---
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-large-ToM1
  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-ToM1

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5759
- Accuracy: 0.8736
- F1: 0.8942
- Precision: 0.8774
- Recall: 0.9118

## 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: 16
- seed: 2015
- 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
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.642         | 1.0   | 93   | 0.3124          | 0.8846   | 0.8966 | 0.9286    | 0.8667 |
| 0.4136        | 2.0   | 186  | 0.2751          | 0.8718   | 0.8810 | 0.9487    | 0.8222 |
| 0.2804        | 3.0   | 279  | 0.2827          | 0.9103   | 0.9263 | 0.88      | 0.9778 |
| 0.1963        | 4.0   | 372  | 0.3018          | 0.8974   | 0.9111 | 0.9111    | 0.9111 |
| 0.1116        | 5.0   | 465  | 0.3308          | 0.9231   | 0.9362 | 0.8980    | 0.9778 |


### Framework versions

- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0