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/twitter-xlm-roberta-base-sentiment-finetunned
+
+This is multilingual XLM-Roberta model sequence classifier fine tunned and based on [Cardiff NLP Group](cardiffnlp/twitter-roberta-base-sentiment) sentiment classification model.
+
+## How to use it
+
+```python
+from transformers import pipeline
+
+model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned"
+
+sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
+sentiment_classifier("this is a lovely message")
+> [{'label': 'Positive', 'score': 0.9918450713157654}]
+
+sentiment_classifier("you are an idiot and you and your family should go back to your country")
+> [{'label': 'Negative', 'score': 0.9849833846092224}]
+
+```
+
+## Evaluation
+
+```
+              precision    recall  f1-score   support
+
+    Negative       0.57      0.14      0.23        28
+     Neutral       0.78      0.94      0.86       132
+    Positive       0.89      0.80      0.85        51
+
+    accuracy                           0.80       211
+   macro avg       0.75      0.63      0.64       211
+weighted avg       0.78      0.80      0.77       211
+```
+