readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: tulio-cased
    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. -->
  
  # tulio-cased
  
  This model is a fine-tuned version of [jorgeortizfuentes/tulio-cased](https://huggingface.co/jorgeortizfuentes/tulio-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1933
  - Accuracy: 0.7472
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 16
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 40.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.4272        | 0.53  | 1000  | 1.3091          | 0.7239   |
  | 1.4209        | 1.07  | 2000  | 1.3182          | 0.7230   |
  | 1.4025        | 1.6   | 3000  | 1.3050          | 0.7242   |
  | 1.3956        | 2.14  | 4000  | 1.3096          | 0.7238   |
  | 1.3854        | 2.67  | 5000  | 1.3074          | 0.7250   |
  | 1.3704        | 3.2   | 6000  | 1.3082          | 0.7238   |
  | 1.3673        | 3.74  | 7000  | 1.2999          | 0.7268   |
  | 1.3463        | 4.27  | 8000  | 1.2969          | 0.7265   |
  | 1.3553        | 4.81  | 9000  | 1.2960          | 0.7273   |
  | 1.328         | 5.34  | 10000 | 1.2979          | 0.7272   |
  | 1.3412        | 5.87  | 11000 | 1.2907          | 0.7278   |
  | 1.3156        | 6.41  | 12000 | 1.2949          | 0.7268   |
  | 1.3256        | 6.94  | 13000 | 1.2884          | 0.7279   |
  | 1.3052        | 7.47  | 14000 | 1.2870          | 0.7284   |
  | 1.3055        | 8.01  | 15000 | 1.2884          | 0.7284   |
  | 1.2942        | 8.54  | 16000 | 1.2855          | 0.7296   |
  | 1.2894        | 9.08  | 17000 | 1.2870          | 0.7296   |
  | 1.2722        | 9.61  | 18000 | 1.2777          | 0.7316   |
  | 1.2752        | 10.14 | 19000 | 1.2814          | 0.7295   |
  | 1.2715        | 10.68 | 20000 | 1.2847          | 0.7292   |
  | 1.2598        | 11.21 | 21000 | 1.2659          | 0.7331   |
  | 1.2565        | 11.75 | 22000 | 1.2656          | 0.7322   |
  | 1.2449        | 12.28 | 23000 | 1.2709          | 0.7329   |
  | 1.2481        | 12.81 | 24000 | 1.2666          | 0.7317   |
  | 1.2331        | 13.35 | 25000 | 1.2636          | 0.7334   |
  | 1.2256        | 13.88 | 26000 | 1.2584          | 0.7336   |
  | 1.2175        | 14.42 | 27000 | 1.2660          | 0.7325   |
  | 1.2315        | 14.95 | 28000 | 1.2572          | 0.7351   |
  | 1.2153        | 15.48 | 29000 | 1.2538          | 0.7347   |
  | 1.2166        | 16.02 | 30000 | 1.2599          | 0.7332   |
  | 1.1978        | 16.55 | 31000 | 1.2507          | 0.7358   |
  | 1.2084        | 17.08 | 32000 | 1.2613          | 0.7342   |
  | 1.1887        | 17.62 | 33000 | 1.2591          | 0.7349   |
  | 1.187         | 18.15 | 34000 | 1.2495          | 0.7361   |
  | 1.1817        | 18.69 | 35000 | 1.2467          | 0.7363   |
  | 1.1738        | 19.22 | 36000 | 1.2479          | 0.7362   |
  | 1.1693        | 19.75 | 37000 | 1.2472          | 0.7367   |
  | 1.1628        | 20.29 | 38000 | 1.2422          | 0.7371   |
  | 1.1715        | 20.82 | 39000 | 1.2453          | 0.7367   |
  | 1.1527        | 21.36 | 40000 | 1.2412          | 0.7383   |
  | 1.1542        | 21.89 | 41000 | 1.2319          | 0.7391   |
  | 1.149         | 22.42 | 42000 | 1.2361          | 0.7383   |
  | 1.1509        | 22.96 | 43000 | 1.2356          | 0.7381   |
  | 1.1417        | 23.49 | 44000 | 1.2380          | 0.7384   |
  | 1.143         | 24.03 | 45000 | 1.2396          | 0.7383   |
  | 1.1321        | 24.56 | 46000 | 1.2279          | 0.7400   |
  | 1.1352        | 25.09 | 47000 | 1.2207          | 0.7404   |
  | 1.1286        | 25.63 | 48000 | 1.2318          | 0.7397   |
  | 1.1203        | 26.16 | 49000 | 1.2222          | 0.7412   |
  | 1.1219        | 26.7  | 50000 | 1.2298          | 0.7397   |
  | 1.1135        | 27.23 | 51000 | 1.2218          | 0.7411   |
  | 1.1139        | 27.76 | 52000 | 1.2155          | 0.7424   |
  | 1.0923        | 28.3  | 53000 | 1.2239          | 0.7415   |
  | 1.1067        | 28.83 | 54000 | 1.2193          | 0.7416   |
  | 1.0923        | 29.36 | 55000 | 1.2212          | 0.7427   |
  | 1.1038        | 29.9  | 56000 | 1.2171          | 0.7424   |
  | 1.0967        | 30.43 | 57000 | 1.2125          | 0.7438   |
  | 1.0856        | 30.97 | 58000 | 1.2099          | 0.7439   |
  | 1.0856        | 31.5  | 59000 | 1.2075          | 0.7449   |
  | 1.0923        | 32.03 | 60