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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: "I love Berlin." |
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--- |
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## English Universal Part-of-Speech Tagging in Flair (fast model) |
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This is the fast universal part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **98,47** (Ontonotes) |
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Predicts universal POS tags: |
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| **tag** | **meaning** | |
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|---------------------------------|-----------| |
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|ADJ | adjective | |
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| ADP | adposition | |
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| ADV | adverb | |
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| AUX | auxiliary | |
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| CCONJ | coordinating conjunction | |
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| DET | determiner | |
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| INTJ | interjection | |
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| NOUN | noun | |
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| NUM | numeral | |
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| PART | particle | |
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| PRON | pronoun | |
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| PROPN | proper noun | |
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| PUNCT | punctuation | |
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| SCONJ | subordinating conjunction | |
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| SYM | symbol | |
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| VERB | verb | |
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| X | other | |
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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/upos-english-fast") |
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# make example sentence |
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sentence = Sentence("I love Berlin.") |
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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('pos'): |
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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]: "I" [− Labels: PRON (0.9996)] |
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Span [2]: "love" [− Labels: VERB (1.0)] |
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Span [3]: "Berlin" [− Labels: PROPN (0.9986)] |
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Span [4]: "." [− Labels: PUNCT (1.0)] |
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``` |
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So, the word "*I*" is labeled as a **pronoun** (PRON), "*love*" is labeled as a **verb** (VERB) and "*Berlin*" is labeled as a **proper noun** (PROPN) in the sentence "*I love Berlin*". |
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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 = 'upos' |
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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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# 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/upos-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{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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