Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:69363
loss:EpochLossWrapper
text-embeddings-inference
Instructions to use eric920609/20-multilingual-e5-large_fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use eric920609/20-multilingual-e5-large_fold_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("eric920609/20-multilingual-e5-large_fold_1") sentences = [ "query: MSI 微星 MSI PRO MP165 E6 可攜式螢幕 (16型/FHD/Type C/喇叭/IPS)+SPACE LED觸控護眼三色螢幕掛燈 33cm款", "passage: 六合一清潔組【Apple】AirPods 4", "passage: 【MSI 微星】PRO MP251 E2 25型 平面商用螢幕(FHD/IPS/120Hz/DP+HDMI+D-Sub/眼部護理技術)", "passage: 【Apple】Apple Watch Series 11 GPS+行動網路 42mm(鋁金屬錶殼搭配運動型錶帶)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "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" | |
| } | |
| ] |