TIES MERGING#

Functions#

models.tak_utils.ties_merging.chunked_disjoint_mean(vectors, chunk_size=10000)[source]#

models.tak_utils.ties_merging.chunked_sum(tensor, chunk_size=10000)[source]#

models.tak_utils.ties_merging.disjoint_merge(Tensor, merge_func, reference_sign_to_mult, weights=None)[source]#

models.tak_utils.ties_merging.resolve_sign(Tensor, mode=None)[source]#

models.tak_utils.ties_merging.resolve_zero_signs(sign_to_mult, method='majority')[source]#

models.tak_utils.ties_merging.ties_merging(vectors, topK=20, merging_type='mean', weights=None, **kwargs)[source]#

models.tak_utils.ties_merging.topk_values_mask(M, K=0.7, return_mask=False)[source]#

models.tak_utils.ties_merging.chunked_disjoint_mean(vectors, chunk_size=10000)[source]#

models.tak_utils.ties_merging.chunked_sum(tensor, chunk_size=10000)[source]#

models.tak_utils.ties_merging.disjoint_merge(Tensor, merge_func, reference_sign_to_mult, weights=None)[source]#

models.tak_utils.ties_merging.resolve_sign(Tensor, mode=None)[source]#

models.tak_utils.ties_merging.resolve_zero_signs(sign_to_mult, method='majority')[source]#

models.tak_utils.ties_merging.ties_merging(vectors, topK=20, merging_type='mean', weights=None, **kwargs)[source]#

models.tak_utils.ties_merging.topk_values_mask(M, K=0.7, return_mask=False)[source]#