Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

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README.md

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+---
+annotations_creators:
+- found
+language_creators:
+- crowdsourced
+languages:
+  release_v1:
+  - en
+  release_v2:
+  - en
+  release_v2-1:
+  - en
+  release_v2-1_constrained:
+  - en
+  release_v2_constrained:
+  - en
+  release_v3-0_en:
+  - en
+  release_v3-0_ru:
+  - ru
+  webnlg_challenge_2017:
+  - en
+licenses:
+- cc-by-sa-3-0
+- cc-by-nc-sa-4-0
+- gfdl-1-1
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|other-db_pedia
+- original
+task_categories:
+  release_v1:
+  - conditional-text-generation
+  release_v2:
+  - conditional-text-generation
+  release_v2-1:
+  - conditional-text-generation
+  release_v2-1_constrained:
+  - conditional-text-generation
+  release_v2_constrained:
+  - conditional-text-generation
+  release_v3-0_en:
+  - conditional-text-generation
+  - structure-prediction
+  release_v3-0_ru:
+  - conditional-text-generation
+  - structure-prediction
+  webnlg_challenge_2017:
+  - conditional-text-generation
+task_ids:
+  release_v1:
+  - other-stuctured-to-text
+  release_v2:
+  - other-stuctured-to-text
+  release_v2-1:
+  - other-stuctured-to-text
+  release_v2-1_constrained:
+  - other-stuctured-to-text
+  release_v2_constrained:
+  - other-stuctured-to-text
+  release_v3-0_en:
+  - conditional-text-generation
+  - parsing
+  release_v3-0_ru:
+  - conditional-text-generation
+  - parsing
+  webnlg_challenge_2017:
+  - other-stuctured-to-text
+---
+
+# Dataset Card for WebNLG
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [WebNLG challenge website](https://webnlg-challenge.loria.fr/)
+- **Repository:** [WebNLG GitLab repository](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/)
+- **Paper:** [Creating Training Corpora for NLG Micro-Planning](https://www.aclweb.org/anthology/P17-1017.pdf)
+- **Leaderboard:** [WebNLG leaderboards](https://gerbil-nlg.dice-research.org/gerbil/webnlg2020results)
+- **Point of Contact:** [anastasia.shimorina@loria.fr](anastasia.shimorina@loria.fr)
+
+### Dataset Summary
+
+The WebNLG challenge consists in mapping data to text. The training data consists
+of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
+of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
+
+```
+a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
+b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
+```
+
+As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
+(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
+aggregation (how to avoid repetitions) and surface realisation
+(how to build a syntactically correct and natural sounding text).
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a `other-structured-to-text` task which requires a model takes a set of RDF (Resource Description Format) triples from a database (DBpedia) of the form (subject, property, object) as input and write out a natural language sentence expressing the information contained in the triples. The dataset has supportd two challenges: the [WebNLG2017](https://www.aclweb.org/anthology/W17-3518/) and [WebNLG2020](https://gerbil-nlg.dice-research.org/gerbil/webnlg2020results) challenge. Results were ordered by their [METEOR](https://huggingface.co/metrics/meteor) to the reference, but the leaderboards report a range of other metrics including [BLEU](https://huggingface.co/metrics/bleu), [BERTscore](https://huggingface.co/metrics/bertscore), and [BLEURT](https://huggingface.co/metrics/bleurt). The v3 release (`release_v3.0_en`, `release_v3.0_ru`) for the WebNLG2020 challenge also supports a semantic `parsing` task.
+
+### Languages
+
+All releases contain English (`en`) data. The v3 release (`release_v3.0_ru`) also contains Russian (`ru`) examples.
+
+## Dataset Structure
+
+### Data Instances
+
+A typical example contains the original RDF triples in the set, a modified version which presented to crowd workers, and a set of possible verbalizations for this set of triples:
+```
+{'2017_test_category': '',
+ 'category': 'Politician',
+ 'eid': 'Id10',
+ 'lex': {'comment': ['good', 'good', 'good'],
+         'lid': ['Id1', 'Id2', 'Id3'],
+         'text': ['World War II had Chiang Kai-shek as a commander and United States Army soldier Abner W. Sibal.',
+                  'Abner W. Sibal served in the United States Army during the Second World War and during that war Chiang Kai-shek was one of the commanders.',
+                  'Abner W. Sibal, served in the United States Army and fought in World War II, one of the commanders of which, was Chiang Kai-shek.']},
+ 'modified_triple_sets': {'mtriple_set': [['Abner_W._Sibal | battle | World_War_II',
+                                           'World_War_II | commander | Chiang_Kai-shek',
+                                           'Abner_W._Sibal | militaryBranch | United_States_Army']]},
+ 'original_triple_sets': {'otriple_set': [['Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | branch | United_States_Army'],
+                                          ['Abner_W._Sibal | militaryBranch | United_States_Army',
+                                           'Abner_W._Sibal | battles | World_War_II',
+                                           'World_War_II | commander | Chiang_Kai-shek']]},
+ 'shape': '(X (X) (X (X)))',
+ 'shape_type': 'mixed',
+ 'size': 3}
+```
+
+### Data Fields
+
+The following fields can be found in the instances:
+- `category`: the category of the DBpedia entites present in the RDF triples.
+- `eid`: an example ID, only unique per split per category.
+- `size`: number of RDF triples in the set.
+- `shape`: (for v3 only) Each set of RDF-triples is a tree, which is characterised by its shape and shape type. `shape` is a string representation of the tree with nested parentheses where X is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format))
+- `shape_type`: (for v3 only) is a type of the tree shape, which can be: `chain` (the object of one triple is the subject of the other); `sibling` (triples with a shared subject); `mixed` (both chain and sibling types present).
+- `2017_test_category`: (for `webnlg_challenge_2017`) tells whether the set of RDF triples was present in the training set or not.
+- `lex`: the lexicalizations, with:
+  - `text`: the text to be predicted.
+  - `lid`: a lexicalizayion ID, unique per example.
+  - `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad`
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+The dataset uses the `cc-by-nc-sa-4.0` license. The source DBpedia project uses the `cc-by-sa-3.0` and `gfdl-1.1` licenses.
+
+### Citation Information
+
+- If you use the WebNLG corpus, cite:
+```
+@inproceedings{web_nlg,
+  author    = {Claire Gardent and
+               Anastasia Shimorina and
+               Shashi Narayan and
+               Laura Perez{-}Beltrachini},
+  editor    = {Regina Barzilay and
+               Min{-}Yen Kan},
+  title     = {Creating Training Corpora for {NLG} Micro-Planners},
+  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational
+               Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
+               1: Long Papers},
+  pages     = {179--188},
+  publisher = {Association for Computational Linguistics},
+  year      = {2017},
+  url       = {https://doi.org/10.18653/v1/P17-1017},
+  doi       = {10.18653/v1/P17-1017}
+}
+```
+
+- If you use `release_v2_constrained` in particular, cite:
+```
+@InProceedings{shimorina2018handling,
+  author = 	"Shimorina, Anastasia
+		and Gardent, Claire",
+  title = 	"Handling Rare Items in Data-to-Text Generation",
+  booktitle = 	"Proceedings of the 11th International Conference on Natural Language Generation",
+  year = 	"2018",
+  publisher = 	"Association for Computational Linguistics",
+  pages = 	"360--370",
+  location = 	"Tilburg University, The Netherlands",
+  url = 	"http://aclweb.org/anthology/W18-6543"
+}
+```

dataset_infos.json

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+{"webnlg_challenge_2017": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "webnlg_challenge_2017", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5439100, "num_examples": 6940, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 687093, "num_examples": 872, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 3037685, "num_examples": 4615, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 9163878, "size_in_bytes": 34554466}, "release_v1": {"description": "The WebNLG challenge consists in mapping data to text. 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John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v1", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"full": {"name": "full", "num_bytes": 11361516, "num_examples": 14237, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 11361516, "size_in_bytes": 36752104}, "release_v2": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v2", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10538445, "num_examples": 12876, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1323317, "num_examples": 1619, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 1288814, "num_examples": 1600, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 13150576, "size_in_bytes": 38541164}, "release_v2_constrained": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v2_constrained", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10560502, "num_examples": 12895, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1385570, "num_examples": 1594, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 1207294, "num_examples": 1606, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 13153366, "size_in_bytes": 38543954}, "release_v2.1": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v2.1", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10556881, "num_examples": 12876, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1325368, "num_examples": 1619, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 1289748, "num_examples": 1600, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 13171997, "size_in_bytes": 38562585}, "release_v2.1_constrained": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v2.1_constrained", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10747616, "num_examples": 12895, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1247988, "num_examples": 1594, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 1176393, "num_examples": 1606, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 13171997, "size_in_bytes": 38562585}, "release_v3.0_en": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v3.0_en", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 10784576, "num_examples": 13211, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1356359, "num_examples": 1667, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 25813556, "num_examples": 39991, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 37954491, "size_in_bytes": 63345079}, "release_v3.0_ru": {"description": "The WebNLG challenge consists in mapping data to text. The training data consists\nof Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation\nof these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).\n\na. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)\nb. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot\n\nAs the example illustrates, the task involves specific NLG subtasks such as sentence segmentation\n(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),\naggregation (how to avoid repetitions) and surface realisation\n(how to build a syntactically correct and natural sounding text).\n", "citation": "@inproceedings{web_nlg,\n  author    = {Claire Gardent and\n               Anastasia Shimorina and\n               Shashi Narayan and\n               Laura Perez{-}Beltrachini},\n  editor    = {Regina Barzilay and\n               Min{-}Yen Kan},\n  title     = {Creating Training Corpora for {NLG} Micro-Planners},\n  booktitle = {Proceedings of the 55th Annual Meeting of the\n               Association for Computational Linguistics,\n               {ACL} 2017, Vancouver, Canada, July 30 - August 4,\n               Volume 1: Long Papers},\n  pages     = {179--188},\n  publisher = {Association for Computational Linguistics},\n  year      = {2017},\n  url       = {https://doi.org/10.18653/v1/P17-1017},\n  doi       = {10.18653/v1/P17-1017}\n}\n", "homepage": "https://webnlg-challenge.loria.fr/", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "size": {"dtype": "int32", "id": null, "_type": "Value"}, "eid": {"dtype": "string", "id": null, "_type": "Value"}, "original_triple_sets": {"feature": {"otriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "modified_triple_sets": {"feature": {"mtriple_set": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}, "shape": {"dtype": "string", "id": null, "_type": "Value"}, "shape_type": {"dtype": "string", "id": null, "_type": "Value"}, "lex": {"feature": {"comment": {"dtype": "string", "id": null, "_type": "Value"}, "lid": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}, "2017_test_category": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "web_nlg", "config_name": "release_v3.0_ru", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7972852, "num_examples": 5573, "dataset_name": "web_nlg"}, "dev": {"name": "dev", "num_bytes": 1097883, "num_examples": 790, "dataset_name": "web_nlg"}, "test": {"name": "test", "num_bytes": 18023181, "num_examples": 23870, "dataset_name": "web_nlg"}}, "download_checksums": {"https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip": {"num_bytes": 25390588, "checksum": "287290957f7352c9e3b64cdc5957faba8ed5d835f34f2106ba5666a77fdb1cfb"}}, "download_size": 25390588, "post_processing_size": null, "dataset_size": 27093916, "size_in_bytes": 52484504}}

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web_nlg.py

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+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The WebNLG corpus"""
+
+from __future__ import absolute_import, division, print_function
+
+import os
+import xml.etree.cElementTree as ET
+from collections import defaultdict
+from glob import glob
+from os.path import join as pjoin
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{web_nlg,
+  author    = {Claire Gardent and
+               Anastasia Shimorina and
+               Shashi Narayan and
+               Laura Perez{-}Beltrachini},
+  editor    = {Regina Barzilay and
+               Min{-}Yen Kan},
+  title     = {Creating Training Corpora for {NLG} Micro-Planners},
+  booktitle = {Proceedings of the 55th Annual Meeting of the
+               Association for Computational Linguistics,
+               {ACL} 2017, Vancouver, Canada, July 30 - August 4,
+               Volume 1: Long Papers},
+  pages     = {179--188},
+  publisher = {Association for Computational Linguistics},
+  year      = {2017},
+  url       = {https://doi.org/10.18653/v1/P17-1017},
+  doi       = {10.18653/v1/P17-1017}
+}
+"""
+
+_DESCRIPTION = """\
+The WebNLG challenge consists in mapping data to text. The training data consists
+of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation
+of these triples. For instance, given the 3 DBpedia triples shown in (a), the aim is to generate a text such as (b).
+
+a. (John_E_Blaha birthDate 1942_08_26) (John_E_Blaha birthPlace San_Antonio) (John_E_Blaha occupation Fighter_pilot)
+b. John E Blaha, born in San Antonio on 1942-08-26, worked as a fighter pilot
+
+As the example illustrates, the task involves specific NLG subtasks such as sentence segmentation
+(how to chunk the input data into sentences), lexicalisation (of the DBpedia properties),
+aggregation (how to avoid repetitions) and surface realisation
+(how to build a syntactically correct and natural sounding text).
+"""
+
+_URL = "https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip"
+
+_FILE_PATHS = {
+    "webnlg_challenge_2017": {
+        "train": [f"webnlg_challenge_2017/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"webnlg_challenge_2017/dev/{i}triples/" for i in range(1, 8)],
+        "test": ["webnlg_challenge_2017/test/"],
+    },
+    "release_v1": {"full": [f"release_v1/xml/{i}triples" for i in range(1, 8)]},
+    "release_v2": {
+        "train": [f"release_v2/xml/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v2/xml/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v2/xml/test/{i}triples/" for i in range(1, 8)],
+    },
+    "release_v2_constrained": {
+        "train": [f"release_v2_constrained/xml/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v2_constrained/xml/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v2_constrained/xml/test/{i}triples/" for i in range(1, 8)],
+    },
+    "release_v2.1": {
+        "train": [f"release_v2.1/xml/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v2.1/xml/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v2.1/xml/test/{i}triples/" for i in range(1, 8)],
+    },
+    "release_v2.1_constrained": {
+        "train": [f"release_v2.1_constrained/xml/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v2.1_constrained/xml/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v2.1_constrained/xml/test/{i}triples/" for i in range(1, 8)],
+    },
+    "release_v3.0_en": {
+        "train": [f"release_v3.0/en/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v3.0/en/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v3.0/en/test/" for i in range(1, 8)],
+    },
+    "release_v3.0_ru": {
+        "train": [f"release_v3.0/ru/train/{i}triples/" for i in range(1, 8)],
+        "dev": [f"release_v3.0/ru/dev/{i}triples/" for i in range(1, 8)],
+        "test": [f"release_v3.0/ru/test/" for i in range(1, 8)],
+    },
+}
+
+
+def et_to_dict(tree):
+    dct = {tree.tag: {} if tree.attrib else None}
+    children = list(tree)
+    if children:
+        dd = defaultdict(list)
+        for dc in map(et_to_dict, children):
+            for k, v in dc.items():
+                dd[k].append(v)
+        dct = {tree.tag: dd}
+    if tree.attrib:
+        dct[tree.tag].update((k, v) for k, v in tree.attrib.items())
+    if tree.text:
+        text = tree.text.strip()
+        if children or tree.attrib:
+            if text:
+                dct[tree.tag]["text"] = text
+        else:
+            dct[tree.tag] = text
+    return dct
+
+
+def parse_entry(entry):
+    res = {}
+    otriple_set_list = entry["originaltripleset"]
+    res["original_triple_sets"] = [{"otriple_set": otriple_set["otriple"]} for otriple_set in otriple_set_list]
+    mtriple_set_list = entry["modifiedtripleset"]
+    res["modified_triple_sets"] = [{"mtriple_set": mtriple_set["mtriple"]} for mtriple_set in mtriple_set_list]
+    res["category"] = entry["category"]
+    res["eid"] = entry["eid"]
+    res["size"] = int(entry["size"])
+    res["lex"] = {
+        "comment": [ex.get("comment", "") for ex in entry.get("lex", [])],
+        "lid": [ex.get("lid", "") for ex in entry.get("lex", [])],
+        "text": [ex.get("text", "") for ex in entry.get("lex", [])],
+    }
+    res["shape"] = entry.get("shape", "")
+    res["shape_type"] = entry.get("shape_type", "")
+    return res
+
+
+def xml_file_to_examples(filename):
+    tree = ET.parse(filename).getroot()
+    examples = et_to_dict(tree)["benchmark"]["entries"][0]["entry"]
+    return [parse_entry(entry) for entry in examples]
+
+
+class WebNlg(datasets.GeneratorBasedBuilder):
+    """The WebNLG corpus"""
+
+    VERSION = datasets.Version("3.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="webnlg_challenge_2017", description="WebNLG Challenge 2017 data, covers 10 DBpedia categories."
+        ),
+        datasets.BuilderConfig(name="release_v1", description="Covers 15 DBpedia categories."),
+        datasets.BuilderConfig(
+            name="release_v2", description="Includes release_v1 and test data from the WebNLG challenge."
+        ),
+        datasets.BuilderConfig(
+            name="release_v2_constrained",
+            description="Same data as v2, the split into train/dev/test is more challenging.",
+        ),
+        datasets.BuilderConfig(name="release_v2.1", description="5,667 texts from v2 were cleaned."),
+        datasets.BuilderConfig(
+            name="release_v2.1_constrained",
+            description="Same data as v2.1, the split into train/dev/test is more challenging.",
+        ),
+        datasets.BuilderConfig(
+            name="release_v3.0_en", description="WebNLG+ data used in the WebNLG challenge 2020. English."
+        ),
+        datasets.BuilderConfig(
+            name="release_v3.0_ru", description="WebNLG+ data used in the WebNLG challenge 2020. Russian."
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "category": datasets.Value("string"),
+                "size": datasets.Value("int32"),
+                "eid": datasets.Value("string"),
+                "original_triple_sets": datasets.Sequence(
+                    {"otriple_set": datasets.Sequence(datasets.Value("string"))}
+                ),
+                "modified_triple_sets": datasets.Sequence(
+                    {"mtriple_set": datasets.Sequence(datasets.Value("string"))}
+                ),
+                "shape": datasets.Value("string"),
+                "shape_type": datasets.Value("string"),
+                "lex": datasets.Sequence(
+                    {
+                        "comment": datasets.Value("string"),
+                        "lid": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                    }
+                ),
+                "2017_test_category": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://webnlg-challenge.loria.fr/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=spl,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filedirs": [
+                        os.path.join(data_dir, "webnlg-dataset-master", dir_suf) for dir_suf in dir_suffix_list
+                    ],
+                },
+            )
+            for spl, dir_suffix_list in _FILE_PATHS[self.config.name].items()
+        ]
+
+    def _generate_examples(self, filedirs):
+        """ Yields examples. """
+
+        id_ = 0
+        for xml_location in filedirs:
+            for xml_file in sorted(glob(pjoin(xml_location, "*.xml"))):
+                test_cat = xml_file.split("/")[-1][:-4] if "webnlg_challenge_2017/test" in xml_file else ""
+                for exple_dict in xml_file_to_examples(xml_file):
+                    exple_dict["2017_test_category"] = test_cat
+                    id_ += 1
+                    yield id_, exple_dict