Pretrained FastText Word Vector for English

Introduction

FastText is a library developed by Facebook's AI Research (FAIR) lab that provides efficient learning of word representations and sentence classification. This implementation provides pretrained word vectors for English, allowing users to obtain vector representations for words.

Architecture

FastText extends the Word2Vec model by representing words as bags of character n-grams. This allows the model to generate meaningful word vectors even for words not present in the training data, handling the problem of out-of-vocabulary words.

Training

The FastText model is trained on a large corpus of text data to learn word vectors that capture semantic similarities between words. The training process involves optimizing the model to predict the context in which words appear, using techniques such as skip-gram or continuous bag of words (CBOW).

Guide: Running Locally

To use FastText pretrained word vectors locally, follow these steps:

1. Install FastText:

   • Ensure you have Python installed.
   • Install the FastText library using pip:

     pip install fasttext
     

2. Download Pretrained Model:

   • Obtain the English word vectors by downloading cc.en.300.bin from the FastText repository.

3. Load and Use the Model:

   • Use the following code to load the model and extract word vectors:

     import fasttext.util
     ft = fasttext.load_model('cc.en.300.bin')
     vector = ft.get_word_vector('hello')
     

Suggested Cloud GPUs

For large-scale processing or training, consider using cloud GPU services such as AWS EC2, Google Cloud, or Azure, which offer instances with powerful GPUs to accelerate computation.

License

FastText is released under the MIT license, allowing for free use and modification with proper attribution to the original authors.