---
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: 1.3123
- Exact Match: 55.7319
- F1: 68.7642

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.3583        | 0.5   | 19   | 4.0413          | 7.7601      | 19.0346 |
| 6.3583        | 1.0   | 38   | 2.8990          | 13.4039     | 26.3293 |
| 4.0604        | 1.5   | 57   | 2.5086          | 21.3404     | 33.1133 |
| 4.0604        | 2.0   | 76   | 2.3326          | 24.6914     | 37.3153 |
| 4.0604        | 2.5   | 95   | 2.2257          | 27.3369     | 38.8270 |
| 2.4855        | 3.0   | 114  | 2.1022          | 31.9224     | 43.8833 |
| 2.4855        | 3.5   | 133  | 2.0109          | 33.1570     | 45.0028 |
| 2.1528        | 4.0   | 152  | 1.8668          | 35.6261     | 48.8805 |
| 2.1528        | 4.5   | 171  | 1.7791          | 38.9771     | 52.1137 |
| 2.1528        | 5.0   | 190  | 1.7014          | 42.1517     | 55.5969 |
| 1.8067        | 5.5   | 209  | 1.5955          | 44.4444     | 58.4357 |
| 1.8067        | 6.0   | 228  | 1.5192          | 48.3245     | 61.1827 |
| 1.8067        | 6.5   | 247  | 1.4530          | 50.7937     | 63.7891 |
| 1.5663        | 7.0   | 266  | 1.4101          | 52.3810     | 65.5927 |
| 1.5663        | 7.5   | 285  | 1.3799          | 53.0864     | 65.9441 |
| 1.3777        | 8.0   | 304  | 1.3490          | 54.1446     | 67.0120 |
| 1.3777        | 8.5   | 323  | 1.3281          | 55.0265     | 68.0820 |
| 1.3777        | 9.0   | 342  | 1.3247          | 55.0265     | 68.0493 |
| 1.3092        | 9.5   | 361  | 1.3147          | 55.3792     | 68.6187 |
| 1.3092        | 10.0  | 380  | 1.3123          | 55.7319     | 68.7642 |


### Framework versions

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