@@ -45,12 +43,15 @@ _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
46
  """
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- _URL = "https://zenodo.org/record/4482922/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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@@ -86,35 +87,28 @@ class Medal(datasets.GeneratorBasedBuilder):
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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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  import csv
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+ import os.path
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  import datasets
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  _CITATION = """\
27
  @inproceedings{wen-etal-2020-medal,
28
  title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
 
43
  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
44
  """
45
 
 
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  _URLS = {
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+ "pretrain": "data/pretrain_subset.zip",
48
+ "full": "data/full_data.csv.zip"
49
+ }
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+ _FILENAMES = {
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+ "train": "train.csv",
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+ "test": "test.csv",
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+ "valid": "valid.csv",
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+ "full": "full_data.csv",
55
  }
56
 
57
 
 
87
  """Returns SplitGenerators."""
88
  # dl_manager is a datasets.download.DownloadManager that can be used to
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  # download and extract URLs
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+ dl_dir = dl_manager.download_and_extract(_URLS)
 
 
 
 
 
 
 
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92
  return [
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  datasets.SplitGenerator(
94
  name=datasets.Split.TRAIN,
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  # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["train"]), "split": "train"},
97
  ),
98
  datasets.SplitGenerator(
99
  name=datasets.Split.TEST,
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  # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["test"]), "split": "test"},
102
  ),
103
  datasets.SplitGenerator(
104
  name=datasets.Split.VALIDATION,
105
  # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(dl_dir["pretrain"], _FILENAMES["valid"]), "split": "val"},
107
  ),
108
  datasets.SplitGenerator(
109
  name="full",
110
  # These kwargs will be passed to _generate_examples
111
+ gen_kwargs={"filepath": os.path.join(dl_dir["full"], _FILENAMES["full"]), "split": "full"},
112
  ),
113
  ]
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