metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
base_model: russellc/roberta-news-classifier
model-index:
  - name: roberta-news-classifier
    results: []

roberta-news-classifier

This model is a fine-tuned version of 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