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# fine-tuned-DatasetQAS-IDK-MRC-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: 1.0883
- Exact Match: 65.4450
- F1: 70.8022

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.2828        | 0.49  | 36   | 2.6576          | 49.7382     | 49.7756 |
| 3.794         | 0.98  | 72   | 1.9936          | 49.8691     | 49.8691 |
| 2.2086        | 1.47  | 108  | 1.8469          | 49.2147     | 49.5992 |
| 2.2086        | 1.96  | 144  | 1.7445          | 50.5236     | 51.9107 |
| 2.0123        | 2.46  | 180  | 1.6178          | 49.8691     | 54.4031 |
| 1.7802        | 2.95  | 216  | 1.4800          | 54.8429     | 58.8765 |
| 1.5945        | 3.44  | 252  | 1.3337          | 57.5916     | 62.8748 |
| 1.5945        | 3.93  | 288  | 1.3153          | 58.2461     | 63.4667 |
| 1.4083        | 4.42  | 324  | 1.2184          | 59.8168     | 65.4478 |
| 1.2513        | 4.91  | 360  | 1.2348          | 58.3770     | 64.1649 |
| 1.2513        | 5.4   | 396  | 1.1415          | 62.6963     | 68.0081 |
| 1.161         | 5.89  | 432  | 1.1463          | 62.6963     | 67.6633 |
| 1.0755        | 6.38  | 468  | 1.1126          | 63.4817     | 68.7554 |
| 1.0099        | 6.87  | 504  | 1.0823          | 63.4817     | 68.9182 |
| 1.0099        | 7.37  | 540  | 1.0547          | 66.2304     | 71.2423 |
| 0.9815        | 7.86  | 576  | 1.0835          | 63.4817     | 69.1031 |
| 0.9464        | 8.35  | 612  | 1.0644          | 66.3613     | 71.4374 |
| 0.9464        | 8.84  | 648  | 1.0642          | 65.9686     | 71.2813 |
| 0.9325        | 9.33  | 684  | 1.0786          | 65.4450     | 70.8541 |
| 0.913         | 9.82  | 720  | 1.0883          | 65.4450     | 70.8022 |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05", "results": []}]}
transformers pytorch tensorboard bert question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze-LR-1e-05
==========================================================================================


This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0883
* Exact Match: 65.4450
* F1: 70.8022


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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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



### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2