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# fine-tuned-DatasetQAS-IDK-MRC-with-indobert-large-p2-with-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: 1.4144
- Exact Match: 54.9738
- F1: 61.7773
- Precision: 63.1273
- Recall: 66.0715

## 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
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1      | Precision | Recall  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 3.4922        | 0.49  | 73   | 2.1228          | 17.8010     | 26.7821 | 24.6611   | 46.2405 |
| 2.3015        | 0.99  | 146  | 1.7236          | 31.9372     | 39.3632 | 39.3151   | 50.4983 |
| 1.5627        | 1.49  | 219  | 1.3562          | 43.9791     | 50.3363 | 50.6305   | 59.6967 |
| 1.3703        | 1.98  | 292  | 1.3352          | 43.0628     | 50.9390 | 51.7207   | 58.2556 |
| 1.0433        | 2.48  | 365  | 1.2210          | 46.7277     | 54.3203 | 55.5971   | 60.2780 |
| 1.0456        | 2.97  | 438  | 1.1553          | 50.3927     | 58.4862 | 59.5577   | 65.4513 |
| 0.8656        | 3.47  | 511  | 1.1815          | 50.3927     | 57.6228 | 58.5436   | 62.8284 |
| 0.8838        | 3.97  | 584  | 1.2030          | 49.0838     | 56.4395 | 57.7457   | 61.5960 |
| 0.6994        | 4.47  | 657  | 1.1820          | 51.9634     | 59.1479 | 59.9674   | 64.7123 |
| 0.7335        | 4.96  | 730  | 1.1825          | 52.6178     | 60.0014 | 61.3988   | 64.7995 |
| 0.596         | 5.46  | 803  | 1.2962          | 52.2251     | 59.6942 | 61.1135   | 63.7633 |
| 0.6165        | 5.95  | 876  | 1.2169          | 53.0105     | 60.3582 | 61.5312   | 65.2088 |
| 0.5917        | 6.45  | 949  | 1.3939          | 53.0105     | 60.1105 | 61.5127   | 64.4837 |
| 0.5275        | 6.95  | 1022 | 1.3169          | 54.8429     | 62.1060 | 63.5898   | 66.5208 |
| 0.5058        | 7.45  | 1095 | 1.3237          | 55.6283     | 62.4607 | 63.7170   | 67.3387 |
| 0.4651        | 7.94  | 1168 | 1.3677          | 53.0105     | 59.7708 | 60.9283   | 64.5730 |
| 0.4616        | 8.44  | 1241 | 1.4120          | 57.4607     | 63.9364 | 65.2036   | 67.4919 |
| 0.4053        | 8.93  | 1314 | 1.3799          | 56.2827     | 62.8043 | 63.9601   | 66.7283 |
| 0.4061        | 9.43  | 1387 | 1.4736          | 55.7592     | 62.3147 | 63.7404   | 66.0129 |
| 0.4037        | 9.93  | 1460 | 1.4144          | 54.9738     | 61.7773 | 63.1273   | 66.0715 |


### 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-large-p2-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-large-p2-with-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: 1.4144
* Exact Match: 54.9738
* F1: 61.7773
* Precision: 63.1273
* Recall: 66.0715


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
* 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
* 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
* 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
* num\_epochs: 16### Training results### Framework versions


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