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@@ -33,7 +33,7 @@ This way, the model learns an inner representation of the English language that
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  useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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  classifier using the features produced by the BERT model as inputs.
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- ## Model description [xaqren/sentiment_analysis]
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  This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for
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  further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.
 
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  useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
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  classifier using the features produced by the BERT model as inputs.
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+ ## Model description [sbcBI/sentiment_analysis]
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  This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for
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  further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.