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--- |
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tags: |
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- flair |
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- token-classification |
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- sequence-tagger-model |
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language: en |
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datasets: |
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- ontonotes |
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widget: |
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- text: "On September 1st George Washington won 1 dollar." |
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--- |
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## English NER in Flair (Ontonotes fast model) |
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This is the fast version of the 18-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **89.3** (Ontonotes) |
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Predicts 18 tags: |
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| **tag** | **meaning** | |
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|---------------------------------|-----------| |
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| CARDINAL | cardinal value | |
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| DATE | date value | |
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| EVENT | event name | |
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| FAC | building name | |
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| GPE | geo-political entity | |
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| LANGUAGE | language name | |
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| LAW | law name | |
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| LOC | location name | |
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| MONEY | money name | |
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| NORP | affiliation | |
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| ORDINAL | ordinal value | |
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| ORG | organization name | |
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| PERCENT | percent value | |
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| PERSON | person name | |
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| PRODUCT | product name | |
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| QUANTITY | quantity value | |
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| TIME | time value | |
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| WORK_OF_ART | name of work of art | |
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Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. |
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--- |
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### Demo: How to use in Flair |
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Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("flair/ner-english-ontonotes-fast") |
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# make example sentence |
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sentence = Sentence("On September 1st George Washington won 1 dollar.") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
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This yields the following output: |
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``` |
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Span [2,3]: "September 1st" [− Labels: DATE (0.9655)] |
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Span [4,5]: "George Washington" [− Labels: PERSON (0.8243)] |
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Span [7,8]: "1 dollar" [− Labels: MONEY (0.8022)] |
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``` |
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So, the entities "*September 1st*" (labeled as a **date**), "*George Washington*" (labeled as a **person**) and "*1 dollar*" (labeled as a **money**) are found in the sentence "*On September 1st George Washington won 1 dollar*". |
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--- |
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### Training: Script to train this model |
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The following Flair script was used to train this model: |
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```python |
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from flair.data import Corpus |
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from flair.datasets import ColumnCorpus |
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from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
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# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself) |
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corpus: Corpus = ColumnCorpus( |
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"resources/tasks/onto-ner", |
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column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"}, |
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tag_to_bioes="ner", |
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) |
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# 2. what tag do we want to predict? |
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tag_type = 'ner' |
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# 3. make the tag dictionary from the corpus |
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tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) |
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# 4. initialize each embedding we use |
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embedding_types = [ |
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# GloVe embeddings |
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WordEmbeddings('en-crawl'), |
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# contextual string embeddings, forward |
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FlairEmbeddings('news-forward-fast'), |
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# contextual string embeddings, backward |
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FlairEmbeddings('news-backward-fast'), |
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] |
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# embedding stack consists of Flair and GloVe embeddings |
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embeddings = StackedEmbeddings(embeddings=embedding_types) |
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# 5. initialize sequence tagger |
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from flair.models import SequenceTagger |
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tagger = SequenceTagger(hidden_size=256, |
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embeddings=embeddings, |
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tag_dictionary=tag_dictionary, |
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tag_type=tag_type) |
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# 6. initialize trainer |
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from flair.trainers import ModelTrainer |
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trainer = ModelTrainer(tagger, corpus) |
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# 7. run training |
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trainer.train('resources/taggers/ner-english-ontonotes-fast', |
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train_with_dev=True, |
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max_epochs=150) |
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``` |
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--- |
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### Cite |
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Please cite the following paper when using this model. |
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``` |
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@inproceedings{akbik2018coling, |
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title={Contextual String Embeddings for Sequence Labeling}, |
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author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, |
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booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, |
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pages = {1638--1649}, |
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year = {2018} |
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} |
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``` |
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--- |
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### Issues? |
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The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/). |
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