def debiasing_gbdd(equality_sets, vocab, vecs, t1_list, t2_list, a1_list,
a2_list):
v_b = gbdd.get_bias_direction(equality_sets, vocab, vecs)
new_vecs = gbdd.debias_direction_linear(v_b, vecs)
t1, t2, a1, a2 = calculation.vocabs_to_dicts(vocab, new_vecs, t1_list,
t2_list, a1_list, a2_list)
return new_vecs, t1, t2, a1, a2
def debiasing_gbdd_bam(equality_sets, vocab, vecs, t1_list, t2_list, a1_list,
a2_list, aug1_list, aug2_list):
v_b = gbdd.get_bias_direction(equality_sets, vocab, vecs)
new_vecs = gbdd.debias_direction_linear(v_b, vecs)
new_vecs, proj_matrix = bam.debias_proc(equality_sets, vocab, new_vecs)
t1, t2, a1, a2, aug1, aug2 = calculation.vocabs_to_dicts(
vocab, new_vecs, t1_list, t2_list, a1_list, a2_list, aug1_list,
aug2_list)
return new_vecs, t1, t2, a1, a2, aug1, aug2
def debiasing_gbdd_bam_full(equality_sets, vocab, vecs):
v_b = gbdd.get_bias_direction(equality_sets, vocab, vecs)
new_vecs = gbdd.debias_direction_linear(v_b, vecs)
new_vecs, proj_matrix = bam.debias_proc(equality_sets, vocab, new_vecs)
return new_vecs
def debiasing_gbdd_full(equality_sets, vocab, vecs):
v_b = gbdd.get_bias_direction(equality_sets, vocab, vecs)
new_vecs = gbdd.debias_direction_linear(v_b, vecs)
return new_vecs