Sentence Similarity
sentence-transformers
ONNX
Vietnamese
roberta
semantic-search
cosine-similarity
Eval Results (legacy)
Instructions to use itdainb/vietnamese-embedding-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use itdainb/vietnamese-embedding-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("itdainb/vietnamese-embedding-onnx") 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
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Evaluation results
- Pearsonr (Onnx) on doanhieung/vi-stsbenchmarkself-reported0.850
- Pearsonr (Optimized) on doanhieung/vi-stsbenchmarkself-reported0.850
- Pearsonr (Dynamic 8b) on doanhieung/vi-stsbenchmarkself-reported0.830
- Pearsonr (Static 8b) on doanhieung/vi-stsbenchmarkself-reported0.840
- Spearmanr (Onnx) on doanhieung/vi-stsbenchmarkself-reported0.840
- Spearmanr (Optimized) on doanhieung/vi-stsbenchmarkself-reported0.840
- Spearmanr (Dynamic 8b) on doanhieung/vi-stsbenchmarkself-reported0.820
- Spearmanr (Static 8b) on doanhieung/vi-stsbenchmarkself-reported0.820