Feature Extraction
sentence-transformers
ONNX
English
onnxruntime
bert
sentence-similarity
int4
int8
quantization
Instructions to use nuvaidev/all-MiniLM-L6-v2-onnx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nuvaidev/all-MiniLM-L6-v2-onnx-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nuvaidev/all-MiniLM-L6-v2-onnx-quantized") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "model_type": "bert", | |
| "hidden_size": 384, | |
| "num_hidden_layers": 6, | |
| "num_attention_heads": 12, | |
| "vocab_size": 30522, | |
| "max_position_embeddings": 512, | |
| "hidden_act": "gelu", | |
| "intermediate_size": 1536, | |
| "type_vocab_size": 2 | |
| } | |