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 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
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