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@@ -23,8 +23,8 @@ In this dataset, given a tweet, the goal was to infer the underlying topic of th
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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- tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification")
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- model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification")
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  nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
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  nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...")
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
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+ tokenizer = AutoTokenizer.from_pretrained("data354/camembert-fr-covid-tweet-sentiment-classification")
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+ model = AutoModelForSequenceClassification.from_pretrained("data354/camembert-fr-covid-tweet-sentiment-classification")
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  nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
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  nlp_topic_classif("tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les '' ont dit ''...")