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---
license: mit
language:
- tl
- en
metrics:
- precision
- recall
- f1
library_name: transformers
pipeline_tag: text-classification
tags:
- nlp
- language
datasets:
- syke9p3/multilabel-tagalog-hate-speech
---
# Multilabel Classification of Tagalog Hate Speech using Bidirectional Encoder Representations from Transformers (BERT)
Multilabel Tagalog Hate Speech Classifier using Bidirectional Encoder Representation From Transformers. Classifies Tagalog Hate Speech with labels Age, Gender, Physical, Race, Religion, and Others.
Hate speech encompasses expressions and behaviors that promote hatred, discrimination, prejudice, or violence against individuals or groups based on specific attributes, with consequences ranging from physical harm to psychological distress, making it a critical issue in today's society.
Bidirectional Encoder Representations from Transformers (BERT) is pre-trained deep learning model used in this study that uses a transformer architecture to generate word embeddings, capturing both left and right context information, and can be fine-tuned for various natural language processing tasks. For this project, we fine-tuned [Jiang et. al.'s pre-trained BERT Tagalog Base Uncased model](https://huggingface.co./GKLMIP/bert-tagalog-base-uncased) in the task of multilabel hate speech classification.
## 👥 Proponents
- Saya-ang, Kenth G. ([@syke9p3](https://github.com/syke9p3))
- Gozum, Denise Julianne S. ([@Xenoxianne](https://github.com/Xenoxianne))
- Hamor, Mary Grizelle D. ([@mnemoria](https://github.com/mnemoria))
- Mabansag, Ria Karen B. ([@riavx](https://github.com/riavx))
## 📋 About the Thesis
### 📄 Abstract
Hate speech promotes hatred, discrimination, prejudice, or violence against individuals or groups based on specific attributes, leading to physical and psychological harm. This study addresses the prevalence of hate speech on social media by proposing a Tagalog hate speech multilabel classification model. Using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, the study classifies hate speech into categories such as Age, Gender, Physical, Race, Religion, and Others. Analyzing 2,116 manually annotated social media posts from Facebook, Reddit, and Twitter, the model achieved varying precision, recall, and f-measure scores across categories, with an overall hamming loss of 3.79%.
### 🔠 Keywords
*Bidirectional Encoder Representations from Transformers; Hate Speech; Multilabel Classification; Social Media; Tagalog; Polytechnic University of the Philippines; Bachelor of Science in Computer Science*
### 💻 Languages and Technologies
#### Model
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://jupyter.org/)
[](https://huggingface.co./)
[](https://huggingface.co./)
[](https://huggingface.co./)
[](https://huggingface.co./)
#### User Interface
[](https://en.wikipedia.org/wiki/HTML5)
[](https://en.wikipedia.org/wiki/CSS)
[](https://en.wikipedia.org/wiki/JavaScript)
[](https://flask.palletsprojects.com/en/3.0.x/)
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### 🖼 Screenshots
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<img src="./Screenshot1.jpg"/>
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### 🎨 Labels
**Multilabel Classification** refers to the task of assigning one or more relevant labels to each text. Each text can be associated with multiple categories simultaneously, such as Age, Gender, Physical, Race, Religion, or Others.
Target of hate speech pertains to one's age bracket or demographic
Target of hate speech pertains to gender identity, sex, or sexual orientation
Target of hate speech pertains to physical attributes or disability
Target of hate speech pertains to racial background, ethnicity, or nationality
Target of hate speech pertains to affiliation, belief, and faith to any of the existing religious or non-religious groups
Target of hate speech pertains other topic that is not relevant as Age, Gender, Physical, Race, or Religion
### 📜 Dataset
2,116 scraped social media posts from Facebook, Reddit, and Twitter manually annotated for determining labels for each data split into three sets:
| Dataset | Number of Posts | Percentage |
|----------------|-----------------|------------|
| Training Set | 1,267 | 60% |
| Validation Set | 212 | 10% |
| Testing Set | 633 | 30% |
### 🔢 Results
The testing set containing 633 annotated hate speech data used to analyze performance of the model in its ability to classify the hate speech input according to different label in terms of Precision, Recall, F-Measure, and overall hamming loss.
| Label | Precision | Recall | F-Measure |
|----------|-----------|--------|-----------|
| Age | 97.12% | 90.18% | 93.52% |
| Gender | 93.23% | 94.66% | 93.94% |
| Physical | 92.23% | 71.43% | 80.51% |
| Race | 90.99% | 88.60% | 89.78% |
| Religion | 99.03% | 94.44% | 96.68% |
| Others | 83.74% | 85.12% | 84.43% |
**Overall Hamming Loss:** 3.79% |