---
base_model: Qwen/Qwen3-4B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-4B
- lora
- transformers
- turn-taking
- multi-party-dialogue
- spgi
- text-classification
---

# Qwen3-4B-SPGI: Proactive Response Prediction in Multi-Party Dialogue

LoRA adapter for **Qwen/Qwen3-4B** fine-tuned on the SPGI earnings call corpus for **proactive response prediction** in multi-party conversations. Given a conversational context and a current utterance, the model predicts whether a target speaker will **SPEAK** next or remain **SILENT**.

## Model Details

- **Model type:** LoRA adapter for causal language model (text classification / sequence classification)
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
- **SPGI2.0 Corpus:** Earnings call transcripts from S&P Global [SPGI2.0](https://datasets.kensho.com/datasets/spgispeech2)

### Model Sources
- **Base model:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)


## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "kraken07/qwen3-4b-spgi")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")

# Your input format should match training: context turns + current turn
# Output: SPEAK or SILENT prediction for the target speaker
```

## Citation

If you use this model, please cite our work:

```bibtex
@misc{bhagtani2026speakstaysilentcontextaware,
  title={Speak or Stay Silent: Context-Aware Turn-Taking in Multi-Party Dialogue},
  author={Bhagtani, Kratika and Anand, Mrinal and Xu, Yu Chen and Yadav, Amit Kumar Singh},
  year={2026},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2603.11409}
}
```