Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use p0x0q-dev/bge-m3-sparse-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use p0x0q-dev/bge-m3-sparse-experimental with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("p0x0q-dev/bge-m3-sparse-experimental") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# Experimental Sparse Vector Repository
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This repository is a fork of the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) repository, aimed at creating sparse vectors. It is an experimental project based on the BGE-M3 model, which is known for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
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# Experimental Sparse Vector Repository
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This repository is a fork of the [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) repository, aimed at creating sparse vectors. It is an experimental project based on the BGE-M3 model, which is known for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
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# Note
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Currently, sparse vectors can only be created when deploying with Inference Endpoints.
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