readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: BiBert-Subjectivity
    results: []
  ---
  
  <!-- 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. -->
  
  # BiBert-Subjectivity
  
  This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1481
  - Accuracy: 0.9583
  - F1: 0.9581
  - Mae: 0.0417
  - Accuracy 2: 0.9583
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - 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 | F1     | Mae   | Accuracy 2 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-----:|:----------:|
  | No log        | 1.0   | 112  | 0.1333          | 0.95     | 0.9508 | 0.05  | 0.95       |
  | No log        | 2.0   | 224  | 0.1517          | 0.953    | 0.9531 | 0.047 | 0.953      |
  | No log        | 3.0   | 336  | 0.2219          | 0.951    | 0.9505 | 0.049 | 0.951      |
  | No log        | 4.0   | 448  | 0.2327          | 0.947    | 0.9479 | 0.053 | 0.947      |
  | 0.0865        | 5.0   | 560  | 0.2557          | 0.953    | 0.9528 | 0.047 | 0.953      |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  