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# deberta-v3-base-mrpc

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5681
- Accuracy: 0.8946
- F1: 0.9244
- Combined Score: 0.9095

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0


### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "deberta-v3-base-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "config": "mrpc", "split": "validation", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8946078431372549, "name": "Accuracy"}, {"type": "f1", "value": 0.9244288224956063, "name": "F1"}]}]}]}
transformers pytorch deberta-v2 text-classification generated_from_trainer en dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# deberta-v3-base-mrpc

This model is a fine-tuned version of microsoft/deberta-v3-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5681
- Accuracy: 0.8946
- F1: 0.9244
- Combined Score: 0.9095

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0


### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3

# deberta-v3-base-mrpc

This model is a fine-tuned version of microsoft/deberta-v3-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5681
- Accuracy: 0.8946
- F1: 0.9244
- Combined Score: 0.9095 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0 ### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # deberta-v3-base-mrpc

This model is a fine-tuned version of microsoft/deberta-v3-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5681
- Accuracy: 0.8946
- F1: 0.9244
- Combined Score: 0.9095 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0 ### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3