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initial model commit

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  1. README.md +5 -5
README.md CHANGED
@@ -13,7 +13,7 @@ inference: false
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  This is the fast phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
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- F1-Score: **96,48** (corrected CoNLL-2000)
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  Predicts 4 tags:
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@@ -43,7 +43,7 @@ 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/chunk-english")
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  # make example sentence
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  sentence = Sentence("The happy man has been eating at the diner")
@@ -98,10 +98,10 @@ tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
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  embedding_types = [
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  # contextual string embeddings, forward
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- FlairEmbeddings('news-forward'),
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  # contextual string embeddings, backward
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- FlairEmbeddings('news-backward'),
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  ]
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  # embedding stack consists of Flair and GloVe embeddings
@@ -121,7 +121,7 @@ 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/chunk-english',
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  train_with_dev=True,
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  max_epochs=150)
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  ```
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  This is the fast phrase chunking model for English that ships with [Flair](https://github.com/flairNLP/flair/).
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+ F1-Score: **96,22** (corrected CoNLL-2000)
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  Predicts 4 tags:
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  from flair.models import SequenceTagger
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  # load tagger
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+ tagger = SequenceTagger.load("flair/chunk-english-fast")
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  # make example sentence
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  sentence = Sentence("The happy man has been eating at the diner")
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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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  trainer = ModelTrainer(tagger, corpus)
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  # 7. run training
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+ trainer.train('resources/taggers/chunk-english-fast',
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  train_with_dev=True,
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  max_epochs=150)
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  ```