Update README.md

README.md

@@ -8,28 +8,23 @@ license: mit
 
 # flaubert_small_cased_sentiment
 
-This is a `flaubert_small_cased` model finetuned for sentiment analysis on product reviews in French. It predicts the sentiment of the review, from `very_negative` (1 star) to `very_positive` (5 stars).
+This is a `flaubert` model finetuned for sentiment analysis on company emails in French. It predicts the sentiment of the email, from `negative` to `positive`.
 
-This model is intended for direct use as a sentiment analysis model for French product reviews, or for further finetuning on related sentiment analysis tasks.
+This model is intended for direct use as a sentiment analysis model for French emails, or for further finetuning on related sentiment analysis tasks.
 
 ## Training data
 
-The training data consists of the French portion of `amazon_reviews_multi`, supplemented with another 140,000 similar reviews.
+The training data consists of the French portion of `emails_multi`, supplemented with another 40,000 similar emails.
 
 ## Accuracy
 
-The finetuned model was evaluated on the French test set of `amazon_reviews_multi`.
+The finetuned model was evaluated on the French test set of `emails_multi`.
 
 - Accuracy (exact) is the exact match on the number of stars.
 - Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the number given by the human reviewer. 
 
 
-| Language | Accuracy (exact) | Accuracy (off-by-1) |
-| -------- | ---------------------- | ------------------- |
-| French  | 61.56%                 | 95.66%
+| Language | Accuracy (exact) | 
+| -------- | ---------------------- 
+| French  |95%                 | 
 
-## Contact 
-
-[NLP Town](https://www.nlp.town) offers a suite of sentiment models for a wide range of languages, including an improved multilingual model through [RapidAPI](https://rapidapi.com/nlp-town-nlp-town-default/api/multilingual-sentiment-analysis2/). 
-
-Feel free to contact us for questions, feedback and/or requests for similar models.