--- license: apache-2.0 library_name: peft widget: - text: "It was good." example_title: "Positive" - text: "Not a fan, don't recommed." example_title: "Negative" - text: "Better than the first one." example_title: "Positive" - text: "This is not worth watching even once. " example_title: "Negative" - text: "This one is a pass." example_title: "Positive" tags: - sentiment datasets: - imdb metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-lora-text-classification results: [] --- # distilbert-base-uncased-lora-text-classification 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: 0.4297 - Accuracy: {'accuracy': 0.86} ## 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: 0.01 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.7852 | 1.0 | 5000 | 0.4518 | {'accuracy': 0.8486} | | 0.6289 | 2.0 | 10000 | 0.4492 | {'accuracy': 0.8528} | | 0.0503 | 3.0 | 15000 | 0.4297 | {'accuracy': 0.86} | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0 ## Training procedure ### Framework versions - PEFT 0.6.2