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

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6056
- F1 Macro: 0.5289
- Precision: 0.5397
- Recall: 0.5373
- Accuracy: 0.7180

## 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  | 1.8365          | 0.3820   | 0.4312    | 0.4000 | 0.6106   |
| 2.2658        | 2.0   | 708  | 1.3285          | 0.5013   | 0.5037    | 0.5722 | 0.6554   |
| 1.5226        | 3.0   | 1062 | 1.2491          | 0.5450   | 0.5421    | 0.6006 | 0.7090   |
| 1.5226        | 4.0   | 1416 | 1.3314          | 0.5476   | 0.5555    | 0.5767 | 0.7180   |
| 1.1009        | 5.0   | 1770 | 1.4114          | 0.5468   | 0.5457    | 0.5754 | 0.7203   |
| 0.7195        | 6.0   | 2124 | 1.6056          | 0.5289   | 0.5397    | 0.5373 | 0.7180   |


### Framework versions

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