Create README.md

README.md

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+---
+pipeline_type: "text-classification"
+
+widget:
+- text: "this is a lovely message"
+  example_title: "Example 1"
+  multi_class: false
+- text: "you are an idiot and you and your family should go back to your country"
+  example_title: "Example 2"
+  multi_class: false
+
+
+language: 
+  - en
+  - nl
+  - fr
+  - pt
+  - it
+  - es
+  - de
+  - da
+  - pl
+  - af
+  
+datasets:
+- jigsaw_toxicity_pred
+metrics:
+- F1 Accuracy
+---
+
+# citizenlab/distilbert-base-multilingual-cased-toxicity
+
+This is multilingual Distil-Bert model sequence classifier trained based on [JIGSAW Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset.
+
+## How to use it
+
+```python
+from transformers import pipeline
+
+model_path = "citizenlab/distilbert-base-multilingual-cased-toxicity"
+
+topic_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
+topic_classifier("this is a lovely message")
+> [{'label': 'not_toxic', 'score': 0.9954179525375366}]
+
+topic_classifier("you are an idiot and you and your family should go back to your country")
+> [{'label': 'toxic', 'score': 0.9948776960372925}]
+
+```
+
+## Evaluation
+
+### Accuracy
+
+```
+  Accuracy Score = 0.9425
+F1 Score (Micro) = 0.9450549450549449
+F1 Score (Macro) = 0.8491432341169309
+```