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# bart-large-mrpc

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947

## 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

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "facebook/bart-large", "model-index": [{"name": "bart-large-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.8774509803921569, "name": "Accuracy"}, {"type": "f1", "value": 0.9119718309859154, "name": "F1"}]}]}]}
transformers pytorch bart text-classification generated_from_trainer en dataset:glue base_model:facebook/bart-large license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text-classification #generated_from_trainer #en #dataset-glue #base_model-facebook/bart-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# bart-large-mrpc

This model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947

## 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

# bart-large-mrpc

This model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947 ## 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 #bart #text-classification #generated_from_trainer #en #dataset-glue #base_model-facebook/bart-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # bart-large-mrpc

This model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947 ## 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 #bart #text-classification #generated_from_trainer #en #dataset-glue #base_model-facebook/bart-large #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# bart-large-mrpc

This model is a fine-tuned version of facebook/bart-large on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5684
- Accuracy: 0.8775
- F1: 0.9120
- Combined Score: 0.8947## 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