---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- hate_speech_filipino
metrics:
- accuracy
- f1
model-index:
- name: scenario-non-kd-from-scratch-data-hate_speech_filipino-model-xlm-roberta-base
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hate_speech_filipino
      type: hate_speech_filipino
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7247164461247637
    - name: F1
      type: f1
      value: 0.7256887214504355
---

<!-- 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-non-kd-from-scratch-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: 1.0612
- Accuracy: 0.7247
- F1: 0.7257

## 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  | 0.6595          | 0.6307   | 0.6853 |
| No log        | 0.64  | 200  | 0.5676          | 0.7032   | 0.6620 |
| No log        | 0.96  | 300  | 0.5294          | 0.7358   | 0.7069 |
| No log        | 1.28  | 400  | 0.5112          | 0.7493   | 0.7084 |
| 0.585         | 1.6   | 500  | 0.5554          | 0.7283   | 0.7420 |
| 0.585         | 1.92  | 600  | 0.5201          | 0.7349   | 0.6679 |
| 0.585         | 2.24  | 700  | 0.5838          | 0.7361   | 0.7415 |
| 0.585         | 2.56  | 800  | 0.5693          | 0.7325   | 0.7421 |
| 0.585         | 2.88  | 900  | 0.5469          | 0.7517   | 0.7128 |
| 0.3954        | 3.19  | 1000 | 0.6406          | 0.7509   | 0.7361 |
| 0.3954        | 3.51  | 1100 | 0.5834          | 0.7401   | 0.7158 |
| 0.3954        | 3.83  | 1200 | 0.6038          | 0.7538   | 0.7324 |
| 0.3954        | 4.15  | 1300 | 0.7079          | 0.7436   | 0.7230 |
| 0.3954        | 4.47  | 1400 | 0.7422          | 0.7474   | 0.7182 |
| 0.2591        | 4.79  | 1500 | 0.6393          | 0.75     | 0.7307 |
| 0.2591        | 5.11  | 1600 | 0.7890          | 0.7481   | 0.7307 |
| 0.2591        | 5.43  | 1700 | 1.0788          | 0.7332   | 0.6651 |
| 0.2591        | 5.75  | 1800 | 0.8036          | 0.7353   | 0.7157 |
| 0.2591        | 6.07  | 1900 | 1.0868          | 0.7474   | 0.7167 |
| 0.1729        | 6.39  | 2000 | 1.3150          | 0.7441   | 0.7027 |
| 0.1729        | 6.71  | 2100 | 1.0097          | 0.7351   | 0.7268 |
| 0.1729        | 7.03  | 2200 | 1.0160          | 0.7389   | 0.7074 |
| 0.1729        | 7.35  | 2300 | 1.0612          | 0.7247   | 0.7257 |


### Framework versions

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