readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: xlm-roberta-large-finetuned-TRAC-DS
    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. -->
  
  # xlm-roberta-large-finetuned-TRAC-DS
  
  This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0992
  - Accuracy: 0.3342
  - Precision: 0.1114
  - Recall: 0.3333
  - F1: 0.1670
  
  ## 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: 4.1187640010910775e-05
  - train_batch_size: 4
  - eval_batch_size: 8
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.1358        | 0.25  | 612  | 1.1003          | 0.4436   | 0.1479    | 0.3333 | 0.2049 |
  | 1.1199        | 0.5   | 1224 | 1.1130          | 0.4436   | 0.1479    | 0.3333 | 0.2049 |
  | 1.1221        | 0.75  | 1836 | 1.0992          | 0.3342   | 0.1114    | 0.3333 | 0.1670 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.1+cu111
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  