readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - tamilmixsentiment
  metrics:
  - accuracy
  model_index:
  - name: tamil-sentiment-distilbert
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: tamilmixsentiment
        type: tamilmixsentiment
        args: default
      metric:
        name: Accuracy
        type: accuracy
        value: 0.665
  ---
  
  <!-- 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. -->
  
  # tamil-sentiment-distilbert
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tamilmixsentiment dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0230
  - Accuracy: 0.665
  
  ## Dataset Information
  - text: Tamil-English code-mixed comment.
  - label: list of the possible sentiments 
    - LABEL_0: "Positive", 
    - LABEL_1: "Negative", 
    - LABEL_2: "Mixed_feelings", 
    - LABEL_3: "unknown_state", 
    - LABEL_4: "not-Tamil"
  
  ## Intended uses & limitations
  
  This model was just created for doing classification task on tamilmixsentiment dataset
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 0
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.0442        | 1.0   | 250  | 0.9883          | 0.674    |
  | 0.9227        | 2.0   | 500  | 0.9782          | 0.673    |
  | 0.7591        | 3.0   | 750  | 1.0230          | 0.665    |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  