readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: beto-sentiment-analysis-finetuned
    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
  
  This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4406
  - Accuracy: 0.7757
  - F1: 0.7773
  
  ## 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: 1e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 3380
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 35
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 2.5384        | 1.45  | 100  | 2.1387          | 0.2831   | 0.3049 |
  | 2.1562        | 2.9   | 200  | 1.6375          | 0.4596   | 0.4873 |
  | 1.5805        | 4.35  | 300  | 1.4332          | 0.5993   | 0.6377 |
  | 1.4242        | 5.8   | 400  | 1.3355          | 0.6544   | 0.6565 |
  | 1.1192        | 7.25  | 500  | 1.2845          | 0.6765   | 0.6854 |
  | 0.9617        | 8.7   | 600  | 1.1512          | 0.6912   | 0.7167 |
  | 0.829         | 10.14 | 700  | 1.0676          | 0.6801   | 0.7079 |
  | 0.6889        | 11.59 | 800  | 1.0715          | 0.7022   | 0.7323 |
  | 0.59          | 13.04 | 900  | 1.1065          | 0.7316   | 0.7392 |
  | 0.5129        | 14.49 | 1000 | 1.1585          | 0.7059   | 0.7382 |
  | 0.4278        | 15.94 | 1100 | 1.1106          | 0.75     | 0.7582 |
  | 0.3728        | 17.39 | 1200 | 1.1561          | 0.7537   | 0.7679 |
  | 0.3142        | 18.84 | 1300 | 1.1755          | 0.7537   | 0.7667 |
  | 0.275         | 20.29 | 1400 | 1.2095          | 0.7574   | 0.7707 |
  | 0.2251        | 21.74 | 1500 | 1.3647          | 0.7574   | 0.7674 |
  | 0.2175        | 23.19 | 1600 | 1.3127          | 0.7537   | 0.7635 |
  | 0.1923        | 24.64 | 1700 | 1.3494          | 0.7794   | 0.7760 |
  | 0.1753        | 26.09 | 1800 | 1.4221          | 0.7684   | 0.7658 |
  | 0.1484        | 27.54 | 1900 | 1.3572          | 0.7684   | 0.7727 |
  | 0.1455        | 28.99 | 2000 | 1.4063          | 0.7757   | 0.7747 |
  | 0.131         | 30.43 | 2100 | 1.3754          | 0.7721   | 0.7730 |
  | 0.1125        | 31.88 | 2200 | 1.4302          | 0.7757   | 0.7740 |
  | 0.1203        | 33.33 | 2300 | 1.4146          | 0.7684   | 0.7714 |
  | 0.1083        | 34.78 | 2400 | 1.4406          | 0.7757   | 0.7773 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  