---
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-gold-silver-data-hate_speech_filipino-model-xlm-roberta
  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-gold-silver-data-hate_speech_filipino-model-xlm-roberta

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: 1.7729
- Accuracy: 0.7559
- F1: 0.7334

## 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  | 3.1640          | 0.6628   | 0.6705 |
| No log        | 0.64  | 200  | 2.7957          | 0.7136   | 0.6553 |
| No log        | 0.96  | 300  | 2.4898          | 0.7141   | 0.7235 |
| No log        | 1.28  | 400  | 2.3972          | 0.7353   | 0.6774 |
| 3.1684        | 1.6   | 500  | 2.1905          | 0.7476   | 0.7243 |
| 3.1684        | 1.92  | 600  | 2.1041          | 0.7481   | 0.7406 |
| 3.1684        | 2.24  | 700  | 2.1459          | 0.7481   | 0.7084 |
| 3.1684        | 2.56  | 800  | 2.3199          | 0.7457   | 0.6848 |
| 3.1684        | 2.88  | 900  | 2.0306          | 0.7422   | 0.7461 |
| 2.009         | 3.19  | 1000 | 2.0210          | 0.7623   | 0.7414 |
| 2.009         | 3.51  | 1100 | 2.1153          | 0.7554   | 0.7045 |
| 2.009         | 3.83  | 1200 | 1.9246          | 0.7583   | 0.7452 |
| 2.009         | 4.15  | 1300 | 1.9316          | 0.7611   | 0.7300 |
| 2.009         | 4.47  | 1400 | 2.5109          | 0.7547   | 0.7107 |
| 1.5147        | 4.79  | 1500 | 2.1018          | 0.7339   | 0.7465 |
| 1.5147        | 5.11  | 1600 | 2.5402          | 0.7344   | 0.6409 |
| 1.5147        | 5.43  | 1700 | 1.9100          | 0.7602   | 0.7427 |
| 1.5147        | 5.75  | 1800 | 2.0015          | 0.7519   | 0.7469 |
| 1.5147        | 6.07  | 1900 | 1.9725          | 0.7389   | 0.7376 |
| 1.2233        | 6.39  | 2000 | 1.8734          | 0.7545   | 0.7499 |
| 1.2233        | 6.71  | 2100 | 1.7899          | 0.7677   | 0.7478 |
| 1.2233        | 7.03  | 2200 | 1.9712          | 0.7583   | 0.7507 |
| 1.2233        | 7.35  | 2300 | 2.0334          | 0.7545   | 0.7049 |
| 1.2233        | 7.67  | 2400 | 1.7572          | 0.7583   | 0.7439 |
| 1.0785        | 7.99  | 2500 | 1.9824          | 0.7455   | 0.7361 |
| 1.0785        | 8.31  | 2600 | 2.1105          | 0.7306   | 0.7505 |
| 1.0785        | 8.63  | 2700 | 1.8427          | 0.7521   | 0.7451 |
| 1.0785        | 8.95  | 2800 | 1.9739          | 0.7569   | 0.7141 |
| 1.0785        | 9.27  | 2900 | 1.8361          | 0.7557   | 0.7401 |
| 0.9726        | 9.58  | 3000 | 1.8516          | 0.7564   | 0.7216 |
| 0.9726        | 9.9   | 3100 | 2.0626          | 0.7346   | 0.7495 |
| 0.9726        | 10.22 | 3200 | 1.8399          | 0.7651   | 0.7407 |
| 0.9726        | 10.54 | 3300 | 1.9179          | 0.7609   | 0.7201 |
| 0.9726        | 10.86 | 3400 | 1.7367          | 0.7625   | 0.7328 |
| 0.9237        | 11.18 | 3500 | 2.0266          | 0.7342   | 0.7462 |
| 0.9237        | 11.5  | 3600 | 2.3553          | 0.7094   | 0.7371 |
| 0.9237        | 11.82 | 3700 | 1.7729          | 0.7559   | 0.7334 |


### Framework versions

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