mirror of
https://github.com/wassname/Castor.git
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Michael Tu
82bf90f4bb
* SST change min_freq and Kim CNN init distribution * Add tuned results for SST-1 and SST-2 * Fix typo
101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
import re
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import torch
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from torchtext.data import Field, TabularDataset
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from torchtext.data.iterator import BucketIterator
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from torchtext.vocab import Vectors
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def clean_str_sst(string):
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"""
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Tokenization/string cleaning for the SST dataset
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"""
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string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
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string = re.sub(r"\s{2,}", " ", string)
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return string.lower().strip().split()
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class SST1(TabularDataset):
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NAME = 'SST-1'
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NUM_CLASSES = 5
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TEXT_FIELD = Field(batch_first=True, tokenize=clean_str_sst)
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LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
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@staticmethod
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def sort_key(ex):
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return len(ex.text)
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@classmethod
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def splits(cls, path, train='stsa.fine.phrases.train', validation='stsa.fine.dev', test='stsa.fine.test', **kwargs):
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return super(SST1, cls).splits(
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path, train=train, validation=validation, test=test,
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format='tsv', fields=[('label', cls.LABEL_FIELD), ('text', cls.TEXT_FIELD)]
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)
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@classmethod
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def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
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unk_init=torch.Tensor.zero_):
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"""
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:param path: directory containing train, test, dev files
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:param vectors_name: name of word vectors file
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:param vectors_cache: path to directory containing word vectors file
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:param batch_size: batch size
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:param device: GPU device
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:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
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:param unk_init: function used to generate vector for OOV words
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:return:
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"""
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if vectors is None:
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vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
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train, val, test = cls.splits(path)
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cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
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return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
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sort_within_batch=True, device=device)
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class SST2(TabularDataset):
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NAME = 'SST-2'
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NUM_CLASSES = 5
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TEXT_FIELD = Field(batch_first=True, tokenize=clean_str_sst)
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LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
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@staticmethod
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def sort_key(ex):
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return len(ex.text)
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@classmethod
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def splits(cls, path, train='stsa.binary.phrases.train', validation='stsa.binary.dev', test='stsa.binary.test', **kwargs):
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return super(SST2, cls).splits(
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path, train=train, validation=validation, test=test,
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format='tsv', fields=[('label', cls.LABEL_FIELD), ('text', cls.TEXT_FIELD)]
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)
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@classmethod
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def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
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unk_init=torch.Tensor.zero_):
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"""
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:param path: directory containing train, test, dev files
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:param vectors_name: name of word vectors file
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:param vectors_cache: path to directory containing word vectors file
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:param batch_size: batch size
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:param device: GPU device
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:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
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:param unk_init: function used to generate vector for OOV words
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:return:
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"""
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if vectors is None:
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vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
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train, val, test = cls.splits(path)
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cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
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return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
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sort_within_batch=True, device=device)
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