---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
- debagreement
- stance
- debate
- disagreement
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-debagreement
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-debagreement

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8424
- Accuracy: 0.6803
- F1: 0.6776

## Model description


## Intended uses & limitations

This model is intended for detecting stance (agreement, disagreement, neutrality) in Reddit comment reply pairs. It was trained on political subreddit data from the DEBAGREEMENT dataset and may not generalize well to other domains or platforms.

## Training and evaluation data

- **Base model**: [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) (125M parameters)
- **Dataset**: [Jiyog/debagreement-cp](https://huggingface.co/datasets/Jiyog/debagreement-cp) (DEBAGREEMENT)
- **Task**: 3-class sequence classification (sentence-pair input)
- **Input format**: `body_parent` (premise) + `body_child` (hypothesis)
- **Epochs**: 3
- **Batch size**: 16
- **Max sequence length**: 512 tokens
- **Optimizer**: AdamW (default HuggingFace Trainer)
- **Weight decay**: 0.01
- **Best model selected by**: Weighted F1

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8592        | 1.0   | 2145 | 0.7596          | 0.6612   | 0.6493 |
| 0.7169        | 2.0   | 4290 | 0.7670          | 0.6780   | 0.6708 |
| 0.4871        | 3.0   | 6435 | 0.8424          | 0.6803   | 0.6776 |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2