EduNLP.ModelZoo

rnn

class EduNLP.ModelZoo.rnn.LM(rnn_type: str, vocab_size: int, embedding_dim: int, hidden_size: int, num_layers=1, bidirectional=False, embedding=None, model_params=None, **kwargs)

Examples

>>> import torch
>>> seq_idx = torch.LongTensor([[1, 2, 3], [1, 2, 0], [3, 0, 0]])
>>> seq_len = torch.LongTensor([3, 2, 1])
>>> lm = LM("RNN", 4, 3, 2)
>>> output, hn = lm(seq_idx, seq_len)
>>> output.shape
torch.Size([3, 3, 2])
>>> hn.shape
torch.Size([1, 3, 2])
>>> lm = LM("RNN", 4, 3, 2, num_layers=2)
>>> output, hn = lm(seq_idx, seq_len)
>>> output.shape
torch.Size([3, 3, 2])
>>> hn.shape
torch.Size([2, 3, 2])

forward(seq_idx, seq_len)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

utils

class EduNLP.ModelZoo.utils.Masker(mask: (<class 'int'>, <class 'str'>, Ellipsis) = 0, per=0.2, seed=None)

Examples

>>> masker = Masker(per=0.5, seed=10)
>>> items = [[1, 1, 3, 4, 6], [2], [5, 9, 1, 4]]
>>> masked_seq, mask_label = masker(items)
>>> masked_seq
[[1, 1, 0, 0, 6], [2], [0, 9, 0, 4]]
>>> mask_label
[[0, 0, 1, 1, 0], [0], [1, 0, 1, 0]]
>>> items = [[1, 2, 3], [1, 1, 0], [2, 0, 0]]
>>> masked_seq, mask_label = masker(items, [3, 2, 1])
>>> masked_seq
[[1, 0, 3], [0, 1, 0], [2, 0, 0]]
>>> mask_label
[[0, 1, 0], [1, 0, 0], [0, 0, 0]]
>>> masker = Masker(mask="[MASK]", per=0.5, seed=10)
>>> items = [["a", "b", "c"], ["d", "[PAD]", "[PAD]"], ["hello", "world", "[PAD]"]]
>>> masked_seq, mask_label = masker(items, length=[3, 1, 2])
>>> masked_seq
[['a', '[MASK]', 'c'], ['d', '[PAD]', '[PAD]'], ['hello', '[MASK]', '[PAD]']]
>>> mask_label
[[0, 1, 0], [0, 0, 0], [0, 1, 0]]

class EduNLP.ModelZoo.utils.PadSequence(length, pad_val=0, clip=True)

Pad the sequence.

Pad the sequence to the given length by inserting pad_val. If clip is set, sequence that has length larger than length will be clipped.

Parameters

• length (int) – The maximum length to pad/clip the sequence

• pad_val (number) – The pad value. Default 0

• clip (bool) –

EduNLP.ModelZoo.utils.pad_sequence(sequence: list, max_length=None, pad_val=0, clip=True)

Parameters

• sequence –

• max_length –

• pad_val –

• clip –

Examples

>>> seq = [[4, 3, 3], [2], [3, 3, 2]]
>>> pad_sequence(seq)
[[4, 3, 3], [2, 0, 0], [3, 3, 2]]
>>> pad_sequence(seq, pad_val=1)
[[4, 3, 3], [2, 1, 1], [3, 3, 2]]
>>> pad_sequence(seq, max_length=2)
[[4, 3], [2, 0], [3, 3]]
>>> pad_sequence(seq, max_length=2, clip=False)
[[4, 3, 3], [2, 0], [3, 3, 2]]

EduNLP.ModelZoo.utils.set_device(_net, ctx, *args, **kwargs)

code from longling v1.3.26