readme: ---
  language:
  - fr
  
  tags:
  - classification
  license: apache-2.0
  
  metrics:
  - accuracy
  
  widget:
  - text: "tchai on est morts. on va se faire vacciner et ils vont contrôler comme les marionnettes avec des fils. d'après les 'ont dit'..."
  ---
  
  # camembert-fr-covid-tweet-classification
  This model is a fine-tune checkpoint of [Yanzhu/bertweetfr-base](https://huggingface.co/Yanzhu/bertweetfr-base), fine-tuned on SST-2.
  This model reaches an accuracy of 66.00% on the dev set.
  
  In this dataset, given a tweet, the goal was to infer the underlying topic of the tweet by choosing from four topics classes:
  - chiffres : this means, the tweet talk about statistics of covid.
  - mesures : this means, the tweet talk about measures take by government  of covid 
  - opinions : this means, the tweet talk about opinion of people like fake new. 
  - symptomes : this means, the tweet talk about symptoms or variant of covid.
  - divers : or other
   
   # Pipelining the Model
   
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
  tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
  model = AutoModelForSequenceClassification.from_pretrained("Monsia/camembert-fr-covid-tweet-classification")
  nlp_topic_classif = transformers.pipeline('topics-classification', model = model, tokenizer = tokenizer)
  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 ''...")
  # Output: [{'label': 'opinions', 'score': 0.831]
  ```
   