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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: 1.0791
- Exact Match: 69.7644
- F1: 75.9108
- Precision: 77.5909
- Recall: 77.7773

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      | Precision | Recall  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 5.5507        | 0.49  | 73   | 3.2003          | 49.6073     | 49.6073 | 49.6073   | 49.6073 |
| 3.6491        | 0.99  | 146  | 1.9800          | 49.8691     | 49.8691 | 49.8691   | 49.8691 |
| 2.1085        | 1.49  | 219  | 1.7880          | 42.0157     | 48.4391 | 47.4995   | 57.0930 |
| 1.926         | 1.98  | 292  | 1.5461          | 54.3194     | 59.1586 | 59.2743   | 63.4653 |
| 1.5331        | 2.48  | 365  | 1.3471          | 57.7225     | 62.7979 | 63.2329   | 68.5704 |
| 1.4896        | 2.98  | 438  | 1.1975          | 59.0314     | 65.0097 | 66.0998   | 69.0900 |
| 1.1584        | 3.47  | 511  | 1.1617          | 60.9948     | 67.2465 | 68.0441   | 71.1982 |
| 1.1448        | 3.97  | 584  | 1.0450          | 65.4450     | 70.7693 | 71.7620   | 73.7743 |
| 0.9692        | 4.47  | 657  | 1.0827          | 65.3141     | 70.8950 | 71.9487   | 74.1019 |
| 0.9078        | 4.96  | 730  | 1.0273          | 66.8848     | 72.6251 | 74.0714   | 75.6255 |
| 0.8139        | 5.46  | 803  | 1.0441          | 66.3613     | 72.1886 | 73.9642   | 74.5072 |
| 0.8035        | 5.96  | 876  | 1.0418          | 66.6230     | 72.3513 | 73.8273   | 74.5317 |
| 0.7829        | 6.45  | 949  | 1.0555          | 67.2775     | 72.9075 | 74.5876   | 75.6701 |
| 0.7168        | 6.95  | 1022 | 1.0134          | 68.7173     | 74.2844 | 75.7597   | 76.3650 |
| 0.6677        | 7.45  | 1095 | 1.0526          | 68.8482     | 74.6640 | 76.4448   | 76.5281 |
| 0.6795        | 7.94  | 1168 | 1.0144          | 69.2408     | 75.2363 | 77.0568   | 76.9687 |
| 0.6109        | 8.44  | 1241 | 1.0488          | 69.3717     | 74.9248 | 76.5687   | 76.9808 |
| 0.5713        | 8.94  | 1314 | 1.0025          | 70.6806     | 76.3889 | 77.8845   | 78.7983 |
| 0.5859        | 9.43  | 1387 | 1.0352          | 70.8115     | 76.1957 | 77.9573   | 78.0250 |
| 0.5204        | 9.93  | 1460 | 1.0295          | 70.9424     | 76.5325 | 78.2172   | 78.3561 |
| 0.4952        | 10.43 | 1533 | 1.0356          | 70.4188     | 76.0822 | 77.7609   | 78.4852 |
| 0.4832        | 10.92 | 1606 | 1.0636          | 70.1571     | 75.9582 | 77.6080   | 78.0054 |
| 0.4613        | 11.42 | 1679 | 1.0791          | 69.7644     | 75.9108 | 77.5909   | 77.7773 |


### 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", "precision", "recall"], "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: 1.0791
* Exact Match: 69.7644
* F1: 75.9108
* Precision: 77.5909
* Recall: 77.7773


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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 16


### 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 16 ### 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 16 ### 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 16### Training results### Framework versions


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