---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: fintunned-v2-roberta_GA
  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. -->

# fintunned-v2-roberta_GA

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1635
- Accuracy: 0.9523
- F1: 0.9527
- Precision: 0.9534
- Recall: 0.9523

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.3896        | 0.45  | 50   | 2.2632          | 0.325    | 0.2696 | 0.4504    | 0.3447 |
| 1.2481        | 0.91  | 100  | 0.4536          | 0.8841   | 0.8873 | 0.8940    | 0.8892 |
| 0.3487        | 1.36  | 150  | 0.2978          | 0.9136   | 0.9161 | 0.9186    | 0.9167 |
| 0.2618        | 1.82  | 200  | 0.2472          | 0.9295   | 0.9319 | 0.9362    | 0.9313 |
| 0.2223        | 2.27  | 250  | 0.1872          | 0.9409   | 0.9415 | 0.9445    | 0.9408 |
| 0.076         | 2.73  | 300  | 0.1635          | 0.9523   | 0.9527 | 0.9534    | 0.9523 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1