/fasttext-embeddings-with-flair

This project contains the code to use custom fasttext embeddings with flair framework.

Primary LanguagePythonApache License 2.0Apache-2.0

Custom Fasttext Embeddings with Flair

Header

To get a better overview and understanding of the need for this project, I would recommend to read my article on the same.

The objective of this script is to be able to use our custom fasttext embeddings with the Flair.

Usage

The integration is very simple:

  1. Copy the fasttext_custom_embeddings_with_flair.py file to your project.
  2. Install the packages from requirements.txt.
  3. Import the FastTextEmbeddings from fasttext_custom_embeddings_with_flair.py file.
  4. Instantiate an object of FastTextEmbeddings by passing either the local path or the remote http(s) url to the constructor.
  5. Use it like you use any other embedding object in Flair.

Usage with local file

from fasttext_custom_embeddings_with_flair import FastTextEmbeddings 
from flair.data import Sentence 

ft_embeddings = FastTextEmbeddings('/path/to/custom_fasttext_embeddings.bin', use_local=True)

sentence = Sentence('The quick brown fox jumps over a lazy dog.', use_tokenizer=True) '
ft_embeddings.embed(sentence)
  1. Initialize the constructor by passing the local path to embeddings .bin file.
  2. Set the boolean use_local to True.

Support for embedding file with ".vec" extension is also added. We need to add one more parameter extension='.vec' to the constructor.

ft_embeddings = FastTextEmbeddings('/path/to/custom_fasttext_embeddings.vec', use_local=True, extension='.vec')

Note: The ".vec" extension files do not support out of vocabulary(OOV) embeddings. In this case, for OOV words, zero vectors are assigned by default.

Usage with remote file

from fasttext_custom_embeddings_with_flair import FastTextEmbeddings 
from flair.data import Sentence    

ft_embeddings = FastTextEmbeddings('/url/to/custom_fattext_embeddings.bin', use_local=False)    

sentence = Sentence('The quick brown fox jumps over a lazy dog.', use_tokenizer=True) 
ft_embeddings.embed(sentence)

  1. Initialize the constructor by passing the remote url to embeddings .bin file. (I have tested this with my embeddings in S3 with an https url.)

  2. Set the boolean use_local to False. (Mandatory step if you are using a remote url.)

Usage with StackedEmbeddings

from fasttext_custom_embeddings_with_flair import FastTextEmbeddings 
from flair.embeddings import WordEmbeddings, StackedEmbeddings
from flair.data import Sentence 

ft_embeddings = FastTextEmbeddings('/url/to/custom_fattext_embeddings.bin', use_local=False) 
glove_embeddings = WordEmbeddings('glove')    

stacked_embeddings = StackedEmbeddings([ft_embeddings, glove_embeddings])

sentence = Sentence('The quick brown fox jumps over a lazy dog.', use_tokenizer=True) 
stacked_embeddings.embed(sentence)

Usage with DocumentEmbeddings

from fasttext_custom_embeddings_with_flair import FastTextEmbeddings 
from flair.embeddings import WordEmbeddings, DocumentRNNEmbeddings 
from flair.data import Sentence    

ft_embeddings = FastTextEmbeddings('/url/to/custom_fattext_embeddings.bin', use_local=True) 
glove_embeddings = WordEmbeddings('glove')    

document_rnn_embeddings = DocumentRNNEmbeddings([ft_embeddings, glove_embeddings])

sentence = Sentence('The quick brown fox jumps over a lazy dog.', use_tokenizer=True) 
document_rnn_embeddings.embed(sentence)

In case of any doubts, get in touch.

Author: Pranay Chandekar

Refernces:

  1. Flair
  2. fastText

License:

This repository is licensed under the Apache 2.0 License.