readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: xlmroberta-2nd-finetune-epru
    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. -->
  
  # xlmroberta-2nd-finetune-epru
  
  This model is a fine-tuned version of [mmillet/xlm-roberta-base_single_finetuned_on_cedr_augmented](https://huggingface.co/mmillet/xlm-roberta-base_single_finetuned_on_cedr_augmented) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3666
  - Accuracy: 0.9325
  - F1: 0.9329
  - Precision: 0.9352
  - Recall: 0.9325
  
  ## 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: 0.0001
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.4757        | 1.0   | 12   | 0.2387          | 0.9264   | 0.9267 | 0.9333    | 0.9264 |
  | 0.3086        | 2.0   | 24   | 0.3059          | 0.9141   | 0.9143 | 0.9270    | 0.9141 |
  | 0.2151        | 3.0   | 36   | 0.2394          | 0.9202   | 0.9214 | 0.9266    | 0.9202 |
  | 0.1629        | 4.0   | 48   | 0.3025          | 0.9325   | 0.9332 | 0.9385    | 0.9325 |
  | 0.0911        | 5.0   | 60   | 0.2597          | 0.9387   | 0.9390 | 0.9434    | 0.9387 |
  | 0.0455        | 6.0   | 72   | 0.3476          | 0.9387   | 0.9389 | 0.9400    | 0.9387 |
  | 0.0521        | 7.0   | 84   | 0.3630          | 0.9325   | 0.9329 | 0.9356    | 0.9325 |
  | 0.029         | 8.0   | 96   | 0.3100          | 0.9509   | 0.9513 | 0.9531    | 0.9509 |
  | 0.0379        | 9.0   | 108  | 0.3044          | 0.9448   | 0.9450 | 0.9455    | 0.9448 |
  | 0.0363        | 10.0  | 120  | 0.4181          | 0.9141   | 0.9147 | 0.9191    | 0.9141 |
  | 0.0165        | 11.0  | 132  | 0.3666          | 0.9325   | 0.9329 | 0.9352    | 0.9325 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  