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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: 1.1675
- Exact Match: 61.4311
- F1: 76.0013
- Precision: 77.2642
- Recall: 81.7278

## 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  |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|:---------:|:-------:|
| 6.2602        | 0.5   | 38   | 4.9592          | 0.5236      | 11.0968 | 11.1980   | 26.6188 |
| 5.556         | 0.99  | 76   | 3.0406          | 12.7400     | 25.1945 | 25.6795   | 39.6592 |
| 3.3395        | 1.5   | 114  | 2.4880          | 21.1169     | 34.7076 | 32.9184   | 52.2869 |
| 2.4731        | 1.99  | 152  | 2.2257          | 27.3997     | 40.0066 | 39.3514   | 53.5252 |
| 2.4731        | 2.5   | 190  | 2.0431          | 32.6353     | 44.5789 | 44.7211   | 55.0020 |
| 2.162         | 2.99  | 228  | 1.8362          | 38.9180     | 50.4876 | 50.7087   | 59.7434 |
| 1.8755        | 3.5   | 266  | 1.6441          | 43.9791     | 56.8266 | 57.4538   | 65.5751 |
| 1.5888        | 3.99  | 304  | 1.4664          | 52.0070     | 63.9616 | 65.2046   | 70.0798 |
| 1.5888        | 4.5   | 342  | 1.3509          | 54.4503     | 68.6979 | 70.2140   | 76.0813 |
| 1.333         | 4.99  | 380  | 1.2571          | 54.7993     | 68.9857 | 70.8728   | 75.8745 |
| 1.2051        | 5.5   | 418  | 1.2440          | 56.5445     | 70.2921 | 72.4571   | 75.9313 |
| 1.0522        | 5.99  | 456  | 1.1808          | 57.5916     | 72.1230 | 73.6246   | 78.8092 |
| 1.0522        | 6.5   | 494  | 1.1575          | 58.9878     | 73.1594 | 74.9064   | 79.2545 |
| 0.9584        | 6.99  | 532  | 1.1553          | 58.9878     | 73.5139 | 75.2615   | 79.3901 |
| 0.9006        | 7.5   | 570  | 1.1112          | 60.0349     | 74.5273 | 75.8170   | 81.1555 |
| 0.8102        | 7.99  | 608  | 1.1164          | 59.8604     | 74.5013 | 76.2748   | 80.2875 |
| 0.8102        | 8.5   | 646  | 1.1371          | 60.0349     | 74.2469 | 75.9082   | 79.9186 |
| 0.773         | 8.99  | 684  | 1.1410          | 60.7330     | 74.9095 | 76.7045   | 80.9178 |
| 0.7482        | 9.5   | 722  | 1.1307          | 60.3839     | 74.7594 | 76.8954   | 80.6364 |
| 0.6878        | 9.99  | 760  | 1.1219          | 61.0820     | 74.9064 | 76.4266   | 81.4087 |
| 0.6878        | 10.5  | 798  | 1.1362          | 62.1291     | 76.5097 | 77.5924   | 82.8049 |
| 0.6401        | 10.99 | 836  | 1.1266          | 61.0820     | 75.8874 | 77.0263   | 81.7467 |
| 0.634         | 11.5  | 874  | 1.1570          | 61.7801     | 75.9638 | 77.5661   | 80.8536 |
| 0.5856        | 11.99 | 912  | 1.1675          | 61.4311     | 76.0013 | 77.2642   | 81.7278 |


### 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-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: 1.1675
* Exact Match: 61.4311
* F1: 76.0013
* Precision: 77.2642
* Recall: 81.7278


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