---
language:
- en
license: mit
tags:
- text-generation
- style-transfer
- rewriting
- humanization
- seq2seq
- bart
- evaluation
- bertscore
- rouge
- chrf
library_name: transformers
base_model: facebook/bart-base
pipeline_tag: text-generation
paper:
- https://arxiv.org/abs/2604.11687v1
---

# cive202/humanize-ai-text-bart-base

Fine-tuned **BART-base** (`facebook/bart-base`) for **AI → Human rewriting** (“humanization”) via prefix-based conditional generation.

- **Architecture**: encoder–decoder (seq2seq)  
- **Parameters**: ~139M  
- **Task format**: `humanize: {ai_text}` → `{human_text}`  

---

## 📄 Paper

**“Rewriting the Machine: Encoder-Decoder vs. Decoder-Only Transformers for AI-to-Human Text Style Transfer”**  
**Authors:** Utsav Paneru et al.  
**arXiv:** https://arxiv.org/abs/2604.11687v1  
**Status:** Preprint (2026)

### Citation

```bibtex
@misc{paneru2026makesoundlikehuman,
      title={Please Make it Sound like Human: Encoder-Decoder vs. Decoder-Only Transformers for AI-to-Human Text Style Transfer}, 
      author={Utsav Paneru},
      year={2026},
      eprint={2604.11687},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.11687}, 
}
```

## Quickstart

```bash
pip install -U "transformers>=4.40.0" torch sentencepiece
```

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "cive202/humanize-ai-text-bart-base"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

ai_text = "Large language models often produce fluent, structured prose with recognizable regularities..."

inputs = tokenizer("humanize: " + ai_text, return_tensors="pt", truncation=True)

out = model.generate(
    **inputs,
    max_new_tokens=256,
    num_beams=4,
)

print(tokenizer.decode(out[0], skip_special_tokens=True))
```

---

## Training note (important)

This checkpoint corresponds to a **smoke-test / pipeline validation run**, not a full training run.

Saved config characteristics:

- `max_steps = 10`  
- `max_train_samples = 128`  
- `num_train_epochs = 1`  

⚠️ Interpret results below as a **lower-bound baseline**, not a fully optimized model.

---

## Dataset

Parallel chunk pairs created via sentence-aware chunking:

- **Train**: 25,140 pairs  
- **Validation**: 1,390  
- **Test**: 1,390  

### Preprocessing

- Sentence tokenization (NLTK)  
- Greedy token packing (≤200 tokens)  
- Filtering short pairs (<10 words)  
- Document-disjoint splits  

---

## Evaluation (test n = 1,390)

### Reference similarity

- **BERTScore F1**: **0.9088**  
- **ROUGE-L**: **0.4448**  
- **chrF++**: **46.4131**  

### Fluency proxy

- **GPT-2 PPL (output)**: **26.6919**  
- **GPT-2 PPL (human)**: **23.6912**  

### Style shift

- **Mean marker shift**: **0.6513**  

This baseline partially shifts text toward human-like distributions but is limited by minimal training.

---

## Limitations

- Not a fully trained model (smoke-test configuration)  
- Limited style transformation strength  
- No guarantee of bypassing AI detectors  
- Lower performance compared to larger/full runs  

---

## Research context

Part of the unpublished 2026 manuscript:

**“Rewriting the Machine: Encoder-Decoder vs. Decoder-Only Transformers for AI-to-Human Text Style Transfer”**

- Status: preprint  
- Link: https://arxiv.org/abs/2604.11687

---

## License

MIT (placeholder). Ensure compatibility with `facebook/bart-base`.

---