readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-expression_epoch5
    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. -->
  
  # bert-finetuned-expression_epoch5
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5897
  - Precision: 0.5835
  - Recall: 0.5688
  - F1: 0.5760
  - Accuracy: 0.8344
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - 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 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 218  | 0.5185          | 0.5076    | 0.5034 | 0.5055 | 0.8207   |
  | No log        | 2.0   | 436  | 0.4972          | 0.4948    | 0.5638 | 0.5271 | 0.8177   |
  | 0.5193        | 3.0   | 654  | 0.5128          | 0.5838    | 0.5554 | 0.5692 | 0.8390   |
  | 0.5193        | 4.0   | 872  | 0.5665          | 0.5612    | 0.6074 | 0.5834 | 0.8224   |
  | 0.2063        | 5.0   | 1090 | 0.5897          | 0.5835    | 0.5688 | 0.5760 | 0.8344   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  