| --- |
| annotations_creators: |
| - machine-generated |
| language_creators: |
| - machine-generated |
| language: |
| - en |
| license: |
| - cc-by-sa-4.0 |
| multilinguality: |
| - monolingual |
| pretty_name: wikitext_linked |
| size_categories: |
| - 1M<n<10M |
| source_datasets: |
| - extended|wikitext |
| task_categories: |
| - fill-mask |
| - token-classification |
| - text-classification |
| task_ids: |
| - masked-language-modeling |
| - named-entity-recognition |
| - part-of-speech |
| - lemmatization |
| - parsing |
| - entity-linking-classification |
| --- |
| |
| # Dataset Card for wikitext_linked |
| |
| ## Table of Contents |
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [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) |
| - [Contributions](#contributions) |
| |
| ## Dataset Description |
| |
| - **Homepage:** - |
| - **Repository:** [https://github.com/GabrielKP/svo/](https://github.com/GabrielKP/svo/) |
| - **Paper:** - |
| - **Leaderboard:** - |
| - **Point of Contact:** [gabriel.kressin@dfki.de](mailto:gabriel.kressin@dfki.de) |
| |
| ### Dataset Summary |
| |
| The WikiText language modeling dataset is a collection of over 100 million tokens extracted from |
| the set of verified Good and Featured articles on Wikipedia. Dependency Relations, POS, NER tags |
| are marked with [trankit](https://github.com/nlp-uoregon/trankit), entities are linked with |
| [entity-fishing](https://nerd.readthedocs.io/en/latest/index.html), which also tags another field |
| of NER tags. The dataset is available under the Creative Commons Attribution-ShareAlike License. |
| |
| Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and |
| WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary |
| and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is |
| composed of full articles, the dataset is well suited for models that can take advantage of long |
| term dependencies. |
| |
| ### Supported Tasks and Leaderboards |
| |
| - masked-language-modeling |
| - named-entity-recognition |
| - part-of-speech |
| - lemmatization |
| - parsing |
| - entity-linking-classification |
| |
| ### Languages |
| |
| English. |
| |
| ## Dataset Structure |
| |
| ### Data Instances |
| |
| #### wikitext2 |
| |
| - **Size of downloaded dataset files:** 27.3 MB |
| - **Size of the generated dataset:** 197.2 MB |
| - **Total amount of disk used:** 197.2 MB |
| |
| An example of 'validation' looks as follows. |
| ```json |
| { |
| 'text': 'It is closely related to the American lobster , H. americanus .', |
| 'original_id': 3, |
| 'tok_span': [[0, 0], [0, 2], [3, 5], [6, 13], [14, 21], [22, 24], [25, 28], [29, 37], [38, 45], [46, 47], [48, 50], [51, 61], [62, 63]], |
| 'tok_upos': ['root', 'PRON', 'AUX', 'ADV', 'ADJ', 'ADP', 'DET', 'ADJ', 'NOUN', 'PUNCT', 'PROPN', 'PROPN', 'PUNCT'], |
| 'tok_xpos': ['root', 'PRP', 'VBZ', 'RB', 'JJ', 'IN', 'DT', 'JJ', 'NN', ',', 'NNP', 'NNP', '.'], |
| 'tok_dephead': [0, 4, 4, 4, 0, 8, 8, 8, 4, 8, 8, 10, 4], |
| 'tok_deprel': ['root', 'nsubj', 'cop', 'advmod', 'root', 'case', 'det', 'amod', 'obl', 'punct', 'appos', 'flat', 'punct'], |
| 'tok_lemma': [None, 'it', 'be', 'closely', 'related', 'to', 'the', 'american', 'lobster', ',', 'H.', 'americanus', '.'], |
| 'tok_ner': [None, 'O', 'O', 'O', 'O', 'O', 'O', 'S-MISC', 'O', 'O', 'O', 'O', 'O'], |
| 'ent_span': [[29, 45]], |
| 'ent_wikipedia_external_ref': ['377397'], |
| 'ent_ner': [None], |
| 'ent_domains': [['Enterprise']], |
| } |
| ``` |
| |
| #### wikitext103 |
|
|
| - **Size of downloaded dataset files:** 1.11 GB |
| - **Size of the generated dataset:** 7.82 GB |
| - **Total amount of disk used:** 7.82 GB |
|
|
| An example of 'train' looks as follows. |
| ```json |
| { |
| 'text': 'Vision for the PlayStation Portable .', |
| 'original_id': 3, |
| 'tok_span': [[0, 0], [0, 6], [7, 10], [11, 14], [15, 26], [27, 35], [36, 37]], |
| 'tok_upos': ['root', 'NOUN', 'ADP', 'DET', 'PROPN', 'PROPN', 'PUNCT'], |
| 'tok_xpos': ['root', 'NN', 'IN', 'DT', 'NNP', 'NNP', '.'], |
| 'tok_dephead': [0, 0, 5, 5, 5, 1, 1], |
| 'tok_deprel': ['root', 'root', 'case', 'det', 'compound', 'nmod', 'punct'], |
| 'tok_lemma': [None, 'vision', 'for', 'the', 'PlayStation', 'Portable', '.'], |
| 'tok_ner': [None, 'O', 'O', 'O', 'B-MISC', 'E-MISC', 'O'], |
| 'ent_span': [[15, 35]], |
| 'ent_wikipedia_external_ref': ['619009'], |
| 'ent_ner': [None], |
| 'ent_domains': [['Electronics', 'Computer_Science']] |
| } |
| ``` |
|
|
| Use following code to print the examples nicely: |
| ```py |
| def print_tokens_entities(example): |
| text = example['text'] |
| print( |
| "Text:\n" |
| f" {text}" |
| "\nOrig-Id: " |
| f"{example['original_id']}" |
| "\nTokens:" |
| ) |
| iterator = enumerate(zip( |
| example["tok_span"], |
| example["tok_upos"], |
| example["tok_xpos"], |
| example["tok_ner"], |
| example["tok_dephead"], |
| example["tok_deprel"], |
| example["tok_lemma"], |
| )) |
| print(f" Id | {'token':12} | {'upos':8} | {'xpos':8} | {'ner':8} | {'deph':4} | {'deprel':9} | {'lemma':12} | Id") |
| print("---------------------------------------------------------------------------------------------------") |
| for idx, (tok_span, upos, xpos, ner, dephead, deprel, lemma) in iterator: |
| print(f" {idx:3} | {text[tok_span[0]:tok_span[1]]:12} | {upos:8} | {xpos:8} | {str(ner):8} | {str(dephead):4} | {deprel:9} | {str(lemma):12} | {idx}") |
| |
| iterator = list(enumerate(zip( |
| example.get("ent_span", []), |
| example.get("ent_wikipedia_external_ref", []), |
| example.get("ent_ner", []), |
| example.get("ent_domains", []), |
| ))) |
| if len(iterator) > 0: |
| print("Entities") |
| print(f" Id | {'entity':21} | {'wiki_ref':7} | {'ner':7} | domains") |
| print("--------------------------------------------------------------------") |
| for idx, ((start, end), wiki_ref, ent_ner, ent_domains) in iterator: |
| print(f" {idx:3} | {text[start:end]:21} | {str(wiki_ref):7} | {str(ent_ner):7} | {ent_domains}") |
| ``` |
|
|
| ### Data Fields |
|
|
| The data fields are the same among all splits. |
|
|
| * text: string feature. |
| * original_id: int feature. Mapping to index within original wikitext dataset. |
| * tok_span: sequence of (int, int) tuples. Denotes token spans (start inclusive, end exclusive) |
| within each sentence. |
| **Note that each sentence includes an artificial root node to align dependency relations.** |
| * tok_upos: string feature. [Universal Dependency POS tag](https://universaldependencies.org/) |
| tags. Aligned with tok_span. Root node has tag "root". |
| * tok_xpos: string geature. [XPOS POS tag](https://trankit.readthedocs.io/en/latest/overview.html#token-list). |
| Aligned with tok_span. Root node has tag "root". |
| * tok_dephead: int feature. |
| [Universal Dependency Head Node](https://universaldependencies.org/introduction.html). Int refers |
| to tokens in tok_span. Root node has head `0` (itself). |
| * tok_deprel: [Universal Dependency Relation Description](https://universaldependencies.org/introduction.html). |
| Refers to the relation between this token and head token. Aligned with tok_span. Root node has |
| dependency relation "root" to itself. |
| * tok_lemma: string feature. Lemma of token. Aligend with tok_span. |
| * tok_ner: string feature. NER tag of token. Marked in BIOS schema (e.g. S-MISC, B-LOC, ...) |
| Aligned with tok_span. Root node has NER tag `None`. |
| * ent_span: sequence of (int, int) tuples. Denotes entities found by entity-fishing |
| (start inclusive, end exclusive). |
| * ent_wikipedia_external_ref: string feature. External Reference to wikipedia page. You can |
| access the wikipedia page via the url `https://en.wikipedia.org/wiki?curid=<ent_wikipedia_external_ref>`. |
| Aligend with ent_span. All entities either have this field, or the `ent_ner` field, but not both. |
| An empty field is denoted by the string `None`. Aligned with ent_span. |
| * ent_ner: string feature. Denotes NER tags. An empty field is denoted by the string `None`. |
| Aligned with ent_span. |
| "ent_domains": sequence of string. Denotes domains of entity. Can be empty sequence. Aligned with |
| ent_span. |
| |
| ### Data Splits |
| |
| | name | train |validation| test| |
| |-------------------|------:|---------:|----:| |
| |wikitext103 |4076530| 8607|10062| |
| |wikitext2 | 82649| 8606|10062| |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| [More Information Needed] |
| |
| ### Source Data |
| |
| #### Initial Data Collection and Normalization |
| |
| [https://huggingface.co/datasets/wikitext](https://huggingface.co/datasets/wikitext) |
| |
| #### Who are the source language producers? |
| |
| [More Information Needed] |
| |
| ### Annotations |
| |
| #### Annotation process |
| |
| 1. Started with `wikitext2-raw-v1` and `wikitext103-raw-v1` from [wikitext](https://huggingface.co/datasets/wikitext) |
| 2. Ran datasets through Trankit. Marked all fields starting with `tok`. |
| |
| In this step, the texts have been split into sentences. To retain the original text sections |
| you can accumulate over `original_id` (examples are in order). |
|
|
| 3. Ran datasets through entity-fishing. Marked all fields starting with `ent`. |
|
|
| #### Who are the annotators? |
|
|
| Machines powered by [DFKI](https://www.dfki.de/web). |
|
|
| ### 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 |
|
|
| Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) |
|
|
| ### Citation Information |
|
|
| Please cite the original creators of wikitext, and the great people |
| developing trankit and entity-fishing. |
| ``` |
| @misc{merity2016pointer, |
| title={Pointer Sentinel Mixture Models}, |
| author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher}, |
| year={2016}, |
| eprint={1609.07843}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| |
| @inproceedings{nguyen2021trankit, |
| title={Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing}, |
| author={Nguyen, Minh Van and Lai, Viet Dac and Veyseh, Amir Pouran Ben and Nguyen, Thien Huu}, |
| booktitle="Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", |
| year={2021} |
| } |
| |
| @misc{entity-fishing, |
| title = {entity-fishing}, |
| howpublished = {\\url{https://github.com/kermitt2/entity-fishing}}, |
| publisher = {GitHub}, |
| year = {2016--2022}, |
| archivePrefix = {swh}, |
| eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c} |
| } |
| ``` |
|
|
| ### Contributions |
|
|
| Thanks to [@GabrielKP](https://github.com/GabrielKP) for adding this dataset. |
|
|
