camembert-fr-covid-tweet-sentiment-classification

This model is a fine-tune checkpoint of Yanzhu/bertweetfr-base, fine-tuned on SST-2. This model reaches an accuracy of 71% 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:

• 0 : negatif
• 1 : neutre
• 2 : positif

Pipelining the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("data354/camembert-fr-covid-tweet-sentiment-classification")
model = AutoModelForSequenceClassification.from_pretrained("data354/camembert-fr-covid-tweet-sentiment-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]