Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K - 100K
ArXiv:
| 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. |