To use fastText for text similarity search on Linux, you need to first install fastText on a Linux distribution with good C++11 support. One command to install fastText could look like this:
sudo apt-get update && sudo apt-get install -y build-essential libbz2-dev libsnappy-dev libgflags-dev libgoogle-glog-dev libboost-iostreams-dev libboost-program-options-dev
Once installed, you can use fastText to train a model on a text corpus and obtain sentence embeddings for the text data. These embeddings can then be used for similarity search using cosine similarity or other distance metrics.
Here’s some sample code for doing text similarity search using fastText in Python:
import fasttext
# Load a pre-trained model or train your own
model = fasttext.load_model('model.bin')
# Get sentence embeddings for a set of sentences
embeddings = model.get_sentence_vector('sentence1', 'sentence2', 'sentence3')
# Compute pairwise cosine similarity between embeddings
similarity = fasttext.cosine_similarity(embeddings)
This code assumes that you have a pre-trained model saved as a binary file named ‘model.bin’ in the current working directory. You can train your own model using fastText by following the instructions provided in the library’s documentation.
Note: Before using fastText for text similarity search on Linux, it’s important to preprocess your text data to ensure that it is in a suitable format for analysis.