readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - tweet_eval
  metrics:
  - accuracy
  - f1
  model-index:
  - name: tweet_eval-sentiment-finetuned
    results: 
    - task:
        name: Sentiment Analysis
        type: sentiment-analysis
      dataset:
        name: tweeteval
        type: tweeteval
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7099
      - name: f1
        type: f1
        value: 0.7097
  ---
  
  <!-- 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. -->
  
  # tweet_eval-sentiment-finetuned
  
  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the Tweet_Eval dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6532
  - Accuracy: 0.744
  - F1: 0.7437
  
  ## 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: 128
  - eval_batch_size: 256
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.7491        | 1.0   | 357  | 0.6089          | 0.7345   | 0.7314 |
  | 0.5516        | 2.0   | 714  | 0.5958          | 0.751    | 0.7516 |
  | 0.4618        | 3.0   | 1071 | 0.6131          | 0.748    | 0.7487 |
  | 0.4066        | 4.0   | 1428 | 0.6532          | 0.744    | 0.7437 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.9.1
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  