---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: roberta-large-pr_tqacd
  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-large-pr_tqacd

This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3352
- F1 Macro: 0.5698
- Precision: 0.5840
- Recall: 0.5633
- Accuracy: 0.7476

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Precision | Recall | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| No log        | 1.0   | 354  | 1.9174          | 0.3780   | 0.4605    | 0.3785 | 0.5650   |
| 2.289         | 2.0   | 708  | 1.2911          | 0.5305   | 0.5284    | 0.5883 | 0.6926   |
| 1.5337        | 3.0   | 1062 | 1.3022          | 0.5340   | 0.5466    | 0.5812 | 0.7001   |
| 1.5337        | 4.0   | 1416 | 1.3496          | 0.5526   | 0.5623    | 0.5737 | 0.7288   |
| 1.0847        | 5.0   | 1770 | 1.4333          | 0.5665   | 0.5774    | 0.5961 | 0.7439   |
| 0.6609        | 6.0   | 2124 | 1.5549          | 0.5681   | 0.5782    | 0.5880 | 0.7444   |
| 0.6609        | 7.0   | 2478 | 1.9182          | 0.5561   | 0.5543    | 0.5754 | 0.7269   |
| 0.388         | 8.0   | 2832 | 2.0856          | 0.5709   | 0.5799    | 0.5793 | 0.7387   |
| 0.1976        | 9.0   | 3186 | 2.4609          | 0.5564   | 0.5545    | 0.5706 | 0.7368   |
| 0.1383        | 10.0  | 3540 | 2.7571          | 0.5801   | 0.5833    | 0.5914 | 0.7467   |
| 0.1383        | 11.0  | 3894 | 3.0987          | 0.5700   | 0.5757    | 0.5711 | 0.75     |
| 0.0618        | 12.0  | 4248 | 3.3352          | 0.5698   | 0.5840    | 0.5633 | 0.7476   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1