⛵ Vela 1.0
Collection
The model family for intelligent routing. • 11 items • Updated • 1
Vela FactCheck
Vela FactCheck identifies requests that need external factual knowledge or retrieval; it does not determine whether a claim is true.
307M parameters · Input capacity: 32,768 tokens, including special tokens.
Evaluation
Compared with the original mmBERT32K FactCheck model for identifying requests that need fact-checking. Scores are on a 0–100 scale; higher is better.
| Development evaluation | Original mmBERT | Vela |
|---|---|---|
| Macro F1 · 186 inputs | 50.26 | 88.08 |
| Accuracy · 186 inputs | 54.30 | 88.17 |
The same 186 reviewed natural requests contain 7–2,868 tokens. Both models use FP32, complete inputs and the highest-scoring label. This development set informed Vela selection; it is not an independent test or a measurement of 32K accuracy.
Quick start
With PyTorch and Transformers 4.57.6 or 5.17.0:
from transformers import pipeline
model_id = "llm-semantic-router/Vela-1.0-Encoder-307M-FactCheck"
model = pipeline("text-classification", model=model_id, device=-1)
print(model("When was the first moon landing?", top_k=None, truncation=False))
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Model tree for llm-semantic-router/Vela-1.0-Encoder-307M-FactCheck
Base model
jhu-clsp/mmBERT-base