Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use atifborntough/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use atifborntough/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("atifborntough/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 atifborntough/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("atifborntough/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("atifborntough/all-MiniLM-L6-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aaa2be7dcf4d404276b27028488280e13965c96819346a4e5ad67095d7990aa5
- Size of remote file:
- 90.9 MB
- SHA256:
- 2d98d96d278348988f2744e6445b8bc16d921c3f6e17c667362f3cb353007aea
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