---
license: cc-by-sa-4.0
base_model: jcblaise/roberta-tagalog-base
tags:
- generated_from_trainer
- tagalog
- filipino
- twitter
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-tagalog-base-philippine-elections-2016-2022-hate-speech
  results: []
datasets:
- hate_speech_filipino
- mapsoriano/2016_2022_hate_speech_filipino
language:
- tl
- en
---

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# roberta-tagalog-base-philippine-elections-2016-2022-hate-speech

This model is a fine-tuned version of [jcblaise/roberta-tagalog-base](https://huggingface.co/jcblaise/roberta-tagalog-base) for the task of Text Classification, classifying hate and non-hate tweets.

The model was fine-tuned on a combined dataset [mapsoriano/2016_2022_hate_speech_filipino](https://huggingface.co/datasets/mapsoriano/2016_2022_hate_speech_filipino) consisting of
the [hate_speech_filipino](https://huggingface.co/datasets/hate_speech_filipino) dataset and a newly crawled 2022 Philippine Presidential Elections-related Tweets Hate Speech Dataset.

It achieves the following results on the evaluation (validation) set:
- Loss: 0.3574
- Accuracy: 0.8743

It achieves the following results on the test set:
- Accuracy: 0.8783
- Precision: 0.8563
- Recall: 0.9077
- F1: 0.8813

Feel free to connect via [LinkedIn](https://www.linkedin.com/in/map-soriano/) for further information on this model or on the study that it was used on.

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## Intended uses & limitations

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## Training and evaluation data

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## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3423        | 1.0   | 1361 | 0.3167          | 0.8693   |
| 0.2194        | 2.0   | 2722 | 0.3574          | 0.8743   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3