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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- hate_speech_filipino
metrics:
- accuracy
- f1
model-index:
- name: scenario-teacher-data-hate_speech_filipino-model-xlm-roberta-base
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. -->
# scenario-teacher-data-hate_speech_filipino-model-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the hate_speech_filipino dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0437
- Accuracy: 0.7817
- F1: 0.7687
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.32 | 100 | 0.5923 | 0.6966 | 0.7200 |
| No log | 0.64 | 200 | 0.5214 | 0.7450 | 0.7202 |
| No log | 0.96 | 300 | 0.5052 | 0.7554 | 0.7372 |
| No log | 1.28 | 400 | 0.5106 | 0.7649 | 0.7442 |
| 0.5444 | 1.6 | 500 | 0.5499 | 0.7559 | 0.7564 |
| 0.5444 | 1.92 | 600 | 0.4998 | 0.7566 | 0.6862 |
| 0.5444 | 2.24 | 700 | 0.5269 | 0.7760 | 0.7653 |
| 0.5444 | 2.56 | 800 | 0.5129 | 0.7836 | 0.7716 |
| 0.5444 | 2.88 | 900 | 0.5132 | 0.7668 | 0.7070 |
| 0.3971 | 3.19 | 1000 | 0.5680 | 0.7805 | 0.7510 |
| 0.3971 | 3.51 | 1100 | 0.5999 | 0.7781 | 0.7696 |
| 0.3971 | 3.83 | 1200 | 0.6097 | 0.7632 | 0.7674 |
| 0.3971 | 4.15 | 1300 | 0.6476 | 0.7795 | 0.7573 |
| 0.3971 | 4.47 | 1400 | 0.6461 | 0.7843 | 0.7629 |
| 0.2704 | 4.79 | 1500 | 0.6329 | 0.7786 | 0.7634 |
| 0.2704 | 5.11 | 1600 | 0.7783 | 0.7729 | 0.7396 |
| 0.2704 | 5.43 | 1700 | 0.6963 | 0.7750 | 0.7285 |
| 0.2704 | 5.75 | 1800 | 0.7857 | 0.7892 | 0.7680 |
| 0.2704 | 6.07 | 1900 | 0.6921 | 0.7762 | 0.7655 |
| 0.215 | 6.39 | 2000 | 0.7196 | 0.7722 | 0.7499 |
| 0.215 | 6.71 | 2100 | 1.0259 | 0.7691 | 0.7671 |
| 0.215 | 7.03 | 2200 | 1.1496 | 0.7767 | 0.7640 |
| 0.215 | 7.35 | 2300 | 1.0437 | 0.7817 | 0.7687 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
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