readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: Lilt_xlmRoberta-finetuned
    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. -->
  
  # Lilt_xlmRoberta-finetuned
  
  This model is a fine-tuned version of [RobertoFont/lilt-xlm-roberta-base](https://huggingface.co/RobertoFont/lilt-xlm-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8585
  - Accuracy: 0.756
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 32
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 78   | 1.6469          | 0.524    |
  | No log        | 2.0   | 156  | 1.2372          | 0.644    |
  | No log        | 3.0   | 234  | 1.0487          | 0.686    |
  | No log        | 4.0   | 312  | 0.9515          | 0.723    |
  | No log        | 5.0   | 390  | 0.9319          | 0.735    |
  | No log        | 6.0   | 468  | 0.8965          | 0.742    |
  | No log        | 7.0   | 546  | 0.8809          | 0.754    |
  | No log        | 8.0   | 624  | 0.8597          | 0.756    |
  | No log        | 9.0   | 702  | 0.8673          | 0.758    |
  | No log        | 10.0  | 780  | 0.8585          | 0.756    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  