Flair_SOTA_NLP

Stay tuned for the use of FLAIR for the NLP datasets in Google Colab Environment. In this repository I would be doing the NLP tasks using the FLAIR library and see the performance difference between Flair and FASTAI approach.

Flair is:

A 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.

Multilingual. FLAIR supports a rapidly growing number of languages. Flair 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.

This is a Pytorch NLP framework. Flair framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.