what's difference between tokenizer.encode and tokenizer.encode_plus in Hugging Face

Question

Here is an example of doing sequence classification using a model to determine if two sequences are paraphrases of each other. The two examples give two different results. Can you help me explain why tokenizer.encode and tokenizer.encode_plus give different results?

Example 1 (with .encode_plus()):

paraphrase = tokenizer.encode_plus(sequence_0, sequence_2, return_tensors="pt")
not_paraphrase = tokenizer.encode_plus(sequence_0, sequence_1, return_tensors="pt")

paraphrase_classification_logits = model(**paraphrase)[0]
not_paraphrase_classification_logits = model(**not_paraphrase)[0]

Example 2 (with .encode()):

paraphrase = tokenizer.encode(sequence_0, sequence_2, return_tensors="pt")
not_paraphrase = tokenizer.encode(sequence_0, sequence_1, return_tensors="pt")

paraphrase_classification_logits = model(paraphrase)[0]
not_paraphrase_classification_logits = model(not_paraphrase)[0]

Answer 1

The main difference is stemming from the additional information that encode_plus is providing. If you read the documentation on the respective functions, then there is a slight difference forencode():

Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary. Same as doing self.convert_tokens_to_ids(self.tokenize(text)).

and the description of encode_plus():

Returns a dictionary containing the encoded sequence or sequence pair and additional information: the mask for sequence classification and the overflowing elements if a max_length is specified.

Depending on your specified model and input sentence, the difference lies in the additionally encoded information, specifically the input mask. Since you are feeding in two sentences at a time, BERT (and likely other model variants), expect some form of masking, which allows the model to discern between the two sequences, see here. Since encode_plus is providing this information, but encode isn't, you get different output results.