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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license: []
-multilinguality:
-- monolingual
-pretty_name: CrossNER is a cross-domain dataset for named entity recognition
-size_categories:
-- 10K<n<100K
-source_datasets:
-- extended|conll2003
-tags:
-- cross domain
-- ai
-- news
-- music
-- literature
-- politics
-- science
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
-dataset_info:
-- config_name: ai
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 65080
-    num_examples: 100
-  - name: validation
-    num_bytes: 189453
-    num_examples: 350
-  - name: test
-    num_bytes: 225691
-    num_examples: 431
-  download_size: 289173
-  dataset_size: 480224
-- config_name: literature
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 63181
-    num_examples: 100
-  - name: validation
-    num_bytes: 244076
-    num_examples: 400
-  - name: test
-    num_bytes: 270092
-    num_examples: 416
-  download_size: 334380
-  dataset_size: 577349
-- config_name: music
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 65077
-    num_examples: 100
-  - name: validation
-    num_bytes: 259702
-    num_examples: 380
-  - name: test
-    num_bytes: 327195
-    num_examples: 465
-  download_size: 414065
-  dataset_size: 651974
-- config_name: conll2003
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 3561081
-    num_examples: 14041
-  - name: validation
-    num_bytes: 891431
-    num_examples: 3250
-  - name: test
-    num_bytes: 811470
-    num_examples: 3453
-  download_size: 2694794
-  dataset_size: 5263982
-- config_name: politics
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 143507
-    num_examples: 200
-  - name: validation
-    num_bytes: 422760
-    num_examples: 541
-  - name: test
-    num_bytes: 472690
-    num_examples: 651
-  download_size: 724168
-  dataset_size: 1038957
-- config_name: science
-  features:
-  - name: id
-    dtype: string
-  - name: tokens
-    sequence: string
-  - name: ner_tags
-    sequence:
-      class_label:
-        names:
-          '0': O
-          '1': B-academicjournal
-          '2': I-academicjournal
-          '3': B-album
-          '4': I-album
-          '5': B-algorithm
-          '6': I-algorithm
-          '7': B-astronomicalobject
-          '8': I-astronomicalobject
-          '9': B-award
-          '10': I-award
-          '11': B-band
-          '12': I-band
-          '13': B-book
-          '14': I-book
-          '15': B-chemicalcompound
-          '16': I-chemicalcompound
-          '17': B-chemicalelement
-          '18': I-chemicalelement
-          '19': B-conference
-          '20': I-conference
-          '21': B-country
-          '22': I-country
-          '23': B-discipline
-          '24': I-discipline
-          '25': B-election
-          '26': I-election
-          '27': B-enzyme
-          '28': I-enzyme
-          '29': B-event
-          '30': I-event
-          '31': B-field
-          '32': I-field
-          '33': B-literarygenre
-          '34': I-literarygenre
-          '35': B-location
-          '36': I-location
-          '37': B-magazine
-          '38': I-magazine
-          '39': B-metrics
-          '40': I-metrics
-          '41': B-misc
-          '42': I-misc
-          '43': B-musicalartist
-          '44': I-musicalartist
-          '45': B-musicalinstrument
-          '46': I-musicalinstrument
-          '47': B-musicgenre
-          '48': I-musicgenre
-          '49': B-organisation
-          '50': I-organisation
-          '51': B-person
-          '52': I-person
-          '53': B-poem
-          '54': I-poem
-          '55': B-politicalparty
-          '56': I-politicalparty
-          '57': B-politician
-          '58': I-politician
-          '59': B-product
-          '60': I-product
-          '61': B-programlang
-          '62': I-programlang
-          '63': B-protein
-          '64': I-protein
-          '65': B-researcher
-          '66': I-researcher
-          '67': B-scientist
-          '68': I-scientist
-          '69': B-song
-          '70': I-song
-          '71': B-task
-          '72': I-task
-          '73': B-theory
-          '74': I-theory
-          '75': B-university
-          '76': I-university
-          '77': B-writer
-          '78': I-writer
-  splits:
-  - name: train
-    num_bytes: 121928
-    num_examples: 200
-  - name: validation
-    num_bytes: 276118
-    num_examples: 450
-  - name: test
-    num_bytes: 334181
-    num_examples: 543
-  download_size: 485191
-  dataset_size: 732227
----
-# Dataset Card for CrossRE
-## 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
-- **Repository:** [CrossNER](https://github.com/zliucr/CrossNER)
-- **Paper:** [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
-
-### Dataset Summary
-CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains 
-(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for 
-different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five 
-domains.
-
-For details, see the paper: 
-[CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Languages
-
-The language data in CrossNER is in English (BCP-47 en)
-
-## Dataset Structure
-
-### Data Instances
-
-#### conll2003
-- **Size of downloaded dataset files:** 2.69 MB
-- **Size of the generated dataset:** 5.26 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."], 
-  "ner_tags": [49, 0, 41, 0, 0, 0, 41, 0, 0]
-}
-```
-
-#### politics
-- **Size of downloaded dataset files:** 0.72 MB
-- **Size of the generated dataset:** 1.04 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."], 
-  "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 55, 56, 0, 0, 0, 0, 0, 55, 56, 56, 56, 56, 56, 0, 55, 56, 56, 56, 56, 0]
-}
-```
-
-#### science
-- **Size of downloaded dataset files:** 0.49 MB
-- **Size of the generated dataset:** 0.73 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."], 
-  "ner_tags": [0, 0, 0, 0, 15, 16, 0, 15, 16, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-}
-```
-
-#### music
-- **Size of downloaded dataset files:** 0.41 MB
-- **Size of the generated dataset:** 0.65 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."], 
-  "ner_tags": [0, 0, 0, 0, 35, 36, 36, 0, 0, 0, 0, 0, 0, 29, 30, 30, 30, 30, 0]
-}
-```
-
-#### literature
-- **Size of downloaded dataset files:** 0.33 MB
-- **Size of the generated dataset:** 0.58 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0",
-  "tokens": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."], 
-  "ner_tags": [0, 0, 0, 0, 0, 0, 0, 51, 52, 52, 0, 0, 21, 22, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 0, 0, 77, 78, 0, 77, 0, 0, 0, 21, 0, 21, 0, 0, 41, 0, 0, 0, 0, 0, 0, 51, 52, 0, 0, 41, 0, 0, 0, 0, 0, 51, 0, 0]
-}
-```
-
-#### ai
-- **Size of downloaded dataset files:** 0.29 MB
-- **Size of the generated dataset:** 0.48 MB
-
-An example of 'train' looks as follows:
-```json
-{
-  "id": "0", 
-  "tokens": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."], 
-  "ner_tags": [0, 0, 0, 59, 60, 60, 0, 0, 0, 0, 31, 32, 0, 71, 72, 0, 71, 72, 0, 0, 0, 71, 72, 72, 0, 0, 31, 32, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 65, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-- `id`: the instance id of this sentence, a `string` feature.
-- `tokens`: the list of tokens of this sentence, a `list` of `string` features.
-- `ner_tags`: the list of entity tags, a `list` of classification labels.
-
-```json
-{"O": 0, "B-academicjournal": 1, "I-academicjournal": 2, "B-album": 3, "I-album": 4, "B-algorithm": 5, "I-algorithm": 6, "B-astronomicalobject": 7, "I-astronomicalobject": 8, "B-award": 9, "I-award": 10, "B-band": 11, "I-band": 12, "B-book": 13, "I-book": 14, "B-chemicalcompound": 15, "I-chemicalcompound": 16, "B-chemicalelement": 17, "I-chemicalelement": 18, "B-conference": 19, "I-conference": 20, "B-country": 21, "I-country": 22, "B-discipline": 23, "I-discipline": 24, "B-election": 25, "I-election": 26, "B-enzyme": 27, "I-enzyme": 28, "B-event": 29, "I-event": 30, "B-field": 31, "I-field": 32, "B-literarygenre": 33, "I-literarygenre": 34, "B-location": 35, "I-location": 36, "B-magazine": 37, "I-magazine": 38, "B-metrics": 39, "I-metrics": 40, "B-misc": 41, "I-misc": 42, "B-musicalartist": 43, "I-musicalartist": 44, "B-musicalinstrument": 45, "I-musicalinstrument": 46, "B-musicgenre": 47, "I-musicgenre": 48, "B-organisation": 49, "I-organisation": 50, "B-person": 51, "I-person": 52, "B-poem": 53, "I-poem": 54, "B-politicalparty": 55, "I-politicalparty": 56, "B-politician": 57, "I-politician": 58, "B-product": 59, "I-product": 60, "B-programlang": 61, "I-programlang": 62, "B-protein": 63, "I-protein": 64, "B-researcher": 65, "I-researcher": 66, "B-scientist": 67, "I-scientist": 68, "B-song": 69, "I-song": 70, "B-task": 71, "I-task": 72, "B-theory": 73, "I-theory": 74, "B-university": 75, "I-university": 76, "B-writer": 77, "I-writer": 78}
-```
-
-### Data Splits
-|              | Train  | Dev   | Test  |
-|--------------|--------|-------|-------|
-| conll2003    | 14,987 | 3,466 | 3,684 |
-| politics     | 200    | 541   | 651   |
-| science      | 200    | 450   | 543   |
-| music        | 100    | 380   | 456   |
-| literature   | 100    | 400   | 416   |
-| ai           | 100    | 350   | 431   |
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the source language producers?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-#### Who are the annotators?
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Personal and Sensitive Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Discussion of Biases
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Other Known Limitations
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Licensing Information
-
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-### Citation Information
-
-```
-@article{liu2020crossner,
-      title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, 
-      author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
-      year={2020},
-      eprint={2012.04373},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
-
-### Contributions
-
-Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.

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cross_ner.py

@@ -1,223 +0,0 @@
-# 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.
-"""CrossNER is a cross-domain dataset for named entity recognition"""
-
-
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@article{liu2020crossner,
-      title={CrossNER: Evaluating Cross-Domain Named Entity Recognition}, 
-      author={Zihan Liu and Yan Xu and Tiezheng Yu and Wenliang Dai and Ziwei Ji and Samuel Cahyawijaya and Andrea Madotto and Pascale Fung},
-      year={2020},
-      eprint={2012.04373},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-"""
-
-_DESCRIPTION = """\
-CrossNER is a fully-labeled collected of named entity recognition (NER) data spanning over five diverse domains 
-(Politics, Natural Science, Music, Literature, and Artificial Intelligence) with specialized entity categories for 
-different domains. Additionally, CrossNER also includes unlabeled domain-related corpora for the corresponding five 
-domains.
-
-For details, see the paper: 
-[CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://arxiv.org/abs/2012.04373)
-"""
-
-_HOMEPAGE = "https://github.com/zliucr/CrossNER"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "conll2003": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/conll2003/test.txt",
-    },
-    "politics": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/politics/test.txt",
-    },
-    "science": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/science/test.txt",
-    },
-    "music": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/music/test.txt",
-    },
-    "literature": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/literature/test.txt",
-    },
-    "ai": {
-        "train": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/train.txt",
-        "validation": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/dev.txt",
-        "test": "https://raw.githubusercontent.com/zliucr/CrossNER/main/ner_data/ai/test.txt",
-    },
-}
-
-
-_CLASS_LABELS = [
-    "O",
-    "B-academicjournal", "I-academicjournal",
-    "B-album", "I-album",
-    "B-algorithm", "I-algorithm",
-    "B-astronomicalobject", "I-astronomicalobject",
-    "B-award", "I-award",
-    "B-band", "I-band",
-    "B-book", "I-book",
-    "B-chemicalcompound", "I-chemicalcompound",
-    "B-chemicalelement", "I-chemicalelement",
-    "B-conference", "I-conference",
-    "B-country", "I-country",
-    "B-discipline", "I-discipline",
-    "B-election", "I-election",
-    "B-enzyme", "I-enzyme",
-    "B-event", "I-event",
-    "B-field", "I-field",
-    "B-literarygenre", "I-literarygenre",
-    "B-location", "I-location",
-    "B-magazine", "I-magazine",
-    "B-metrics", "I-metrics",
-    "B-misc", "I-misc",
-    "B-musicalartist", "I-musicalartist",
-    "B-musicalinstrument", "I-musicalinstrument",
-    "B-musicgenre", "I-musicgenre",
-    "B-organisation", "I-organisation",
-    "B-person", "I-person",
-    "B-poem", "I-poem",
-    "B-politicalparty", "I-politicalparty",
-    "B-politician", "I-politician",
-    "B-product", "I-product",
-    "B-programlang", "I-programlang",
-    "B-protein", "I-protein",
-    "B-researcher", "I-researcher",
-    "B-scientist", "I-scientist",
-    "B-song", "I-song",
-    "B-task", "I-task",
-    "B-theory", "I-theory",
-    "B-university", "I-university",
-    "B-writer", "I-writer",
-]
-
-
-class CrossNER(datasets.GeneratorBasedBuilder):
-    """CrossNER is a cross-domain dataset for named entity recognition"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="conll2003", version=VERSION,
-                               description="This part of CrossNER covers data from the news domain"),
-        datasets.BuilderConfig(name="politics", version=VERSION,
-                               description="This part of CrossNER covers data from the politics domain"),
-        datasets.BuilderConfig(name="science", version=VERSION,
-                               description="This part of CrossNER covers data from the science domain"),
-        datasets.BuilderConfig(name="music", version=VERSION,
-                               description="This part of CrossNER covers data from the music domain"),
-        datasets.BuilderConfig(name="literature", version=VERSION,
-                               description="This part of CrossNER covers data from the literature domain"),
-        datasets.BuilderConfig(name="ai", version=VERSION,
-                               description="This part of CrossNER covers data from the AI domain"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=_CLASS_LABELS)),
-            }
-        )
-        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, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        downloaded_files = dl_manager.download_and_extract(urls)
-        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
-                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath):
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            guid = 0
-            tokens = []
-            ner_tags = []
-            for line in f:
-                if line == "" or line == "\n":
-                    if tokens:
-                        yield guid, {
-                            "id": str(guid),
-                            "tokens": tokens,
-                            "ner_tags": ner_tags,
-                        }
-                        guid += 1
-                        tokens = []
-                        ner_tags = []
-                else:
-                    splits = line.split("\t")
-                    tokens.append(splits[0])
-                    ner_tags.append(splits[1].rstrip())
-            # last example
-            if tokens:
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }

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