Sentence Similarity
sentence-transformers
PyTorch
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use HashNuke/all-MiniLM-L12-v2-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HashNuke/all-MiniLM-L12-v2-coreml with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HashNuke/all-MiniLM-L12-v2-coreml") 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 HashNuke/all-MiniLM-L12-v2-coreml with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("HashNuke/all-MiniLM-L12-v2-coreml") model = AutoModel.from_pretrained("HashNuke/all-MiniLM-L12-v2-coreml") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c703f06bebc92db4ce35dfa0bbea7c3f6e8b4b4acce635b982cc8727b8e573ef
- Size of remote file:
- 134 MB
- SHA256:
- e6187a3017729198d4f826677a4d9f42b16da2052c1a6df2518587596702867d
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