src/mlm/scorers.py [457:473]:
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        else:
            scores = np.zeros((len(corpus),))

        sent_count = 0
        batch_log_interval = 20

        batch_score_accumulation = 1
        batch_sent_idxs_per_ctx = [[] for ctx in self._ctxs]
        batch_scores_per_ctx = [[] for ctx in self._ctxs]
        batch_masked_positions_per_ctx = [[] for ctx in self._ctxs]

        def sum_accumulated_scores():
            for ctx_idx in range(len(self._ctxs)):
                for batch_sent_idxs, batch_scores, batch_masked_positions in zip(batch_sent_idxs_per_ctx[ctx_idx], batch_scores_per_ctx[ctx_idx], batch_masked_positions_per_ctx[ctx_idx]):
                    if per_token:
                        # Slow; only use when necessary
                        for batch_sent_idx, batch_score, batch_masked_position in zip(batch_sent_idxs, batch_scores, batch_masked_positions):
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src/mlm/scorers.py [667:683]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        else:
            scores = np.zeros((len(corpus),))

        sent_count = 0
        batch_log_interval = 20

        batch_score_accumulation = 1
        batch_sent_idxs_per_ctx = [[] for ctx in self._ctxs]
        batch_scores_per_ctx = [[] for ctx in self._ctxs]
        batch_masked_positions_per_ctx = [[] for ctx in self._ctxs]

        def sum_accumulated_scores():
            for ctx_idx in range(len(self._ctxs)):
                for batch_sent_idxs, batch_scores, batch_masked_positions in zip(batch_sent_idxs_per_ctx[ctx_idx], batch_scores_per_ctx[ctx_idx], batch_masked_positions_per_ctx[ctx_idx]):
                    if per_token:
                        # Slow; only use when necessary
                        for batch_sent_idx, batch_score, batch_masked_position in zip(batch_sent_idxs, batch_scores, batch_masked_positions):
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