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# fine-tuned-DatasetQAS-Squad-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: 1.6868
- Exact Match: 45.1218
- F1: 61.3575
- Precision: 62.6921
- Recall: 67.7425

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 2.4472        | 0.5   | 1023 | 2.2865          | 29.5933     | 42.4059 | 43.5205   | 52.5488 |
| 1.8722        | 1.0   | 2046 | 1.8562          | 39.5825     | 55.5286 | 56.6097   | 63.1751 |
| 1.6329        | 1.5   | 3069 | 1.7271          | 42.6516     | 59.0745 | 60.4591   | 65.8650 |
| 1.5391        | 2.0   | 4092 | 1.6703          | 43.7412     | 60.5632 | 62.1124   | 67.0088 |
| 1.4011        | 2.5   | 5115 | 1.6518          | 44.4565     | 61.1817 | 62.7045   | 66.6741 |
| 1.3955        | 3.0   | 6138 | 1.6557          | 45.0803     | 62.3404 | 63.2191   | 68.5525 |
| 1.2678        | 3.5   | 7161 | 1.6473          | 45.3131     | 61.8217 | 63.1073   | 67.9864 |
| 1.2641        | 4.0   | 8184 | 1.6277          | 45.5627     | 61.9713 | 63.2872   | 68.0141 |
| 1.1456        | 4.5   | 9207 | 1.6868          | 45.1218     | 61.3575 | 62.6921   | 67.7425 |


### 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-Squad-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-Squad-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: 1.6868
* Exact Match: 45.1218
* F1: 61.3575
* Precision: 62.6921
* Recall: 67.7425


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