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flair
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upos-multi-fast

Token Classification
Flair
PyTorch
sequence-tagger-model
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initial model commit

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  1. README.md +189 -0
  2. loss.tsv +151 -0
  3. pytorch_model.bin +3 -0
  4. training.log +0 -0
README.md ADDED
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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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+
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+ ## Multilingual Universal Part-of-Speech Tagging in Flair (fast model)
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+
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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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+
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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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+
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+ Predicts universal POS tags:
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+
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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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+
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+
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+
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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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+ ---
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+
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+ ### Demo: How to use in Flair
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+
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+ Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
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+
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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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+
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+ # load tagger
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+ tagger = SequenceTagger.load("flair/upos-multi-fast")
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+
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+ # make example sentence
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+ sentence = Sentence("Ich liebe Berlin, as they say. ")
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+
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+ # predict NER tags
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+ tagger.predict(sentence)
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+
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+ # print sentence
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+ print(sentence)
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+
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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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+
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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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+
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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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+
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+ ---
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+
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+ ### Training: Script to train this model
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+
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+ The following Flair script was used to train this model:
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+
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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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+
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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),
124
+ UD_NORWEGIAN(in_memory=False),
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+ UD_FINNISH(in_memory=False),
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+ ])
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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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+
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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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+
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+ # 4. initialize each embedding we use
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+ embedding_types = [
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+
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+ # contextual string embeddings, forward
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+ FlairEmbeddings('multi-forward-fast'),
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+
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+ # contextual string embeddings, backward
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+ FlairEmbeddings('multi-backward-fast'),
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+ ]
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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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+
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+ # 5. initialize sequence tagger
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+ from flair.models import SequenceTagger
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+
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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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+
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+ # 6. initialize trainer
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+ from flair.trainers import ModelTrainer
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+
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+ trainer = ModelTrainer(tagger, corpus)
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+
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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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+
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+
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+ ---
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+
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+ ### Cite
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+
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+ Please cite the following paper when using this model.
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+
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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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+ ---
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+
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+ ### Issues?
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+
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+ The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
loss.tsv ADDED
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