"""
ModelWrapper class
--------------------------
"""
from abc import ABC, abstractmethod
[docs]class ModelWrapper(ABC):
"""A model wrapper queries a model with a list of text inputs.
Classification-based models return a list of lists, where each
sublist represents the model's scores for a given input.
Text-to-text models return a list of strings, where each string is
the output – like a translation or summarization – for a given
input.
"""
@abstractmethod
def __call__(self, text_input_list, **kwargs):
raise NotImplementedError()
[docs] def get_grad(self, text_input):
"""Get gradient of loss with respect to input tokens."""
raise NotImplementedError()
def _tokenize(self, inputs):
"""Helper method for `tokenize`"""
raise NotImplementedError()
[docs] def tokenize(self, inputs, strip_prefix=False):
"""Helper method that tokenizes input strings
Args:
inputs (list[str]): list of input strings
strip_prefix (bool): If `True`, we strip auxiliary characters added to tokens as prefixes (e.g. "##" for BERT, "Ġ" for RoBERTa)
Returns:
tokens (list[list[str]]): List of list of tokens as strings
"""
tokens = self._tokenize(inputs)
if strip_prefix:
# `aux_chars` are known auxiliary characters that are added to tokens
strip_chars = ["##", "Ġ", "__"]
# TODO: Find a better way to identify prefixes. These depend on the model, so cannot be resolved in ModelWrapper.
def strip(s, chars):
for c in chars:
s = s.replace(c, "")
return s
tokens = [[strip(t, strip_chars) for t in x] for x in tokens]
return tokens