Text Classification
Transformers
ONNX
Safetensors
modernbert
semantic-router
vela
text-embeddings-inference
Instructions to use llm-semantic-router/Vela-1.0-Encoder-307M-FactCheck 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-FactCheck 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-FactCheck")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/Vela-1.0-Encoder-307M-FactCheck") model = AutoModelForSequenceClassification.from_pretrained("llm-semantic-router/Vela-1.0-Encoder-307M-FactCheck", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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