Sentence Similarity
sentence-transformers
ONNX
Safetensors
Transformers
Vietnamese
roberta
feature-extraction
phobert
vietnamese
sentence-embedding
Instructions to use dangvantuan/vietnamese-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dangvantuan/vietnamese-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("dangvantuan/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] - Transformers
How to use dangvantuan/vietnamese-embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dangvantuan/vietnamese-embedding") model = AutoModel.from_pretrained("dangvantuan/vietnamese-embedding", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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| [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) | 78.05 | 77.94|135M |
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### Metric for all dataset of [Semantic Textual Similarity on STS Benchmark](https://huggingface.co/datasets/doanhieung/vi-stsbenchmark)
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**Pearson score**
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| Model | [STSB] | [STS12]| [STS13] | [STS14] | [STS15] | [STS16] | [SICK] | Mean |
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| [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) | 78.05 | 77.94|135M |
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### Metric for all dataset of [Semantic Textual Similarity on STS Benchmark](https://huggingface.co/datasets/doanhieung/vi-stsbenchmark)
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You can run an evaluation on this [Colab](https://colab.research.google.com/drive/1JZLWKiknSUnA92UY2RIhvS65WtP6sgqW?hl=fr#scrollTo=IkTAwPqxDTOK)
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**Pearson score**
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| Model | [STSB] | [STS12]| [STS13] | [STS14] | [STS15] | [STS16] | [SICK] | Mean |
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