| | --- |
| | tags: |
| | - flair |
| | - token-classification |
| | - sequence-tagger-model |
| | language: nl |
| | datasets: |
| | - conll2003 |
| | widget: |
| | - text: "George Washington ging naar Washington." |
| | --- |
| | |
| | # Dutch NER in Flair (default model) |
| |
|
| | This is the standard 4-class NER model for Dutch that ships with [Flair](https://github.com/flairNLP/flair/). |
| |
|
| | F1-Score: **92,58** (CoNLL-03) |
| |
|
| | Predicts 4 tags: |
| |
|
| | | **tag** | **meaning** | |
| | |---------------------------------|-----------| |
| | | PER | person name | |
| | | LOC | location name | |
| | | ORG | organization name | |
| | | MISC | other name | |
| |
|
| | Based on Transformer embeddings 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-dutch") |
| | |
| | # make example sentence |
| | sentence = Sentence("George Washington ging naar 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 (0.997)] |
| | Span [5]: "Washington" [− Labels: LOC (0.9996)] |
| | ``` |
| |
|
| | So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging naar 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 CONLL_03_DUTCH |
| | from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
| | |
| | |
| | # 1. get the corpus |
| | corpus: Corpus = CONLL_03_DUTCH() |
| | |
| | # 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 embeddings |
| | embeddings = TransformerWordEmbeddings('wietsedv/bert-base-dutch-cased') |
| | |
| | # 5. initialize sequence tagger |
| | tagger: SequenceTagger = SequenceTagger(hidden_size=256, |
| | embeddings=embeddings, |
| | tag_dictionary=tag_dictionary, |
| | tag_type=tag_type) |
| | |
| | # 6. initialize trainer |
| | trainer: ModelTrainer = ModelTrainer(tagger, corpus) |
| | |
| | # 7. run training |
| | trainer.train('resources/taggers/ner-dutch', |
| | train_with_dev=True, |
| | max_epochs=150) |
| | ``` |
| |
|
| |
|
| | --- |
| |
|
| | ### Cite |
| |
|
| | Please cite the following paper when using this model. |
| |
|
| | ``` |
| | @inproceedings{akbik-etal-2019-flair, |
| | 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 = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)", |
| | year = "2019", |
| | url = "https://www.aclweb.org/anthology/N19-4010", |
| | pages = "54--59", |
| | } |
| | ``` |
| |
|
| | --- |
| |
|
| | ### Issues? |
| |
|
| | The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/). |
| |
|
