fjibj/flair

a very simple framework for state-of-the-art NLP. Developed by Zalando Research.

Flair uses hyper-powerful word embeddings to achieve state-of-the-art accuracies on a range of natural language processing (NLP) tasks.

Flair is:

A word embedding library. There are many different types of word embeddings out there, with wildly different properties. Flair packages many of them behind a simple interface, so you can mix and match embeddings for your experiments. In particular, you can try out our proposed contextual string embeddings, to build your own state-of-the-art NLP methods.
A powerful syntactic / semantic tagger. Flair allows you to apply our state-of-the-art models for named entity recognition (NER), part-of-speech tagging (PoS) and chunking to your text.

Embedding your text for state-of-the-art NLP has never been easier.

Comparison with State-of-the-Art

Flair outperforms the previous best methods on a range of NLP tasks:

Task	Dataset	Our Result	Previous best
Named Entity Recognition (English)	Conll-03	93.09 (F1)	92.22 (Peters et al., 2018)
Named Entity Recognition (English)	Ontonotes	89.71 (F1)	86.28 (Chiu et al., 2016)
Named Entity Recognition (German)	Conll-03	88.32 (F1)	78.76 (Lample et al., 2016)
Named Entity Recognition (German)	Germeval	84.65 (F1)	79.08 (Hänig et al, 2014)
Part-of-Speech tagging	WSJ	97.85	97.64 (Choi, 2016)
Chunking	Conll-2000	96.72 (F1)	96.36 (Peters et al., 2017)

Here's how to reproduce these numbers using Flair. You can also find a detailed evaluation and discussion in our paper:

Contextual String Embeddings for Sequence Labeling. Alan Akbik, Duncan Blythe and Roland Vollgraf. 27th International Conference on Computational Linguistics, COLING 2018.

Quick Start

Let's run named entity recognition (NER) over an example sentence. All you need to do is make a Sentence, load a pre-trained model and use it to predict tags for the sentence:

from flair.data import Sentence
from flair.tagging_model import SequenceTaggerLSTM

# make a sentence
sentence = Sentence('I love Berlin .')

# load the NER tagger
tagger = SequenceTaggerLSTM.load('ner')

# run NER over sentence
tagger.predict(sentence)

Done! The Sentence now has entity annotations. Print the sentence to see what the tagger found.

print('Analysing %s' % sentence)
print('\nThe following NER tags are found: \n')
print(sentence.to_tag_string())

This should print:

Analysing Sentence: "I love Berlin ." - 4 Tokens

The following NER tags are found: 

I love Berlin <S-LOC> .

More Examples

Let's look into some core functionality to understand the library better. You can find more information in the TUTORIAL!

NLP base types

First, you need to construct Sentence objects for your text.

# The sentence objects holds a sentence that we may want to embed
from flair.data import Sentence

# Make a sentence object by passing a whitespace tokenized string
sentence = Sentence('The grass is green .')

# Print the object to see what's in there
print(sentence)

You can access the tokens of a sentence via their token id, or iterate through the tokens in a sentence.

print(sentence[4])

# The Sentence object has a list of Token objects (each token represents a word)
for token in sentence:
    print(token)

Tokens can also have tags, such as a named entity tag. In this example, we're adding an NER tag of type 'color' to the word 'green' in the example sentence.

# add a tag to a word in the sentence
sentence[4].add_tag('ner', 'color')

# print the sentence with all tags of this type
print(sentence.to_tag_string('ner'))

Tagging with Pre-Trained Model

Now, lets use a pre-trained model for named entity recognition (NER). This model was trained over the English CoNLL-03 task and can recognize 4 different entity types.

from flair.tagging_model import SequenceTaggerLSTM

tagger = SequenceTaggerLSTM.load('ner')

All you need to do is use the predict() method of the tagger on a sentence. This will add predicted tags to the tokens in the sentence. Lets use a sentence with two named entities:

sentence = Sentence('George Washington went to Washington .')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence.to_tag_string())

This should print:

George <B-PER> Washington <E-PER> went <O> to <O> Washington <S-LOC> . <O>

Word Embeddings

A main use case of Flair is to make embedding words with powerful embeddings easy. All you need to do is instantiate an embedding class and call the method embed() over a list of sentences. We illustrate this with a simple GloVe embedding.

# Init a simple GloVe embedding.
from flair.embeddings import WordEmbeddings
embedder = WordEmbeddings('glove')

# embed a sentence using GloVe.
from flair.data import Sentence
sentence = Sentence('The grass is green .')
embedder.embed(sentences=[sentence])

If you want to check the embeddings of the words, just call the embedding property of any Token:

# now check out the embedded tokens.
for token in sentence:
    print(token)
    print(token.embedding)

Contextual String Embeddings

Contextual string embeddings are powerful embeddings that capture latent syntactic-semantic information that goes beyond standard word embeddings. Key differences are: (1) they are trained without any explicit notion of words and thus fundamentally model words as sequences of characters. And (2) they are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use.

With Flair, you can use these embeddings simply by instantiating the appropriate embedding class, same as before:

# the CharLMEmbedding also inherits from the TextEmbeddings class
from flair.embeddings import CharLMEmbeddings
embedder = CharLMEmbeddings('news-forward')

# embed a sentence using CharLM.
from flair.data import Sentence
sentence = Sentence('The grass is green .')
embedder.embed(sentences=[sentence])

Set up

Python 3.6

This library runs on Python 3.6 - because methods signatures and type hints are beautiful. If you do not have Python 3.6, install it first. Here is how for Ubuntu 16.04.

Requirements

The requirements.txt lists all libraries that we depend on. Install them first. For instance, if you use pip and virtualenv, run these commands:

virtualenv --python=[path/to/python3.6] [path/to/this/virtualenv]

source [path/to/this/virtualenv]/bin/activate

pip install -r requirements.txt

Check if everything works

Go into the virtualenv and run the following command.

python predict.py

This should download a pre-trained NER model for English and apply it to an example sentence. It should print out the following:

Analysing Sentence: "George Washington went to Washington ." - 6 Tokens

The following NER tags are found: 

George <B-PER> Washington <E-PER> went to Washington <S-LOC> .

Citing Flair

Please cite the following paper when using Flair:

@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     = {(forthcoming)},
  year      = {2018}
}

Contributing

Thanks for your interest in contributing! There are many ways to get involved; start with our contributor guidelines and then check these open issues for specific tasks.

For contributors looking to get deeper into the API we suggest cloning the repository and checking out the unit tests for examples of how to call methods. Nearly all classes and methods are documented, so finding your way around the code should hopefully be easy.

License

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

fjibj / flair