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Convert dataset to Parquet

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Convert dataset to Parquet.

README.md CHANGED
@@ -34,7 +34,7 @@ dataset_info:
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  - name: test
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  num_bytes: 713732
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  num_examples: 1319
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- download_size: 4915944
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  dataset_size: 4676934
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  - config_name: socratic
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  features:
@@ -51,6 +51,13 @@ dataset_info:
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  num_examples: 1319
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  download_size: 6374717
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  dataset_size: 6134967
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for GSM8K
 
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  - name: test
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  num_bytes: 713732
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  num_examples: 1319
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+ download_size: 2725633
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  dataset_size: 4676934
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  - config_name: socratic
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  features:
 
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  num_examples: 1319
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  download_size: 6374717
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  dataset_size: 6134967
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+ configs:
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+ - config_name: main
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+ data_files:
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+ - split: train
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+ path: main/train-*
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+ - split: test
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+ path: main/test-*
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  ---
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  # Dataset Card for GSM8K
dataset_infos.json CHANGED
@@ -1 +1,102 @@
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- {"main": {"description": "GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality\nlinguistically diverse grade school math word problems. The\ndataset was created to support the task of question answering\non basic mathematical problems that require multi-step reasoning.\n", "citation": "@misc{cobbe2021training,\n title={Training Verifiers to Solve Math Word Problems},\n author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n year={2021},\n eprint={2110.14168},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n", "homepage": "https://openai.com/blog/grade-school-math", "license": "MIT", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "gsm8k", "config_name": "main", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3963202, "num_examples": 7473, "dataset_name": "gsm8k"}, "test": {"name": "test", "num_bytes": 713732, "num_examples": 1319, "dataset_name": "gsm8k"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl": {"num_bytes": 4166206, "checksum": "17f347dc51477c50d4efb83959dbb7c56297aba886e5544ee2aaed3024813465"}, "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl": {"num_bytes": 749738, "checksum": "3730d312f6e3440559ace48831e51066acaca737f6eabec99bccb9e4b3c39d14"}}, "download_size": 4915944, "post_processing_size": null, "dataset_size": 4676934, "size_in_bytes": 9592878}, "socratic": {"description": "GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality\nlinguistically diverse grade school math word problems. The\ndataset was created to support the task of question answering\non basic mathematical problems that require multi-step reasoning.\n", "citation": "@misc{cobbe2021training,\n title={Training Verifiers to Solve Math Word Problems},\n author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n year={2021},\n eprint={2110.14168},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n", "homepage": "https://openai.com/blog/grade-school-math", "license": "MIT", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "gsm8k", "config_name": "socratic", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5198108, "num_examples": 7473, "dataset_name": "gsm8k"}, "test": {"name": "test", "num_bytes": 936859, "num_examples": 1319, "dataset_name": "gsm8k"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train_socratic.jsonl": {"num_bytes": 5401739, "checksum": "153d86551187cfd64ef7afb59bfd0ef75cea3ae9388e7ad31e43920b6dd77872"}, "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test_socratic.jsonl": {"num_bytes": 972978, "checksum": "c96673362fa7a699f4836a9b6474a067448f95fe58064727501ee63ba4c3fdb6"}}, "download_size": 6374717, "post_processing_size": null, "dataset_size": 6134967, "size_in_bytes": 12509684}}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "main": {
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+ "description": "GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality\nlinguistically diverse grade school math word problems. The\ndataset was created to support the task of question answering\non basic mathematical problems that require multi-step reasoning.\n",
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+ "citation": "@misc{cobbe2021training,\n title={Training Verifiers to Solve Math Word Problems},\n author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n year={2021},\n eprint={2110.14168},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n",
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+ "description": "GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality\nlinguistically diverse grade school math word problems. The\ndataset was created to support the task of question answering\non basic mathematical problems that require multi-step reasoning.\n",
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+ "citation": "@misc{cobbe2021training,\n title={Training Verifiers to Solve Math Word Problems},\n author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n year={2021},\n eprint={2110.14168},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n",
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