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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: |
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- en |
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- de |
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- fr |
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- it |
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- nl |
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- pl |
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- es |
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- sv |
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- da |
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- no |
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- fi |
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- cs |
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datasets: |
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- ontonotes |
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inference: false |
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--- |
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## Multilingual Universal Part-of-Speech Tagging in Flair (fast model) |
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This is the fast multilingual universal part-of-speech tagging model that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **92,88** (12 UD Treebanks covering English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech) |
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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-multi-fast") |
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# make example sentence |
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sentence = Sentence("Ich liebe Berlin, as they say. ") |
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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]: "Ich" [− Labels: PRON (0.9999)] |
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Span [2]: "liebe" [− Labels: VERB (0.9999)] |
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Span [3]: "Berlin" [− Labels: PROPN (0.9997)] |
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Span [4]: "," [− Labels: PUNCT (1.0)] |
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Span [5]: "as" [− Labels: SCONJ (0.9991)] |
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Span [6]: "they" [− Labels: PRON (0.9998)] |
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Span [7]: "say" [− Labels: VERB (0.9998)] |
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Span [8]: "." [− Labels: PUNCT (1.0)] |
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``` |
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So, the words "*Ich*" and "*they*" are labeled as **pronouns** (PRON), while "*liebe*" and "*say*" are labeled as **verbs** (VERB) in the multilingual sentence "*Ich liebe Berlin, as they say*". |
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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 MultiCorpus |
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from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \ |
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UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH |
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from flair.embeddings import StackedEmbeddings, FlairEmbeddings |
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# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large) |
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corpus = MultiCorpus([ |
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UD_ENGLISH(in_memory=False), |
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UD_GERMAN(in_memory=False), |
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UD_DUTCH(in_memory=False), |
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UD_FRENCH(in_memory=False), |
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UD_ITALIAN(in_memory=False), |
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UD_SPANISH(in_memory=False), |
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UD_POLISH(in_memory=False), |
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UD_CZECH(in_memory=False), |
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UD_DANISH(in_memory=False), |
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UD_SWEDISH(in_memory=False), |
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UD_NORWEGIAN(in_memory=False), |
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UD_FINNISH(in_memory=False), |
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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('multi-forward-fast'), |
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# contextual string embeddings, backward |
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FlairEmbeddings('multi-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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use_crf=False) |
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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-multi-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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