---
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