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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imdb |
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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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model-index: |
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- name: results |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: imdb |
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type: imdb |
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config: plain_text |
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split: test |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9133333333333333 |
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- name: F1 |
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type: f1 |
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value: 0.9161290322580645 |
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- name: Precision |
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type: precision |
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value: 0.8875 |
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- name: Recall |
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type: recall |
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value: 0.9466666666666667 |
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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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# results |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2250 |
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- Accuracy: 0.9133 |
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- F1: 0.9161 |
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- Precision: 0.8875 |
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- Recall: 0.9467 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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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: 3 |
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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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| 0.6922 | 0.98 | 46 | 0.6867 | 0.7433 | 0.6778 | 0.9101 | 0.54 | |
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| 0.2634 | 1.98 | 93 | 0.3428 | 0.8833 | 0.8736 | 0.9528 | 0.8067 | |
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| 0.1736 | 2.94 | 138 | 0.2250 | 0.9133 | 0.9161 | 0.8875 | 0.9467 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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