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
- Xet hash:
- c9abfbcdb4ffc00d355a5aceeb3df3cad5ed9f079223b9434115e96b6423ffb1
- Size of remote file:
- 17.1 MB
- SHA256:
- cad551d5600a84242d0973327029452a1e3672ba6313c2a3c3d69c4310e12719
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