Update README.md

README.md

@@ -1,34 +1,34 @@
----
-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
-
+---
+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 = "Kwaai/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.