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Models by this creator
The bert-base-multilingual-uncased-sentiment model is a text classification model that uses the BERT architecture. It is trained on a multilingual dataset and is designed to predict the sentiment of a given text. The model is capable of understanding text in multiple languages and can classify it into different sentiment categories such as positive, negative, or neutral.
The flaubert_small_cased_sentiment model is a sentiment analysis model that has been finetuned on product reviews in French. It predicts the sentiment of a review, ranging from very negative (1 star) to very positive (5 stars). The model has been trained on a combination of the French portion of amazon_reviews_multi dataset and additional similar reviews. The accuracy of the model has been evaluated on the French test set of amazon_reviews_multi, with metrics including exact match accuracy and accuracy within one star. This model can be used as is for sentiment analysis of French product reviews, or can be further finetuned for related sentiment analysis tasks.