--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 - precision - recall model-index: - name: distilbert-imdb results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test[6250:12500]+test[-12500:-6250] args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93024 - name: F1 type: f1 value: 0.9302304818748904 - name: Precision type: precision value: 0.93047490567909 - name: Recall type: recall value: 0.93024 --- # distilbert-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2118 - Accuracy: 0.9302 - F1: 0.9302 - Precision: 0.9305 - Recall: 0.9302 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 9072 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.2803 | 1.0 | 782 | 0.1874 | 0.9276 | 0.9276 | 0.9276 | 0.9276 | | 0.1143 | 2.0 | 1564 | 0.2118 | 0.9302 | 0.9302 | 0.9305 | 0.9302 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2