Upload dataloading script and README.md

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.

cross_ner.py

@@ -0,0 +1,223 @@
+# 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,
+                }