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# mrpc

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158

## 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_ratio: 0.06
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE MRPC", "type": "glue", "args": "mrpc"}, "metrics": [{"type": "accuracy", "value": 0.9019607843137255, "name": "Accuracy"}, {"type": "f1", "value": 0.9295774647887324, "name": "F1"}]}, {"task": {"type": "natural-language-inference", "name": "Natural Language Inference"}, "dataset": {"name": "glue", "type": "glue", "config": "mrpc", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.9019607843137255, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTgxMmY3ZTkyZmYyZTJhZjQzNzkxYWRhMzRkNjQ4MDU3NmRhNzJmNDUwMmI5NWQyYTQ1ODRmMGVhOGI3NzMxZCIsInZlcnNpb24iOjF9.E6AhJwh_S4LfzhJjvlUzGWDmJYzxwbzL0IKqIIiNhFGg-_N5G9_VJAgqiQz-6i9xGHB2fJM-G5XinjHRk4SeBA"}, {"type": "precision", "value": 0.9134948096885813, "name": "Precision", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2NmZThjNDI0YThmMzE4MjdhNjM3OTFmYzAwNzY4ZTM4ZDc4ZDA3NTYzYWRhNTdlNWMyZWI1NTMwZmFhNzQ5NyIsInZlcnNpb24iOjF9.nOkbqzXVD3r9LrIePn7o9Ny8_GiPoSBskCx3ey3Hrexrx00Gj6B9wkVvc8EcV5bAsBTeAJSeqO7ncS_-WJjlCQ"}, {"type": "recall", "value": 0.946236559139785, "name": "Recall", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzA2NDgzYTkzMTY4ZDQxYTdlZmM2ODY4YzM4N2E0ODk0YzRkNDI3YTFhMGIwNDZhNTI0MmIyNGU0YmFlMzRjYyIsInZlcnNpb24iOjF9.jNL0IQk6XnUd6zFfHwTSL41Ax35OdoE8xQA-2PqEFs9UtT2O9fo6cZyXDln6QPMGHOlwNgPp_PX6mLrmDHN6Cw"}, {"type": "auc", "value": 0.9536411880747964, "name": "AUC", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE0ZWZlNGFkMzdhNTdjZjY0NDkzNDZhOTJmY2Q1MWU4MTc3NGMwYmRjNTlkMTZjOTBiNjIwOTUzZWZhZTcwNSIsInZlcnNpb24iOjF9.ZVekwshvwAi8K6gYJmKEDk8riyiOqDhsfzbSxXa-AWKvREksbNtsDo_u6iOEYImGLbcEFfgesDE-cBnEsmMdAg"}, {"type": "f1", "value": 0.9295774647887324, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDQwMmE1Y2FhMGE4M2Q5YjU3NTAyZTljZWQ5ODRkMGEyZmI4M2FhNDJjYjlkMzllMzU5NDQ1ZWI2YjNiNmM0OCIsInZlcnNpb24iOjF9.a2jDnaSZhCJ_3f1rBJ8mXfyLCRR6Y9tYb_Hayi00NPWrejDML8Bc-LoobxlPdbd8x8LVJ2vOWhbH5LP4J9kOBg"}, {"type": "loss", "value": 0.48942330479621887, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODFkMWQ5NTQ0ODMwNjQ2MzcyODA1ODlhZGUzNTg4NjE2M2U5MmIzYjQ3NzgxNTQyZDkyMGNiM2ZhYzc4ZGY0MSIsInZlcnNpb24iOjF9.K6fAIi21ZNtOqKS5c9jlO7kXISNHb0DD4pzdgLsESVjjOYxqS4C9f_OBJjIV-KtuwQGbi3yNC5Y4jTWk2HvNCQ"}]}]}]}
transformers pytorch tensorboard safetensors roberta text-classification generated_from_trainer en dataset:glue base_model:roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 




# mrpc

This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158

## 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_ratio: 0.06
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

# mrpc

This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158 ## 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_ratio: 0.06
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # mrpc

This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158 ## 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_ratio: 0.06
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #en #dataset-glue #base_model-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# mrpc

This model is a fine-tuned version of roberta-base on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4898
- Accuracy: 0.9020
- F1: 0.9296
- Combined Score: 0.9158## 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_ratio: 0.06
- num_epochs: 5.0### Training results### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1