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--- |
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license: mit |
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base_model: gpt2 |
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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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- precision |
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- recall |
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- f1 |
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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: train |
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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: 0.9330666661262512 |
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- name: Precision |
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type: precision |
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value: 0.9330666661262512 |
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- name: Recall |
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type: recall |
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value: 0.9330666661262512 |
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- name: F1 |
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type: f1 |
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value: 0.9330666661262512 |
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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 [gpt2](https://huggingface.co/gpt2) on the imdb dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2797 |
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- Accuracy: 0.9331 |
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- Precision: 0.9331 |
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- Recall: 0.9331 |
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- F1: 0.9331 |
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- Auroc: 0.9810 |
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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: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Auroc | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:-----:|:----:|:--------:|:------:|:------:|:---------------:|:---------:|:------:| |
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| 0.1436 | 0.46 | 500 | 0.8935 | 0.9751 | 0.8935 | 0.2923 | 0.8935 | 0.8935 | |
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| 0.1621 | 0.91 | 1000 | 0.9261 | 0.9789 | 0.9261 | 0.1984 | 0.9261 | 0.9261 | |
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| 0.2196 | 1.37 | 1500 | 0.9289 | 0.9810 | 0.9289 | 0.2082 | 0.9289 | 0.9289 | |
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| 0.1457 | 1.83 | 2000 | 0.9325 | 0.9816 | 0.9325 | 0.2282 | 0.9325 | 0.9325 | |
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| 0.1103 | 2.29 | 2500 | 0.9305 | 0.9806 | 0.9305 | 0.3201 | 0.9305 | 0.9305 | |
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| 0.0679 | 2.74 | 3000 | 0.2797 | 0.9331 | 0.9331 | 0.9331 | 0.9331 | 0.9810 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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