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# DeBERTa-finetuned-SNLI4

This model is a fine-tuned version of [gyeoldere/DeBERTa-finetuned-SNLI2](https://huggingface.co/gyeoldere/DeBERTa-finetuned-SNLI2) on the snli dataset.

## Model description

fliped_forth used

## Intended uses & limitations

More information needed

## Training and evaluation data

final training loss : 1.216

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["snli"], "model-index": [{"name": "DeBERTa-finetuned-SNLI4", "results": []}]}
transformers pytorch deberta generated_from_trainer dataset:snli license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta #generated_from_trainer #dataset-snli #license-mit #endpoints_compatible #region-us 




# DeBERTa-finetuned-SNLI4

This model is a fine-tuned version of gyeoldere/DeBERTa-finetuned-SNLI2 on the snli dataset.

## Model description

fliped_forth used

## Intended uses & limitations

More information needed

## Training and evaluation data

final training loss : 1.216

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

# DeBERTa-finetuned-SNLI4

This model is a fine-tuned version of gyeoldere/DeBERTa-finetuned-SNLI2 on the snli dataset. ## Model description

fliped_forth used ## Intended uses & limitations

More information needed ## Training and evaluation data

final training loss : 1.216 ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #generated_from_trainer #dataset-snli #license-mit #endpoints_compatible #region-us 
 # DeBERTa-finetuned-SNLI4

This model is a fine-tuned version of gyeoldere/DeBERTa-finetuned-SNLI2 on the snli dataset. ## Model description

fliped_forth used ## Intended uses & limitations

More information needed ## Training and evaluation data

final training loss : 1.216 ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3 ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #generated_from_trainer #dataset-snli #license-mit #endpoints_compatible #region-us 
# DeBERTa-finetuned-SNLI4

This model is a fine-tuned version of gyeoldere/DeBERTa-finetuned-SNLI2 on the snli dataset.## Model description

fliped_forth used## Intended uses & limitations

More information needed## Training and evaluation data

final training loss : 1.216## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3### Training results### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2