Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use sentence-transformers/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") 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-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
Add base_model metadata (nreimers/MiniLM-L6-H384-uncased)
#159
by victor HF Staff - opened
This repo currently has no base_model set in its card metadata, so it doesn't appear in any model's tree:
base_model:
- nreimers/MiniLM-L6-H384-uncased
Setting this will surface the model in nreimers/MiniLM-L6-H384-uncased's Model Tree (under its quantizations/finetunes) and improve discoverability for people browsing from the base model.
Feel free to merge directly (or close).
tomaarsen changed pull request status to merged