Feature Extraction
sentence-transformers
ONNX
xlm-roberta
mteb
Sentence Transformers
sentence-similarity
Eval Results (legacy)
text-embeddings-inference
Instructions to use morgendigital/multilingual-e5-large-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use morgendigital/multilingual-e5-large-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("morgendigital/multilingual-e5-large-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
Download quantize_config.json from morgendigital/multilingual-e5-large-quantized: direct link, hf CLI and curl.
- Browser
- Download file 834 Bytes
-
https://huggingface.co/morgendigital/multilingual-e5-large-quantized/resolve/main/quantize_config.json
- Command line
-
hf download hf://morgendigital/multilingual-e5-large-quantized/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/morgendigital/multilingual-e5-large-quantized/resolve/main/quantize_config.json
834 Bytes
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "Reshape", | |
| "ConstantOfShape", | |
| "Shape", | |
| "Div", | |
| "Slice", | |
| "Transpose", | |
| "ReduceMean", | |
| "Pow", | |
| "Equal", | |
| "Unsqueeze", | |
| "Mul", | |
| "Concat", | |
| "MatMul", | |
| "Add", | |
| "Softmax", | |
| "Constant", | |
| "Sqrt", | |
| "Erf", | |
| "Where", | |
| "Expand", | |
| "CumSum", | |
| "Gather", | |
| "Sub", | |
| "Not", | |
| "Cast" | |
| ], | |
| "weight_type": "QInt8" | |
| } | |
| } | |
| } |