
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fine-tuned-DatasetQAS-TYDI-QA-ID-with-indobert-large-p2-without-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: 1.3674
- Exact Match: 61.6056
- F1: 75.5602
- Precision: 77.4368
- Recall: 81.6689

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 6.0141        | 0.49  | 38   | 2.9934          | 18.3246     | 28.5746 | 29.9986   | 38.5826 |
| 3.7088        | 0.99  | 76   | 2.2203          | 26.8761     | 39.4873 | 41.1034   | 53.1907 |
| 2.2959        | 1.48  | 114  | 1.6080          | 44.8517     | 57.3435 | 59.1495   | 67.1641 |
| 1.6372        | 1.97  | 152  | 1.3376          | 51.8325     | 64.2537 | 66.2962   | 72.3325 |
| 1.6372        | 2.47  | 190  | 1.2596          | 54.4503     | 68.1380 | 70.6221   | 74.2239 |
| 1.22          | 2.96  | 228  | 1.1852          | 56.8935     | 70.9543 | 73.3635   | 75.8557 |
| 1.0455        | 3.45  | 266  | 1.1563          | 57.5916     | 71.5590 | 73.6178   | 77.8050 |
| 0.9065        | 3.95  | 304  | 1.1665          | 59.6859     | 73.4100 | 75.5409   | 78.9196 |
| 0.9065        | 4.44  | 342  | 1.1836          | 60.7330     | 74.8281 | 76.8589   | 79.7466 |
| 0.7931        | 4.94  | 380  | 1.1379          | 60.7330     | 74.7075 | 76.7252   | 79.6857 |
| 0.6928        | 5.43  | 418  | 1.2231          | 60.3839     | 74.8252 | 77.1086   | 79.3933 |
| 0.6134        | 5.92  | 456  | 1.2237          | 61.0820     | 75.0180 | 76.8374   | 80.0757 |
| 0.6134        | 6.42  | 494  | 1.2708          | 62.4782     | 75.6353 | 77.9711   | 80.0176 |
| 0.5184        | 6.91  | 532  | 1.2351          | 62.1291     | 75.4447 | 77.4664   | 80.9526 |
| 0.4996        | 7.4   | 570  | 1.2836          | 61.9546     | 75.7055 | 77.5004   | 80.8263 |
| 0.4253        | 7.9   | 608  | 1.2907          | 61.9546     | 75.7304 | 77.4574   | 81.1694 |
| 0.4253        | 8.39  | 646  | 1.3289          | 62.3037     | 75.8263 | 77.7220   | 80.7753 |
| 0.4077        | 8.88  | 684  | 1.3006          | 61.6056     | 75.4850 | 77.2571   | 80.4104 |
| 0.3478        | 9.38  | 722  | 1.3455          | 61.2565     | 74.7989 | 76.8151   | 80.0213 |
| 0.3074        | 9.87  | 760  | 1.3674          | 61.6056     | 75.5602 | 77.4368   | 81.6689 |


### Framework versions

- Transformers 4.27.0
- Pytorch 2.0.0+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-large-p2-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-large-p2-without-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: 1.3674
* Exact Match: 61.6056
* F1: 75.5602
* Precision: 77.4368
* Recall: 81.6689


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 4.27.0
* Pytorch 2.0.0+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 4.27.0
* Pytorch 2.0.0+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 4.27.0
* Pytorch 2.0.0+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 4.27.0
* Pytorch 2.0.0+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2