| --- |
| tags: |
| - flair |
| - token-classification |
| - sequence-tagger-model |
| language: fr |
| datasets: |
| - conll2003 |
| widget: |
| - text: "George Washington est allé à Washington" |
| --- |
| |
| ## French NER in Flair (default model) |
|
|
| This is the standard 4-class NER model for French that ships with [Flair](https://github.com/flairNLP/flair/). |
|
|
| F1-Score: **90,61** (WikiNER) |
|
|
| Predicts 4 tags: |
|
|
| | **tag** | **meaning** | |
| |---------------------------------|-----------| |
| | PER | person name | |
| | LOC | location name | |
| | ORG | organization name | |
| | MISC | other name | |
|
|
| Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. |
|
|
| --- |
|
|
| ### Demo: How to use in Flair |
|
|
| 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/ner-french") |
| |
| # make example sentence |
| sentence = Sentence("George Washington est allé à Washington") |
| |
| # 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 [1,2]: "George Washington" [− Labels: PER (0.7394)] |
| Span [6]: "Washington" [− Labels: LOC (0.9161)] |
| ``` |
|
|
| So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington est allé à Washington*". |
|
|
|
|
| --- |
|
|
| ### Training: Script to train this model |
|
|
| The following Flair script was used to train this model: |
|
|
| ```python |
| from flair.data import Corpus |
| from flair.datasets import WIKINER_FRENCH |
| from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
| |
| # 1. get the corpus |
| corpus: Corpus = WIKINER_FRENCH() |
| |
| # 2. what tag do we want to predict? |
| tag_type = 'ner' |
| |
| # 3. make the tag dictionary from the corpus |
| tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) |
| |
| # 4. initialize each embedding we use |
| embedding_types = [ |
| |
| # GloVe embeddings |
| WordEmbeddings('fr'), |
| |
| # contextual string embeddings, forward |
| FlairEmbeddings('fr-forward'), |
| |
| # contextual string embeddings, backward |
| FlairEmbeddings('fr-backward'), |
| ] |
| |
| # embedding stack consists of Flair and GloVe embeddings |
| embeddings = StackedEmbeddings(embeddings=embedding_types) |
| |
| # 5. initialize sequence tagger |
| from flair.models import SequenceTagger |
| |
| tagger = SequenceTagger(hidden_size=256, |
| embeddings=embeddings, |
| tag_dictionary=tag_dictionary, |
| tag_type=tag_type) |
| |
| # 6. initialize trainer |
| from flair.trainers import ModelTrainer |
| |
| trainer = ModelTrainer(tagger, corpus) |
| |
| # 7. run training |
| trainer.train('resources/taggers/ner-french', |
| train_with_dev=True, |
| max_epochs=150) |
| ``` |
|
|
|
|
|
|
| --- |
|
|
| ### Cite |
|
|
| Please cite the following paper when using this model. |
|
|
| ``` |
| @inproceedings{akbik2018coling, |
| title={Contextual String Embeddings for Sequence Labeling}, |
| author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, |
| booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, |
| pages = {1638--1649}, |
| year = {2018} |
| } |
| ``` |
|
|
|
|
| --- |
|
|
| ### Issues? |
|
|
| The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/). |
|
|
