| --- |
| library_name: transformers |
| license: apache-2.0 |
| pipeline_tag: token-classification |
| base_model: vllm-sr/Vela-1.0-Encoder-307M |
| base_model_relation: finetune |
| language: |
| - en |
| - zh |
| - es |
| - fr |
| - de |
| - ja |
| tags: |
| - modernbert |
| - semantic-router |
| - vela |
| --- |
| |
| <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 PII |
|
|
| Vela PII finds sensitive entity spans for privacy-aware routing and redaction. |
|
|
| **307M parameters · Input capacity: 32,768 tokens, including special tokens.** |
|
|
| Outputs use 35 BIO labels across 17 entity types. The example returns spans with Unicode character offsets. |
|
|
| ## Evaluation |
|
|
| Exact-span micro F1 (×100) on the same synthetic development sets, compared with [the original mmBERT32K PII model](https://huggingface.co/vllm-sr/mmbert32k-pii-detector-merged). Higher is better. |
|
|
| | Evaluation | Original mmBERT | Vela | |
| |---|---:|---:| |
| | Short inputs · 888 | 23.66 | **90.76** | |
| | Controlled 4K context · 30 | 0.44 | **89.03** | |
| | Controlled 8K context · 30 | 0.28 | **89.88** | |
| | Controlled 16K context · 30 | 0.36 | **89.73** | |
| | Controlled 32K context · 30 | 0.23 | **89.24** | |
|
|
| Synthetic examples cover six languages. Long inputs include sparse entities, densely packed repeated entities and negative examples; micro F1 weights each entity equally. Both models process complete inputs in FP32 with the same exact-span scorer. These development sets informed Vela selection; they are not an independent natural-document benchmark. |
|
|
| ## Quick start |
|
|
| With PyTorch and Transformers 4.57.6 or 5.17.0: |
|
|
| ```python |
| from transformers import pipeline |
| |
| model_id = "vllm-sr/Vela-1.0-Encoder-307M-PII" |
| model = pipeline("token-classification", model=model_id, aggregation_strategy="simple", device=-1) |
| text = "Contact Mara Wells at mara.wells@example.com." |
| assert len(model.tokenizer.encode(text)) <= model.model.config.max_position_embeddings |
| print(model(text)) |
| ``` |
|
|
| [Explore the Vela model collection](https://huggingface.co/collections/vllm-sr/vela-10-router-models-6aa555ba70cc6997d6d67798) |
|
|
