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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## Model Description:
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[**vietnamese-embedding**](https://huggingface.co/dangvantuan/
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The model utilizes PhoBERT to encode Vietnamese sentences into a 768-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of Vietnamese sentences, reflecting both the lexical and contextual layers of the language.
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## Full Model Architecture
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## Model Description:
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[**vietnamese-embedding**](https://huggingface.co/dangvantuan/vietnamese-embedding) is the Embedding Model for Vietnamese language. This model is a specialized sentence-embedding trained specifically for the Vietnamese language, leveraging the robust capabilities of PhoBERT, a pre-trained language model based on the RoBERTa architecture.
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The model utilizes PhoBERT to encode Vietnamese sentences into a 768-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of Vietnamese sentences, reflecting both the lexical and contextual layers of the language.
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## Full Model Architecture
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