flair is a very simple framework for state-of-the-art Natural Language Processing (NLP) to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.
The core idea of the framework is to present a simple, unified interface for conceptually very different types of word and document embeddings
The project is based on PyTorch 0.4+ and Python 3.6+.
The software is released under an open source license.
Key Features
- Powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
- Ships with a “model zoo” of pre-trained models to allow researchers to use state-of-the-art NLP models in their applications.
- Multilingual. Thanks to the Flair community, there’s support for a rapidly growing number of languages. The project also includes ‘one model, many languages’ taggers, i.e. single models that predict PoS or NER tags for input text in various languages.
- A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings, BERT embeddings and ELMo embeddings.
- A Pytorch NLP framework. Our framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.
Website: github.com/zalandoresearch/flair
Support: Tutorials
Developer: Zalando Research
License: MIT License
flair is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Natural Language Processing | |
|---|---|
| PyTorch-Transformers | Library of state-of-the-art pre-trained models |
| Natural Language Toolkit | Suite of open source Python modules, data sets and tutorials |
| Stanford CoreNLP | Extensible annotation-based NLP pipeline |
| spaCy | Industrial strength natural language processing |
| scikit-learn | Machine learning library for Python |
| Gensim | Python-based vector space modeling and topic modeling toolkit |
| flair | Simple framework for state-of-the-art NLP |
| Apache OpenNLP | Machine learning based toolkit |
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Read our verdict in the software roundup.
| Python Natural Language Processing Tools | |
|---|---|
| PyTorch-Transformers | Library of state-of-the-art pre-trained models for NLP |
| NLTK | Natural Language Toolkit |
| spaCy | Industrial strength natural language processing |
| scikit-learn | Machine learning library |
| Gensim | Vector space modeling and topic modeling toolkit |
| flair | Simple framework for state-of-the-art NLP |
| TextBlob | Python (2 and 3) library for processing textual data |
| textacy | Python library for performing NLP tasks |
| polyglot | Multilingual text (NLP) processing toolkit |
| AllenNLP | Apache 2.0 NLP research library |
| Snips NLU | Natural Language Understanding Python library |
| PyNLPI | Various modules useful for common, and less common, NLP tasks |
| nlpnet | Natural Language Processing with neural networks |
| Pattern | Web mining module |
| GluonNLP | Deep Learning for NLP |
| PyTorch-NLP | Neural network layers, text processing modules and datasets |
| NLP Architect | Deep Learning NLP/NLU library |
Read our verdict in the software roundup.
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