---
license: cc-by-sa-4.0
library_name: transformers
tags:
- flan-t5
- detoxification
- polite-rewriting
- text-to-text
model_name: flan-paradetox-full
---

# FLAN-T5-Large **Polite-Rewrite** (full fine-tune)

**Model:** `google/flan-t5-large` fine-tuned for toxic → polite rewriting.

## Training details
| Parameter | Value |
|-----------|-------|
| epochs | 3 |
| effective batch | 32 (16 × grad_acc=2, fp16) |
| lr / schedule | 3 e-5, cosine, 3 % warm-up |
| total steps | 1 800 |
| optimizer | AdamW, weight_decay=0.01 |
| hardware | 1 × A100-40 GB |

### Data
Merged **29 k** parallel pairs  
* ParaDetox (19 k)  
* Polite Insult (1.6 k, oversample×2)  
* PseudoParaDetox Llama-3 (8.6 k, tox≤0.3, cosine≥0.8)

### Metrics (dev 3 %)
| metric | score |
|--------|-------|
| BLEU | 0.82 |
| Avg toxicity (Detoxify) | **0.12** (src 0.71 → tgt 0.12) |
| Success rate (tox≤0.5 AND -20 %) | 89 % |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tok = AutoTokenizer.from_pretrained("RinaldiDev/flan-paradetox-full")
model = AutoModelForSeq2SeqLM.from_pretrained("RinaldiDev/flan-paradetox-full")

def rewrite_polite(text):
    inp = f"Rewrite politely:\\nInput: {text}\\nPolite:"
    ids = tok(inp, return_tensors="pt").input_ids
    out = model.generate(ids, num_beams=4, max_length=96)
    return tok.decode(out[0], skip_special_tokens=True)

print(rewrite_polite("Shut up, idiot!"))
# → "Stop talking"

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### Direct Use

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AI moderation helper
Toxic-to-polite assistants
Not for hallucination-free tasks; may still miss subtle hate speech.

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Trained largely on English; fails on code-switching.
Llama-generated pairs could contain artifacts.

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## Training Details

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