
## Model description
This model is a fine-tuned version of the [bert-base-uncased](https://huggingface.co/transformers/model_doc/bert.html) model to classify toxic comments.

## How to use

You can use the model with the following code.

```python
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline

model_path = "JungleLee/bert-toxic-comment-classification"
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=2)

pipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(pipeline("You're a fucking nerd."))
```

## Training data
The training data comes from this [Kaggle competition](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data). We use 90% of the `train.csv` data to train the model.

## Evaluation results

The model achieves 0.95 AUC in a 1500 rows held-out test set.
{"language": ["en"], "license": "afl-3.0", "library_name": "transformers", "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification en license:afl-3.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #en #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 


## Model description
This model is a fine-tuned version of the bert-base-uncased model to classify toxic comments.

## How to use

You can use the model with the following code.



## Training data
The training data comes from this Kaggle competition. We use 90% of the 'URL' data to train the model.

## Evaluation results

The model achieves 0.95 AUC in a 1500 rows held-out test set.
## Model description
This model is a fine-tuned version of the bert-base-uncased model to classify toxic comments. ## How to use

You can use the model with the following code. ## Training data
The training data comes from this Kaggle competition. We use 90% of the 'URL' data to train the model. ## Evaluation results

The model achieves 0.95 AUC in a 1500 rows held-out test set.
TAGS
#transformers #pytorch #bert #text-classification #en #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Model description
This model is a fine-tuned version of the bert-base-uncased model to classify toxic comments. ## How to use

You can use the model with the following code. ## Training data
The training data comes from this Kaggle competition. We use 90% of the 'URL' data to train the model. ## Evaluation results

The model achieves 0.95 AUC in a 1500 rows held-out test set.
TAGS
#transformers #pytorch #bert #text-classification #en #license-afl-3.0 #autotrain_compatible #endpoints_compatible #region-us 
## Model description
This model is a fine-tuned version of the bert-base-uncased model to classify toxic comments.## How to use

You can use the model with the following code.## Training data
The training data comes from this Kaggle competition. We use 90% of the 'URL' data to train the model.## Evaluation results

The model achieves 0.95 AUC in a 1500 rows held-out test set.