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
Commit ·
b025097
1
Parent(s): ac3ea59
chore: Add FlagEmbedding and peft to requirements.txt
Browse files- requirements.txt +2 -0
requirements.txt
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optimum[onnxruntime]==1.2.3
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mkl-include
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mkl
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optimum[onnxruntime]==1.2.3
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mkl-include
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mkl
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FlagEmbedding
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peft
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