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# wnli_roberta-base_144_v2

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.5634

## 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": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "wnli_roberta-base_144_v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI", "type": "glue", "args": "wnli"}, "metrics": [{"type": "accuracy", "value": 0.5633802816901409, "name": "Accuracy"}]}]}]}
pytorch generated_from_trainer en dataset:glue license:mit model-index region:us
TAGS
#pytorch #generated_from_trainer #en #dataset-glue #license-mit #model-index #region-us 




# wnli_roberta-base_144_v2

This model is a fine-tuned version of roberta-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.5634

## 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

# wnli_roberta-base_144_v2

This model is a fine-tuned version of roberta-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.5634 ## 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-mit #model-index #region-us 
 # wnli_roberta-base_144_v2

This model is a fine-tuned version of roberta-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.5634 ## 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-mit #model-index #region-us 
# wnli_roberta-base_144_v2

This model is a fine-tuned version of roberta-base on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6920
- Accuracy: 0.5634## 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