Update files from the datasets library (from 1.2.0)

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

dataset_infos.json

@@ -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"}}, "supervised_keys": null, "builder_name": "social_bias_frames", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 5169855, "num_examples": 17501, "dataset_name": "social_bias_frames"}, "validation": {"name": "validation", "num_bytes": 4904733, "num_examples": 16738, "dataset_name": "social_bias_frames"}, "train": {"name": "train", "num_bytes": 32739659, "num_examples": 112900, "dataset_name": "social_bias_frames"}}, "download_checksums": {"https://homes.cs.washington.edu/~msap/social-bias-frames/SocialBiasFrames_v2.tgz": {"num_bytes": 6250039, "checksum": "9d572fc25530789602c58b433ac8a4d3c8e4962e3ba09cab5d135e851b8cec78"}}, "download_size": 6250039, "dataset_size": 42814247, "size_in_bytes": 49064286}}
+{"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}}

dummy/0.0.0/dummy_data.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d9e85caa0f8d436bc287363b98c65197f109cb680b2f514d68ac6ec8288ee28d
-size 1953
+oid sha256:5b2ff486df1d229a596aac518e89660ba4d14ddc9a3df05bbeb00b4f67f8b38c
+size 1923

social_bias_frames.py

@@ -39,7 +39,7 @@ For example, these frames are meant to distill the implication that "women (cand
 behind the statement "we shouldn’t lower our standards to hire more women."
 """
 
-_DATA_URL = "https://homes.cs.washington.edu/~msap/social-bias-frames/SocialBiasFrames_v2.tgz"
+_DATA_URL = "https://homes.cs.washington.edu/~msap/social-bias-frames/SBIC.v2.tgz"
 
 
 class SocialBiasFrames(datasets.GeneratorBasedBuilder):
@@ -68,6 +68,7 @@ class SocialBiasFrames(datasets.GeneratorBasedBuilder):
                     "targetMinority": datasets.Value("string"),
                     "targetCategory": datasets.Value("string"),
                     "targetStereotype": datasets.Value("string"),
+                    "dataSource": datasets.Value("string"),
                 }
             ),
             # No default supervised_keys (as we have to pass both premise
@@ -81,13 +82,13 @@ class SocialBiasFrames(datasets.GeneratorBasedBuilder):
         dl_dir = dl_manager.download_and_extract(_DATA_URL)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "SBFv2.tst.csv")}
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.tst.csv")}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "SBFv2.dev.csv")}
+                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.dev.csv")}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "SBFv2.trn.csv")}
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "SBIC.v2.trn.csv")}
             ),
         ]