readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
    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. -->
  
  # electricidad-small-discriminator-finetuned-usElectionTweets1Jul11Nov-spanish
  
  This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3327
  - Accuracy: 0.7642
  - F1: 0.7642
  
  ## 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: 60
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
  | 0.88          | 1.0   | 1222  | 0.7491          | 0.6943   | 0.6943 |
  | 0.7292        | 2.0   | 2444  | 0.6253          | 0.7544   | 0.7544 |
  | 0.6346        | 3.0   | 3666  | 0.5292          | 0.7971   | 0.7971 |
  | 0.565         | 4.0   | 4888  | 0.4831          | 0.8168   | 0.8168 |
  | 0.4898        | 5.0   | 6110  | 0.4086          | 0.8532   | 0.8532 |
  | 0.4375        | 6.0   | 7332  | 0.3411          | 0.8831   | 0.8831 |
  | 0.3968        | 7.0   | 8554  | 0.2735          | 0.9100   | 0.9100 |
  | 0.3321        | 8.0   | 9776  | 0.2343          | 0.9253   | 0.9253 |
  | 0.3045        | 9.0   | 10998 | 0.1855          | 0.9450   | 0.9450 |
  | 0.2837        | 10.0  | 12220 | 0.1539          | 0.9591   | 0.9591 |
  | 0.2411        | 11.0  | 13442 | 0.1309          | 0.9650   | 0.9650 |
  | 0.2203        | 12.0  | 14664 | 0.1100          | 0.9716   | 0.9716 |
  | 0.1953        | 13.0  | 15886 | 0.1067          | 0.9760   | 0.9760 |
  | 0.1836        | 14.0  | 17108 | 0.0755          | 0.9813   | 0.9813 |
  | 0.1611        | 15.0  | 18330 | 0.0731          | 0.9829   | 0.9829 |
  | 0.1479        | 16.0  | 19552 | 0.0746          | 0.9839   | 0.9839 |
  | 0.138         | 17.0  | 20774 | 0.0516          | 0.9895   | 0.9895 |
  | 0.129         | 18.0  | 21996 | 0.0481          | 0.9903   | 0.9903 |
  | 0.1182        | 19.0  | 23218 | 0.0401          | 0.9926   | 0.9926 |
  | 0.1065        | 20.0  | 24440 | 0.0488          | 0.9895   | 0.9895 |
  | 0.096         | 21.0  | 25662 | 0.0333          | 0.9928   | 0.9928 |
  | 0.0889        | 22.0  | 26884 | 0.0222          | 0.9951   | 0.9951 |
  | 0.0743        | 23.0  | 28106 | 0.0236          | 0.9951   | 0.9951 |
  | 0.0821        | 24.0  | 29328 | 0.0322          | 0.9931   | 0.9931 |
  | 0.0866        | 25.0  | 30550 | 0.0135          | 0.9974   | 0.9974 |
  | 0.0616        | 26.0  | 31772 | 0.0100          | 0.9980   | 0.9980 |
  | 0.0641        | 27.0  | 32994 | 0.0112          | 0.9977   | 0.9977 |
  | 0.0603        | 28.0  | 34216 | 0.0071          | 0.9987   | 0.9987 |
  | 0.0491        | 29.0  | 35438 | 0.0088          | 0.9982   | 0.9982 |
  | 0.0563        | 30.0  | 36660 | 0.0071          | 0.9982   | 0.9982 |
  | 0.0467        | 31.0  | 37882 | 0.0045          | 0.9990   | 0.9990 |
  | 0.0545        | 32.0  | 39104 | 0.0057          | 0.9987   | 0.9987 |
  | 0.0519        | 33.0  | 40326 | 0.0048          | 0.9992   | 0.9992 |
  | 0.0524        | 34.0  | 41548 | 0.0030          | 0.9995   | 0.9995 |
  | 0.044         | 35.0  | 42770 | 0.0046          | 0.9990   | 0.9990 |
  | 0.0442        | 36.0  | 43992 | 0.0029          | 0.9995   | 0.9995 |
  | 0.0352        | 37.0  | 45214 | 0.0035          | 0.9995   | 0.9995 |
  | 0.0348        | 38.0  | 46436 | 0.0029          | 0.9995   | 0.9995 |
  | 0.0295        | 39.0  | 47658 | 0.0023          | 0.9995   | 0.9995 |
  | 0.0289        | 40.0  | 48880 | 0.0035          | 0.9995   | 0.9995 |
  | 0.0292        | 41.0  | 50102 | 0.0023          | 0.9995   | 0.9995 |
  | 0.0259        | 42.0  | 51324 | 0.0027          | 0.9995   | 0.9995 |
  | 0.0217        | 43.0  | 52546 | 0.0031          | 0.9995   | 0.9995 |
  | 0.0278        | 44.0  | 53768 | 0.0018          | 0.9995   | 0.9995 |
  | 0.0254        | 45.0  | 54990 | 0.0023          | 0.9995   | 0.9995 |
  | 0.0164        | 46.0  | 56212 | 0.0016          | 0.9997   | 0.9997 |
  | 0.0277        | 47.0  | 57434 | 0.0027          | 0.9997   | 0.9997 |
  | 0.0158        | 48.0  | 58656 | 0.0029          | 0.9997   | 0.9997 |
  | 0.0178        | 49.0  | 59878 | 0.0023          | 0.9997   | 0.9997 |
  | 0.022         | 50.0  | 61100 | 0.0019          | 0.9997   | 0.9997 |
  | 0.0167        | 51.0  | 62322 | 0.0018          | 0.9997 