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# eval_masked_v4_mrpc

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6346
- Accuracy: 0.7941
- F1: 0.8595
- Combined Score: 0.8268

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "eval_masked_v4_mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.7941176470588235, "name": "Accuracy"}, {"type": "f1", "value": 0.8595317725752508, "name": "F1"}]}]}]}
pytorch generated_from_trainer en dataset:glue license:apache-2.0 model-index region:us
TAGS
#pytorch #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #region-us 




# eval_masked_v4_mrpc

This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6346
- Accuracy: 0.7941
- F1: 0.8595
- Combined Score: 0.8268

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1

# eval_masked_v4_mrpc

This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6346
- Accuracy: 0.7941
- F1: 0.8595
- Combined Score: 0.8268 ## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
TAGS
#pytorch #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #region-us 
 # eval_masked_v4_mrpc

This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6346
- Accuracy: 0.7941
- F1: 0.8595
- Combined Score: 0.8268 ## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1
TAGS
#pytorch #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #region-us 
# eval_masked_v4_mrpc

This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6346
- Accuracy: 0.7941
- F1: 0.8595
- Combined Score: 0.8268## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0### Training results### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1