---
license: apache-2.0
base_model: convaiinnovations/laya
library_name: onnxruntime
pipeline_tag: text-classification
tags: [typed-decisions, laya, modernbert, fiction, character-traits]
---

# DeepQuill trait verifier

A fine-tune of [Laya](https://huggingface.co/convaiinnovations/laya) (ModernBERT-large encoder plus a typed-decision head). [DeepQuill](https://deepquill.app) uses it to check character-trait claims that its local extractor pulls from fiction.

For each claim (a target character, `field = value`, the quote, and the passage), the model answers four yes/no questions:

- **aboutTarget**: is it about that character?
- **lasting**: a lasting attribute, not a momentary state?
- **literal**: literal, not figurative?
- **asserted**: asserted by the narration?

The claim is kept when all four pass.

## Files

- `model.w8.onnx`: weight-only 8-bit (MatMulNBits). Activations run in fp32, so a claim's result doesn't depend on what it's batched with.
- `tokenizer.json`
- `verifier_config.json`: the exact question texts, sequence limits and calibration temperature.

## Training data

Everything the model was trained on is public:

- 3,760 labelled claims from real extractor output over US-public-domain novels from 1894–1930 (Project Gutenberg).
- Synthetic contemporary passages written for this purpose.

The model was never trained on user manuscripts.

## Evaluation

The model was scored on held-out sets that share no books or characters with the training data. Metric: keep-F1.

| Test set | This model | Qwen3-4B LLM verifier | No verifier |
|---|---|---|---|
| 1920s public-domain claims (524) | 0.75 | 0.32 | 0.45 |
| Contemporary manuscript claims (157) | 0.88 | 0.24 | 0.45 |