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Update files from the datasets library (from 1.8.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.8.0

Files changed (3) hide show
  1. README.md +22 -3
  2. dataset_infos.json +1 -1
  3. squad_v2.py +6 -0
README.md CHANGED
@@ -1,4 +1,23 @@
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  paperswithcode_id: squad
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  ---
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@@ -95,9 +114,9 @@ The data fields are the same among all splits.
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  ### Data Splits
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- | name |train |validation|
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- |--------|-----:|---------:|
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- |squad_v2|130319| 11873|
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  ## Dataset Creation
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  ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - crowdsourced
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+ languages:
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+ - en
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+ licenses:
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+ - cc-by-sa-4-0
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+ multilinguality:
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+ - monolingual
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+ size_categories:
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+ - 100K<n<1M
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - open-domain-qa
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+ - extractive-qa
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  paperswithcode_id: squad
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  ---
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  ### Data Splits
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+ | name | train | validation |
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+ | -------- | -----: | ---------: |
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+ | squad_v2 | 130319 | 11873 |
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  ## Dataset Creation
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dataset_infos.json CHANGED
@@ -1 +1 @@
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- {"squad_v2": {"description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but \n also determine when no answer is supported by the paragraph and abstain from answering.\n", "citation": "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n Konstantin and {Liang}, Percy},\n title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n journal = {arXiv e-prints},\n year = 2016,\n eid = {arXiv:1606.05250},\n pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "supervised_keys": null, "builder_name": "squad_v2", "config_name": "squad_v2", "version": {"version_str": "2.0.0", "description": null, "datasets_version_to_prepare": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 116851642, "num_examples": 130319, "dataset_name": "squad_v2"}, "validation": {"name": "validation", "num_bytes": 11677230, "num_examples": 11873, "dataset_name": "squad_v2"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}}, "download_size": 46494161, "dataset_size": 128528872, "size_in_bytes": 175023033}}
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+ {"squad_v2": {"description": "combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers\n to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but\n also determine when no answer is supported by the paragraph and abstain from answering.\n", "citation": "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n Konstantin and {Liang}, Percy},\n title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n journal = {arXiv e-prints},\n year = 2016,\n eid = {arXiv:1606.05250},\n pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n eprint = {1606.05250},\n}\n", "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "question-answering-extractive", "question_column": "question", "context_column": "context", "answers_column": "answers"}], "builder_name": "squad_v2", "config_name": "squad_v2", "version": {"version_str": "2.0.0", "description": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 116699950, "num_examples": 130319, "dataset_name": "squad_v2"}, "validation": {"name": "validation", "num_bytes": 11660302, "num_examples": 11873, "dataset_name": "squad_v2"}}, "download_checksums": {"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json": {"num_bytes": 42123633, "checksum": "68dcfbb971bd3e96d5b46c7177b16c1a4e7d4bdef19fb204502738552dede002"}, "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json": {"num_bytes": 4370528, "checksum": "80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8"}}, "download_size": 46494161, "post_processing_size": null, "dataset_size": 128360252, "size_in_bytes": 174854413}}
squad_v2.py CHANGED
@@ -4,6 +4,7 @@
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  import json
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  import datasets
 
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  # TODO(squad_v2): BibTeX citation
@@ -82,6 +83,11 @@ class SquadV2(datasets.GeneratorBasedBuilder):
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  # Homepage of the dataset for documentation
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  homepage="https://rajpurkar.github.io/SQuAD-explorer/",
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  citation=_CITATION,
 
 
 
 
 
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  )
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  def _split_generators(self, dl_manager):
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  import json
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  import datasets
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+ from datasets.tasks import QuestionAnsweringExtractive
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  # TODO(squad_v2): BibTeX citation
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  # Homepage of the dataset for documentation
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  homepage="https://rajpurkar.github.io/SQuAD-explorer/",
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  citation=_CITATION,
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+ task_templates=[
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+ QuestionAnsweringExtractive(
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+ question_column="question", context_column="context", answers_column="answers"
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+ )
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+ ],
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  )
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  def _split_generators(self, dl_manager):