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# fine-tuned-IndoNLI-Basic-with-indobert-large-p2-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: 0.6963
- Accuracy: 0.7724
- F1: 0.7724

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3098        | 0.5   | 40   | 0.8899          | 0.6231   | 0.6268 |
| 1.0199        | 0.99  | 80   | 0.7268          | 0.6996   | 0.6999 |
| 0.767         | 1.49  | 120  | 0.6616          | 0.7406   | 0.7418 |
| 0.6649        | 1.98  | 160  | 0.6224          | 0.7547   | 0.7557 |
| 0.5796        | 2.48  | 200  | 0.6114          | 0.7656   | 0.7645 |
| 0.5796        | 2.97  | 240  | 0.6236          | 0.7524   | 0.7540 |
| 0.54          | 3.47  | 280  | 0.6223          | 0.7615   | 0.7624 |
| 0.4757        | 3.96  | 320  | 0.5965          | 0.7706   | 0.7721 |
| 0.4492        | 4.46  | 360  | 0.6216          | 0.7679   | 0.7681 |
| 0.3981        | 4.95  | 400  | 0.6347          | 0.7651   | 0.7669 |
| 0.3981        | 5.45  | 440  | 0.6373          | 0.7715   | 0.7727 |
| 0.352         | 5.94  | 480  | 0.6505          | 0.7674   | 0.7690 |
| 0.3294        | 6.44  | 520  | 0.6627          | 0.7720   | 0.7731 |
| 0.3058        | 6.93  | 560  | 0.6743          | 0.7660   | 0.7674 |
| 0.2692        | 7.43  | 600  | 0.6846          | 0.7665   | 0.7678 |
| 0.2692        | 7.93  | 640  | 0.6963          | 0.7724   | 0.7724 |


### 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-large-p2-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-large-p2-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: 0.6963
* Accuracy: 0.7724
* F1: 0.7724


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