readme: ---
  license: gpl-3.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: electra-tagalog-base-uncased-discriminator-ner-v1
    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. -->
  
  # electra-tagalog-base-uncased-discriminator-ner-v1
  
  This model is a fine-tuned version of [jcblaise/electra-tagalog-base-uncased-discriminator](https://huggingface.co/jcblaise/electra-tagalog-base-uncased-discriminator) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2224
  - Precision: 0.9458
  - Recall: 0.9221
  - F1: 0.9338
  - Accuracy: 0.9584
  
  ## 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   | 205  | 0.5797          | 0.7108    | 0.5981 | 0.6496 | 0.8354   |
  | No log        | 2.0   | 410  | 0.3364          | 0.8057    | 0.8323 | 0.8188 | 0.9069   |
  | 0.4976        | 3.0   | 615  | 0.2628          | 0.8653    | 0.8727 | 0.8689 | 0.9317   |
  | 0.4976        | 4.0   | 820  | 0.2323          | 0.9173    | 0.8823 | 0.8994 | 0.9440   |
  | 0.1335        | 5.0   | 1025 | 0.2212          | 0.9154    | 0.9102 | 0.9128 | 0.9473   |
  | 0.1335        | 6.0   | 1230 | 0.2235          | 0.9335    | 0.9102 | 0.9217 | 0.9524   |
  | 0.1335        | 7.0   | 1435 | 0.2207          | 0.9169    | 0.9221 | 0.9195 | 0.9541   |
  | 0.0509        | 8.0   | 1640 | 0.2214          | 0.9414    | 0.9221 | 0.9316 | 0.9582   |
  | 0.0509        | 9.0   | 1845 | 0.2266          | 0.9416    | 0.9255 | 0.9335 | 0.9571   |
  | 0.0301        | 10.0  | 2050 | 0.2224          | 0.9458    | 0.9221 | 0.9338 | 0.9584   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  