---
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