@@ -16,6 +16,7 @@ __all__ = [ | |||
'CSVLoader', | |||
'JsonLoader', | |||
'ConllLoader', | |||
'MatchingLoader', | |||
'SNLILoader', | |||
'SSTLoader', | |||
'PeopleDailyCorpusLoader', | |||
@@ -26,6 +27,6 @@ __all__ = [ | |||
] | |||
from .embed_loader import EmbedLoader | |||
from .dataset_loader import DataSetLoader, CSVLoader, JsonLoader, ConllLoader, SNLILoader, SSTLoader, \ | |||
PeopleDailyCorpusLoader, Conll2003Loader | |||
from .dataset_loader import DataSetLoader, CSVLoader, JsonLoader, ConllLoader, MatchingLoader,\ | |||
SNLILoader, SSTLoader, PeopleDailyCorpusLoader, Conll2003Loader | |||
from .model_io import ModelLoader, ModelSaver |
@@ -16,19 +16,24 @@ __all__ = [ | |||
'CSVLoader', | |||
'JsonLoader', | |||
'ConllLoader', | |||
'MatchingLoader', | |||
'SNLILoader', | |||
'SSTLoader', | |||
'PeopleDailyCorpusLoader', | |||
'Conll2003Loader', | |||
] | |||
import os | |||
from nltk import Tree | |||
from typing import Union, Dict | |||
from ..core.vocabulary import Vocabulary | |||
from ..core.dataset import DataSet | |||
from ..core.instance import Instance | |||
from .file_reader import _read_csv, _read_json, _read_conll | |||
from .base_loader import DataSetLoader | |||
from .base_loader import DataSetLoader, DataInfo | |||
from .data_loader.sst import SSTLoader | |||
from ..core.const import Const | |||
from ..modules.encoder._bert import BertTokenizer | |||
class PeopleDailyCorpusLoader(DataSetLoader): | |||
@@ -244,6 +249,162 @@ class JsonLoader(DataSetLoader): | |||
return ds | |||
class MatchingLoader(DataSetLoader): | |||
""" | |||
别名::class:`fastNLP.io.MatchingLoader` :class:`fastNLP.io.dataset_loader.MatchingLoader` | |||
读取Matching数据集,根据数据集做预处理并返回DataInfo。 | |||
数据来源: | |||
SNLI: https://nlp.stanford.edu/projects/snli/snli_1.0.zip | |||
""" | |||
def __init__(self, data_format: str='snli', for_model: str='esim', bert_dir=None): | |||
super(MatchingLoader, self).__init__() | |||
self.data_format = data_format.lower() | |||
self.for_model = for_model.lower() | |||
self.bert_dir = bert_dir | |||
def _load(self, path: str) -> DataSet: | |||
raise NotImplementedError | |||
def process(self, paths: Union[str, Dict[str, str]], **options) -> DataInfo: | |||
if isinstance(paths, str): | |||
paths = {'train': paths} | |||
data_set = {} | |||
for n, p in paths.items(): | |||
if self.data_format == 'snli': | |||
data = self._load_snli(p) | |||
else: | |||
raise RuntimeError(f'Your data format is {self.data_format}, ' | |||
f'Please choose data format from [snli]') | |||
if self.for_model == 'esim': | |||
data = self._for_esim(data) | |||
elif self.for_model == 'bert': | |||
data = self._for_bert(data, self.bert_dir) | |||
else: | |||
raise RuntimeError(f'Your model is {self.data_format}, ' | |||
f'Please choose from [esim, bert]') | |||
data_set[n] = data | |||
print(f'successfully load {n} set!') | |||
if not hasattr(self, 'vocab'): | |||
raise RuntimeError(f'There is NOT vocab attribute built!') | |||
if not hasattr(self, 'label_vocab'): | |||
raise RuntimeError(f'There is NOT label vocab attribute built!') | |||
if self.for_model != 'bert': | |||
from fastNLP.modules.encoder.embedding import StaticEmbedding | |||
embedding = StaticEmbedding(self.vocab, model_dir_or_name='en') | |||
data_info = DataInfo(vocabs={'vocab': self.vocab, 'target_vocab': self.label_vocab}, | |||
embeddings={'glove': embedding} if self.for_model != 'bert' else None, | |||
datasets=data_set) | |||
return data_info | |||
@staticmethod | |||
def _load_snli(path: str) -> DataSet: | |||
""" | |||
读取SNLI数据集 | |||
数据来源: https://nlp.stanford.edu/projects/snli/snli_1.0.zip | |||
:param str path: 数据集路径 | |||
:return: | |||
""" | |||
raw_ds = JsonLoader( | |||
fields={ | |||
'sentence1_parse': Const.INPUTS(0), | |||
'sentence2_parse': Const.INPUTS(1), | |||
'gold_label': Const.TARGET, | |||
} | |||
)._load(path) | |||
return raw_ds | |||
def _for_esim(self, raw_ds: DataSet): | |||
if self.data_format == 'snli' or self.data_format == 'mnli': | |||
def parse_tree(x): | |||
t = Tree.fromstring(x) | |||
return t.leaves() | |||
raw_ds.apply(lambda ins: parse_tree( | |||
ins[Const.INPUTS(0)]), new_field_name=Const.INPUTS(0)) | |||
raw_ds.apply(lambda ins: parse_tree( | |||
ins[Const.INPUTS(1)]), new_field_name=Const.INPUTS(1)) | |||
raw_ds.drop(lambda x: x[Const.TARGET] == '-') | |||
if not hasattr(self, 'vocab'): | |||
self.vocab = Vocabulary().from_dataset(raw_ds, [Const.INPUTS(0), Const.INPUTS(1)]) | |||
if not hasattr(self, 'label_vocab'): | |||
self.label_vocab = Vocabulary(padding=None, unknown=None).from_dataset(raw_ds, field_name=Const.TARGET) | |||
raw_ds.apply(lambda ins: [self.vocab.to_index(w) for w in ins[Const.INPUTS(0)]], new_field_name=Const.INPUTS(0)) | |||
raw_ds.apply(lambda ins: [self.vocab.to_index(w) for w in ins[Const.INPUTS(1)]], new_field_name=Const.INPUTS(1)) | |||
raw_ds.apply(lambda ins: self.label_vocab.to_index(Const.TARGET), new_field_name=Const.TARGET) | |||
raw_ds.set_input(Const.INPUTS(0), Const.INPUTS(1)) | |||
raw_ds.set_target(Const.TARGET) | |||
return raw_ds | |||
def _for_bert(self, raw_ds: DataSet, bert_dir: str): | |||
if self.data_format == 'snli' or self.data_format == 'mnli': | |||
def parse_tree(x): | |||
t = Tree.fromstring(x) | |||
return t.leaves() | |||
raw_ds.apply(lambda ins: parse_tree( | |||
ins[Const.INPUTS(0)]), new_field_name=Const.INPUTS(0)) | |||
raw_ds.apply(lambda ins: parse_tree( | |||
ins[Const.INPUTS(1)]), new_field_name=Const.INPUTS(1)) | |||
raw_ds.drop(lambda x: x[Const.TARGET] == '-') | |||
tokenizer = BertTokenizer.from_pretrained(bert_dir) | |||
vocab = Vocabulary(padding=None, unknown=None) | |||
with open(os.path.join(bert_dir, 'vocab.txt')) as f: | |||
lines = f.readlines() | |||
vocab_list = [] | |||
for line in lines: | |||
vocab_list.append(line.strip()) | |||
vocab.add_word_lst(vocab_list) | |||
vocab.build_vocab() | |||
vocab.padding = '[PAD]' | |||
vocab.unknown = '[UNK]' | |||
if not hasattr(self, 'vocab'): | |||
self.vocab = vocab | |||
else: | |||
for w, idx in self.vocab: | |||
if vocab[w] != idx: | |||
raise AttributeError(f"{self.__class__.__name__} has ") | |||
for i in range(2): | |||
raw_ds.apply(lambda x: tokenizer.tokenize(" ".join(x[Const.INPUTS(i)])), new_field_name=Const.INPUTS(i)) | |||
raw_ds.apply(lambda x: ['[CLS]'] + x[Const.INPUTS(0)] + ['[SEP]'] + x[Const.INPUTS(1)] + ['[SEP]'], | |||
new_field_name=Const.INPUT) | |||
raw_ds.apply(lambda x: [0] * (len(x[Const.INPUTS(0)]) + 2) + [1] * (len(x[Const.INPUTS(1)]) + 1), | |||
new_field_name=Const.INPUT_LENS(0)) | |||
raw_ds.apply(lambda x: [1] * len(x[Const.INPUT_LENS(0)]), new_field_name=Const.INPUT_LENS(1)) | |||
max_len = 512 | |||
raw_ds.apply(lambda x: x[Const.INPUT][: max_len], new_field_name=Const.INPUT) | |||
raw_ds.apply(lambda x: [self.vocab.to_index(w) for w in x[Const.INPUT]], new_field_name=Const.INPUT) | |||
raw_ds.apply(lambda x: x[Const.INPUT_LENS(0)][: max_len], new_field_name=Const.INPUT_LENS(0)) | |||
raw_ds.apply(lambda x: x[Const.INPUT_LENS(1)][: max_len], new_field_name=Const.INPUT_LENS(1)) | |||
if not hasattr(self, 'label_vocab'): | |||
self.label_vocab = Vocabulary(padding=None, unknown=None) | |||
self.label_vocab.from_dataset(raw_ds, field_name=Const.TARGET) | |||
raw_ds.apply(lambda x: self.label_vocab.to_index(x[Const.TARGET]), new_field_name=Const.TARGET) | |||
raw_ds.set_input(Const.INPUT, Const.INPUT_LENS(0), Const.INPUT_LENS(1)) | |||
raw_ds.set_target(Const.TARGET) | |||
class SNLILoader(JsonLoader): | |||
""" | |||
别名::class:`fastNLP.io.SNLILoader` :class:`fastNLP.io.dataset_loader.SNLILoader` | |||
@@ -7,6 +7,12 @@ __all__ = [ | |||
"ConvMaxpool", | |||
"Embedding", | |||
"StaticEmbedding", | |||
"ElmoEmbedding", | |||
"BertEmbedding", | |||
"StackEmbedding", | |||
"LSTMCharEmbedding", | |||
"CNNCharEmbedding", | |||
"LSTM", | |||
@@ -21,7 +27,8 @@ __all__ = [ | |||
from .bert import BertModel | |||
from .char_encoder import ConvolutionCharEncoder, LSTMCharEncoder | |||
from .conv_maxpool import ConvMaxpool | |||
from .embedding import Embedding | |||
from .embedding import Embedding, StaticEmbedding, ElmoEmbedding, BertEmbedding, \ | |||
StackEmbedding, LSTMCharEmbedding, CNNCharEmbedding | |||
from .lstm import LSTM | |||
from .star_transformer import StarTransformer | |||
from .transformer import TransformerEncoder | |||
@@ -9,7 +9,7 @@ | |||
import torch | |||
from torch import nn | |||
from ... import Vocabulary | |||
from ...core.vocabulary import Vocabulary | |||
import collections | |||
import os | |||
@@ -1,10 +1,16 @@ | |||
__all__ = [ | |||
"Embedding" | |||
"Embedding", | |||
"StaticEmbedding", | |||
"ElmoEmbedding", | |||
"BertEmbedding", | |||
"StackEmbedding", | |||
"LSTMCharEmbedding", | |||
"CNNCharEmbedding", | |||
] | |||
import torch.nn as nn | |||
from ..utils import get_embeddings | |||
from .lstm import LSTM | |||
from ... import Vocabulary | |||
from ...core.vocabulary import Vocabulary | |||
from abc import abstractmethod | |||
import torch | |||
from ...io import EmbedLoader | |||
@@ -15,7 +21,9 @@ from ...io.file_utils import cached_path, _get_base_url | |||
from ._bert import _WordBertModel | |||
from typing import List | |||
from ... import DataSet, DataSetIter, SequentialSampler | |||
from ...core.dataset import DataSet | |||
from ...core.batch import DataSetIter | |||
from ...core.sampler import SequentialSampler | |||
from ...core.utils import _move_model_to_device, _get_model_device | |||
@@ -0,0 +1,44 @@ | |||
import os | |||
import torch | |||
from fastNLP.core import Trainer, Tester, Adam, AccuracyMetric | |||
from fastNLP.io.dataset_loader import MatchingLoader | |||
from reproduction.matching.model.bert import BertForNLI | |||
# bert_dirs = 'path/to/bert/dir' | |||
bert_dirs = '/remote-home/ygxu/BERT/BERT_English_uncased_L-12_H-768_A_12' | |||
# load data set | |||
data_info = MatchingLoader(data_format='snli', for_model='bert', bert_dir=bert_dirs).process( | |||
{#'train': './data/snli/snli_1.0_train.jsonl', | |||
'dev': './data/snli/snli_1.0_dev.jsonl', | |||
'test': './data/snli/snli_1.0_test.jsonl'} | |||
) | |||
print('successfully load data sets!') | |||
model = BertForNLI(bert_dir=bert_dirs) | |||
trainer = Trainer(train_data=data_info.datasets['dev'], model=model, | |||
optimizer=Adam(lr=2e-5, model_params=model.parameters()), | |||
batch_size=torch.cuda.device_count() * 12, n_epochs=4, print_every=-1, | |||
dev_data=data_info.datasets['dev'], | |||
metrics=AccuracyMetric(), metric_key='acc', device=[i for i in range(torch.cuda.device_count())], | |||
check_code_level=-1) | |||
trainer.train(load_best_model=True) | |||
tester = Tester( | |||
data=data_info.datasets['test'], | |||
model=model, | |||
metrics=AccuracyMetric(), | |||
batch_size=torch.cuda.device_count() * 12, | |||
device=[i for i in range(torch.cuda.device_count())], | |||
) | |||
tester.test() | |||
@@ -1,88 +0,0 @@ | |||
import os | |||
import torch | |||
from fastNLP.core import Vocabulary, DataSet, Trainer, Tester, Const, Adam, AccuracyMetric | |||
from reproduction.matching.data.SNLIDataLoader import SNLILoader | |||
from legacy.component.bert_tokenizer import BertTokenizer | |||
from reproduction.matching.model.bert import BertForNLI | |||
def preprocess_data(data: DataSet, bert_dir): | |||
""" | |||
preprocess data set to bert-need data set. | |||
:param data: | |||
:param bert_dir: | |||
:return: | |||
""" | |||
tokenizer = BertTokenizer.from_pretrained(os.path.join(bert_dir, 'vocab.txt')) | |||
vocab = Vocabulary(padding=None, unknown=None) | |||
with open(os.path.join(bert_dir, 'vocab.txt')) as f: | |||
lines = f.readlines() | |||
vocab_list = [] | |||
for line in lines: | |||
vocab_list.append(line.strip()) | |||
vocab.add_word_lst(vocab_list) | |||
vocab.build_vocab() | |||
vocab.padding = '[PAD]' | |||
vocab.unknown = '[UNK]' | |||
for i in range(2): | |||
data.apply(lambda x: tokenizer.tokenize(" ".join(x[Const.INPUTS(i)])), | |||
new_field_name=Const.INPUTS(i)) | |||
data.apply(lambda x: ['[CLS]'] + x[Const.INPUTS(0)] + ['[SEP]'] + x[Const.INPUTS(1)] + ['[SEP]'], | |||
new_field_name=Const.INPUT) | |||
data.apply(lambda x: [0] * (len(x[Const.INPUTS(0)]) + 2) + [1] * (len(x[Const.INPUTS(1)]) + 1), | |||
new_field_name=Const.INPUT_LENS(0)) | |||
data.apply(lambda x: [1] * len(x[Const.INPUT_LENS(0)]), new_field_name=Const.INPUT_LENS(1)) | |||
max_len = 512 | |||
data.apply(lambda x: x[Const.INPUT][: max_len], new_field_name=Const.INPUT) | |||
data.apply(lambda x: [vocab.to_index(w) for w in x[Const.INPUT]], new_field_name=Const.INPUT) | |||
data.apply(lambda x: x[Const.INPUT_LENS(0)][: max_len], new_field_name=Const.INPUT_LENS(0)) | |||
data.apply(lambda x: x[Const.INPUT_LENS(1)][: max_len], new_field_name=Const.INPUT_LENS(1)) | |||
target_vocab = Vocabulary(padding=None, unknown=None) | |||
target_vocab.add_word_lst(['neutral', 'contradiction', 'entailment']) | |||
target_vocab.build_vocab() | |||
data.apply(lambda x: target_vocab.to_index(x[Const.TARGET]), new_field_name=Const.TARGET) | |||
data.set_input(Const.INPUT, Const.INPUT_LENS(0), Const.INPUT_LENS(1), Const.TARGET) | |||
data.set_target(Const.TARGET) | |||
return data | |||
bert_dirs = 'path/to/bert/dir' | |||
# load raw data set | |||
train_data = SNLILoader().load('./data/snli/snli_1.0_train.jsonl') | |||
dev_data = SNLILoader().load('./data/snli/snli_1.0_dev.jsonl') | |||
test_data = SNLILoader().load('./data/snli/snli_1.0_test.jsonl') | |||
print('successfully load data sets!') | |||
train_data = preprocess_data(train_data, bert_dirs) | |||
dev_data = preprocess_data(dev_data, bert_dirs) | |||
test_data = preprocess_data(test_data, bert_dirs) | |||
model = BertForNLI(bert_dir=bert_dirs) | |||
trainer = Trainer(train_data=train_data, model=model, optimizer=Adam(lr=2e-5, model_params=model.parameters()), | |||
batch_size=torch.cuda.device_count() * 12, n_epochs=4, print_every=-1, dev_data=dev_data, | |||
metrics=AccuracyMetric(), metric_key='acc', device=[i for i in range(torch.cuda.device_count())], | |||
check_code_level=-1) | |||
trainer.train(load_best_model=True) | |||
tester = Tester( | |||
data=test_data, | |||
model=model, | |||
metrics=AccuracyMetric(), | |||
batch_size=torch.cuda.device_count() * 12, | |||
device=[i for i in range(torch.cuda.device_count())], | |||
) | |||
tester.test() | |||