scikit-embeddings 0.3.1

Tools for training word and document embeddings in scikit-learn.

scikit-embeddings

Utilites for training, storing and using word and document embeddings in scikit-learn pipelines.

Features

• Train Word and Paragraph embeddings in scikit-learn compatible pipelines.
• Fast and performant trainable tokenizer components from tokenizers.
• Easy to integrate components and pipelines in your scikit-learn workflows and machine learning pipelines.
• Easy serialization and integration with HugginFace Hub for quickly publishing your embedding pipelines.

What scikit-embeddings is not for:

• Training transformer models and deep neural language models (if you want to do this, do it with transformers)
• Using pretrained sentence transformers (use embetter)

Installation

You can easily install scikit-embeddings from PyPI:

pip install scikit-embeddings

If you want to use GloVe embedding models, install alogn with glovpy:

pip install scikit-embeddings[glove]

Example Pipelines

You can use scikit-embeddings with many many different pipeline architectures, I will list a few here:

Word Embeddings

You can train classic vanilla word embeddings by building a pipeline that contains a WordLevel tokenizer and an embedding model:

from skembedding.tokenizers import WordLevelTokenizer
from skembedding.models import Word2VecEmbedding
from skembeddings.pipeline import EmbeddingPipeline

embedding_pipe = EmbeddingPipeline(
    WordLevelTokenizer(),
    Word2VecEmbedding(n_components=100, algorithm="cbow")
)
embedding_pipe.fit(texts)

Fasttext-like

You can train an embedding pipeline that uses subword information by using a tokenizer that does that. You may want to use Unigram, BPE or WordPiece for these purposes. Fasttext also uses skip-gram by default so let's change to that.

from skembedding.tokenizers import UnigramTokenizer
from skembedding.models import Word2VecEmbedding
from skembeddings.pipeline import EmbeddingPipeline

embedding_pipe = EmbeddingPipeline(
    UnigramTokenizer(),
    Word2VecEmbedding(n_components=250, algorithm="sg")
)
embedding_pipe.fit(texts)

Paragraph Embeddings

You can train Doc2Vec paragpraph embeddings with the chosen choice of tokenization.

from skembedding.tokenizers import WordPieceTokenizer
from skembedding.models import ParagraphEmbedding
from skembeddings.pipeline import EmbeddingPipeline, PretrainedPipeline

embedding_pipe = EmbeddingPipeline(
    WordPieceTokenizer(),
    ParagraphEmbedding(n_components=250, algorithm="dm")
)
embedding_pipe.fit(texts)

Serialization

Pipelines can be safely serialized to disk:

embedding_pipe.to_disk("output_folder/")

pretrained = PretrainedPipeline("output_folder/")

Or published to HugginFace Hub:

from huggingface_hub import login

login()
embedding_pipe.to_hub("username/name_of_pipeline")

pretrained = PretrainedPipeline("username/name_of_pipeline")

Text Classification

You can include an embedding model in your classification pipelines by adding some classification head.

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(X, y)

cls_pipe = make_pipeline(pretrained, LogisticRegression())
cls_pipe.fit(X_train, y_train)

y_pred = cls_pipe.predict(X_test)
print(classification_report(y_test, y_pred))