readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - dutch_social
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: robbert-twitter-sentiment-custom
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: dutch_social
        type: dutch_social
        args: dutch_social
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.788
      - name: F1
        type: f1
        value: 0.7878005279207152
      - name: Precision
        type: precision
        value: 0.7877102066609215
      - name: Recall
        type: recall
        value: 0.788
  ---
  
  <!-- 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. -->
  
  # robbert-twitter-sentiment-custom
  
  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6656
  - Accuracy: 0.788
  - F1: 0.7878
  - Precision: 0.7877
  - Recall: 0.788
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.8287        | 1.0   | 282  | 0.7178          | 0.7007   | 0.6958 | 0.6973    | 0.7007 |
  | 0.4339        | 2.0   | 564  | 0.5873          | 0.7667   | 0.7668 | 0.7681    | 0.7667 |
  | 0.2045        | 3.0   | 846  | 0.6656          | 0.788    | 0.7878 | 0.7877    | 0.788  |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.11.0+cpu
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  