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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: "George returned to Berlin to return his hat." |
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--- |
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## English Verb Disambiguation in Flair (fast model) |
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This is the fast verb disambiguation model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **88,27** (Ontonotes) - predicts [Proposition Bank verb frames](http://verbs.colorado.edu/propbank/framesets-english-aliases/). |
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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/frame-english-fast") |
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# make example sentence |
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sentence = Sentence("George returned to Berlin to return his hat.") |
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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 frame tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('frame'): |
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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]: "returned" [− Labels: return.01 (0.9867)] |
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Span [6]: "return" [− Labels: return.02 (0.4741)] |
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``` |
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So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*". |
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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 = ColumnCorpus( |
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"resources/tasks/srl", column_format={1: "text", 11: "frame"} |
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) |
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# 2. what tag do we want to predict? |
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tag_type = 'frame' |
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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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BytePairEmbeddings("en"), |
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FlairEmbeddings("news-forward-fast"), |
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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/frame-english-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{akbik2019flair, |
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title={FLAIR: An easy-to-use framework for state-of-the-art NLP}, |
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author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland}, |
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booktitle={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)}, |
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pages={54--59}, |
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year={2019} |
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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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