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--- |
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pipeline_type: "text-classification" |
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widget: |
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- text: "this is a lovely message" |
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example_title: "Example 1" |
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multi_class: false |
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- text: "you are an idiot and you and your family should go back to your country" |
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example_title: "Example 2" |
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multi_class: false |
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language: |
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- en |
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- nl |
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- fr |
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- pt |
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- it |
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- es |
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- de |
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- da |
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- pl |
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- af |
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datasets: |
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- jigsaw_toxicity_pred |
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metrics: |
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- F1 Accuracy |
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--- |
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# citizenlab/twitter-xlm-roberta-base-sentiment-finetunned |
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This is multilingual XLM-Roberta model sequence classifier fine tunned and based on [Cardiff NLP Group](cardiffnlp/twitter-roberta-base-sentiment) sentiment classification model. |
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## How to use it |
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```python |
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from transformers import pipeline |
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model_path = "citizenlab/twitter-xlm-roberta-base-sentiment-finetunned" |
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sentiment_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path) |
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sentiment_classifier("this is a lovely message") |
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> [{'label': 'Positive', 'score': 0.9918450713157654}] |
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sentiment_classifier("you are an idiot and you and your family should go back to your country") |
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> [{'label': 'Negative', 'score': 0.9849833846092224}] |
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``` |
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## Evaluation |
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``` |
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precision recall f1-score support |
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Negative 0.57 0.14 0.23 28 |
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Neutral 0.78 0.94 0.86 132 |
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Positive 0.89 0.80 0.85 51 |
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accuracy 0.80 211 |
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macro avg 0.75 0.63 0.64 211 |
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weighted avg 0.78 0.80 0.77 211 |
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``` |
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