README.md

---
language:
  - en
  - zh
  - es
  - fr
  - pt
  - de
  - it
  - nl
license: apache-2.0
library_name: dotcheck
pipeline_tag: text-classification
tags:
  - text-classification
  - ai-detection
  - multilingual
  - apache-2.0
  - dotcheck
  - valla
base_model:
  - Oxidane/tmr-ai-text-detector
  - fakespot-ai/roberta-base-ai-text-detection-v1
  - hfl/chinese-macbert-base
base_model_relation: adapter
model-index:
  - name: Valla (inhouse-text@11)
    results:
      - task:
          type: text-classification
          name: binary AI-likeness (text, en)
        dataset:
          name: DotCheck text holdout EN
          type: other
          split: holdout
        metrics:
          - name: mean_P_AI_human
            type: mean_score_human
            value: 0.005
          - name: mean_P_AI_ai
            type: mean_score_ai
            value: 0.973
          - name: balanced_accuracy
            type: balanced_accuracy
            value: 0.986
        source:
          name: text_gates_v11 / Data.json
          url: https://dotcheck.ai/docs
---

# DotCheck/valla-text-v11

Apache-2.0 text AI-likeness heads for DotCheck (English, Chinese, and six Latin language heads). This repo includes the live .npz heads, model card, license, and notices.

| Field | Value |
|-------|--------|
| Hub id | `DotCheck/valla-text-v11` |
| Wire id (EN) | `inhouse-text@11` |
| Label | Valla |
| EN artifact | `text_stack_head_v11.npz` |
| Latin feature bases | [`Oxidane/tmr-ai-text-detector`](https://huggingface.co/Oxidane/tmr-ai-text-detector) (MIT), [`fakespot-ai/roberta-base-ai-text-detection-v1`](https://huggingface.co/fakespot-ai/roberta-base-ai-text-detection-v1) (Apache-2.0) |
| Chinese feature base | [`hfl/chinese-macbert-base`](https://huggingface.co/hfl/chinese-macbert-base) (Apache-2.0; `cls_emb`) |
| Heads | EN + `zh_v1` / `es_v3` / `pt_BR_v3` / `fr_v3` / `it_v3` / `de_v1` / `nl_v1` (separate `.npz`) |
| Output | `p ∈ [0,1]` — P(AI-like) |
| Serve | CPU FastAPI `POST /v1/analyze-text` + required `lang` |
| `lang` | `en\|zh\|es\|pt\|fr\|it\|de\|nl` (`pt` → `pt_BR` head; `zh` = Simplified Chinese) |

## Model description

**Latin group:** Frozen TMR + Fakespot encoders → shared feature vector → language-specific logistic head.  
**Chinese group:** Frozen MacBERT `cls_emb` → `zh_v1` logistic head (train=serve contract).  

Serve loads the Latin base pair for all Latin heads; the Chinese tower loads when `text_stack_head_zh_v1.npz` is present. Unsupported `lang` → fail closed (`unsupported_language`; no silent EN fallback). Traditional-Chinese-dominant product input is not scored (`und`).

Text hygiene (TC1) applied before hash/score: strip wiki-style cites, collapse whitespace, drop `#category` tokens (mirrored in Express / extension).

**Files in this repo:** `README.md`, `LICENSE`, `NOTICE`, `CITATION.cff`, and the `.npz` head file(s) listed above.

## Architecture

```text
raw text
  → text_clean (TC1)
  → if lang in {en,es,pt,fr,it,de,nl}:
        TMR + Fakespot frozen forward (shared Latin group)
  → if lang == zh:
        MacBERT cls_emb frozen forward (Chinese group)
  → lang-selected logistic head (npz)
  → p_AI
```

## Inference

Open weights: the live `.npz` head(s) in this repo (Apache-2.0), for use with the frozen upstream backbone named above. This is not a transformers `AutoModel.from_pretrained("DotCheck/…")` package.

Product scoring: Check or Pro API (below). Leviathan (shared memory and related product path) is not in these files.

```bash
curl -sS -X POST "https://dotcheck-server-c221c1f32c68.herokuapp.com/analyze-text" \
  -H "Authorization: Bearer dc_YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"text\":\"...\",\"lang\":\"en\"}"
```

UI: https://dotcheck.ai/check · API: https://dotcheck.ai/api · docs: https://dotcheck.ai/docs  
Min length / FUP enforced at Express (product policy).

## Training data

| | EN / Latin | Chinese (zh-Hans) |
|--|----|-------------|
| Fit AI | Qwen2.5-7B + Mistral-7B (multi-temp) | Qwen2.5-7B + Mistral; wiki-style prompts |
| Holdout AI | **Qwen2.5-1.5B** | Qwen2.5-1.5B (~200/200) |
| Human | hard-neg wiki / WikiText / Gutenberg mix | Wikipedia lead prose (OpenCC t2s) |
| License | commercial-clean only (no NC banned sets) | same |

No published holdout from live ChatGPT/Claude scrapes. Evidence: `text_gates_v11.json`, `text_gates_{lang}.json`.

## Evaluation

### English (public claim SSOT)

| Metric | Target | Measured |
|--------|--------|---------:|
| mean P(AI) \| human | ≤ 0.12 | **0.005** |
| mean P(AI) \| AI | ≥ 0.85 | **0.973** |
| bal_acc @ thr | ≥ 0.90 | **0.986** |

Also: beat prior `@6` holdout; wiki-monitor mean ~\<0.001; OOD / RAID-lite protocol OK (`TEXT_GATES_OK`).

### Language heads (holdout; same absolute floors)

<!-- sync:text-lang-start -->
| Lang | Wire | Human | AI | bal_acc |
|------|------|------:|---:|--------:|
| zh | `inhouse-text-zh_v1` | 0.033 | 0.973 | 0.972 |
| es | `inhouse-text-es_v3` | 0.029 | 0.993 | 0.982 |
| fr | `inhouse-text-fr_v3` | 0.021 | 0.990 | 0.978 |
| pt | `inhouse-text-pt_BR_v3` | 0.042 | 0.964 | 0.958 |
| de | `inhouse-text-de_v3` | 0.034 | 0.966 | 0.978 |
| it | `inhouse-text-it_v3` | 0.016 | 0.943 | 0.972 |
| nl | `inhouse-text-nl_v3` | 0.056 | 0.963 | 0.950 |
<!-- sync:text-lang-end -->

## Intended use

- Supported-language AI-likeness scoring in DotCheck inference.
- Citation of the tables above.

### Out of scope

- Product scoring SLA / Leviathan / FUP via Hub download
- Languages outside `{en,zh,es,pt,fr,it,de,nl}`
- Traditional Chinese as a scored variant (fail closed)
- Plagiarism / authorship adjudication

## Limitations

- Short, MT-heavy, or heavily edited text: higher variance.
- Eight languages only (Simplified Chinese for `zh`).
- Output is not generator attribution.

## License

[`LICENSE`](LICENSE) — Apache License 2.0 for DotCheck heads in this repo. Upstream backbones: see [`NOTICE`](NOTICE).

## Citation

[`CITATION.cff`](CITATION.cff) · wire `inhouse-text@11` / Valla@11 · https://dotcheck.ai/docs