readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: sentiment-10Epochs-3
    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. -->
  
  # sentiment-10Epochs-3
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7703
  - Accuracy: 0.8568
  - F1: 0.8526
  - Precision: 0.8787
  - Recall: 0.8279
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.3637        | 1.0   | 7088  | 0.3830          | 0.8571   | 0.8418 | 0.9429    | 0.7603 |
  | 0.37          | 2.0   | 14176 | 0.4128          | 0.8676   | 0.8582 | 0.9242    | 0.8010 |
  | 0.325         | 3.0   | 21264 | 0.4656          | 0.8737   | 0.8664 | 0.9189    | 0.8197 |
  | 0.2948        | 4.0   | 28352 | 0.4575          | 0.8703   | 0.8652 | 0.9007    | 0.8324 |
  | 0.3068        | 5.0   | 35440 | 0.4751          | 0.8705   | 0.8653 | 0.9016    | 0.8317 |
  | 0.2945        | 6.0   | 42528 | 0.5509          | 0.8668   | 0.8618 | 0.8956    | 0.8305 |
  | 0.2568        | 7.0   | 49616 | 0.6201          | 0.8632   | 0.8567 | 0.8994    | 0.8178 |
  | 0.2107        | 8.0   | 56704 | 0.6836          | 0.8614   | 0.8576 | 0.8819    | 0.8346 |
  | 0.1966        | 9.0   | 63792 | 0.7030          | 0.8583   | 0.8532 | 0.8848    | 0.8238 |
  | 0.1675        | 10.0  | 70880 | 0.7703          | 0.8568   | 0.8526 | 0.8787    | 0.8279 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  