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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta-news-classifier
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-news-classifier
This model is a fine-tuned version of [russellc/roberta-news-classifier](https://huggingface.co/russellc/roberta-news-classifier) on the custom(Kaggle) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1043
- Accuracy: 0.9786
- F1: 0.9786
- Precision: 0.9786
- Recall: 0.9786
## 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-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1327 | 1.0 | 123 | 0.1043 | 0.9786 | 0.9786 | 0.9786 | 0.9786 |
| 0.1103 | 2.0 | 246 | 0.1157 | 0.9735 | 0.9735 | 0.9735 | 0.9735 |
| 0.102 | 3.0 | 369 | 0.1104 | 0.9735 | 0.9735 | 0.9735 | 0.9735 |
| 0.0825 | 4.0 | 492 | 0.1271 | 0.9714 | 0.9714 | 0.9714 | 0.9714 |
| 0.055 | 5.0 | 615 | 0.1296 | 0.9724 | 0.9724 | 0.9724 | 0.9724 |
### Evaluation results
***** Running Prediction *****
Num examples = 980
Batch size = 64
precision recall f1-score support
dunya 0.99 0.96 0.97 147
ekonomi 0.96 0.96 0.96 141
kultur 0.97 0.99 0.98 142
saglik 0.99 0.98 0.98 148
siyaset 0.98 0.98 0.98 134
spor 1.00 1.00 1.00 139
teknoloji 0.96 0.98 0.97 129
accuracy -- -- 0.98 980
macro avg 0.98 0.98 0.98 980
weighted avg 0.98 0.98 0.98 980
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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