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
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Supported Modality: Text
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]])
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