
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 1.0444
- Exact Match: 67.9319
- F1: 73.5776
- Precision: 75.1324
- Recall: 76.1210

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 5.7583        | 0.49  | 73   | 3.4717          | 48.6911     | 48.7120 | 48.7565   | 48.7036 |
| 3.8675        | 0.99  | 146  | 2.0063          | 49.8691     | 49.8691 | 49.8691   | 49.8691 |
| 2.1019        | 1.49  | 219  | 1.7934          | 43.5864     | 48.7113 | 47.9108   | 55.2027 |
| 1.9506        | 1.98  | 292  | 1.5978          | 52.4869     | 55.5648 | 55.8810   | 58.4501 |
| 1.582         | 2.48  | 365  | 1.4043          | 56.5445     | 61.2297 | 61.9432   | 64.9738 |
| 1.5478        | 2.98  | 438  | 1.2214          | 61.6492     | 66.1051 | 66.6829   | 69.1297 |
| 1.2069        | 3.47  | 511  | 1.1363          | 62.1728     | 67.2788 | 68.1474   | 70.3092 |
| 1.2033        | 3.97  | 584  | 1.0647          | 64.1361     | 69.5232 | 70.4180   | 72.9446 |
| 1.0169        | 4.47  | 657  | 1.0578          | 66.4921     | 72.1170 | 73.7120   | 74.5930 |
| 0.9691        | 4.96  | 730  | 1.0995          | 63.4817     | 69.3250 | 70.7996   | 71.9125 |
| 0.8492        | 5.46  | 803  | 1.0184          | 67.2775     | 72.1515 | 73.4105   | 75.0396 |
| 0.8327        | 5.96  | 876  | 1.1143          | 65.5759     | 71.0294 | 72.7764   | 73.1126 |
| 0.8248        | 6.45  | 949  | 1.0151          | 68.3246     | 73.9214 | 75.4324   | 76.0018 |
| 0.7498        | 6.95  | 1022 | 1.0034          | 68.8482     | 74.2840 | 76.1810   | 75.9452 |
| 0.6847        | 7.45  | 1095 | 1.0601          | 68.0628     | 73.5761 | 75.3716   | 75.4001 |
| 0.7117        | 7.94  | 1168 | 1.0448          | 68.0628     | 74.0368 | 75.6591   | 75.9829 |
| 0.6365        | 8.44  | 1241 | 1.0444          | 67.9319     | 73.5776 | 75.1324   | 76.1210 |


### 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-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: 1.0444
* Exact Match: 67.9319
* F1: 73.5776
* Precision: 75.1324
* Recall: 76.1210


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