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# fine-tuned-DatasetQAS-IDK-MRC-with-indobert-base-uncased-with-ITTL-without-freeze

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: 1.0604
- Exact Match: 69.1099
- F1: 72.5511
- Precision: 73.1008
- Recall: 72.6415

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 5.5481        | 0.49  | 72   | 3.2112          | 48.4293     | 51.4398 | 51.4398   | 51.4398 |
| 3.5187        | 0.98  | 144  | 2.0492          | 49.8691     | 53.0105 | 53.0105   | 53.0105 |
| 2.1378        | 1.47  | 216  | 1.8265          | 49.6073     | 52.8432 | 52.7883   | 53.0432 |
| 2.0191        | 1.96  | 288  | 1.6413          | 50.0        | 55.3856 | 55.2323   | 56.6140 |
| 1.6485        | 2.45  | 360  | 1.4309          | 57.0681     | 60.8658 | 61.0064   | 61.4124 |
| 1.5878        | 2.94  | 432  | 1.2824          | 59.2932     | 63.1427 | 63.3864   | 63.6375 |
| 1.2111        | 3.43  | 504  | 1.1649          | 62.3037     | 65.7367 | 66.2226   | 65.9932 |
| 1.1951        | 3.92  | 576  | 1.0818          | 64.2670     | 68.3155 | 68.7235   | 68.5007 |
| 1.1498        | 4.41  | 648  | 1.1072          | 63.6126     | 66.9459 | 67.3311   | 67.2027 |
| 0.9706        | 4.89  | 720  | 1.0439          | 66.8848     | 70.6183 | 71.0568   | 70.9028 |
| 0.9326        | 5.39  | 792  | 1.0014          | 68.0628     | 71.3521 | 71.8608   | 71.4544 |
| 0.8384        | 5.87  | 864  | 1.0279          | 65.3141     | 68.8561 | 69.2826   | 69.0155 |
| 0.8171        | 6.37  | 936  | 1.0405          | 68.4555     | 72.0273 | 72.5898   | 72.1179 |
| 0.7371        | 6.85  | 1008 | 1.0032          | 67.1466     | 70.4649 | 70.9701   | 70.6563 |
| 0.6918        | 7.35  | 1080 | 1.0841          | 66.0995     | 69.2320 | 69.6566   | 69.3692 |
| 0.6891        | 7.83  | 1152 | 0.9651          | 69.2408     | 72.6165 | 73.1959   | 72.5760 |
| 0.6171        | 8.33  | 1224 | 1.0881          | 68.4555     | 71.7433 | 72.3149   | 71.7580 |
| 0.5754        | 8.81  | 1296 | 0.9857          | 69.5026     | 73.0417 | 73.6169   | 73.0734 |
| 0.6054        | 9.31  | 1368 | 1.1012          | 68.7173     | 72.1025 | 72.7347   | 72.0089 |
| 0.5516        | 9.79  | 1440 | 1.0517          | 68.0628     | 71.8207 | 72.4355   | 71.8125 |
| 0.5177        | 10.28 | 1512 | 1.0604          | 69.1099     | 72.5511 | 73.1008   | 72.6415 |


### 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", "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
=================================================================================


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: 1.0604
* Exact Match: 69.1099
* F1: 72.5511
* Precision: 73.1008
* Recall: 72.6415


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