Sentence Similarity
sentence-transformers
ONNX
Safetensors
Vietnamese
xlm-roberta
Embedding
text-embeddings-inference
Instructions to use AITeamVN/Vietnamese_Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AITeamVN/Vietnamese_Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AITeamVN/Vietnamese_Embedding") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Vietnamese Reranker (Phase 2) and Vietnamese Reranker (Phased) was trained on 1100000 triplets. Although the score on the legal domain drops a bit, since this phase data is much larger, it is very good for other domains.
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You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).
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**Developer**
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| BGE-M3 | 0.5682 | 0.7728 | 0.8382 | 0.8921 | 0.9772 | 0.6822 |
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Vietnamese Reranker (Phase 2) and Vietnamese Reranker (Phased) was trained on 1100000 triplets. Although the score on the legal domain drops a bit, since this phase data is much larger, it is very good for other domains.
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You can reproduce the evaluation result by running code python evaluation_model.py (data downloaded from Kaggle).
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**Developer**
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