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# roBERTa_practice

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Accuracy: 0.92
- F1: 0.9195

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "roBERTa_practice", "results": []}]}
transformers pytorch tensorboard roberta generated_from_trainer dataset:imdb license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #generated_from_trainer #dataset-imdb #license-mit #endpoints_compatible #region-us 




# roBERTa_practice

This model is a fine-tuned version of roberta-base on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Accuracy: 0.92
- F1: 0.9195

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2

# roBERTa_practice

This model is a fine-tuned version of roberta-base on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Accuracy: 0.92
- F1: 0.9195 ## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #generated_from_trainer #dataset-imdb #license-mit #endpoints_compatible #region-us 
 # roBERTa_practice

This model is a fine-tuned version of roberta-base on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Accuracy: 0.92
- F1: 0.9195 ## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Training results ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #roberta #generated_from_trainer #dataset-imdb #license-mit #endpoints_compatible #region-us 
# roBERTa_practice

This model is a fine-tuned version of roberta-base on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2800
- Accuracy: 0.92
- F1: 0.9195## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2### Training results### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2