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Flair: Hands-on Guide to Robust NLP Framework Built Upon PyTorch

Flair: Hands-on Guide to Robust NLP Framework Built Upon PyTorch

Flair is a powerful open-source library for natural language processing. It is mainly used to get insight from text extraction, word embedding, named entity recognition, parts of speech tagging, and text classification. All these features are pre-trained in flair for NLP models. It also supports biomedical data that is more than 32 biomedical datasets already using flair library for natural language processing tasks. Easily integrated with Pytorch NLP framework for embedding in document and sentence.

Humboldt University of Berlin and friends mainly develop flair. The Humboldt University of Berlin maintains the Flair library and has already done more than a hundred industry project implementations and research-based projects using Flair.


Research Paper: 


Let’s look at Flair’s performance based on the nlp task such as named entity recognition, parts of speech tagging, and chunking with their accuracy in the table below.


Using pip:

pip install flair


Using conda:

conda install -c bioconda flair


Flair Model:

First, import sentences from flair’s data library, then import the model for SequenceTagger. Make a sentence using the Sentence object, then load Named entity recognition on SequenceTagger, then run the code.

For an example of the flair model, see the code below.

 from import Sentence
 from flair.models import SequenceTagger
 # make a sentence
 sentence = Sentence('I love India .')
 # load the NER tagger
 tagger = SequenceTagger.load('ner')
 # run NER over sentence

Flair has  the following pre-trained models for NLP Tasks:

  • Name-Entity Recognition 
  • Parts-of-Speech Tagging 
  • Text Classification
  • Training Custom Models


In the flair library, there is a predefined tokenizer using the segtok library of python. To

use the tokenization just the “use_tokenizer”  flag value is true. If not want to implement the write false. We can also define the label of each sentence and its related topic using the function add_tag.

For example, see the code below:

 from import Sentence
 # Make a sentence object by passing an untokenized string and the 'use_tokenizer' flag
 untokenized_sentence = Sentence('The grass is green.', use_tokenizer=False
 # Print the object to see what's in there

In this case, no tokenization occurs use_tokenizer is false.


Word Embeddings:

Here is the list of embedding in the library. We will learn about flair library in detail, and there code implementation.

See Also

Flair Embedding:

Effective embeddings are contextual string embeddings that capture latent syntactic-semantic data that goes beyond standard word embedding. The main differences are: 

(1) Without any clear notion of vocabulary, they are educated and thus essentially model words as character sequences. 

(2) they are contextualized by their surrounding text, meaning that depending on their contextual use, the same word will have distinct embeddings.


 from flair.embeddings import FlairEmbeddings
 # init embedding
 flair_embedding_forward = FlairEmbeddings('news-forward')
 # create a sentence
 sentence = Sentence('The grass is green .')
 # embed words in sentence

Training a Text Classification Model:

We are training a text classifier over the TREC-6 corpus, using a combination of simple GloVe embeddings and Flair embeddings.

In this code, import Corpus and TREC_6 for datasets, WordEmbeddings, FlairEmbeddings, and Document RNN Embeddings, TextClassifier, ModelTrainer.  


 from import Corpus
 from flair.datasets import TREC_6
 from flair.embeddings import WordEmbeddings, FlairEmbeddings, DocumentRNNEmbeddings
 from flair.models import TextClassifier
 from flair.trainers import ModelTrainer
 # 1. get the corpus
 corpus: Corpus = TREC_6()
 # 2. create the label dictionary
 label_dict = corpus.make_label_dictionary()
 # 3. make a list of word embeddings
 word_embeddings = [WordEmbeddings('glove')]
 # 4. initialize document embedding by passing a list of word embeddings
 # Can choose between many RNN types (GRU by default, to change use rnn_type parameter)
 document_embeddings = DocumentRNNEmbeddings(word_embeddings, hidden_size=256)
 # 5. create the text classifier
 classifier = TextClassifier(document_embeddings, label_dictionary=label_dict)
 # 6. initialize the text classifier trainer
 trainer = ModelTrainer(classifier, corpus)
 # 7. start the training



We learn about the Flair open-source library for NLP problems. We also covered the area about NLP and the use of Flair to solve the tasks and their use in the industry. Some important Flair pipelines and their code in the development of pre-trained NLP models.

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