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README.md

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 DistilCamemBERT-Sentiment
 =========================
 
-We present DistilCamemBERT-Sentiment which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine tuned for the sentiment analysis task for the French language. This model is construct over 2 datasets: [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) and [allocine](https://huggingface.co/datasets/allocine) to aims minimize the biais. Inded, Amazon review are very similare beetwen the messages and relatevely short. To opposate Allocine criticims are long and rich text.
+We present DistilCamemBERT-Sentiment which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine tuned for the sentiment analysis task for the French language. This model is construct over 2 datasets: [Amazon Reviews](https://huggingface.co/datasets/amazon_reviews_multi) and [Allociné.fr](https://huggingface.co/datasets/allocine) to aims minimize the biais. Inded, Amazon review are very similare beetwen the messages and relatevely short. To opposate Allocine criticims are long and rich text.
 
 This modelisation is closely of [tblard/tf-allocine](https://huggingface.co/tblard/tf-allocine) base on [CamemBERT](https://huggingface.co/camembert-base) 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](https://huggingface.co/cmarkea/distilcamembert-base).