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# fine-tuned-DatasetQAS-TYDI-QA-ID-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: 1.2532
- Exact Match: 54.3210
- F1: 68.9707

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.5175        | 0.5   | 19   | 3.9501          | 7.5838      | 20.4343 |
| 6.5175        | 1.0   | 38   | 2.9055          | 20.1058     | 31.7904 |
| 4.0122        | 1.5   | 57   | 2.4641          | 20.8113     | 34.2803 |
| 4.0122        | 2.0   | 76   | 2.2939          | 26.1023     | 39.8860 |
| 4.0122        | 2.5   | 95   | 2.1319          | 28.9242     | 40.9501 |
| 2.4422        | 3.0   | 114  | 2.0104          | 32.8042     | 45.3945 |
| 2.4422        | 3.5   | 133  | 1.9276          | 34.9206     | 47.5124 |
| 2.0745        | 4.0   | 152  | 1.7986          | 37.5661     | 50.2540 |
| 2.0745        | 4.5   | 171  | 1.7248          | 41.6226     | 55.5665 |
| 2.0745        | 5.0   | 190  | 1.6329          | 43.9153     | 57.2818 |
| 1.7428        | 5.5   | 209  | 1.5379          | 46.7372     | 61.1075 |
| 1.7428        | 6.0   | 228  | 1.4441          | 49.9118     | 63.9580 |
| 1.7428        | 6.5   | 247  | 1.3759          | 51.8519     | 65.6742 |
| 1.4867        | 7.0   | 266  | 1.3336          | 52.5573     | 66.5658 |
| 1.4867        | 7.5   | 285  | 1.3048          | 53.0864     | 67.1039 |
| 1.3148        | 8.0   | 304  | 1.2778          | 54.3210     | 69.0039 |
| 1.3148        | 8.5   | 323  | 1.2593          | 53.9683     | 68.5121 |
| 1.3148        | 9.0   | 342  | 1.2590          | 53.6155     | 68.3475 |
| 1.2395        | 9.5   | 361  | 1.2532          | 54.3210     | 68.9707 |


### Framework versions

- Transformers 4.26.1
- 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-TYDI-QA-ID-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-TYDI-QA-ID-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: 1.2532
* Exact Match: 54.3210
* F1: 68.9707


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



### Framework versions


* Transformers 4.26.1
* 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: 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 ### Framework versions


* Transformers 4.26.1
* 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: 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 ### Framework versions


* Transformers 4.26.1
* 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: 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### Framework versions


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