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# roberta-base-finetuned

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4057
- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned", "results": []}]}
transformers pytorch tensorboard roberta fill-mask generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# roberta-base-finetuned

This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4057
- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3

# roberta-base-finetuned

This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4057
- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053 ## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base-finetuned

This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4057
- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053 ## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #roberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base-finetuned

This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.4057
- eval_runtime: 3.7087
- eval_samples_per_second: 167.712
- eval_steps_per_second: 2.696
- epoch: 2.11
- step: 2053## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3