Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:43318
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use leewonjun/e5-mul-0910a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use leewonjun/e5-mul-0910a with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("leewonjun/e5-mul-0910a") sentences = [ "query: 3PL 사용 시의 비용 절감 메커니즘은 어떤 것이 있나요?", "passage: 3 Dimension-Through Silicon Via (Technical)", "passage: Third Party Logistics (상업)", "passage: Authorization Account Answer (Technical)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from leewonjun/e5-mul-0910a: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/leewonjun/e5-mul-0910a/resolve/main/modules.json
- Command line
-
hf download hf://leewonjun/e5-mul-0910a/modules.json
-
curl -L -o modules.json https://huggingface.co/leewonjun/e5-mul-0910a/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |