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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/bge-micro") model = AutoModel.from_pretrained("TaylorAI/bge-micro", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from TaylorAI/bge-micro: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/TaylorAI/bge-micro/resolve/b7d720baec4addbc2fb06acfb4cd515ffc68c248/sentence_bert_config.json
- Command line
-
hf download hf://TaylorAI/bge-micro@b7d720baec4addbc2fb06acfb4cd515ffc68c248/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/TaylorAI/bge-micro/resolve/b7d720baec4addbc2fb06acfb4cd515ffc68c248/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |