Text Classification
Transformers
ONNX
Safetensors
modernbert
feature-extraction
semantic-router
vela
matryoshka
custom_code
text-embeddings-inference
Instructions to use llm-semantic-router/Vela-1.0-Encoder-307M-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-semantic-router/Vela-1.0-Encoder-307M-Reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="llm-semantic-router/Vela-1.0-Encoder-307M-Reranker", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/Vela-1.0-Encoder-307M-Reranker", trust_remote_code=True) model = AutoModel.from_pretrained("llm-semantic-router/Vela-1.0-Encoder-307M-Reranker", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fdbdb0307b65058d3b81a88cba0082cd1bbe8e85d34295d2271d6fa85a31c7a
- Size of remote file:
- 34.4 MB
- SHA256:
- 609d8f4c067cd3950f88594c5a802616cea245823836ef5848ee4fc40aab5b6f
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