metadata
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
roberta-tagalog-base-philippine-elections-2016-2022-hate-speech
This model is a fine-tuned version of 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 consisting of the 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
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: 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