---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-with-ITTL-without-freeze-LR-1e-05
  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. -->

# fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-with-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1732
- Exact Match: 60.2113
- F1: 73.6360

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.3258        | 0.5   | 19   | 3.5172          | 10.7394     | 21.8207 |
| 6.3258        | 0.99  | 38   | 2.7869          | 18.3099     | 28.8508 |
| 3.5421        | 1.5   | 57   | 2.3403          | 23.4155     | 35.8166 |
| 3.5421        | 1.99  | 76   | 2.0205          | 30.2817     | 42.8646 |
| 3.5421        | 2.5   | 95   | 1.7202          | 38.7324     | 51.9337 |
| 2.0678        | 2.99  | 114  | 1.4839          | 44.5423     | 59.5131 |
| 2.0678        | 3.5   | 133  | 1.3583          | 50.5282     | 64.3127 |
| 1.4302        | 3.99  | 152  | 1.2706          | 52.2887     | 66.7363 |
| 1.4302        | 4.5   | 171  | 1.2389          | 55.1056     | 69.3028 |
| 1.4302        | 4.99  | 190  | 1.2065          | 55.8099     | 69.9972 |
| 1.0965        | 5.5   | 209  | 1.1840          | 56.8662     | 70.7353 |
| 1.0965        | 5.99  | 228  | 1.1887          | 58.4507     | 71.9049 |
| 1.0965        | 6.5   | 247  | 1.1797          | 58.6268     | 72.5495 |
| 0.9291        | 6.99  | 266  | 1.1685          | 59.6831     | 73.0495 |
| 0.9291        | 7.5   | 285  | 1.1713          | 59.1549     | 73.1298 |
| 0.839         | 7.99  | 304  | 1.1721          | 60.0352     | 73.0300 |
| 0.839         | 8.5   | 323  | 1.1735          | 60.0352     | 73.5065 |
| 0.839         | 8.99  | 342  | 1.1725          | 60.2113     | 73.6033 |
| 0.7854        | 9.5   | 361  | 1.1738          | 60.2113     | 73.6432 |
| 0.7854        | 9.99  | 380  | 1.1732          | 60.2113     | 73.6360 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2