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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("vudanghuy/phobert-rag-vietnamese-edu-v1")
# 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, 0.3276, 0.4231],
#         [0.3276, 1.0000, 0.3157],
#         [0.4231, 0.3157, 1.0000]])

Training Details

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.5.0
Transformers: 5.0.0
PyTorch: 2.10.0+cpu
Accelerate: 1.13.0
Datasets: 4.0.0
Tokenizers: 0.22.2

vudanghuy/
phobert-rag-vietnamese-edu-v1

SentenceTransformer

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Framework Versions

Citation

BibTeX