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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 |
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datasets: |
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- ontonotes |
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widget: |
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- text: "I love Berlin." |
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--- |
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## English Part-of-Speech Tagging in Flair (default model) |
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This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/). |
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F1-Score: **98,19** (Ontonotes) |
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Predicts fine-grained POS tags: |
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| **tag** | **meaning** | |
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|---------------------------------|-----------| |
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|ADD | Email | |
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|AFX | Affix | |
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|CC | Coordinating conjunction | |
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|CD | Cardinal number | |
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|DT | Determiner | |
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|EX | Existential there | |
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|FW | Foreign word | |
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|HYPH | Hyphen | |
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|IN | Preposition or subordinating conjunction | |
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|JJ | Adjective | |
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|JJR |Adjective, comparative | |
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|JJS | Adjective, superlative | |
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|LS | List item marker | |
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|MD | Modal | |
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|NFP | Superfluous punctuation | |
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|NN | Noun, singular or mass | |
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|NNP |Proper noun, singular | |
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|NNPS | Proper noun, plural | |
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|NNS |Noun, plural | |
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|PDT | Predeterminer | |
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|POS | Possessive ending | |
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|PRP | Personal pronoun | |
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|PRP$ | Possessive pronoun | |
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|RB | Adverb | |
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|RBR | Adverb, comparative | |
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|RBS | Adverb, superlative | |
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|RP | Particle | |
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|SYM | Symbol | |
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|TO | to | |
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|UH | Interjection | |
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|VB | Verb, base form | |
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|VBD | Verb, past tense | |
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|VBG | Verb, gerund or present participle | |
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|VBN | Verb, past participle | |
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|VBP | Verb, non-3rd person singular present | |
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|VBZ | Verb, 3rd person singular present | |
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|WDT | Wh-determiner | |
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|WP | Wh-pronoun | |
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|WP$ | Possessive wh-pronoun | |
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|WRB | Wh-adverb | |
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|XX | Unknown | |
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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/pos-english") |
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# make example sentence |
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sentence = Sentence("I love Berlin.") |
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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]: "I" [− Labels: PRP (1.0)] |
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Span [2]: "love" [− Labels: VBP (1.0)] |
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Span [3]: "Berlin" [− Labels: NNP (0.9999)] |
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Span [4]: "." [− Labels: . (1.0)] |
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``` |
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So, the word "*I*" is labeled as a **pronoun** (PRP), "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*I love Berlin*". |
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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 Corpus |
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from flair.datasets import ColumnCorpus |
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from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings |
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# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself) |
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corpus: Corpus = ColumnCorpus( |
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"resources/tasks/onto-ner", |
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column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"}, |
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tag_to_bioes="ner", |
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) |
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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 each embedding we use |
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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 |
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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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# 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/pos-english', |
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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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