paragraphs(3); ?>
tokenize($sentence); $input = $tokenizer->convertTokensToFeatures( $tokens, $tokenizer->getPaddingTokenId(), $tokenizer->getMaxModelInputSizes()['input_ids'] ); $outputs = $model($input['input_ids'], $input['attention_mask']); $predicted_class = $outputs->argmax()->item(); if($predicted_class == 0) { echo "Negative sentiment"; } elseif($predicted_class == 1) { echo "Neutral sentiment"; } elseif($predicted_class == 2) { echo "Positive sentiment"; } ?>This code uses the HuggingFace Transformers package/library to perform text sentiment analysis on a given sentence. It uses a pretrained BERT-based model to predict the sentiment of the text.