| | --- |
| | license: afl-3.0 |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | library_name: transformers |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | ## 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 = "pt-sk/bert-toxic-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. |
