---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-tagalog-profanity-classifier
  results: []
base_model: https://huggingface.co/jcblaise/roberta-tagalog-base
---

<!-- 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