README.md

metadata

title: README
emoji: ❤️
colorFrom: red
colorTo: red
sdk: static
pinned: false

SentenceTransformers 🤗 is a Python framework for state-of-the-art sentence, text and image embeddings.

Install the Sentence Transformers library.

pip install -U sentence-transformers

The usage is as simple as:

from sentence_transformers import SentenceTransformer

# 1. Load a pretrained Sentence Transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")

# The sentences to encode
sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium.",
]

# 2. Calculate embeddings by calling model.encode()
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 3. Calculate the embedding similarities
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6660, 0.1046],
#         [0.6660, 1.0000, 0.1411],
#         [0.1046, 0.1411, 1.0000]])

Hugging Face makes it easy to collaboratively build and showcase your Sentence Transformers models! You can collaborate with your organization, upload and showcase your own models in your profile ❤️

Documentation

Push your Sentence Transformers models to the Hub ❤️

Find all Sentence Transformers models on the 🤗 Hub

To upload your Sentence Transformers models to the Hugging Face Hub, log in with huggingface-cli login and use the save_to_hub method within the Sentence Transformers library.

from sentence_transformers import SentenceTransformer

# Load or train a model
model = SentenceTransformer(...)
# Push to Hub
model.push_to_hub("my_new_model")