| {"emilylearning--cond_ft_none_on_reddit__prcnt_na__test_run_True__bert-base-uncased": { | |
| "description": "\nThis corpus contains preprocessed posts from the Reddit dataset.\nThe dataset consists of 3,848,330 posts with an average length of 270 words for content,\nand 28 words for the summary.\n\nFeatures includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.\nContent is used as document and summary is used as summary.\n", | |
| "citation": "\n@inproceedings{volske-etal-2017-tl,\n title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},\n author = {V{\"o}lske, Michael and Potthast, Martin and Syed, Shahbaz and Stein, Benno},\n booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},\n month = {sep},\n year = {2017},\n address = {Copenhagen, Denmark},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W17-4508},\n doi = {10.18653/v1/W17-4508},\n pages = {59--63},\n abstract = {Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.},\n}\n", | |
| "homepage": "https://github.com/webis-de/webis-tldr-17-corpus", | |
| "license": "", | |
| "features": { | |
| "input_ids": { | |
| "feature": { | |
| "dtype": "int32", | |
| "id": null, | |
| "_type": "Value" | |
| }, | |
| "length": -1, | |
| "id": null, | |
| "_type": "Sequence" | |
| }, | |
| "attention_mask": { | |
| "feature": { | |
| "dtype": "int8", | |
| "id": null, | |
| "_type": "Value" | |
| }, | |
| "length": -1, | |
| "id": null, | |
| "_type": "Sequence" | |
| }, | |
| "labels": { | |
| "feature": { | |
| "dtype": "int64", | |
| "id": null, | |
| "_type": "Value" | |
| }, | |
| "length": -1, | |
| "id": null, | |
| "_type": "Sequence" | |
| } | |
| }, | |
| "post_processed": null, | |
| "supervised_keys": null, | |
| "task_templates": null, | |
| "builder_name": "reddit", | |
| "config_name": "default", | |
| "version": { | |
| "version_str": "1.0.0", | |
| "description": null, | |
| "major": 1, | |
| "minor": 0, | |
| "patch": 0 | |
| }, | |
| "splits": { | |
| "train[:1000]": { | |
| "name": "train[:1000]", | |
| "num_bytes": 653640, | |
| "num_examples": 390, | |
| "dataset_name": "cond_ft_none_on_reddit__prcnt_na__test_run_True__bert-base-uncased" | |
| } | |
| }, | |
| "download_checksums": null, | |
| "download_size": 106355, | |
| "post_processing_size": null, | |
| "dataset_size": 653640, | |
| "size_in_bytes": 759995 | |
| }} |