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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: russellc/roberta-news-classifier |
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model-index: |
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- name: roberta-news-classifier |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-news-classifier |
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This model is a fine-tuned version of [russellc/roberta-news-classifier](https://huggingface.co/russellc/roberta-news-classifier) on the custom(Kaggle) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1043 |
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- Accuracy: 0.9786 |
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- F1: 0.9786 |
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- Precision: 0.9786 |
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- Recall: 0.9786 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1327 | 1.0 | 123 | 0.1043 | 0.9786 | 0.9786 | 0.9786 | 0.9786 | |
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| 0.1103 | 2.0 | 246 | 0.1157 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | |
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| 0.102 | 3.0 | 369 | 0.1104 | 0.9735 | 0.9735 | 0.9735 | 0.9735 | |
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| 0.0825 | 4.0 | 492 | 0.1271 | 0.9714 | 0.9714 | 0.9714 | 0.9714 | |
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| 0.055 | 5.0 | 615 | 0.1296 | 0.9724 | 0.9724 | 0.9724 | 0.9724 | |
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### Evaluation results |
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***** Running Prediction ***** |
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Num examples = 980 |
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Batch size = 64 |
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precision recall f1-score support |
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dunya 0.99 0.96 0.97 147 |
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ekonomi 0.96 0.96 0.96 141 |
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kultur 0.97 0.99 0.98 142 |
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saglik 0.99 0.98 0.98 148 |
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siyaset 0.98 0.98 0.98 134 |
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spor 1.00 1.00 1.00 139 |
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teknoloji 0.96 0.98 0.97 129 |
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accuracy -- -- 0.98 980 |
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macro avg 0.98 0.98 0.98 980 |
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weighted avg 0.98 0.98 0.98 980 |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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