Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
ONNX
Safetensors
OpenVINO
Transformers
English
bert
feature-extraction
text-embeddings-inference
Instructions to use gaepiaz/all-MiniLM-L6-v2-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gaepiaz/all-MiniLM-L6-v2-java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gaepiaz/all-MiniLM-L6-v2-java") 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 gaepiaz/all-MiniLM-L6-v2-java with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gaepiaz/all-MiniLM-L6-v2-java") model = AutoModel.from_pretrained("gaepiaz/all-MiniLM-L6-v2-java") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37b48972eade46999fb41bf05b5b3dbed517bf7b8bd7471d1722b30d7d00f2fa
- Size of remote file:
- 90.3 MB
- SHA256:
- a44f671e364dddbac31f203f07b91be6b0a35e51936e5ebfab65b6d9538b83ff
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