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# fine-tuned-IndoNLI-Basic-with-indobert-base-uncased-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: 0.6185
- Accuracy: 0.7629
- F1: 0.7622

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1098        | 0.5   | 40   | 1.0813          | 0.4110   | 0.4037 |
| 1.0991        | 0.99  | 80   | 0.9440          | 0.5653   | 0.5613 |
| 1.0022        | 1.49  | 120  | 0.8605          | 0.6249   | 0.6215 |
| 0.876         | 1.98  | 160  | 0.7910          | 0.6582   | 0.6563 |
| 0.7978        | 2.48  | 200  | 0.7613          | 0.6800   | 0.6777 |
| 0.7978        | 2.97  | 240  | 0.7216          | 0.7005   | 0.7020 |
| 0.7667        | 3.47  | 280  | 0.6940          | 0.7178   | 0.7179 |
| 0.7091        | 3.96  | 320  | 0.6762          | 0.7310   | 0.7309 |
| 0.6752        | 4.46  | 360  | 0.6569          | 0.7424   | 0.7413 |
| 0.6425        | 4.95  | 400  | 0.6440          | 0.7610   | 0.7618 |
| 0.6425        | 5.45  | 440  | 0.6302          | 0.7619   | 0.7618 |
| 0.6153        | 5.94  | 480  | 0.6266          | 0.7615   | 0.7613 |
| 0.5945        | 6.44  | 520  | 0.6291          | 0.7638   | 0.7634 |
| 0.5587        | 6.93  | 560  | 0.6222          | 0.7606   | 0.7593 |
| 0.5452        | 7.43  | 600  | 0.6212          | 0.7633   | 0.7631 |
| 0.5452        | 7.93  | 640  | 0.6185          | 0.7629   | 0.7622 |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "fine-tuned-IndoNLI-Basic-with-indobert-base-uncased-LR-1e-05", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

fine-tuned-IndoNLI-Basic-with-indobert-base-uncased-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: 0.6185
* Accuracy: 0.7629
* F1: 0.7622


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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* 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: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 16
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 1.13.1+cu117
* Datasets 2.2.0
* Tokenizers 0.13.2