def test_load_word_pairs(self):
ids1, ids2 = load_word_pairs(self.pairs_fname, WhitespaceTokenizer(),
self.pairs_vocab, "tokens")
# first two token IDs reserved for [CLS] and [SEP]
assert torch.equal(torch.tensor([i.item() for i in ids1]),
torch.arange(2, self.num_pairs + 2, step=2))
assert torch.equal(torch.tensor([i.item() for i in ids2]),
torch.arange(3, self.num_pairs + 3, step=2))
def __init__(
self,
embedding_layer: torch.nn.Embedding,
seed_word_pairs_file: Union[PathLike, str],
tokenizer: Tokenizer,
mitigator_vocab: Optional[Vocabulary] = None,
namespace: str = "tokens",
):
self.ids1, self.ids2 = load_word_pairs(seed_word_pairs_file, tokenizer,
mitigator_vocab, namespace)
self.mitigator = INLPBiasMitigator()
def __init__(
self,
seed_word_pairs_file: Union[PathLike, str],
tokenizer: Tokenizer,
direction_vocab: Optional[Vocabulary] = None,
namespace: str = "tokens",
noise: float = 1e-10,
):
self.ids1, self.ids2 = load_word_pairs(seed_word_pairs_file, tokenizer,
direction_vocab, namespace)
self.direction = ClassificationNormalBiasDirection()
self.noise = noise
def __init__(
self,
seed_word_pairs_file: Union[PathLike, str],
tokenizer: Tokenizer,
direction_vocab: Optional[Vocabulary] = None,
namespace: str = "tokens",
requires_grad: bool = False,
noise: float = 1e-10,
):
self.ids1, self.ids2 = load_word_pairs(seed_word_pairs_file, tokenizer,
direction_vocab, namespace)
self.direction = TwoMeansBiasDirection(requires_grad=requires_grad)
self.noise = noise
def __init__(
self,
bias_direction: BiasDirectionWrapper,
embedding_layer: torch.nn.Embedding,
equalize_word_pairs_file: Union[PathLike, str],
tokenizer: Tokenizer,
mitigator_vocab: Optional[Vocabulary] = None,
namespace: str = "tokens",
requires_grad: bool = True,
):
# use predetermined bias direction
self.bias_direction = bias_direction
self.predetermined_bias_direction = self.bias_direction(
embedding_layer)
self.ids1, self.ids2 = load_word_pairs(equalize_word_pairs_file,
tokenizer, mitigator_vocab,
namespace)
self.mitigator = HardBiasMitigator(requires_grad=requires_grad)