---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: roberta-base-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-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: 2.7467
- F1 Macro: 0.2528
- Precision: 0.2683
- Recall: 0.2648
- Accuracy: 0.3515

## 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   | 114  | 2.3961          | 0.0436   | 0.0603    | 0.1103 | 0.1584   |
| No log        | 2.0   | 228  | 2.3381          | 0.1234   | 0.1119    | 0.1476 | 0.3564   |
| No log        | 3.0   | 342  | 2.1873          | 0.2052   | 0.3773    | 0.2457 | 0.2673   |
| No log        | 4.0   | 456  | 2.2524          | 0.1800   | 0.2231    | 0.2043 | 0.3267   |
| 2.241         | 5.0   | 570  | 2.2141          | 0.2128   | 0.2340    | 0.2494 | 0.3218   |
| 2.241         | 6.0   | 684  | 2.3365          | 0.2238   | 0.2480    | 0.2391 | 0.2822   |
| 2.241         | 7.0   | 798  | 2.4779          | 0.2805   | 0.3501    | 0.2756 | 0.3713   |
| 2.241         | 8.0   | 912  | 2.5194          | 0.2518   | 0.2908    | 0.2667 | 0.3416   |
| 0.9793        | 9.0   | 1026 | 2.7467          | 0.2528   | 0.2683    | 0.2648 | 0.3515   |


### Framework versions

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