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# fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-without-ITTL-without-freeze-LR-1e-05

This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1935
- Exact Match: 57.2183
- F1: 71.7072

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.3204        | 0.5   | 19   | 3.6469          | 10.9155     | 20.4300 |
| 6.3204        | 0.99  | 38   | 2.7834          | 17.9577     | 28.8829 |
| 3.5802        | 1.5   | 57   | 2.3114          | 24.2958     | 36.4160 |
| 3.5802        | 1.99  | 76   | 2.0209          | 29.4014     | 42.5434 |
| 3.5802        | 2.5   | 95   | 1.7380          | 38.3803     | 51.5950 |
| 2.0482        | 2.99  | 114  | 1.4687          | 44.8944     | 59.1567 |
| 2.0482        | 3.5   | 133  | 1.3680          | 50.0        | 64.4849 |
| 1.3956        | 3.99  | 152  | 1.2840          | 50.5282     | 65.7446 |
| 1.3956        | 4.5   | 171  | 1.2633          | 52.6408     | 67.0356 |
| 1.3956        | 4.99  | 190  | 1.2035          | 53.5211     | 68.4126 |
| 1.0901        | 5.5   | 209  | 1.2142          | 54.5775     | 69.1038 |
| 1.0901        | 5.99  | 228  | 1.1843          | 55.6338     | 69.8223 |
| 1.0901        | 6.5   | 247  | 1.1881          | 56.6901     | 70.7746 |
| 0.9217        | 6.99  | 266  | 1.1898          | 56.1620     | 70.2471 |
| 0.9217        | 7.5   | 285  | 1.1882          | 56.5141     | 70.7193 |
| 0.8307        | 7.99  | 304  | 1.2073          | 56.8662     | 71.6134 |
| 0.8307        | 8.5   | 323  | 1.1930          | 57.0423     | 71.3981 |
| 0.8307        | 8.99  | 342  | 1.1980          | 57.0423     | 71.8225 |
| 0.7811        | 9.5   | 361  | 1.1940          | 57.2183     | 71.7072 |
| 0.7811        | 9.99  | 380  | 1.1935          | 57.2183     | 71.7072 |


### 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-large-p2-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-large-p2-without-ITTL-without-freeze-LR-1e-05
============================================================================================


This model is a fine-tuned version of indobenchmark/indobert-large-p2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1935
* Exact Match: 57.2183
* F1: 71.7072


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