T5 multilabel classification using tf
I am trying to do multilabel classification on a corpus of data which has labels too. WhenThe data looks like this after adding the tag in the front for each row:print(texts)0        multilabel classification: how time changes th…1        multilabel classification: hawaii has been in …2        multilabel classification: not all alaskans ar…3        multilabel classification: you should read rap…4        multilabel classification: giving stupid kids …I am trying to tokenize above and since there are multiple rows, I am guessing i have to go in a loop. What I am trying understand is, how do I get the input_ids, attention_mask? should I go in a loop to get for each row or for the entire text?am I doing it right above by adding the tag multilabel classification: for each row? am totally confused whether my assumption is wrong or whether this is the way to do it.my code is:src_tokenized = TOKENIZER.encode_plus(texts[0],max_length=SRC_MAX_LENGTH,pad_to_max_length=True,truncation=True,return_attention_mask=True,return_token_type_ids=False,return_tensors=‘tf’)src_input_ids = src_tokenized[‘input_ids’]src_attention_mask = src_tokenized[‘attention_mask’]t5_summary_ids = t5_model.generate(src_input_ids)am feeling am doing wrong by running it row by row. but am not sure. I googled for it and all i see multilabel classification example is using pytorch not tf.Appreciate all the help. TIA