language: fr
license: mit
datasets:
- amazon_reviews_multi
- allocine
widget:
- text: Je pensais lire un livre nul, mais finalement je l'ai trouvé super...
- text: >-
Cette banque est bien, mais elle n'offre pas les services de paiements
sans contact.
- text: >-
Cette banque est bien et elle offre en plus les services de paiements sans
contact.
DistilCamemBERT-Sentiment
We present DistilCamemBERT-Sentiment which is DistilCamemBERT fine tuned for the sentiment analysis task for the French language. This model is constructed over 2 datasets: Amazon Reviews and Allociné.fr in order to minimize the bias. Indeed, Amazon reviews are very similar in the messages and relatively shorts, contrary to Allociné critics which are long and rich texts.
This modelization is close to tblard/tf-allocine based on CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by 2 with the same consumption power thanks to DistilCamemBERT.