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# fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-without-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: 1.0844
- Exact Match: 65.5759
- F1: 70.8360

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.4526        | 0.49  | 36   | 2.4554          | 49.8691     | 49.8691 |
| 3.7633        | 0.98  | 72   | 1.9974          | 49.7382     | 49.7438 |
| 2.2184        | 1.47  | 108  | 1.8592          | 49.7382     | 50.4880 |
| 2.2184        | 1.96  | 144  | 1.7572          | 49.3455     | 51.5995 |
| 2.0352        | 2.45  | 180  | 1.6433          | 49.3455     | 54.2883 |
| 1.8246        | 2.94  | 216  | 1.5385          | 54.0576     | 57.3719 |
| 1.6495        | 3.43  | 252  | 1.4355          | 57.9843     | 62.1664 |
| 1.6495        | 3.92  | 288  | 1.3803          | 57.7225     | 62.1946 |
| 1.5079        | 4.41  | 324  | 1.3151          | 57.4607     | 63.1030 |
| 1.3449        | 4.9   | 360  | 1.2581          | 59.2932     | 65.0643 |
| 1.3449        | 5.39  | 396  | 1.1867          | 62.1728     | 67.8726 |
| 1.2429        | 5.88  | 432  | 1.1721          | 63.0890     | 68.1874 |
| 1.1547        | 6.37  | 468  | 1.1433          | 64.2670     | 69.1473 |
| 1.0805        | 6.86  | 504  | 1.1245          | 64.5288     | 69.0480 |
| 1.0805        | 7.35  | 540  | 1.0896          | 65.8377     | 70.8688 |
| 1.0457        | 7.84  | 576  | 1.0936          | 66.0995     | 70.8063 |
| 1.0152        | 8.33  | 612  | 1.0979          | 65.9686     | 70.8546 |
| 1.0152        | 8.82  | 648  | 1.0924          | 65.8377     | 71.0837 |
| 0.9966        | 9.31  | 684  | 1.0849          | 65.4450     | 70.6378 |
| 0.965         | 9.8   | 720  | 1.0844          | 65.5759     | 70.8360 |


### 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-without-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-without-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: 1.0844
* Exact Match: 65.5759
* F1: 70.8360


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