
# roberta-base-spam-detector

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [0x7194633/spam_detector](https://huggingface.co/datasets/0x7194633/spam_detector) dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_accuracy: 0.9979
- eval_f1: 0.9980
- eval_precision: 0.9960
- eval_recall: 1.0
- eval_runtime: 30.7625
- eval_samples_per_second: 30.882
- eval_steps_per_second: 1.95
- epoch: 1.16
- step: 1446

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "datasets": ["0x7194633/spam_detector"], "metrics": ["accuracy"], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-spam-detector", "results": []}]}
transformers pytorch roberta text-classification en dataset:0x7194633/spam_detector license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/spam_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# roberta-base-spam-detector

This model is a fine-tuned version of roberta-base on the 0x7194633/spam_detector dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_accuracy: 0.9979
- eval_f1: 0.9980
- eval_precision: 0.9960
- eval_recall: 1.0
- eval_runtime: 30.7625
- eval_samples_per_second: 30.882
- eval_steps_per_second: 1.95
- epoch: 1.16
- step: 1446

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
# roberta-base-spam-detector

This model is a fine-tuned version of roberta-base on the 0x7194633/spam_detector dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_accuracy: 0.9979
- eval_f1: 0.9980
- eval_precision: 0.9960
- eval_recall: 1.0
- eval_runtime: 30.7625
- eval_samples_per_second: 30.882
- eval_steps_per_second: 1.95
- epoch: 1.16
- step: 1446 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3 ### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/spam_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base-spam-detector

This model is a fine-tuned version of roberta-base on the 0x7194633/spam_detector dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_accuracy: 0.9979
- eval_f1: 0.9980
- eval_precision: 0.9960
- eval_recall: 1.0
- eval_runtime: 30.7625
- eval_samples_per_second: 30.882
- eval_steps_per_second: 1.95
- epoch: 1.16
- step: 1446 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3 ### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/spam_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base-spam-detector

This model is a fine-tuned version of roberta-base on the 0x7194633/spam_detector dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0211
- eval_accuracy: 0.9979
- eval_f1: 0.9980
- eval_precision: 0.9960
- eval_recall: 1.0
- eval_runtime: 30.7625
- eval_samples_per_second: 30.882
- eval_steps_per_second: 1.95
- epoch: 1.16
- step: 1446## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

More information needed### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3