embedders 0.0.1

High-level API for creating sentence and token embeddings

embedders

With embedders, you can easily convert your text into sentence- or token-level embeddings within a few lines of code. Use cases for this include similarity search between texts, information extraction such as named entity recognition, or basic text classification.

How to install

You can set up this library via either running pip install embedders, or via cloning this repository and running pip install -r requirements.txt in your repository.

Example

Calculating sentence embeddings

from embedders.classification.contextual import TransformerSentenceEmbedder
from embedders.classification.reduce import PCASentenceReducer

corpus = [
    "I went to Cologne in 2009",
    "My favorite number is 41",
    ...
]

embedder = TransformerSentenceEmbedder("bert-base-cased")
embeddings = embedder.encode(corpus) # contains a list of shape [num_texts, embedding_dimension]

# if the dimension is too large, you can also apply dimensionality reduction
reducer = PCASentenceReducer(embedder)
embeddings_reduced = reducer.fit_transform(corpus)

Calculating token embeddings

from embedders.extraction.count_based import CharacterTokenEmbedder
from embedders.extraction.reduce import PCATokenReducer

corpus = [
    "I went to Cologne in 2009",
    "My favorite number is 41",
    ...
]

embedder = CharacterTokenEmbedder("en_core_web_sm")
embeddings = embedder.encode(corpus) # contains a list of ragged shape [num_texts, num_tokens (text-specific), embedding_dimension]

# if the dimension is too large, you can also apply dimensionality reduction
reducer = PCATokenReducer(embedder)
embeddings_reduced = reducer.fit_transform(corpus)

How to contribute

Currently, the best way to contribute is via adding issues for the kind of transformations you like and starring this repository :-)