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@@ -3,31 +3,22 @@ language: en
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  tags:
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  - text-classification
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  - tensorflow
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- - roberta
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- datasets:
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- - go_emotions
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  license: mit
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  ---
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-
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- ## What is the GoEmotions Dataset?
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-
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- The dataset is comprised of 58000 Reddit comments with 28 emotions.
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-
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- - admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise
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  ## Usage
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  ```python
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  from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
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- tokenizer = RobertaTokenizerFast.from_pretrained("cappuch/EmoRoBERTa_Retrain")
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- model = TFRobertaForSequenceClassification.from_pretrained("cappuch/EmoRoBERTa_Retrain")
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- emotion = pipeline('sentiment-analysis',
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- model='cappuch/EmoRoBERTa_Retrain')
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- emotion_labels = emotion("Hello!")
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- print(emotion_labels)
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  #[{'label': 'neutral', 'score': 0.9964383244514465}]
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  ```
 
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  tags:
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  - text-classification
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  - tensorflow
 
 
 
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  license: mit
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  ---
 
 
 
 
 
 
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  ## Usage
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  ```python
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  from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
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+ tokenizer = RobertaTokenizerFast.from_pretrained("cappuch/Emotions_BERT")
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+ model = TFRobertaForSequenceClassification.from_pretrained("cappuch/Emotions_BERT")
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+ classific = pipeline('sentiment-analysis',
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+ model='cappuch/Emotions_BERT')
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+ labels = classific("Hello!")
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+ print(labels)
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  #[{'label': 'neutral', 'score': 0.9964383244514465}]
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  ```