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dataset_infos.json

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+{"kapilchauhan--processed_bert_dataset_free_speech": {
+    "description": "This 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.",
+    "citation": "@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}",
+    "homepage": "https://github.com/webis-de/webis-tldr-17-corpus",
+    "license": "",
+    "features": {
+        "author": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "body": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "normalizedBody": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "subreddit": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "subreddit_id": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "id": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "summary": {
+            "feature": {
+                "dtype": "string",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "input_ids": {
+            "feature": {
+                "dtype": "int32",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        },
+        "token_type_ids": {
+            "feature": {
+                "dtype": "int8",
+                "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"
+        },
+        "special_tokens_mask": {
+            "feature": {
+                "dtype": "int8",
+                "id": null,
+                "_type": "Value"
+            },
+            "length": -1,
+            "id": null,
+            "_type": "Sequence"
+        }
+    },
+    "post_processed": null,
+    "supervised_keys": null,
+    "task_templates": null,
+    "builder_name": null,
+    "config_name": null,
+    "version": null,
+    "splits": {
+        "train": {
+            "name": "train",
+            "num_bytes": 1570623293.0,
+            "num_examples": 78093,
+            "dataset_name": "processed_bert_dataset_free_speech"
+        }
+    },
+    "download_checksums": null,
+    "download_size": 253310712,
+    "post_processing_size": null,
+    "dataset_size": 1570623293.0,
+    "size_in_bytes": 1823934005.0
+}}