README.md

metadata

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-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: -positif -negatif -neutre

Pipelining the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("Monsia/camembert-fr-covid-tweet-sentiment-classification")
model = AutoModelForSequenceClassification.from_pretrained("Monsia/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]