readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-tagalog-large-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. -->
  # Description
  - The dataset consists of 148 Filipino storytelling books, 4,523 sentences, 7,118 tokens, and 868 unique tokens.
  - This NER model only supports the Filipino language and does not include proper nouns, verbs, adjectives, and adverbs as of the moment
  - The input must undergo preprocessing. Soon I will upload the code to GitHub for preprocessing the input
  - To replicate the preprocessed input use this example as a guide 
  - Input: "May umaapoy na bahay "
  - Preprocessed Input: "apoy bahay"
  # roberta-tagalog-large-ner-v1
  
  This model is a fine-tuned version of [jcblaise/roberta-tagalog-large](https://huggingface.co/jcblaise/roberta-tagalog-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1866
  - Precision: 0.9546
  - Recall: 0.9557
  - F1: 0.9551
  - Accuracy: 0.9724
  
  ## 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  | 0.2044          | 0.8945    | 0.8920 | 0.8933 | 0.9414   |
  | No log        | 2.0   | 410  | 0.1421          | 0.9410    | 0.9341 | 0.9375 | 0.9625   |
  | 0.2423        | 3.0   | 615  | 0.1485          | 0.9309    | 0.9500 | 0.9403 | 0.9670   |
  | 0.2423        | 4.0   | 820  | 0.1543          | 0.9473    | 0.9505 | 0.9489 | 0.9689   |
  | 0.0154        | 5.0   | 1025 | 0.1749          | 0.9494    | 0.9494 | 0.9494 | 0.9706   |
  | 0.0154        | 6.0   | 1230 | 0.1706          | 0.9459    | 0.9545 | 0.9502 | 0.9713   |
  | 0.0154        | 7.0   | 1435 | 0.1822          | 0.9490    | 0.9522 | 0.9506 | 0.9717   |
  | 0.003         | 8.0   | 1640 | 0.1841          | 0.9529    | 0.9540 | 0.9534 | 0.9723   |
  | 0.003         | 9.0   | 1845 | 0.1870          | 0.9540    | 0.9551 | 0.9545 | 0.9729   |
  | 0.0007        | 10.0  | 2050 | 0.1866          | 0.9546    | 0.9557 | 0.9551 | 0.9724   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  