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# fine-tuned-IndoNLI-Basic-with-xlm-roberta-large-LR-1e-05

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5019
- Accuracy: 0.8243
- F1: 0.8245

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.1396        | 0.5   | 40   | 1.0955          | 0.3696   | 0.2355 |
| 1.108         | 0.99  | 80   | 1.0433          | 0.4406   | 0.3665 |
| 1.0644        | 1.49  | 120  | 0.9406          | 0.5321   | 0.5293 |
| 0.963         | 1.98  | 160  | 0.9097          | 0.6154   | 0.6192 |
| 0.8825        | 2.48  | 200  | 0.7810          | 0.6891   | 0.6898 |
| 0.8825        | 2.97  | 240  | 0.7141          | 0.7196   | 0.7216 |
| 0.8145        | 3.47  | 280  | 0.7784          | 0.7219   | 0.7238 |
| 0.7253        | 3.96  | 320  | 0.6165          | 0.7711   | 0.7716 |
| 0.6706        | 4.46  | 360  | 0.6133          | 0.7597   | 0.7582 |
| 0.6356        | 4.95  | 400  | 0.5849          | 0.7833   | 0.7826 |
| 0.6356        | 5.45  | 440  | 0.5443          | 0.7979   | 0.7980 |
| 0.5919        | 5.94  | 480  | 0.5335          | 0.8093   | 0.8101 |
| 0.5509        | 6.44  | 520  | 0.5256          | 0.8157   | 0.8165 |
| 0.5286        | 6.93  | 560  | 0.5127          | 0.8107   | 0.8101 |
| 0.5081        | 7.43  | 600  | 0.5160          | 0.8170   | 0.8173 |
| 0.5081        | 7.93  | 640  | 0.5037          | 0.8220   | 0.8222 |
| 0.5077        | 8.42  | 680  | 0.4961          | 0.8207   | 0.8210 |
| 0.4829        | 8.92  | 720  | 0.5016          | 0.8266   | 0.8268 |
| 0.4585        | 9.41  | 760  | 0.5043          | 0.8229   | 0.8227 |
| 0.4712        | 9.91  | 800  | 0.5019          | 0.8243   | 0.8245 |


### 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-xlm-roberta-large-LR-1e-05", "results": []}]}
transformers pytorch tensorboard xlm-roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

fine-tuned-IndoNLI-Basic-with-xlm-roberta-large-LR-1e-05
========================================================


This model is a fine-tuned version of xlm-roberta-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.5019
* Accuracy: 0.8243
* F1: 0.8245


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 #xlm-roberta #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 #xlm-roberta #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