Text Classification models give "you should probably train your model"
I tried using a few of the top downloaded transformer models for text classificationSamLowe/roberta-base-go_emotions · Hugging Face,ProsusAI/finbert · Hugging Face. The pages seems to imply it should work out of the box to yield sentiment given a sentence. However, I get a warning saying:If you want to useRobertaLMHeadModelas a standalone, addis_decoder=True.Some weights of RobertaForCausalLM were not initialized from the model checkpoint at SamLowe/roberta-base-go_emotions and are newly initialized: [‘lm_head.bias’, ‘lm_head.decoder.bias’, ‘lm_head.dense.bias’, ‘lm_head.dense.weight’, ‘lm_head.layer_norm.bias’, ‘lm_head.layer_norm.weight’]You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.Using pipeline(“text-generation”, model=“SamLowe/roberta-base-go_emotions”, top_k=None, is_decoder=True) throws unrecognized kwarg.If I try to use it as is, it appends characters to input text rather than trying to classify sentiment. Am I using this model wrong?Thanks much