| --- |
| library_name: flair |
| tags: |
| - flair |
| - token-classification |
| - sequence-tagger-model |
| language: |
| - en |
| - ja |
| - es |
| - fr |
| - it |
| - de |
| - nl |
| - da |
| - sv |
| - pt |
| license: other |
| license_name: flukes-nc-1.0 |
| license_link: https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md |
| widget: |
| - text: Santos star Pelé won the 1970 World Cup in Mexico. |
| --- |
| |
|
|
| ## English NER in Flair (4-class) |
|
|
| This is the multilingual 4-class NER model for [Flair](https://github.com/flairNLP/flair/). |
|
|
| Our model can predict entities in any language text. However, for some languages you should use a language-specific tokenizer (see examples below). |
|
|
| Predicts 4 tags: |
|
|
| | **tag** | **meaning** | |
| |---------------------------------|-----------| |
| | PER | person name | |
| | LOC | location name | |
| | ORG | organization name | |
| | MISC | other name | |
|
|
| > ⚠️ **Default license: noncommercial use only.** This model is released under the |
| > [Flukes NC 1.0 License](https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md). |
| > Commercial use — including using this model's predictions in a commercial |
| > product or service — requires a separate license. Contact |
| > `alan.akbik@gmail.com`. |
|
|
|
|
|
|
| --- |
|
|
| ### Example 1: Named Entity Recognition in Spanish |
|
|
| Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) |
|
|
| ```python |
| from flair.data import Sentence |
| from flair.models import SequenceTagger |
| |
| # load tagger |
| tagger = SequenceTagger.load("flair/entity-multilingual-8class") |
| |
| # make example sentence with Spanish text |
| sentence = Sentence("La selección española de fútbol, dirigida por Vicente del Bosque, ganó en Johannesburgo la final del Mundial 2010 contra los Países Bajos.") |
| |
| # predict NER tags |
| tagger.predict(sentence) |
| |
| # print sentence |
| print(sentence) |
| |
| # print predicted NER spans |
| print('The following NER tags are found:') |
| # iterate over entities and print |
| for entity in sentence.get_spans('ner'): |
| print(entity) |
| ``` |
|
|
| This yields the following output: |
| ``` |
| Span[1:5]: "selección española de fútbol" → ORG (1.0000) |
| Span[8:11]: "Vicente del Bosque" → PER (1.0000) |
| Span[14:15]: "Johannesburgo" → LOC (1.0000) |
| Span[18:20]: "Mundial 2010" → MISC (1.0000) |
| Span[22:24]: "Países Bajos" → ORG (1.0000) |
| ``` |
|
|
| So, the entities "*selección española de fútbol*" and "*Países Bajos*" are labeled as a **organization** (soccer teams), "*Vicente del Bosque*" is labeled as a **person**, |
| "*Johannesburgo*" is labeled as a **location** and "*Mundial 2010*", labeled as **other entity** (MISC). |
|
|
| --- |
|
|
| ### Example 2: Named Entity Recognition in French |
|
|
| Flair by default uses SegTok for tokenization, which does not work so well for French, as it does not split contractions with elision like "L'équipe". |
| In the newest Flair version, you can add additional split tokens, which alleviates this issue. |
|
|
| ```python |
| from flair.data import Sentence |
| from flair.models import SequenceTagger |
| |
| # load tagger |
| tagger = SequenceTagger.load("flair/entity-multilingual-8class") |
| |
| # make a tokenizer for French that splits on contraction characters |
| tokenizer = SegtokTokenizer(additional_split_characters=["'"]) |
| |
| # make example sentence with French text, pass the special tokenizer |
| sentence = Sentence("L'équipe de France de football, entraînée par Aimé Jacquet, a remporté à Saint-Denis la finale de la Coupe du monde 1998 face au Brésil.", use_tokenizer=tokenizer) |
| |
| # predict NER tags |
| tagger.predict(sentence) |
| |
| # print sentence |
| print(sentence) |
| |
| # print predicted NER spans |
| print('The following NER tags are found:') |
| # iterate over entities and print |
| for entity in sentence.get_spans('ner'): |
| print(entity) |
| ``` |
|
|
| This yields the following output: |
| ``` |
| Span[2:5]: "équipe de France" → ORG (1.0000) |
| Span[10:12]: "Aimé Jacquet" → PER (1.0000) |
| Span[16:17]: "Saint-Denis" → LOC (1.0000) |
| Span[21:25]: "Coupe du monde 1998" → MISC (1.0000) |
| Span[27:28]: "Brésil" → ORG (1.0000) |
| ``` |
|
|
| So, the entities "*équipe de France*" and "*Brésil*" are labeled as a **organization** (soccer teams), "*Aimé Jacquet*" is labeled as a **person**, |
| "*Saint-Denis*" is labeled as a **location** and "*Coupe du monde 1998*" is labeled as **other entity** (MISC). |
|
|
|
|
| --- |
|
|
| ### Example 3: Named Entity Recognition in Japanese |
|
|
| Flair ships external tokenizers for Japanese. |
|
|
| ```python |
| from flair.data import Sentence |
| from flair.models import SequenceTagger |
| |
| # load tagger |
| tagger = SequenceTagger.load("flair/entity-multilingual-8class") |
| |
| # make a tokenizer for Japanese |
| tokenizer = JapaneseTokenizer("janome") |
| |
| # make example sentence with Japanese text, pass the special tokenizer |
| sentence = Sentence("森保一監督率いる日本代表は、カタールで開催された2022年のワールドカップで、元世界王者のスペインを破る歴史的な勝利を収めました。", use_tokenizer=tokenizer) |
| |
| # predict NER tags |
| tagger.predict(sentence) |
| |
| # print sentence |
| print(sentence) |
| |
| # print predicted NER spans |
| print('The following NER tags are found:') |
| # iterate over entities and print |
| for entity in sentence.get_spans('ner'): |
| print(entity) |
| ``` |
|
|
| This yields the following output: |
| ``` |
| Span[0:2]: "森保一" → PER (1.0000) |
| Span[4:5]: "日本" → ORG (1.0000) |
| Span[8:9]: "カタール" → LOC (1.0000) |
| Span[17:18]: "ワールドカップ" → MISC (1.0000) |
| Span[24:25]: "スペイン" → ORG (1.0000) |
| ``` |
|
|
| So, the entities "*日本*" and "*スペイン*" are labeled as a **organization** (soccer teams), "*森保一*" is labeled as a **person**, |
| "*カタール*" is labeled as a **location** and "*ワールドカップ*" is labeled as **other entity** (MISC). |
|
|
|
|
| --- |
|
|
| ### Cite |
|
|
| Please cite the following paper when using this model. |
|
|
| ``` |
| @inproceedings{akbik2019flair, |
| title={{FLAIR}: An easy-to-use framework for state-of-the-art {NLP}}, |
| author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland}, |
| booktitle={{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)}, |
| pages={54--59}, |
| year={2019} |
| } |
| ``` |
|
|
| --- |
|
|
| ## License |
|
|
| **Model weights**: [Flukes Noncommercial License 1.0](https://github.com/flairNLP/flukes/blob/main/MODEL_LICENSE.md). |
| Personal, academic, and other noncommercial use permitted. Commercial use |
| requires a separate license — contact `alan.akbik@gmail.com`. |
