Embedding funciton

This python package converts sentences into tokens and passes tokens through a model to get the sentence embedding. Designed to take dataloader format as input.

How to use:

First, within your environment, install the package.

pip install git+https://github.com/stair-lab/embedder.git

In your script, include the module:

from embed_text_package.embed_text_v2 import Embedder

Then you can initialize an embedder, load the model and call it:

NOTE: the load() function will load both, the model and embedder.

model_name = "<HF_repo>/<HF_model>"
embdr = Embedder()
embdr.load(model_name)
emb = embdr.get_embeddings(dataloader, MODEL_NAME, cols_to_be_embded)

Where dataloader is type Dataloader, model_name is type str. cols_to_be_embded is type list and should contain the names of the columns of the dataloader dataset which shall be embedded.

How to test:

First, within your environment, install the package pytest.

pip install pytest

Then, cd to main folder of the package ("embedder") and type:

pytest