system HF staff commited on
Commit
db13b45
1 Parent(s): 35cc764

Update files from the datasets library (from 1.16.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.16.0

Files changed (3) hide show
  1. README.md +1 -0
  2. dataset_infos.json +1 -1
  3. social_bias_frames.py +15 -10
README.md CHANGED
@@ -1,4 +1,5 @@
1
  ---
 
2
  annotations_creators:
3
  - crowdsourced
4
  language_creators:
 
1
  ---
2
+ pretty_name: Social Bias Frames
3
  annotations_creators:
4
  - crowdsourced
5
  language_creators:
dataset_infos.json CHANGED
@@ -1 +1 @@
1
- {"default": {"description": "Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.\nFor example, these frames are meant to distill the implication that \"women (candidates) are less qualified\"\nbehind the statement \"we shouldn\u2019t lower our standards to hire more women.\"\n", "citation": "@inproceedings{sap2020socialbiasframes,\n title={Social Bias Frames: Reasoning about Social and Power Implications of Language},\n author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},\n year={2020},\n booktitle={ACL},\n}\n", "homepage": "https://homes.cs.washington.edu/~msap/social-bias-frames/", "license": "", "features": {"whoTarget": {"dtype": "string", "id": null, "_type": "Value"}, "intentYN": {"dtype": "string", "id": null, "_type": "Value"}, "sexYN": {"dtype": "string", "id": null, "_type": "Value"}, "sexReason": {"dtype": "string", "id": null, "_type": "Value"}, "offensiveYN": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorGender": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorMinority": {"dtype": "string", "id": null, "_type": "Value"}, "sexPhrase": {"dtype": "string", "id": null, "_type": "Value"}, "speakerMinorityYN": {"dtype": "string", "id": null, "_type": "Value"}, "WorkerId": {"dtype": "string", "id": null, "_type": "Value"}, "HITId": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorPolitics": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorRace": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorAge": {"dtype": "string", "id": null, "_type": "Value"}, "post": {"dtype": "string", "id": null, "_type": "Value"}, "targetMinority": {"dtype": "string", "id": null, "_type": "Value"}, "targetCategory": {"dtype": "string", "id": null, "_type": "Value"}, "targetStereotype": {"dtype": "string", "id": null, "_type": "Value"}, "dataSource": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "social_bias_frames", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 5371665, "num_examples": 17501, "dataset_name": "social_bias_frames"}, "validation": {"name": "validation", "num_bytes": 5096009, "num_examples": 16738, "dataset_name": "social_bias_frames"}, "train": {"name": "train", "num_bytes": 34006886, "num_examples": 112900, "dataset_name": "social_bias_frames"}}, "download_checksums": {"https://homes.cs.washington.edu/~msap/social-bias-frames/SBIC.v2.tgz": {"num_bytes": 6326977, "checksum": "6f7ecfbdf4f3d4f030496665a5371dba5c7c45fcfed32c14b7e225a0de40d7bb"}}, "download_size": 6326977, "post_processing_size": null, "dataset_size": 44474560, "size_in_bytes": 50801537}}
 
1
+ {"default": {"description": "Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.\nFor example, these frames are meant to distill the implication that \"women (candidates) are less qualified\"\nbehind the statement \"we shouldn\u2019t lower our standards to hire more women.\"\n", "citation": "@inproceedings{sap2020socialbiasframes,\n title={Social Bias Frames: Reasoning about Social and Power Implications of Language},\n author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},\n year={2020},\n booktitle={ACL},\n}\n", "homepage": "https://homes.cs.washington.edu/~msap/social-bias-frames/", "license": "", "features": {"whoTarget": {"dtype": "string", "id": null, "_type": "Value"}, "intentYN": {"dtype": "string", "id": null, "_type": "Value"}, "sexYN": {"dtype": "string", "id": null, "_type": "Value"}, "sexReason": {"dtype": "string", "id": null, "_type": "Value"}, "offensiveYN": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorGender": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorMinority": {"dtype": "string", "id": null, "_type": "Value"}, "sexPhrase": {"dtype": "string", "id": null, "_type": "Value"}, "speakerMinorityYN": {"dtype": "string", "id": null, "_type": "Value"}, "WorkerId": {"dtype": "string", "id": null, "_type": "Value"}, "HITId": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorPolitics": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorRace": {"dtype": "string", "id": null, "_type": "Value"}, "annotatorAge": {"dtype": "string", "id": null, "_type": "Value"}, "post": {"dtype": "string", "id": null, "_type": "Value"}, "targetMinority": {"dtype": "string", "id": null, "_type": "Value"}, "targetCategory": {"dtype": "string", "id": null, "_type": "Value"}, "targetStereotype": {"dtype": "string", "id": null, "_type": "Value"}, "dataSource": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "social_bias_frames", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 5371665, "num_examples": 17501, "dataset_name": "social_bias_frames"}, "validation": {"name": "validation", "num_bytes": 5096009, "num_examples": 16738, "dataset_name": "social_bias_frames"}, "train": {"name": "train", "num_bytes": 34006886, "num_examples": 112900, "dataset_name": "social_bias_frames"}}, "download_checksums": {"https://homes.cs.washington.edu/~msap/social-bias-frames/SBIC.v2.tgz": {"num_bytes": 9464583, "checksum": "07cabae1ee0289392ea11b494c0012578bd39a582d58dc5ebd8edc3deda6bc5b"}}, "download_size": 9464583, "post_processing_size": null, "dataset_size": 44474560, "size_in_bytes": 53939143}}
social_bias_frames.py CHANGED
@@ -18,7 +18,6 @@
18
 
19
 
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  import csv
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- import os
22
 
23
  import datasets
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@@ -78,22 +77,28 @@ class SocialBiasFrames(datasets.GeneratorBasedBuilder):
78
  )
79
 
80
  def _split_generators(self, dl_manager):
81
- dl_dir = dl_manager.download_and_extract(_DATA_URL)
82
  return [
83
  datasets.SplitGenerator(
84
- name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.tst.csv")}
 
85
  ),
86
  datasets.SplitGenerator(
87
- name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.dev.csv")}
 
88
  ),
89
  datasets.SplitGenerator(
90
- name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.trn.csv")}
 
91
  ),
92
  ]
93
 
94
- def _generate_examples(self, filepath):
95
  """This function returns the examples in the raw (text) form."""
96
- with open(filepath, encoding="utf-8") as f:
97
- reader = csv.DictReader(f)
98
- for idx, row in enumerate(reader):
99
- yield idx, row
 
 
 
 
18
 
19
 
20
  import csv
 
21
 
22
  import datasets
23
 
 
77
  )
78
 
79
  def _split_generators(self, dl_manager):
80
+ archive = dl_manager.download(_DATA_URL)
81
  return [
82
  datasets.SplitGenerator(
83
+ name=datasets.Split.TEST,
84
+ gen_kwargs={"filepath": "SBIC.v2.tst.csv", "files": dl_manager.iter_archive(archive)},
85
  ),
86
  datasets.SplitGenerator(
87
+ name=datasets.Split.VALIDATION,
88
+ gen_kwargs={"filepath": "SBIC.v2.dev.csv", "files": dl_manager.iter_archive(archive)},
89
  ),
90
  datasets.SplitGenerator(
91
+ name=datasets.Split.TRAIN,
92
+ gen_kwargs={"filepath": "SBIC.v2.trn.csv", "files": dl_manager.iter_archive(archive)},
93
  ),
94
  ]
95
 
96
+ def _generate_examples(self, filepath, files):
97
  """This function returns the examples in the raw (text) form."""
98
+ for path, f in files:
99
+ if path == filepath:
100
+ lines = (line.decode("utf-8") for line in f)
101
+ reader = csv.DictReader(lines)
102
+ for idx, row in enumerate(reader):
103
+ yield idx, row
104
+ break