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ner-multi-fast

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

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  1. README.md +148 -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: en de nl es
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+ datasets:
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+ - conll2003
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+ inference: false
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+ ---
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+
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+ ## 4-Language NER in Flair (English, German, Dutch and Spanish)
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+
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+ This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
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+
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+ F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish)
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+
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+
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+ Predicts 4 tags:
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+
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+ | **tag** | **meaning** |
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+ |---------------------------------|-----------|
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+ | PER | person name |
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+ | LOC | location name |
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+ | ORG | organization name |
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+ | MISC | other name |
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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/ner-multi")
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+
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+ # make example sentence in any of the four languages
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+ sentence = Sentence("George Washington ging nach Washington")
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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('ner'):
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+ print(entity)
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+
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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 [1,2]: "George Washington" [− Labels: PER (0.9977)]
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+ Span [5]: "Washington" [− Labels: LOC (0.9895)]
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+ ```
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+
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+ So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
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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 Corpus
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+ from flair.datasets import CONLL_03, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
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+ from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
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+
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+ # 1. get the multi-language corpus
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+ corpus: Corpus = MultiCorpus([
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+ CONLL_03(), # English corpus
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+ CONLL_03_GERMAN(), # German corpus
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+ CONLL_03_DUTCH(), # Dutch corpus
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+ CONLL_03_SPANISH(), # Spanish corpus
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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 = 'ner'
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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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+ # GloVe embeddings
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+ WordEmbeddings('glove'),
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+
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+ # FastText embeddings
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+ WordEmbeddings('de'),
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+
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+ # contextual string embeddings, forward
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+ FlairEmbeddings('multi-forward'),
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+
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+ # contextual string embeddings, backward
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+ FlairEmbeddings('multi-backward'),
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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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+
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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/ner-multi',
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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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+ ```
loss.tsv ADDED
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