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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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- conll2003 |
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inference: false |
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--- |
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## English NER in Flair (fast model) |
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This is the fast 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **92,92** (corrected CoNLL-03) |
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Predicts 4 tags: |
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| **tag** | **meaning** | |
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|---------------------------------|-----------| |
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| PER | person name | |
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| LOC | location name | |
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| ORG | organization name | |
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| MISC | other name | |
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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-fast") |
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# make example sentence |
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sentence = Sentence("George Washington went to Washington") |
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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 [1,2]: "George Washington" [− Labels: PER (0.9515)] |
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Span [5]: "Washington" [− Labels: LOC (0.992)] |
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``` |
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So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*". |
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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 CONLL_03 |
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from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
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# 1. get the corpus |
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corpus: Corpus = CONLL_03() |
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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('glove'), |
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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', |
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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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