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Swarmauri Embedding Doc2vec

A Gensim-based Doc2Vec implementation for document embedding in the Swarmauri ecosystem. This package provides document vectorization capabilities using the Doc2Vec algorithm.

Installation

pip install swarmauri_embedding_doc2vec

Usage

from swarmauri.embeddings.Doc2VecEmbedding import Doc2VecEmbedding

# Initialize the embedder
embedder = Doc2VecEmbedding(vector_size=3000)

# Prepare your documents
documents = ["This is the first document.", "Here is another document.", "And a third one"]

# Fit and transform documents
vectors = embedder.fit_transform(documents)

# Transform new documents
new_doc = "This is a new document"
vector = embedder.transform([new_doc])

# Save and load the model
embedder.save_model("doc2vec.model")
embedder.load_model("doc2vec.model")

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If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.

Metadata

Release files for swarmauri_embedding_doc2vec 0.7.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for swarmauri_embedding_doc2vec 0.7.5
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Built distribution (wheel)

Table of built distributions (wheels) for swarmauri_embedding_doc2vec 0.7.5
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swarmauri_embedding_doc2vec-0.7.5-py3-none-any.whl Python 3 none any Details

Total release size: 15.0 kB

Release files / swarmauri_embedding_doc2vec-0.7.5.tar.gz

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0.7.5 This release

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