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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`.
---
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