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The dataset preview is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    error
Message:      Error -3 while decompressing data: invalid distance code
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/utils.py", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
                  for key, example in self._iter():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
                  yield from ex_iterable
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/tmp/modules-cache/datasets_modules/datasets/biomrc/63965db7e579497111466e7e19b7b435776a23d7daac74ddb2d2d851e15d8194/biomrc.py", line 183, in _generate_examples
                  biomrc = json.load(fp)
                File "/usr/local/lib/python3.9/json/__init__.py", line 293, in load
                  return loads(fp.read(),
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 351, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/gzip.py", line 300, in read
                  return self._buffer.read(size)
                File "/usr/local/lib/python3.9/gzip.py", line 495, in read
                  uncompress = self._decompressor.decompress(buf, size)
              zlib.error: Error -3 while decompressing data: invalid distance code

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Dataset Card for "biomrc"

Dataset Summary

We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

biomrc_large_A

  • Size of downloaded dataset files: 389.18 MB
  • Size of the generated dataset: 1831.85 MB
  • Total amount of disk used: 2221.02 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\"OBJECTIVES: @entity9 is a @entity10 that may result from greater occipital nerve entrapment. Entrapped peripheral nerves typica...",
    "answer": "@entity9 :: (MESH:D009437,Disease) :: ['unilateral occipital neuralgia']\n",
    "entities_list": ["@entity1 :: ('9606', 'Species') :: ['patients']", "@entity10 :: ('MESH:D006261', 'Disease') :: ['headache', 'Headache']", "@entity9 :: ('MESH:D009437', 'Disease') :: ['Occipital neuralgia', 'unilateral occipital neuralgia']"],
    "title": "Sonographic evaluation of the greater occipital nerve in XXXX .\n"
}

biomrc_large_B

  • Size of downloaded dataset files: 327.17 MB
  • Size of the generated dataset: 1469.61 MB
  • Total amount of disk used: 1796.78 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\"BACKGROUND: Adults with physical disabilities are less likely than others to receive @entity2 screening. It is not known, howev...",
    "answer": "@entity2",
    "entities_list": ["@entity2", "@entity1", "@entity0", "@entity3"],
    "title": "Does a standard measure of self-reported physical disability correlate with clinician perception of impairment related to XXXX screening?\n"
}

biomrc_small_A

  • Size of downloaded dataset files: 65.69 MB
  • Size of the generated dataset: 225.37 MB
  • Total amount of disk used: 291.06 MB

An example of 'validation' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\"PURPOSE: @entity120 ( @entity120 ) is a life-limiting @entity102 that presents as an elevated blood pressure in the pulmonary a...",
    "answer": "@entity148 :: (MESH:D001008,Disease) :: ['anxiety']\n",
    "entities_list": "[\"@entity1 :: ('9606', 'Species') :: ['patients']\", \"@entity308 :: ('MESH:D003866', 'Disease') :: ['depression']\", \"@entity146 :...",
    "title": "A predictive model of the effects of @entity308 , XXXX , stress, 6-minute-walk distance, and social support on health-related quality of life in an adult pulmonary hypertension population.\n"
}

biomrc_small_B

  • Size of downloaded dataset files: 55.03 MB
  • Size of the generated dataset: 180.84 MB
  • Total amount of disk used: 235.87 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\"Single-agent activity for @entity12 reflected by response rates of 10%-30% has been reported in @entity0 with @entity3 ( @entit...",
    "answer": "@entity10",
    "entities_list": ["@entity0", "@entity6", "@entity2", "@entity5", "@entity12", "@entity11", "@entity1", "@entity7", "@entity9", "@entity10", "@entity3", "@entity4", "@entity8"],
    "title": "No synergistic activity of @entity7 and XXXX in the treatment of @entity3 .\n"
}

biomrc_tiny_A

  • Size of downloaded dataset files: 0.02 MB
  • Size of the generated dataset: 0.07 MB
  • Total amount of disk used: 0.09 MB

An example of 'test' looks as follows.

This example was too long and was cropped:

{
    "abstract": "\"OBJECTIVE: Decompressive craniectomy (DC) requires later cranioplasty (CP) in survivors. However, if additional ventriculoperit...",
    "answer": "@entity260 :: (MESH:D011183,Disease) :: ['Postoperative Complications']\n",
    "entities_list": ["@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'Patient']", "@entity260 :: ('MESH:D011183', 'Disease') :: ['VPS regarding postoperative complications']", "@entity1276 :: ('MESH:D006849', 'Disease') :: ['hydrocephalus']"],
    "title": "Cranioplasty and Ventriculoperitoneal Shunt Placement after Decompressive Craniectomy: Staged Surgery Is Associated with Fewer XXXX .\n"
}

Data Fields

The data fields are the same among all splits.

biomrc_large_A

  • abstract: a string feature.
  • title: a string feature.
  • entities_list: a list of string features.
  • answer: a string feature.

biomrc_large_B

  • abstract: a string feature.
  • title: a string feature.
  • entities_list: a list of string features.
  • answer: a string feature.

biomrc_small_A

  • abstract: a string feature.
  • title: a string feature.
  • entities_list: a list of string features.
  • answer: a string feature.

biomrc_small_B

  • abstract: a string feature.
  • title: a string feature.
  • entities_list: a list of string features.
  • answer: a string feature.

biomrc_tiny_A

  • abstract: a string feature.
  • title: a string feature.
  • entities_list: a list of string features.
  • answer: a string feature.

Data Splits

biomrc_large_A

train validation test
biomrc_large_A 700000 50000 62707

biomrc_large_B

train validation test
biomrc_large_B 700000 50000 62707

biomrc_small_A

train validation test
biomrc_small_A 87500 6250 6250

biomrc_small_B

train validation test
biomrc_small_B 87500 6250 6250

biomrc_tiny_A

test
biomrc_tiny_A 30

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

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{pappas-etal-2020-biomrc,
    title = "{B}io{MRC}: A Dataset for Biomedical Machine Reading Comprehension",
    author = "Pappas, Dimitris  and
      Stavropoulos, Petros  and
      Androutsopoulos, Ion  and
      McDonald, Ryan",
    booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.bionlp-1.15",
    pages = "140--149",
    abstract = "We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas et al. (2018). Experiments show that simple heuristics do not perform well on the new dataset and that two neural MRC models that had been tested on BIOREAD perform much better on BIOMRC, indicating that the new dataset is indeed less noisy or at least that its task is more feasible. Non-expert human performance is also higher on the new dataset compared to BIOREAD, and biomedical experts perform even better. We also introduce a new BERT-based MRC model, the best version of which substantially outperforms all other methods tested, reaching or surpassing the accuracy of biomedical experts in some experiments. We make the new dataset available in three different sizes, also releasing our code, and providing a leaderboard.",
}

Contributions

Thanks to @lewtun, @PetrosStav, @lhoestq, @thomwolf for adding this dataset.