I am applying pretrained NLI models such as
roberta-large-mnli to my own sentence pairs. However, I am slightly confused by how to separate the promise and hypothesis sentences. By checking through the models available on Huggingface and the examples they show on hosted inference API, some use
</s></s> between sentences, some use
[CLS] ... [SEP] ... [SEP], and some such as your own model do not add any placeholders
I just want to know more about how
pipeline(task="sentiment-analysis", model="xxx-nli") works under-the-hood. I assume it feeds each sentence pair separately into
tokenizer.encode_plus like what is done here. But what
max_length does the model though?
Any information would be really appreciated! Thanks!