vietnamese-embedding

SentenceTransformer based on VoVanPhuc/sup-SimCSE-VietNamese-phobert-base

This is a sentence-transformers model finetuned from VoVanPhuc/sup-SimCSE-VietNamese-phobert-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: VoVanPhuc/sup-SimCSE-VietNamese-phobert-base <!-- at revision 608779b86741a8acd8c8d38132974ff04086b138 -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'RobertaModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.3155, 0.3182],
#         [0.3155, 1.0000, 0.6233],
#         [0.3182, 0.6233, 1.0000]])

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Training Details

Framework Versions

• Python: 3.10.11
• Sentence Transformers: 5.6.0
• Transformers: 5.12.1
• PyTorch: 2.12.1
• Accelerate:
• Datasets:
• Tokenizers: 0.22.2

Citation

BibTeX

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