Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
albertvillanova HF staff commited on
Commit
e2067ff
1 Parent(s): 045248c

Delete legacy JSON metadata

Browse files

Delete legacy `dataset_infos.json`.

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  1. dataset_infos.json +0 -1
dataset_infos.json DELETED
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- {"default": {"description": "Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human judgement scores.\n", "citation": "@inproceedings{Chen2020MOCHAAD,\n author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},\n title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},\n booktitle={EMNLP},\n year={2020}\n}\n", "homepage": "https://allennlp.org/mocha", "license": "https://creativecommons.org/licenses/by-sa/4.0/legalcode", "features": {"constituent_dataset": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "reference": {"dtype": "string", "id": null, "_type": "Value"}, "candidate": {"dtype": "string", "id": null, "_type": "Value"}, "score": {"dtype": "float32", "id": null, "_type": "Value"}, "metadata": {"scores": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "candidate2": {"dtype": "string", "id": null, "_type": "Value"}, "score2": {"dtype": "float32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "mocha", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 33292592, "num_examples": 31069, "dataset_name": "mocha"}, "validation": {"name": "validation", "num_bytes": 4236883, "num_examples": 4009, "dataset_name": "mocha"}, "test": {"name": "test", "num_bytes": 6767409, "num_examples": 6321, "dataset_name": "mocha"}, "minimal_pairs": {"name": "minimal_pairs", "num_bytes": 193560, "num_examples": 200, "dataset_name": "mocha"}}, "download_checksums": {"https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz": {"num_bytes": 14452311, "checksum": "036a14c1ad2eb554b77d32051940cb2808f20557f5edcc24b658df703d3c4302"}}, "download_size": 14452311, "post_processing_size": null, "dataset_size": 44490444, "size_in_bytes": 58942755}}