---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-base-outputs
  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-outputs

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: 0.5836
- Accuracy: 0.6636
- F1: 0.6948
- Precision: 0.6409
- Recall: 0.7587

## 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6984        | 0.1778 | 1000  | 0.6931          | 0.5072   | 0.4296 | 0.5167    | 0.3677 |
| 0.6952        | 0.3556 | 2000  | 0.6932          | 0.4956   | 0.0032 | 0.6667    | 0.0016 |
| 0.6931        | 0.5333 | 3000  | 0.6922          | 0.5314   | 0.3417 | 0.5874    | 0.2409 |
| 0.6927        | 0.7111 | 4000  | 0.6901          | 0.5272   | 0.6625 | 0.5179    | 0.9192 |
| 0.6883        | 0.8889 | 5000  | 0.6792          | 0.5714   | 0.6346 | 0.5570    | 0.7373 |
| 0.6756        | 1.0667 | 6000  | 0.6521          | 0.6114   | 0.5702 | 0.6455    | 0.5107 |
| 0.6476        | 1.2444 | 7000  | 0.6317          | 0.627    | 0.6909 | 0.5939    | 0.8257 |
| 0.6278        | 1.4222 | 8000  | 0.6058          | 0.6474   | 0.6799 | 0.6276    | 0.7417 |
| 0.6134        | 1.6    | 9000  | 0.5959          | 0.6564   | 0.6909 | 0.6328    | 0.7607 |
| 0.6119        | 1.7778 | 10000 | 0.5870          | 0.6618   | 0.6933 | 0.6393    | 0.7571 |
| 0.6033        | 1.9556 | 11000 | 0.5836          | 0.6636   | 0.6948 | 0.6409    | 0.7587 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1