readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - sentiment140
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: distilbert-base-uncasedv1-finetuned-twitter-sentiment
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: sentiment140
        type: sentiment140
        config: sentiment140
        split: train
        args: sentiment140
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.82475
      - name: F1
        type: f1
        value: 0.8246033480256058
      - name: Precision
        type: precision
        value: 0.825087861584212
      - name: Recall
        type: recall
        value: 0.8016811137378513
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncasedv1-finetuned-twitter-sentiment
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sentiment140 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3985
  - Accuracy: 0.8247
  - F1: 0.8246
  - Precision: 0.8251
  - Recall: 0.8017
  
  ## 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: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | No log        | 1.0   | 500  | 0.4049          | 0.8181   | 0.8178 | 0.8236    | 0.7862 |
  | No log        | 2.0   | 1000 | 0.3985          | 0.8247   | 0.8246 | 0.8251    | 0.8017 |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  