---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: roberta-base_binary
  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_binary

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: 0.1729
- Precision: 0.8178
- Recall: 0.6136
- F1: 0.7012
- F0.5: 0.7668
- Macro Precision: 0.8824
- Macro Recall: 0.7971
- Macro F1: 0.8323
- Macro F0.5: 0.8602

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | F0.5   | Macro Precision | Macro Recall | Macro F1 | Macro F0.5 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:---------------:|:------------:|:--------:|:----------:|
| 0.1963        | 1.0    | 1926 | 0.1702          | 0.8148    | 0.6179 | 0.7028 | 0.7660 | 0.8814          | 0.7991       | 0.8333   | 0.8601     |
| 0.1621        | 1.9992 | 3850 | 0.1698          | 0.8027    | 0.6472 | 0.7166 | 0.7659 | 0.8772          | 0.8124       | 0.8405   | 0.8613     |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1