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- from __future__ import annotations
- import math
- import random
- class NoDuplicatesDataLoader:
- def __init__(self, train_examples, batch_size):
- """
- A special data loader to be used with MultipleNegativesRankingLoss.
- The data loader ensures that there are no duplicate sentences within the same batch
- """
- self.batch_size = batch_size
- self.data_pointer = 0
- self.collate_fn = None
- self.train_examples = train_examples
- random.shuffle(self.train_examples)
- def __iter__(self):
- for _ in range(self.__len__()):
- batch = []
- texts_in_batch = set()
- while len(batch) < self.batch_size:
- example = self.train_examples[self.data_pointer]
- valid_example = True
- for text in example.texts:
- if text.strip().lower() in texts_in_batch:
- valid_example = False
- break
- if valid_example:
- batch.append(example)
- for text in example.texts:
- texts_in_batch.add(text.strip().lower())
- self.data_pointer += 1
- if self.data_pointer >= len(self.train_examples):
- self.data_pointer = 0
- random.shuffle(self.train_examples)
- yield self.collate_fn(batch) if self.collate_fn is not None else batch
- def __len__(self):
- return math.floor(len(self.train_examples) / self.batch_size)
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