
# roberta-base-ad-detector

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an [ad_detector](https://huggingface.co/0x7194633/ad_detector) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0

## Model description

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: 1

{"language": ["en"], "license": "apache-2.0", "datasets": ["0x7194633/ad_detector"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "text-classification", "model-index": [{"name": "roberta-base-ad-detector", "results": []}]}
transformers pytorch roberta text-classification en dataset:0x7194633/ad_detector license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/ad_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# roberta-base-ad-detector

This model is a fine-tuned version of roberta-base on an ad_detector dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0

## Model description

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: 1

# roberta-base-ad-detector

This model is a fine-tuned version of roberta-base on an ad_detector dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0 ## Model description

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: 1
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/ad_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # roberta-base-ad-detector

This model is a fine-tuned version of roberta-base on an ad_detector dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0 ## Model description

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: 1
TAGS
#transformers #pytorch #roberta #text-classification #en #dataset-0x7194633/ad_detector #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# roberta-base-ad-detector

This model is a fine-tuned version of roberta-base on an ad_detector dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0010
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0## Model description

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: 1