Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
gte
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use Mihaiii/Venusaur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Mihaiii/Venusaur with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Mihaiii/Venusaur") 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] - Notebooks
- Google Colab
- Kaggle
Download quantize_config.json from Mihaiii/Venusaur: direct link, hf CLI and curl.
- Browser
- Download file 674 Bytes
-
https://huggingface.co/Mihaiii/Venusaur/resolve/main/quantize_config.json
- Command line
-
hf download hf://Mihaiii/Venusaur/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/Mihaiii/Venusaur/resolve/main/quantize_config.json
674 Bytes
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Erf", | |
| "Pow", | |
| "Shape", | |
| "Softmax", | |
| "ReduceMean", | |
| "Cast", | |
| "Reshape", | |
| "MatMul", | |
| "Transpose", | |
| "Div", | |
| "Sqrt", | |
| "Add", | |
| "Concat", | |
| "Slice", | |
| "Sub", | |
| "Constant", | |
| "Mul", | |
| "Gather", | |
| "Unsqueeze" | |
| ], | |
| "weight_type": "QInt8" | |
| } | |
| } | |
| } |