SentenceTransformer
This is a sentence-transformers model trained. It maps sentences & paragraphs to a 2560-dimensional dense vector space and can be used for retrieval.
Model Details
Model Description
- Model Type: Sentence Transformer
- Maximum Sequence Length: 2048 tokens
- Output Dimensionality: 2560 dimensions
- Similarity Function: Cosine Similarity
- Supported Modalities: Text, Message
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'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'flat'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'})
(1): Pooling({'embedding_dimension': 2560, 'pooling_mode': 'lasttoken', 'include_prompt': True})
(2): Normalize({})
)
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, 2560]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9141, 0.6523],
# [0.9141, 1.0000, 0.6367],
# [0.6523, 0.6367, 1.0078]], dtype=torch.bfloat16)
Training Details
Framework Versions
- Python: 3.11.15
- Sentence Transformers: 5.4.1
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2