Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
ArXiv:
Tags:
License:
albertvillanova HF staff commited on
Commit
247d103
1 Parent(s): 87e8984

Add v1.11 data files

Browse files
README.md CHANGED
@@ -59,13 +59,13 @@ dataset_info:
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  dtype: string
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  splits:
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  - name: train
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- num_bytes: 2717420
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  num_examples: 9741
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  - name: validation
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- num_bytes: 331760
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  num_examples: 1221
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- download_size: 6535534
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- dataset_size: 3049180
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  configs:
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  - config_name: v1.0
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  data_files:
@@ -73,6 +73,12 @@ configs:
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  path: v1.0/train-*
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  - split: validation
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  path: v1.0/validation-*
 
 
 
 
 
 
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  ---
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  # Dataset Card for "cos_e"
 
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  dtype: string
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  splits:
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  - name: train
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+ num_bytes: 2702777
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  num_examples: 9741
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  - name: validation
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+ num_bytes: 329897
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  num_examples: 1221
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+ download_size: 1947552
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+ dataset_size: 3032674
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  configs:
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  - config_name: v1.0
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  data_files:
 
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  path: v1.0/train-*
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  - split: validation
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  path: v1.0/validation-*
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+ - config_name: v1.11
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+ data_files:
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+ - split: train
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+ path: v1.11/train-*
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+ - split: validation
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+ path: v1.11/validation-*
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  ---
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  # Dataset Card for "cos_e"
dataset_infos.json CHANGED
@@ -63,53 +63,44 @@
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  },
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  "v1.11": {
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  "description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n",
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- "citation": "\n@inproceedings{rajani2019explain,\n title = \"Explain Yourself! Leveraging Language models for Commonsense Reasoning\",\n author = \"Rajani, Nazneen Fatema and\n McCann, Bryan and\n Xiong, Caiming and\n Socher, Richard\",\n year=\"2019\",\n booktitle = \"Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)\",\n url =\"https://arxiv.org/abs/1906.02361\"\n}\n",
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  "homepage": "https://github.com/salesforce/cos-e",
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  "license": "",
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  "id": {
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  "dtype": "string",
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  "question": {
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  "answer": {
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  "_type": "Value"
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- "supervised_keys": null,
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  "builder_name": "cos_e",
 
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  "config_name": "v1.11",
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  "version": {
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  "version_str": "1.11.0",
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  "description": "",
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- "datasets_version_to_prepare": null,
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  "major": 1,
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  "minor": 11,
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  "patch": 0
@@ -117,41 +108,19 @@
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  "splits": {
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  "train": {
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  "name": "train",
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- "num_bytes": 2717420,
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  "num_examples": 9741,
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- "dataset_name": "cos_e"
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  },
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  "validation": {
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  "name": "validation",
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  "num_examples": 1221,
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- "dataset_name": "cos_e"
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- "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl": {
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  }
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  }
 
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  },
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  "v1.11": {
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  "description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n",
66
+ "citation": "\n@inproceedings{rajani2019explain,\n title = {Explain Yourself! Leveraging Language models for Commonsense Reasoning},\n author = {Rajani, Nazneen Fatema and\n McCann, Bryan and\n Xiong, Caiming and\n Socher, Richard}\n year={2019}\n booktitle = {Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)}\n url ={https://arxiv.org/abs/1906.02361}\n}\n",
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  "homepage": "https://github.com/salesforce/cos-e",
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  "license": "",
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  "answer": {
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  "_type": "Value"
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  },
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  "extractive_explanation": {
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  "dtype": "string",
 
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  "_type": "Value"
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  }
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  },
 
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  "builder_name": "cos_e",
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+ "dataset_name": "cos_e",
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  "config_name": "v1.11",
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  "version": {
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  "version_str": "1.11.0",
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  "description": "",
 
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  "major": 1,
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  "minor": 11,
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  "patch": 0
 
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  "validation": {
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  "name": "validation",
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  "num_examples": 1221,
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  }
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  },
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+ "download_size": 1947552,
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+ "dataset_size": 3032674,
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+ "size_in_bytes": 4980226
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  }
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  }
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