sister

SISTER (SImple SenTence EmbeddeR)

Installation

pip install sister

Basic Usage

import sister
sentence_embedding = sister.MeanEmbedding(lang="en")

sentence = "I am a dog."
vector = sentence_embedding(sentence)

If you have custom model file by yourself, you can load it too. (Data Format has to be loadable as gensim.models.KeyedVectors for word2vec model files)

import sister
from sister.word_embedders import Word2VecEmbedding

sentence_embedding = sister.MeanEmbedding(
    lang="ja", Word2VecEmbedding(model_path="/path/to/model")
)

sentence = "I am a dog."
vector = sentence_embedding(sentence)

Supported languages.

• English
• Japanese
• French

In order to support a new language, please implement Tokenizer (inheriting sister.tokenizers.Tokenizer) and add fastText pre-trained url to word_embedders.get_fasttext() (List of model urls).

Bert models are supported for en, fr, ja (2020-06-29).

Actually Albert for English, CamemBERT for French and BERT for Japanese.
To use BERT, you need to install sister by pip install 'sister[bert]'.

import sister
bert_embedding = sister.BertEmbedding(lang="en")

sentence = "I am a dog."
vector = bert_embedding(sentence)

You can also give multiple sentences to it (more efficient).

import sister
bert_embedding = sister.BertEmbedding(lang="en")

sentences = ["I am a dog.", "I want be a cat."]
vectors = bert_embedding(sentences)