flair README

The project's README from github.com/flairNLP/flair. As one markdown file: README.md.

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PyPI version GitHub Issues Contributions welcome License: MIT

A very simple framework for state-of-the-art NLP. Developed by Humboldt University of Berlin and friends.

Flair is:

Now at version 0.15.1!

State-of-the-Art Models

Flair ships with state-of-the-art models for a range of NLP tasks. For instance, check out our latest NER models:

Language Dataset Flair Best published Model card & demo
English Conll-03 (4-class) 94.09 94.3 (Yamada et al., 2020) Flair English 4-class NER demo
English Ontonotes (18-class) 90.93 91.3 (Yu et al., 2020) Flair English 18-class NER demo
German Conll-03 (4-class) 92.31 90.3 (Yu et al., 2020) Flair German 4-class NER demo
Dutch Conll-03 (4-class) 95.25 93.7 (Yu et al., 2020) Flair Dutch 4-class NER demo
Spanish Conll-03 (4-class) 90.54 90.3 (Yu et al., 2020) Flair Spanish 4-class NER demo

Many Flair sequence tagging models (named entity recognition, part-of-speech tagging etc.) are also hosted on the 🤗 Hugging Face model hub! You can browse models, check detailed information on how they were trained, and even try each model out online!

Quick Start

Requirements and Installation

In your favorite virtual environment, simply do:

pip install flair

Flair requires Python 3.9+.

Example 1: Tag Entities in Text

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.nn import Classifier

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

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

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence: "I love Berlin ." → ["Berlin"/LOC]

This means that "Berlin" was tagged as a location entity in this sentence.

Example 2: Detect Sentiment

Let's run sentiment analysis over an example sentence to determine whether it is POSITIVE or NEGATIVE. Same code as above, just a different model: 

from flair.data import Sentence
from flair.nn import Classifier

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

# load the NER tagger
tagger = Classifier.load('sentiment')

# run NER over sentence
tagger.predict(sentence)

# print the sentence with all annotations
print(sentence)

This should print:

Sentence[4]: "I love Berlin ." → POSITIVE (0.9983)

This means that the sentence "I love Berlin" was tagged as having POSITIVE sentiment.

Tutorials

On our new :fire: Flair documentation page you will find many tutorials to get you started!

In particular:

There is also a dedicated landing page for our biomedical NER and datasets with installation instructions and tutorials.

More Documentation

Another great place to start is the book Natural Language Processing with Flair and its accompanying code repository, though it was written for an older version of Flair and some examples may no longer work.

There are also good third-party articles and posts that illustrate how to use Flair:

Citing Flair

Please cite the following paper when using Flair embeddings:

@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}
}

If you use the Flair framework for your experiments, please cite this paper:

@inproceedings{akbik2019flair,
  title={{FLAIR}: An easy-to-use framework for state-of-the-art {NLP}},
  author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
  booktitle={{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
  pages={54--59},
  year={2019}
}

If you use our new "FLERT" models or approach, please cite this paper:

@misc{schweter2020flert,
    title={{FLERT}: Document-Level Features for Named Entity Recognition},
    author={Stefan Schweter and Alan Akbik},
    year={2020},
    eprint={2011.06993},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

If you use our TARS approach for few-shot and zero-shot learning, please cite this paper:

@inproceedings{halder2020coling,
  title={Task Aware Representation of Sentences for Generic Text Classification},
  author={Halder, Kishaloy and Akbik, Alan and Krapac, Josip and Vollgraf, Roland},
  booktitle = {{COLING} 2020, 28th International Conference on Computational Linguistics},
  year      = {2020}
}

Contact

Please email your questions or comments to Alan Akbik.

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.

License

The MIT License (MIT)

Flair is licensed under the following MIT license: The MIT License (MIT) Copyright © 2018 Zalando SE, https://tech.zalando.com

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.

flair (flairNLP/flair)

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14.4K ·GitHub
Flair
What is this?Flair is a very simple framework for state-of-the-art natural language processing: a Python library for applying pre-trained NLP models to your text, developed by Humboldt University of Berlin and friends. The current release is version 0.15.1.
Is it a Python library I call from my own code, or a standalone tool with its own interface?It is a Python library, not an app: you wrap text in a Sentence object, load a pre-trained model and call predict on it. Flair also works as a text embedding library and as a PyTorch framework for building your own models.
Do I need PyTorch or anything else set up first?No separate stack needed: Flair builds directly on PyTorch, which the pip install brings in automatically. After that you only load models and run them on plain strings.
Does it only work for English text?No — besides English there are pre-trained models listed for German, Dutch and Spanish, Flair supports a rapidly growing number of languages, and it has special support for biomedical texts.
Is it free to use, including commercially?Yes — Flair is MIT-licensed (copyright Zalando SE), so you can use, copy, modify and sell copies subject to keeping the license notice.
Where can I get or try the pre-trained models?Many Flair tagging models (NER, part-of-speech tagging and others) are hosted on the Hugging Face model hub: you can browse them, read how each was trained and even try each model online. The README also links per-language NER demos.
Can I train my own models, or is it only for running pre-trained ones?Both. Pre-trained models work with a few lines of code, and because Flair builds on PyTorch you can also train your own state-of-the-art models — Tutorial 2 on the Flair documentation page covers training.
How accurate are the NER models?Close to the state of the art: the English 4-class NER model scores 94.09 on CoNLL-03, with 92.31 for German, 95.25 for Dutch and 90.54 for Spanish — each within about a point of the best published result.
Is there a book or other material for learning Flair?Yes — the book 'Natural Language Processing with Flair' with an accompanying code repository, plus tutorials on the Flair docs page (basic tagging, training, embeddings, biomedical text). Note the book was written for an older Flair version, so some examples may no longer work.
What are similar projects?spaCy, funNLP, Chinese-Word-Vectors, bert, GLiClass, HanLP, Hy-MT2, Awesome-pytorch-list
Which topics is it in?word-embeddings, named-entity-recognition, text-classification, tutorials, pretrained-models, natural-language-processing, transformers, embeddings, nlp, multilingual, data-science, artificial-intelligence
Creator
flairNLP
Size
386.3 MB
Stars
14.4K
Updated
CommitJun 12Releasev0.15.1Feb 05
Age
8y
Commits · 90 Days