Update files from the datasets library (from 1.12.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.12.0

README.md

@@ -18,9 +18,10 @@ task_categories:
 task_ids:
 - table-to-text
 paperswithcode_id: totto
+pretty_name: ToTTo
 ---
 
-# Dataset Card Creation Guide
+# Dataset Card for ToTTo
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -56,7 +57,8 @@ paperswithcode_id: totto
 
 ### Dataset Summary
 
-[More Information Needed]
+ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled 
+generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
 
 ### Supported Tasks and Leaderboards
 
@@ -70,25 +72,6 @@ paperswithcode_id: totto
 
 ### Data Instances
 
-```
-DatasetDict({
-    train: Dataset({
-        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
-        num_rows: 120761
-    })
-    validation: Dataset({
-        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
-        num_rows: 7700
-    })
-    test: Dataset({
-        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
-        num_rows: 7700
-    })
-})
-```
-
-### Data Fields
-
 A sample training set is provided below
 
 ```
@@ -385,9 +368,40 @@ A sample training set is provided below
 
 Please note that in test set sentence annotations are not available and thus values inside `sentence_annotations` can be safely ignored.
 
+### Data Fields
+
+- `table_webpage_url` (`str`): Table webpage URL.
+- `table_page_title` (`str`): Table metadata with context about the table.
+- `table_section_title` (`str`): Table metadata with context about the table.
+- `table_section_text` (`str`): Table metadata with context about the table.
+- `table` (`List[List[Dict]]`): The outer lists represents rows and the inner lists columns. Each Dict has the fields:
+  - `column_span` (`int`)
+  - `is_header` (`bool`)
+  - `row_span` (`int`)
+  - `value` (`str`)
+- `highlighted_cells` (`List[[row_index, column_index]]`): Where each `[row_index, column_index]` pair indicates that `table[row_index][column_index]` is highlighted.
+- `example_id` (`int`): A unique id for this example.
+- `sentence_annotations`: Consists of the `original_sentence` and the sequence of revised sentences performed in order to produce the `final_sentence`.
+
 ### Data Splits
 
-[More Information Needed]
+```
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
+        num_rows: 120761
+    })
+    validation: Dataset({
+        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
+        num_rows: 7700
+    })
+    test: Dataset({
+        features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
+        num_rows: 7700
+    })
+})
+```
+
 ## Dataset Creation
 
 ### Curation Rationale
@@ -448,7 +462,14 @@ Please note that in test set sentence annotations are not available and thus val
 
 ### Citation Information
 
-[More Information Needed]
+```
+@inproceedings{parikh2020totto,
+  title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
+  author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
+  booktitle={Proceedings of EMNLP},
+  year={2020}
+ }
+```
 
 ### Contributions
 

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.\n", "citation": "@inproceedings{parikh2020totto,\n  title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},\n  author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},\n  booktitle={Proceedings of EMNLP},\n  year={2020}\n }\n", "homepage": "", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_page_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_webpage_url": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_text": {"dtype": "string", "id": null, "_type": "Value"}, "table": [[{"column_span": {"dtype": "int32", "id": null, "_type": "Value"}, "is_header": {"dtype": "bool", "id": null, "_type": "Value"}, "row_span": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}}]], "highlighted_cells": {"feature": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "example_id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_annotations": {"feature": {"original_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_deletion": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_ambiguity": {"dtype": "string", "id": null, "_type": "Value"}, "final_sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "overlap_subset": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "totto", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 652754806, "num_examples": 120761, "dataset_name": "totto"}, "validation": {"name": "validation", "num_bytes": 47277039, "num_examples": 7700, "dataset_name": "totto"}, "test": {"name": "test", "num_bytes": 40883586, "num_examples": 7700, "dataset_name": "totto"}}, "download_checksums": {"https://storage.googleapis.com/totto/totto_data.zip": {"num_bytes": 187724372, "checksum": "0aab72597057394514fd9659745fd2b318d1a64bf0b2ca1b2c339abe0692fdf2"}}, "download_size": 187724372, "post_processing_size": null, "dataset_size": 740915431, "size_in_bytes": 928639803}}
+{"default": {"description": "ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.\n", "citation": "@inproceedings{parikh2020totto,\n  title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},\n  author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},\n  booktitle={Proceedings of EMNLP},\n  year={2020}\n }\n", "homepage": "", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "table_page_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_webpage_url": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_title": {"dtype": "string", "id": null, "_type": "Value"}, "table_section_text": {"dtype": "string", "id": null, "_type": "Value"}, "table": [[{"column_span": {"dtype": "int32", "id": null, "_type": "Value"}, "is_header": {"dtype": "bool", "id": null, "_type": "Value"}, "row_span": {"dtype": "int32", "id": null, "_type": "Value"}, "value": {"dtype": "string", "id": null, "_type": "Value"}}]], "highlighted_cells": {"feature": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "example_id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_annotations": {"feature": {"original_sentence": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_deletion": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_after_ambiguity": {"dtype": "string", "id": null, "_type": "Value"}, "final_sentence": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "overlap_subset": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "totto", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 652754806, "num_examples": 120761, "dataset_name": "totto"}, "validation": {"name": "validation", "num_bytes": 47277039, "num_examples": 7700, "dataset_name": "totto"}, "test": {"name": "test", "num_bytes": 40883586, "num_examples": 7700, "dataset_name": "totto"}}, "download_checksums": {"https://storage.googleapis.com/totto-public/totto_data.zip": {"num_bytes": 187724372, "checksum": "0aab72597057394514fd9659745fd2b318d1a64bf0b2ca1b2c339abe0692fdf2"}}, "download_size": 187724372, "post_processing_size": null, "dataset_size": 740915431, "size_in_bytes": 928639803}}

totto.py

@@ -24,7 +24,7 @@ _DESCRIPTION = """\
 ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
 """
 _HOMEPAGE_URL = ""
-_URL = "https://storage.googleapis.com/totto/totto_data.zip"
+_URL = "https://storage.googleapis.com/totto-public/totto_data.zip"
 _CITATION = """\
 @inproceedings{parikh2020totto,
   title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},