Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TaylorAI/bge-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaylorAI/bge-micro with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaylorAI/bge-micro") 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] - Transformers
How to use TaylorAI/bge-micro with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/bge-micro") model = AutoModel.from_pretrained("TaylorAI/bge-micro") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
9ef0864
1
Parent(s): 37b6c54
Pushing sentencetransformers model
Browse filesModel distilled from bge-small-v1.5 but ~1/4 the size
- added_tokens.json +7 -0
added_tokens.json
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{
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"[CLS]": 101,
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"[MASK]": 103,
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"[PAD]": 0,
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"[SEP]": 102,
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"[UNK]": 100
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}
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