| | --- |
| | license: cc-by-sa-4.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | base_model: jcblaise/roberta-tagalog-base |
| | model-index: |
| | - name: roberta-tagalog-profanity-classifier |
| | 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. --> |
| |
|
| | # roberta-tagalog-profanity-classifier |
| |
|
| | This model is a fine-tuned version of [jcblaise/roberta-tagalog-base](https://huggingface.co/jcblaise/roberta-tagalog-base) on [mginoben/tagalog-profanity-dataset](https://huggingface.co/datasets/mginoben/tagalog-profanity-dataset) dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.3019 |
| | - Accuracy: 0.8898 |
| | - Precision: 0.8523 |
| | - Recall: 0.8944 |
| | - F1: 0.8728 |
| |
|
| | ## Model description |
| |
|
| | The Model classifies tagalog texts that contains profanities as either Abusive or Non-Abusive. |
| |
|
| | It only classifies texts with the following profanities: |
| | - bobo |
| | - bwiset |
| | - gago |
| | - kupal |
| | - pakshet |
| | - pakyu |
| | - pucha |
| | - punyeta |
| | - puta |
| | - putangina |
| | - tanga |
| | - tangina |
| | - tarantado |
| | - ulol |
| |
|
| | ## Intended uses & limitations |
| |
|
| | For content moderation accross different social medias |
| |
|
| | ## Training and evaluation data |
| |
|
| | - Training: 11,110 |
| | - Validation: 2,778 |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 1e-05 |
| | - train_batch_size: 64 |
| | - eval_batch_size: 64 |
| | - 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 | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | No log | 1.0 | 174 | 0.3006 | 0.8776 | 0.8620 | 0.8458 | 0.8538 | |
| | | No log | 2.0 | 348 | 0.2899 | 0.8834 | 0.8801 | 0.8382 | 0.8586 | |
| | | 0.2993 | 3.0 | 522 | 0.2869 | 0.8873 | 0.8491 | 0.8918 | 0.8700 | |
| | | 0.2993 | 4.0 | 696 | 0.3019 | 0.8898 | 0.8523 | 0.8944 | 0.8728 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| |
|
