| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imdb |
| metrics: |
| - accuracy |
| - f1 |
| model-index: |
| - name: tiny-vanilla-target-imdb |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: imdb |
| type: imdb |
| config: plain_text |
| split: train |
| args: plain_text |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.83488 |
| - name: F1 |
| type: f1 |
| value: 0.9100104638995464 |
| --- |
| |
| <!-- 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. --> |
|
|
| # tiny-vanilla-target-imdb |
|
|
| This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the imdb dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.4589 |
| - Accuracy: 0.8349 |
| - F1: 0.9100 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: constant |
| - num_epochs: 200 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
| | 0.5912 | 0.64 | 500 | 0.4160 | 0.8295 | 0.9068 | |
| | 0.3949 | 1.28 | 1000 | 0.4095 | 0.8228 | 0.9028 | |
| | 0.3386 | 1.92 | 1500 | 0.2948 | 0.8804 | 0.9364 | |
| | 0.2993 | 2.56 | 2000 | 0.4798 | 0.7868 | 0.8807 | |
| | 0.2791 | 3.2 | 2500 | 0.4555 | 0.8205 | 0.9014 | |
| | 0.2585 | 3.84 | 3000 | 0.2815 | 0.8859 | 0.9395 | |
| | 0.2371 | 4.48 | 3500 | 0.4446 | 0.8316 | 0.9081 | |
| | 0.2189 | 5.12 | 4000 | 0.6102 | 0.7693 | 0.8696 | |
| | 0.1989 | 5.75 | 4500 | 0.4589 | 0.8349 | 0.9100 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.25.1 |
| - Pytorch 1.12.1 |
| - Datasets 2.7.1 |
| - Tokenizers 0.13.2 |
|
|
