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# fine-tuned-IndoNLI-Translated-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: 0.4945
- Accuracy: 0.8553
- F1: 0.8555

## 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: 8
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4916        | 0.5   | 1533  | 0.4336          | 0.8335   | 0.8342 |
| 0.4465        | 1.0   | 3066  | 0.4120          | 0.8454   | 0.8463 |
| 0.3666        | 1.5   | 4599  | 0.4001          | 0.8537   | 0.8538 |
| 0.3876        | 2.0   | 6132  | 0.3928          | 0.8530   | 0.8528 |
| 0.3347        | 2.5   | 7665  | 0.4415          | 0.8502   | 0.8505 |
| 0.3372        | 3.0   | 9198  | 0.4174          | 0.8582   | 0.8583 |
| 0.2641        | 3.5   | 10731 | 0.4568          | 0.8532   | 0.8529 |
| 0.2747        | 4.0   | 12264 | 0.4262          | 0.8576   | 0.8577 |
| 0.231         | 4.5   | 13797 | 0.4945          | 0.8553   | 0.8555 |


### 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-Translated-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-Translated-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: 0.4945
* Accuracy: 0.8553
* F1: 0.8555


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: 8
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* 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: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 8
* 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