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# fine-tuned-DatasetQAS-TYDI-QA-ID-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: 1.2784
- Exact Match: 53.4392
- F1: 68.7244

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- 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      |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| 6.1764        | 0.5   | 19   | 3.7674          | 10.4056     | 23.6332 |
| 6.1764        | 1.0   | 38   | 2.7985          | 19.5767     | 32.6228 |
| 3.8085        | 1.49  | 57   | 2.4169          | 22.0459     | 35.4084 |
| 3.8085        | 1.99  | 76   | 2.2811          | 25.9259     | 38.3963 |
| 3.8085        | 2.49  | 95   | 2.1607          | 28.0423     | 40.3901 |
| 2.3932        | 2.99  | 114  | 2.0488          | 31.0406     | 43.7059 |
| 2.3932        | 3.49  | 133  | 1.9787          | 34.3915     | 46.3655 |
| 2.0772        | 3.98  | 152  | 1.8661          | 37.2134     | 49.1483 |
| 2.0772        | 4.48  | 171  | 1.7893          | 40.2116     | 52.4989 |
| 2.0772        | 4.98  | 190  | 1.7014          | 41.9753     | 54.9197 |
| 1.7645        | 5.48  | 209  | 1.5940          | 44.2681     | 58.2134 |
| 1.7645        | 5.98  | 228  | 1.4972          | 46.2081     | 60.4997 |
| 1.7645        | 6.47  | 247  | 1.4214          | 48.8536     | 63.4371 |
| 1.5035        | 6.97  | 266  | 1.3676          | 50.6173     | 65.4663 |
| 1.5035        | 7.47  | 285  | 1.3357          | 52.2046     | 67.1759 |
| 1.3206        | 7.97  | 304  | 1.3149          | 53.0864     | 68.0698 |
| 1.3206        | 8.47  | 323  | 1.2988          | 53.4392     | 68.3971 |
| 1.3206        | 8.96  | 342  | 1.2894          | 53.6155     | 68.8897 |
| 1.2472        | 9.46  | 361  | 1.2820          | 53.4392     | 68.5835 |
| 1.2472        | 9.96  | 380  | 1.2784          | 53.4392     | 68.7244 |


### Framework versions

- Transformers 4.27.4
- 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-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-TYDI-QA-ID-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: 1.2784
* Exact Match: 53.4392
* F1: 68.7244


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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 4.27.4
* 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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 4.27.4
* 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: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 32
* 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 4.27.4
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2