Update README.md

README.md

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 DistilCamemBERT-Sentiment
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-We present DistilCamemBERT-Sentiment which is [DistilCamemBERT](https://huggingface.co/cmarkea/distilcamembert-base) fine tuned for the sentiment analysis task for the French language. 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).
+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.
+
+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).
 
 Dataset
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@@ -22,5 +24,5 @@ Evaluation results
 Benchmark
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-How to use DistilCamemBERT-NER
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+How to use DistilCamemBERT-Sentiment
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