from __future__ import annotations
from abc import ABC
from .base import MultilabelTrainingDataset
# typing imports
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ...config import Config
from ...data_models import Taxonomy
from logging import Logger
from transformers import AutoTokenizer
# other imports
from .toplevel_general import MultiLabelTopLevelFullText
from torch import device
[docs]
class MultiLabelTopLevelMotivationBased(MultiLabelTopLevelFullText):
"""
[Adaptation from base class] This implementation responds with motivation only
PARENT DOCS:
---
"""
__doc__ += MultilabelTrainingDataset.__doc__
def __init__(
self,
config: Config,
logger: Logger,
taxonomy: Taxonomy,
dataset: list[dict[str, str]],
tokenizer: AutoTokenizer = None,
_device: device = device("cpu"),
sub_node: str = None
):
super().__init__(
config=config,
logger=logger,
taxonomy=taxonomy,
tokenizer=tokenizer,
dataset=dataset,
_device=device(_device),
sub_node=sub_node
)
[docs]
def _get_text(self, idx: int) -> str:
"""
[Adapted implementation] get text only motivation
"""
__doc__ = MultilabelTrainingDataset._get_text.__doc__
data_record = self.dataset[idx]
short_title = data_record.get("short_title", "")
motivation = data_record.get("motivation", "")
return f"""\
Een besluit over: {motivation}
"""
_get_text.__doc__ += MultilabelTrainingDataset._get_text.__doc__