Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L12-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L12-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L12-v1") 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 sentence-transformers/all-MiniLM-L12-v1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L12-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from sentence-transformers/all-MiniLM-L12-v1: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/sentence-transformers/all-MiniLM-L12-v1/resolve/f4230849d31c21e96d33bb7bc5bc5611ff0f16c4/1_Pooling/config.json
- Command line
-
hf download hf://sentence-transformers/all-MiniLM-L12-v1@f4230849d31c21e96d33bb7bc5bc5611ff0f16c4/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/sentence-transformers/all-MiniLM-L12-v1/resolve/f4230849d31c21e96d33bb7bc5bc5611ff0f16c4/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 384, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |