
<!-- 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-with-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: 1.2832
- Exact Match: 59.3368
- F1: 73.6394
- Precision: 75.6497
- Recall: 79.2494

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 6.1305        | 0.49  | 38   | 2.9545          | 18.3246     | 28.7037 | 30.7234   | 39.3266 |
| 3.6666        | 0.99  | 76   | 2.0933          | 29.3194     | 41.5386 | 41.3158   | 57.3278 |
| 2.2221        | 1.48  | 114  | 1.5088          | 46.0733     | 59.6910 | 61.3465   | 70.0645 |
| 1.5513        | 1.97  | 152  | 1.2788          | 52.7051     | 67.6237 | 68.9352   | 76.7287 |
| 1.5513        | 2.47  | 190  | 1.2375          | 56.0209     | 70.0861 | 72.2276   | 76.3275 |
| 1.1584        | 2.96  | 228  | 1.1617          | 56.3700     | 70.9542 | 72.5147   | 77.8564 |
| 1.0032        | 3.45  | 266  | 1.1656          | 57.9407     | 72.1620 | 73.8214   | 78.2817 |
| 0.8661        | 3.95  | 304  | 1.1443          | 57.5916     | 72.5053 | 73.8808   | 80.3537 |
| 0.8661        | 4.44  | 342  | 1.1663          | 58.4642     | 73.4761 | 75.0381   | 80.0108 |
| 0.7541        | 4.94  | 380  | 1.1414          | 58.2897     | 73.1853 | 74.9363   | 78.6912 |
| 0.6687        | 5.43  | 418  | 1.2151          | 60.0349     | 73.6810 | 75.7886   | 79.3854 |
| 0.5926        | 5.92  | 456  | 1.1805          | 60.5585     | 74.6182 | 76.2757   | 81.1406 |
| 0.5926        | 6.42  | 494  | 1.2740          | 60.5585     | 74.4135 | 76.4582   | 80.1876 |
| 0.4761        | 6.91  | 532  | 1.2221          | 59.8604     | 74.5837 | 75.8985   | 80.5858 |
| 0.4644        | 7.4   | 570  | 1.2832          | 59.3368     | 73.6394 | 75.6497   | 79.2494 |


### 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-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-large-p2-with-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: 1.2832
* Exact Match: 59.3368
* F1: 73.6394
* Precision: 75.6497
* Recall: 79.2494


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