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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: 1.1493
- Exact Match: 60.5585
- F1: 75.1071
- Precision: 76.3329
- Recall: 81.4497

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 6.1192        | 0.5   | 38   | 4.8873          | 4.0140      | 16.4529 | 16.4981   | 38.0734 |
| 5.4384        | 0.99  | 76   | 2.8628          | 16.7539     | 29.8825 | 29.0280   | 50.2974 |
| 3.1591        | 1.5   | 114  | 2.4374          | 24.2583     | 36.1059 | 35.6027   | 53.7380 |
| 2.4014        | 1.99  | 152  | 2.2367          | 30.0175     | 41.9697 | 41.9505   | 53.7706 |
| 2.4014        | 2.5   | 190  | 2.0861          | 33.5079     | 45.2875 | 45.6044   | 55.6393 |
| 2.1121        | 2.99  | 228  | 1.8134          | 41.1867     | 52.1539 | 53.0988   | 60.0665 |
| 1.8437        | 3.5   | 266  | 1.5977          | 46.0733     | 59.5453 | 60.0688   | 69.5715 |
| 1.5105        | 3.99  | 304  | 1.3928          | 51.4834     | 65.0228 | 65.8592   | 72.3641 |
| 1.5105        | 4.5   | 342  | 1.3275          | 54.9738     | 68.7090 | 69.9803   | 75.8245 |
| 1.2337        | 4.99  | 380  | 1.2185          | 56.8935     | 70.5705 | 72.3556   | 75.7959 |
| 1.1333        | 5.5   | 418  | 1.2537          | 57.2426     | 70.9476 | 72.6953   | 75.6818 |
| 0.9915        | 5.99  | 456  | 1.1484          | 58.4642     | 73.3124 | 75.0975   | 78.1646 |
| 0.9915        | 6.5   | 494  | 1.1665          | 59.3368     | 74.0503 | 75.6279   | 79.6335 |
| 0.8931        | 6.99  | 532  | 1.1316          | 59.6859     | 74.4803 | 75.9433   | 79.8837 |
| 0.8498        | 7.5   | 570  | 1.1414          | 60.9075     | 75.3350 | 76.5606   | 81.1204 |
| 0.7783        | 7.99  | 608  | 1.1332          | 60.3839     | 75.2719 | 76.8970   | 81.1038 |
| 0.7783        | 8.5   | 646  | 1.1133          | 61.2565     | 75.3214 | 76.9111   | 81.1566 |
| 0.7209        | 8.99  | 684  | 1.1493          | 60.5585     | 75.1071 | 76.3329   | 81.4497 |


### 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-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: 1.1493
* Exact Match: 60.5585
* F1: 75.1071
* Precision: 76.3329
* Recall: 81.4497


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