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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: sl |
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widget: |
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- text: "Danes je lep dan." |
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--- |
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## Slovene Part-of-speech (PoS) Tagging for Flair |
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This is a Slovene part-of-speech (PoS) tagger trained on the [Slovenian UD Treebank](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) using Flair NLP framework. |
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The tagger is trained using a combination of forward Slovene contextual string embeddings, backward Slovene contextual string embeddings and classic Slovene FastText embeddings. |
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F-score (micro): **94,96** |
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The model is trained on a large (500+) number of different tags that described at [https://universaldependencies.org/tagset-conversion/sl-multext-uposf.html](https://universaldependencies.org/tagset-conversion/sl-multext-uposf.html). |
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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("tadejmagajna/flair-sl-pos") |
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# make example sentence |
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sentence = Sentence("Danes je lep dan.") |
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# predict PoS 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 PoS spans |
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print('The following PoS tags are found:') |
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# iterate over parts of speech and print |
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for tag in sentence.get_spans('pos'): |
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print(tag) |
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``` |
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This prints out the following output: |
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``` |
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Sentence: "Danes je lep dan ." [− Tokens: 5 − Token-Labels: "Danes <Rgp> je <Va-r3s-n> lep <Agpmsnn> dan <Ncmsn> . <Z>"] |
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The following PoS tags are found: |
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Span [1]: "Danes" [− Labels: Rgp (1.0)] |
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Span [2]: "je" [− Labels: Va-r3s-n (1.0)] |
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Span [3]: "lep" [− Labels: Agpmsnn (0.9999)] |
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Span [4]: "dan" [− Labels: Ncmsn (1.0)] |
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Span [5]: "." [− Labels: Z (1.0)] |
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``` |
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--- |
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### Training: Script to train this model |
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The following standard 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 UD_SLOVENIAN |
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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 = UD_SLOVENIAN() |
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# 2. what tag do we want to predict? |
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tag_type = 'pos' |
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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 embeddings |
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embedding_types = [ |
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WordEmbeddings('sl'), |
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FlairEmbeddings('sl-forward'), |
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FlairEmbeddings('sl-backward'), |
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] |
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embeddings: StackedEmbeddings = 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 = 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 = ModelTrainer(tagger, corpus) |
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# 7. start training |
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trainer.train('resources/taggers/pos-slovene', |
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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/). |
