--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - accuracy model-index: - name: prova_Classi results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: sentiment metrics: - name: Accuracy type: accuracy value: 0.716 --- # prova_Classi This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.5530 - Accuracy: 0.716 ## 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: 0.00013441028267541125 - train_batch_size: 32 - eval_batch_size: 16 - seed: 17 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7022 | 1.0 | 1426 | 0.6581 | 0.7105 | | 0.5199 | 2.0 | 2852 | 0.6835 | 0.706 | | 0.2923 | 3.0 | 4278 | 0.7941 | 0.7075 | | 0.1366 | 4.0 | 5704 | 1.0761 | 0.7115 | | 0.0645 | 5.0 | 7130 | 1.5530 | 0.716 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3