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This model is a fine-tune checkpoint of 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

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]
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