readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: finetuning-sentiment-model_withGPU
    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. -->
  
  # finetuning-sentiment-model-10-samples_withGPU
  
  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: 0.3893
  - Accuracy: 0.8744
  - F1: 0.8684
  - Precision: 0.9126
  - Recall: 0.8283
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
  | 0.3631        | 1.0   | 7088  | 0.3622          | 0.8638   | 0.8519 | 0.9334    | 0.7835 |
  | 0.35          | 2.0   | 14176 | 0.3875          | 0.8714   | 0.8622 | 0.9289    | 0.8044 |
  | 0.3262        | 3.0   | 21264 | 0.3893          | 0.8744   | 0.8684 | 0.9126    | 0.8283 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  