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

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2740
- Exact Match: 56.0847
- F1: 70.6246

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.306         | 0.5   | 19   | 3.7982          | 6.5256      | 20.3655 |
| 6.306         | 1.0   | 38   | 2.8932          | 14.1093     | 26.0679 |
| 3.9254        | 1.5   | 57   | 2.4798          | 19.4004     | 32.1438 |
| 3.9254        | 2.0   | 76   | 2.2955          | 26.1023     | 37.6331 |
| 3.9254        | 2.5   | 95   | 2.1688          | 26.9841     | 39.2632 |
| 2.4328        | 3.0   | 114  | 2.0701          | 30.1587     | 41.3438 |
| 2.4328        | 3.5   | 133  | 1.9789          | 33.1570     | 45.0539 |
| 2.1127        | 4.0   | 152  | 1.8465          | 37.2134     | 48.9042 |
| 2.1127        | 4.5   | 171  | 1.7699          | 38.9771     | 50.9760 |
| 2.1127        | 5.0   | 190  | 1.6885          | 41.0935     | 54.1550 |
| 1.7875        | 5.5   | 209  | 1.5785          | 45.1499     | 58.6783 |
| 1.7875        | 6.0   | 228  | 1.4954          | 49.2063     | 62.7869 |
| 1.7875        | 6.5   | 247  | 1.4186          | 51.8519     | 65.7461 |
| 1.5267        | 7.0   | 266  | 1.3734          | 53.4392     | 67.6141 |
| 1.5267        | 7.5   | 285  | 1.3419          | 54.1446     | 68.2563 |
| 1.3317        | 8.0   | 304  | 1.3116          | 55.5556     | 69.1996 |
| 1.3317        | 8.5   | 323  | 1.2936          | 56.0847     | 69.9806 |
| 1.3317        | 9.0   | 342  | 1.2900          | 56.2610     | 70.1634 |
| 1.2556        | 9.5   | 361  | 1.2771          | 55.7319     | 70.1143 |
| 1.2556        | 10.0  | 380  | 1.2740          | 56.0847     | 70.6246 |


### Framework versions

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