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# DeBERTa-finetuned-SNLI2

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

Test_trainer model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the snli dataset.

This model achieves the following results on the evaluation set:
- NLI accuracy : 0.86
- MLM accuracy : 0.68


## Model description

This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.

Output vector of DeBERTa processed through two different fc layer to predict.
I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
[https://huggingface.co/docs/transformers/index]

BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
final_loss = MLM_loss + NLI_loss

## 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: 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 2.9.0
- Tokenizers 0.13.2
{"license": "mit", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["snli"], "metrics": ["accuracy"], "model-index": [{"name": "DeBERTa-finetuned-SNLI2", "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-SNLI2

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

Test_trainer model is a fine-tuned version of microsoft/deberta-base on the snli dataset.

This model achieves the following results on the evaluation set:
- NLI accuracy : 0.86
- MLM accuracy : 0.68


## Model description

This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.

Output vector of DeBERTa processed through two different fc layer to predict.
I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
[URL

BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
final_loss = MLM_loss + NLI_loss

## 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: 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 2.9.0
- Tokenizers 0.13.2
# DeBERTa-finetuned-SNLI2

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

Test_trainer model is a fine-tuned version of microsoft/deberta-base on the snli dataset.

This model achieves the following results on the evaluation set:
- NLI accuracy : 0.86
- MLM accuracy : 0.68 ## Model description

This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.

Output vector of DeBERTa processed through two different fc layer to predict.
I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
[URL

BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
final_loss = MLM_loss + NLI_loss ## 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: 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 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #generated_from_trainer #dataset-snli #license-mit #endpoints_compatible #region-us 
 # DeBERTa-finetuned-SNLI2

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

Test_trainer model is a fine-tuned version of microsoft/deberta-base on the snli dataset.

This model achieves the following results on the evaluation set:
- NLI accuracy : 0.86
- MLM accuracy : 0.68 ## Model description

This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.

Output vector of DeBERTa processed through two different fc layer to predict.
I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
[URL

BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
final_loss = MLM_loss + NLI_loss ## 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: 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 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #deberta #generated_from_trainer #dataset-snli #license-mit #endpoints_compatible #region-us 
# DeBERTa-finetuned-SNLI2

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

Test_trainer model is a fine-tuned version of microsoft/deberta-base on the snli dataset.

This model achieves the following results on the evaluation set:
- NLI accuracy : 0.86
- MLM accuracy : 0.68## Model description

This model fine-tuned to perform 2 tasks simultaneously; NLI task and MLM task.

Output vector of DeBERTa processed through two different fc layer to predict.
I used layer structure introduced in BERT paper, which is implemented on huggingface transformers; DebertaForTokenClassification and DebertaForMaskedLM.
[URL

BinaryCrossEntrophyLoss are used for each class, and two losses are added to obtain final loss
final_loss = MLM_loss + NLI_loss## 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: 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 2.9.0
- Tokenizers 0.13.2