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# roberta-large-finetuned-winogrande

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6939
- eval_accuracy: 0.5036
- eval_runtime: 392.378
- eval_samples_per_second: 3.229
- eval_steps_per_second: 0.808
- epoch: 1.01
- step: 1169

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 11262
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 8

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-large-finetuned-winogrande", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# roberta-large-finetuned-winogrande

This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6939
- eval_accuracy: 0.5036
- eval_runtime: 392.378
- eval_samples_per_second: 3.229
- eval_steps_per_second: 0.808
- epoch: 1.01
- step: 1169

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 11262
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 8

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2

# roberta-large-finetuned-winogrande

This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6939
- eval_accuracy: 0.5036
- eval_runtime: 392.378
- eval_samples_per_second: 3.229
- eval_steps_per_second: 0.808
- epoch: 1.01
- step: 1169 ## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 11262
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 8 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-large-finetuned-winogrande

This model is a fine-tuned version of roberta-large on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6939
- eval_accuracy: 0.5036
- eval_runtime: 392.378
- eval_samples_per_second: 3.229
- eval_steps_per_second: 0.808
- epoch: 1.01
- step: 1169 ## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 11262
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 8 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cpu
- Datasets 2.10.1
- Tokenizers 0.13.2