pos-english / README.md
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
widget:
- text: "I love Berlin."
---
## English Part-of-Speech Tagging in Flair (default model)
This is the standard part-of-speech tagging model for English that ships with [Flair](https://github.com/flairNLP/flair/).
F1-Score: **98,19** (Ontonotes)
Predicts fine-grained POS tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
|ADD | Email |
|AFX | Affix |
|CC | Coordinating conjunction |
|CD | Cardinal number |
|DT | Determiner |
|EX | Existential there |
|FW | Foreign word |
|HYPH | Hyphen |
|IN | Preposition or subordinating conjunction |
|JJ | Adjective |
|JJR |Adjective, comparative |
|JJS | Adjective, superlative |
|LS | List item marker |
|MD | Modal |
|NFP | Superfluous punctuation |
|NN | Noun, singular or mass |
|NNP |Proper noun, singular |
|NNPS | Proper noun, plural |
|NNS |Noun, plural |
|PDT | Predeterminer |
|POS | Possessive ending |
|PRP | Personal pronoun |
|PRP$ | Possessive pronoun |
|RB | Adverb |
|RBR | Adverb, comparative |
|RBS | Adverb, superlative |
|RP | Particle |
|SYM | Symbol |
|TO | to |
|UH | Interjection |
|VB | Verb, base form |
|VBD | Verb, past tense |
|VBG | Verb, gerund or present participle |
|VBN | Verb, past participle |
|VBP | Verb, non-3rd person singular present |
|VBZ | Verb, 3rd person singular present |
|WDT | Wh-determiner |
|WP | Wh-pronoun |
|WP$ | Possessive wh-pronoun |
|WRB | Wh-adverb |
|XX | Unknown |
Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/pos-english")
# make example sentence
sentence = Sentence("I love Berlin.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('pos'):
print(entity)
```
This yields the following output:
```
Span [1]: "I" [− Labels: PRP (1.0)]
Span [2]: "love" [− Labels: VBP (1.0)]
Span [3]: "Berlin" [− Labels: NNP (0.9999)]
Span [4]: "." [− Labels: . (1.0)]
```
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*".
---
### Training: Script to train this model
The following Flair script was used to train this model:
```python
from flair.data import Corpus
from flair.datasets import ColumnCorpus
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
"resources/tasks/onto-ner",
column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
tag_to_bioes="ner",
)
# 2. what tag do we want to predict?
tag_type = 'pos'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize each embedding we use
embedding_types = [
# contextual string embeddings, forward
FlairEmbeddings('news-forward'),
# contextual string embeddings, backward
FlairEmbeddings('news-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/pos-english',
train_with_dev=True,
max_epochs=150)
```
---
### Cite
Please cite the following paper when using this model.
```
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
---
### Issues?
The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).