Sentence Similarity
sentence-transformers
ONNX
Transformers
Korean
roberta
feature-extraction
text-embeddings-inference
Instructions to use anpigon/ko-sroberta-multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use anpigon/ko-sroberta-multitask with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("anpigon/ko-sroberta-multitask") 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] - Transformers
How to use anpigon/ko-sroberta-multitask with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anpigon/ko-sroberta-multitask") model = AutoModel.from_pretrained("anpigon/ko-sroberta-multitask") - Notebooks
- Google Colab
- Kaggle
| { | |
| "per_channel": true, | |
| "reduce_range": true, | |
| "per_model_config": { | |
| "model": { | |
| "op_types": [ | |
| "CumSum", | |
| "ReduceMean", | |
| "Transpose", | |
| "Equal", | |
| "Not", | |
| "Erf", | |
| "Mul", | |
| "Concat", | |
| "Cast", | |
| "Sqrt", | |
| "MatMul", | |
| "Gather", | |
| "Where", | |
| "Unsqueeze", | |
| "Sub", | |
| "Shape", | |
| "Reshape", | |
| "Expand", | |
| "Softmax", | |
| "Slice", | |
| "Pow", | |
| "ConstantOfShape", | |
| "Add", | |
| "Constant", | |
| "Div" | |
| ], | |
| "weight_type": "QInt8" | |
| } | |
| } | |
| } |