---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- hate_speech_filipino
metrics:
- accuracy
- f1
model-index:
- name: scenario-kd-from-scratch-silver-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-kd-from-scratch-silver-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: 1.1354
- Accuracy: 0.7665
- F1: 0.7412

## 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  | 2.4557          | 0.6656   | 0.6952 |
| No log        | 0.64  | 200  | 2.0812          | 0.7063   | 0.7186 |
| No log        | 0.96  | 300  | 1.9137          | 0.7079   | 0.7300 |
| No log        | 1.28  | 400  | 1.8171          | 0.7172   | 0.7401 |
| 2.5353        | 1.6   | 500  | 1.7305          | 0.7462   | 0.6959 |
| 2.5353        | 1.92  | 600  | 2.3251          | 0.6645   | 0.7221 |
| 2.5353        | 2.24  | 700  | 1.5004          | 0.7571   | 0.7299 |
| 2.5353        | 2.56  | 800  | 1.7161          | 0.7431   | 0.6752 |
| 2.5353        | 2.88  | 900  | 1.3750          | 0.7519   | 0.7400 |
| 1.5143        | 3.19  | 1000 | 1.6104          | 0.7561   | 0.6968 |
| 1.5143        | 3.51  | 1100 | 1.4419          | 0.7561   | 0.7104 |
| 1.5143        | 3.83  | 1200 | 1.3306          | 0.7450   | 0.7496 |
| 1.5143        | 4.15  | 1300 | 1.4285          | 0.7668   | 0.7352 |
| 1.5143        | 4.47  | 1400 | 1.3335          | 0.7576   | 0.7552 |
| 1.1029        | 4.79  | 1500 | 1.3649          | 0.7394   | 0.7487 |
| 1.1029        | 5.11  | 1600 | 1.5830          | 0.7224   | 0.7434 |
| 1.1029        | 5.43  | 1700 | 1.2794          | 0.7592   | 0.7560 |
| 1.1029        | 5.75  | 1800 | 1.2877          | 0.7547   | 0.7165 |
| 1.1029        | 6.07  | 1900 | 1.2428          | 0.7637   | 0.7325 |
| 0.8948        | 6.39  | 2000 | 1.2774          | 0.7387   | 0.7494 |
| 0.8948        | 6.71  | 2100 | 1.2324          | 0.7628   | 0.7354 |
| 0.8948        | 7.03  | 2200 | 1.3675          | 0.7387   | 0.7505 |
| 0.8948        | 7.35  | 2300 | 1.2021          | 0.7670   | 0.7490 |
| 0.8948        | 7.67  | 2400 | 1.3012          | 0.7682   | 0.7348 |
| 0.7714        | 7.99  | 2500 | 1.2338          | 0.7580   | 0.7210 |
| 0.7714        | 8.31  | 2600 | 1.2189          | 0.7628   | 0.7519 |
| 0.7714        | 8.63  | 2700 | 1.2962          | 0.7410   | 0.7526 |
| 0.7714        | 8.95  | 2800 | 1.3151          | 0.7675   | 0.7416 |
| 0.7714        | 9.27  | 2900 | 1.1539          | 0.7616   | 0.7528 |
| 0.7096        | 9.58  | 3000 | 1.3696          | 0.7561   | 0.7523 |
| 0.7096        | 9.9   | 3100 | 1.2055          | 0.7514   | 0.7533 |
| 0.7096        | 10.22 | 3200 | 1.1354          | 0.7665   | 0.7412 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3