readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-finetuned-gest-pred
    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. -->
  
  # roberta-finetuned-gest-pred
  
  This model is a fine-tuned version of [xlm-roberta-large-finetuned-conll03-english](https://huggingface.co/xlm-roberta-large-finetuned-conll03-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1049
  - Precision: 0.6712
  - Recall: 0.745
  - F1: 0.7062
  - Accuracy: 0.8288
  
  ## 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: 16
  - eval_batch_size: 16
  - 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 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 147  | 0.8821          | 0.4307    | 0.59   | 0.4979 | 0.7390   |
  | No log        | 2.0   | 294  | 0.7175          | 0.4731    | 0.66   | 0.5511 | 0.8008   |
  | No log        | 3.0   | 441  | 0.6889          | 0.5754    | 0.725  | 0.6416 | 0.8163   |
  | 0.9454        | 4.0   | 588  | 0.7699          | 0.5504    | 0.71   | 0.6201 | 0.7931   |
  | 0.9454        | 5.0   | 735  | 0.7815          | 0.6494    | 0.75   | 0.6961 | 0.8282   |
  | 0.9454        | 6.0   | 882  | 0.8929          | 0.6754    | 0.77   | 0.7196 | 0.8312   |
  | 0.2156        | 7.0   | 1029 | 0.9394          | 0.6468    | 0.76   | 0.6989 | 0.8234   |
  | 0.2156        | 8.0   | 1176 | 1.0083          | 0.6564    | 0.745  | 0.6979 | 0.8157   |
  | 0.2156        | 9.0   | 1323 | 1.0866          | 0.6549    | 0.74   | 0.6948 | 0.8199   |
  | 0.2156        | 10.0  | 1470 | 1.1049          | 0.6712    | 0.745  | 0.7062 | 0.8288   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  