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# camembert-base-mrpc

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716

## 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
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6

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transformers pytorch camembert text-classification generated_from_trainer en dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# camembert-base-mrpc

This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716

## 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
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6

# camembert-base-mrpc

This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716 ## 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
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #camembert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # camembert-base-mrpc

This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716 ## 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
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #camembert #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# camembert-base-mrpc

This model is a fine-tuned version of camembert-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4286
- Accuracy: 0.8505
- F1: 0.8928
- Combined Score: 0.8716## 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
- num_epochs: 5.0### Training results### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu102
- Datasets 2.1.0
- Tokenizers 0.11.6