readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: beto-sentiment-analysis-finetuned-ner
    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. -->
  
  # beto-sentiment-analysis-finetuned-ner
  
  This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9250
  - Precision: 0.5603
  - Recall: 0.6436
  - F1: 0.5991
  - Accuracy: 0.9863
  
  ## 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: 8e-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: 24
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.4102        | 1.0   | 3    | 1.2732          | 0.0455    | 0.0198 | 0.0276 | 0.9723   |
  | 0.7776        | 2.0   | 6    | 0.9025          | 0.1056    | 0.1485 | 0.1235 | 0.9663   |
  | 0.6861        | 3.0   | 9    | 0.7874          | 0.1176    | 0.1980 | 0.1476 | 0.9694   |
  | 0.2837        | 4.0   | 12   | 0.8528          | 0.1067    | 0.2376 | 0.1472 | 0.9534   |
  | 0.3182        | 5.0   | 15   | 0.7798          | 0.2360    | 0.3762 | 0.2901 | 0.9729   |
  | 0.1673        | 6.0   | 18   | 0.8645          | 0.1461    | 0.2574 | 0.1864 | 0.9604   |
  | 0.2065        | 7.0   | 21   | 0.8130          | 0.2941    | 0.5446 | 0.3819 | 0.9765   |
  | 0.0794        | 8.0   | 24   | 0.6841          | 0.4276    | 0.6139 | 0.5041 | 0.9822   |
  | 0.0543        | 9.0   | 27   | 0.7113          | 0.4104    | 0.5446 | 0.4681 | 0.9815   |
  | 0.0278        | 10.0  | 30   | 0.7865          | 0.4565    | 0.6238 | 0.5272 | 0.9833   |
  | 0.0598        | 11.0  | 33   | 0.8356          | 0.4155    | 0.5842 | 0.4856 | 0.9824   |
  | 0.0108        | 12.0  | 36   | 0.8104          | 0.4460    | 0.6139 | 0.5167 | 0.9826   |
  | 0.0235        | 13.0  | 39   | 0.7986          | 0.5194    | 0.6634 | 0.5826 | 0.9844   |
  | 0.0134        | 14.0  | 42   | 0.8175          | 0.6182    | 0.6733 | 0.6445 | 0.9865   |
  | 0.0124        | 15.0  | 45   | 0.8575          | 0.6036    | 0.6634 | 0.6321 | 0.9875   |
  | 0.0049        | 16.0  | 48   | 0.8822          | 0.6019    | 0.6436 | 0.6220 | 0.9871   |
  | 0.0097        | 17.0  | 51   | 0.8696          | 0.5556    | 0.6436 | 0.5963 | 0.9862   |
  | 0.0067        | 18.0  | 54   | 0.8728          | 0.5410    | 0.6535 | 0.5919 | 0.9859   |
  | 0.0045        | 19.0  | 57   | 0.8807          | 0.5159    | 0.6436 | 0.5727 | 0.9848   |
  | 0.004         | 20.0  | 60   | 0.8938          | 0.52      | 0.6436 | 0.5752 | 0.9851   |
  | 0.0038        | 21.0  | 63   | 0.9108          | 0.5203    | 0.6337 | 0.5714 | 0.9852   |
  | 0.004         | 22.0  | 66   | 0.9243          | 0.5702    | 0.6436 | 0.6047 | 0.9864   |
  | 0.0106        | 23.0  | 69   | 0.9261          | 0.5702    | 0.6436 | 0.6047 | 0.9865   |
  | 0.004         | 24.0  | 72   | 0.9250          | 0.5603    | 0.6436 | 0.5991 | 0.9863   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  