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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
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