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Vela-1.0-Encoder-307M-Feedback
vllm-sr/Vela-1.0-Encoder-307M-Feedback
Vela-1.0-Encoder-307M-Feedback is a text classification model from vllm-sr. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
<div align="center" <img src="https://vllm-sr.ai/img/vllm-sr-logo.social.png" alt="vLLM Semantic Router" width="560" / <p <a href="https://vllm-sr.ai/"<strongDocs</strong</a | <a href="https://vllm-sr.ai/blog/"<strong…
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308M
5.2 GB on disk
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.data1.2 GB · 49%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- modernbert
- Access
- Public
- Created
- Sep 12, 2026
- Updated
- Oct 2, 2026
- SHA
- b24cff0c
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Model card
From the Hugging Face model README
<div align="center">
<img src="https://vllm-sr.ai/img/vllm-sr-logo.social.png" alt="vLLM Semantic Router" width="560" />
<p>
<a href="https://vllm-sr.ai/"><strong>Docs</strong></a> |
<a href="https://vllm-sr.ai/blog/"><strong>Blog</strong></a> |
<a href="https://vllm-dev.slack.com/archives/C09CTGF8KCN"><strong>Slack</strong></a> |
<a href="https://github.com/vllm-project/semantic-router"><strong>GitHub</strong></a>
</p>
</div>
Vela Feedback
Vela Feedback distinguishes satisfaction, clarification, reported errors, requested changes, and independent new requests.
307M parameters · Input capacity: 32,768 tokens, including special tokens.
Labels: SAT, NEED_CLARIFICATION, WRONG_ANSWER, WANT_DIFFERENT, and NO_FEEDBACK. Quoted praise or criticism is not automatically feedback.
Evaluation
Compared with the original mmBERT32K Feedback model on the same development inputs. F1 and accuracy are reported on a 0–100 scale.
| Evaluation | Original mmBERT | Vela |
|---|---|---|
| Four shared feedback classes · Macro F1 · 472 inputs | 30.74 | 98.81 |
| Shared-class accuracy · 472 inputs | 38.77 | 97.88 |
| False feedback alerts on 288 neutral inputs · confidence ≥0.7 · lower is better | 254/288 | 6/288 |
Both models use FP32 and complete inputs. The development set includes authored requests, controlled long contexts and reviewed natural requests, and informed Vela selection. The original model has four labels: neutral coverage is measured separately, while Vela’s NO_FEEDBACK predictions count as errors on shared-class examples.
Quick start
With PyTorch and Transformers 4.57.6 or 5.17.0:
from transformers import pipeline
model_id = "vllm-sr/Vela-1.0-Encoder-307M-Feedback"
model = pipeline("text-classification", model=model_id, device=-1)
print(model("That answered my question, thank you.", top_k=None, truncation=False))
- Task
- Text Classification
- Library
- transformers
- Type
- modernbert
- License
- apache-2.0
- Languages
- en, zh, es, fr, de
- Created
- Sep 12, 2026
- Updated
- Oct 2, 2026