readme: ---
  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
  ---
  
  <!-- 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. -->
  
  # 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
  