---
library_name: transformers
base_model: pilotj/roberta-base-pretrained-v1
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: roberta-base-v1
  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-v1

This model is a fine-tuned version of [pilotj/roberta-base-pretrained-v1](https://huggingface.co/pilotj/roberta-base-pretrained-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3920
- Accuracy: 0.8867
- F1 Macro: 0.8576
- F1 W: 0.8880
- Precision: 0.8909
- Recall: 0.8867

## 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: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro | F1 W   | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|:------:|
| 0.3932        | 0.1896 | 500  | 0.4138          | 0.8803   | 0.8505   | 0.8816 | 0.8847    | 0.8803 |
| 0.3997        | 0.3792 | 1000 | 0.4097          | 0.8809   | 0.8499   | 0.8824 | 0.8861    | 0.8809 |
| 0.3997        | 0.5688 | 1500 | 0.4126          | 0.8818   | 0.8514   | 0.8834 | 0.8874    | 0.8818 |
| 0.3907        | 0.7584 | 2000 | 0.3988          | 0.8844   | 0.8544   | 0.8856 | 0.8887    | 0.8844 |
| 0.3881        | 0.9480 | 2500 | 0.3956          | 0.8862   | 0.8549   | 0.8871 | 0.8901    | 0.8862 |
| 0.3558        | 1.1377 | 3000 | 0.3971          | 0.8863   | 0.8570   | 0.8874 | 0.8902    | 0.8863 |
| 0.3526        | 1.3273 | 3500 | 0.3999          | 0.8852   | 0.8558   | 0.8867 | 0.8902    | 0.8852 |
| 0.3435        | 1.5169 | 4000 | 0.3991          | 0.8858   | 0.8565   | 0.8870 | 0.8903    | 0.8858 |
| 0.3428        | 1.7065 | 4500 | 0.3929          | 0.8859   | 0.8572   | 0.8871 | 0.8901    | 0.8859 |
| 0.3392        | 1.8961 | 5000 | 0.3920          | 0.8867   | 0.8576   | 0.8880 | 0.8909    | 0.8867 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.2.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0