From 089009f9f24c4b4438f0a4d65c546210ee33a1ed Mon Sep 17 00:00:00 2001 From: xuyige Date: Sat, 6 Jul 2019 01:08:55 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A4=A7=E5=B9=85=E5=BA=A6=E6=9B=B4=E6=96=B0?= =?UTF-8?q?=EF=BC=9A=201=E3=80=81=E6=9B=B4=E6=96=B0requirements=E4=BB=A5?= =?UTF-8?q?=E5=8F=8AREADME.md=202=E3=80=81=E6=9B=B4=E6=96=B0DataLoader=203?= =?UTF-8?q?=E3=80=81=E6=9B=B4=E6=96=B0loss=204=E3=80=81=E6=9B=B4=E6=96=B0m?= =?UTF-8?q?odel/bert.py=E5=86=85=E5=AE=B9=E5=8F=8A=E9=80=82=E9=85=8D?= =?UTF-8?q?=E7=9A=84=E6=B5=8B=E8=AF=95=E4=BB=A3=E7=A0=81=205=E3=80=81?= =?UTF-8?q?=E6=9B=B4=E6=96=B0reproduction/README.md=206=E3=80=81=E4=BF=AE?= =?UTF-8?q?=E5=A4=8D=E5=85=B6=E4=BB=96=E6=B5=8B=E8=AF=95=E4=BB=A3=E7=A0=81?= =?UTF-8?q?=E7=9A=84=E6=8A=A5=E9=94=99=E7=9A=84=E5=9C=B0=E6=96=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 30 +++++----- fastNLP/core/losses.py | 60 +++++++++---------- fastNLP/io/base_loader.py | 9 +-- fastNLP/io/data_loader/__init__.py | 19 ++++++ fastNLP/models/bert.py | 70 +++++++++++------------ fastNLP/modules/decoder/mlp.py | 3 +- fastNLP/modules/encoder/_bert.py | 23 +++++--- reproduction/README.md | 6 +- reproduction/Star_transformer/datasets.py | 3 +- requirements.txt | 8 +-- test/io/test_dataset_loader.py | 4 +- test/models/test_bert.py | 12 ++-- test/modules/encoder/test_bert.py | 7 ++- 13 files changed, 140 insertions(+), 114 deletions(-) create mode 100644 fastNLP/io/data_loader/__init__.py diff --git a/README.md b/README.md index 9d949482..a5ce3c64 100644 --- a/README.md +++ b/README.md @@ -6,13 +6,14 @@ ![Hex.pm](https://img.shields.io/hexpm/l/plug.svg) [![Documentation Status](https://readthedocs.org/projects/fastnlp/badge/?version=latest)](http://fastnlp.readthedocs.io/?badge=latest) -fastNLP 是一款轻量级的 NLP 处理套件。你既可以使用它快速地完成一个命名实体识别（NER）、中文分词或文本分类任务； 也可以使用他构建许多复杂的网络模型，进行科研。它具有如下的特性： +fastNLP 是一款轻量级的 NLP 处理套件。你既可以使用它快速地完成一个序列标注（[NER](reproduction/seqence_labelling/ner/)、POS-Tagging等）、中文分词、文本分类、[Matching](reproduction/matching/)、指代消解、摘要等任务； 也可以使用它构建许多复杂的网络模型，进行科研。它具有如下的特性： -- 统一的Tabular式数据容器，让数据预处理过程简洁明了。内置多种数据集的DataSet Loader，省去预处理代码。 -- 各种方便的NLP工具，例如预处理embedding加载; 中间数据cache等; -- 详尽的中文文档以供查阅； +- 统一的Tabular式数据容器，让数据预处理过程简洁明了。内置多种数据集的DataSet Loader，省去预处理代码; +- 多种训练、测试组件，例如训练器Trainer；测试器Tester；以及各种评测metrics等等; +- 各种方便的NLP工具，例如预处理embedding加载（包括EMLo和BERT）; 中间数据cache等; +- 详尽的中文[文档](https://fastnlp.readthedocs.io/)、教程以供查阅; - 提供诸多高级模块，例如Variational LSTM, Transformer, CRF等; -- 封装CNNText，Biaffine等模型可供直接使用; +- 在序列标注、中文分词、文本分类、Matching、指代消解、摘要等任务上封装了各种模型可供直接使用; [详细链接](reproduction/) - 便捷且具有扩展性的训练器; 提供多种内置callback函数，方便实验记录、异常捕获等。 @@ -20,13 +21,14 @@ fastNLP 是一款轻量级的 NLP 处理套件。你既可以使用它快速地 fastNLP 依赖如下包: -+ numpy -+ torch>=0.4.0 -+ tqdm -+ nltk ++ numpy>=1.14.2 ++ torch>=1.0.0 ++ tqdm>=4.28.1 ++ nltk>=3.4.1 ++ requests -其中torch的安装可能与操作系统及 CUDA 的版本相关，请参见 PyTorch 官网 。 -在依赖包安装完成的情况，您可以在命令行执行如下指令完成安装 +其中torch的安装可能与操作系统及 CUDA 的版本相关，请参见 [PyTorch 官网](https://pytorch.org/) 。 +在依赖包安装完成后，您可以在命令行执行如下指令完成安装 ```shell pip install fastNLP @@ -77,8 +79,8 @@ fastNLP 在 modules 模块中内置了三种模块的诸多组件，可以帮助 fastNLP 为不同的 NLP 任务实现了许多完整的模型，它们都经过了训练和测试。 你可以在以下两个地方查看相关信息 -- [介绍](reproduction/) -- [源码](fastNLP/models/) +- [模型介绍](reproduction/) +- [模型源码](fastNLP/models/) ## 项目结构 @@ -93,7 +95,7 @@ fastNLP的大致工作流程如上图所示，而项目结构如下： fastNLP.core - 实现了核心功能，包括数据处理组件、训练器、测速器等 + 实现了核心功能，包括数据处理组件、训练器、测试器等 fastNLP.models diff --git a/fastNLP/core/losses.py b/fastNLP/core/losses.py index 46a72802..14aacef0 100644 --- a/fastNLP/core/losses.py +++ b/fastNLP/core/losses.py @@ -20,6 +20,7 @@ from collections import defaultdict import torch import torch.nn.functional as F +from ..core.const import Const from .utils import _CheckError from .utils import _CheckRes from .utils import _build_args @@ -28,6 +29,7 @@ from .utils import _check_function_or_method from .utils import _get_func_signature from .utils import seq_len_to_mask + class LossBase(object): """ 所有loss的基类。如果想了解其中的原理，请查看源码。 @@ -95,22 +97,7 @@ class LossBase(object): # if func_spect.varargs: # raise NameError(f"Delete `*{func_spect.varargs}` in {get_func_signature(self.get_loss)}(Do not use " # f"positional argument.).") - - def _fast_param_map(self, pred_dict, target_dict): - """Only used as inner function. When the pred_dict, target is unequivocal. Don't need users to pass key_map. - such as pred_dict has one element, target_dict has one element - :param pred_dict: - :param target_dict: - :return: dict, if dict is not {}, pass it to self.evaluate. Otherwise do mapping. - """ - fast_param = {} - if len(self._param_map) == 2 and len(pred_dict) == 1 and len(target_dict) == 1: - fast_param['pred'] = list(pred_dict.values())[0] - fast_param['target'] = list(target_dict.values())[0] - return fast_param - return fast_param - def __call__(self, pred_dict, target_dict, check=False): """ :param dict pred_dict: 模型的forward函数返回的dict @@ -118,11 +105,7 @@ class LossBase(object): :param Boolean check: 每一次执行映射函数的时候是否检查映射表，默认为不检查 :return: """ - fast_param = self._fast_param_map(pred_dict, target_dict) - if fast_param: - loss = self.get_loss(**fast_param) - return loss - + if not self._checked: # 1. check consistence between signature and _param_map func_spect = inspect.getfullargspec(self.get_loss) @@ -212,7 +195,6 @@ class LossFunc(LossBase): if not isinstance(key_map, dict): raise RuntimeError(f"Loss error: key_map except a {type({})} but got a {type(key_map)}") self._init_param_map(key_map, **kwargs) - class CrossEntropyLoss(LossBase): @@ -226,7 +208,7 @@ class CrossEntropyLoss(LossBase): :param seq_len: 句子的长度, 长度之外的token不会计算loss。。 :param padding_idx: padding的index，在计算loss时将忽略target中标号为padding_idx的内容, 可以通过该值代替 传入seq_len. - :param str reduction: 支持'elementwise_mean'和'sum'. + :param str reduction: 支持'mean'，'sum'和'none'. Example:: @@ -234,16 +216,16 @@ class CrossEntropyLoss(LossBase): """ - def __init__(self, pred=None, target=None, seq_len=None, padding_idx=-100, reduction='elementwise_mean'): + def __init__(self, pred=None, target=None, seq_len=None, padding_idx=-100, reduction='mean'): super(CrossEntropyLoss, self).__init__() self._init_param_map(pred=pred, target=target, seq_len=seq_len) self.padding_idx = padding_idx - assert reduction in ('elementwise_mean', 'sum') + assert reduction in ('mean', 'sum', 'none') self.reduction = reduction def get_loss(self, pred, target, seq_len=None): - if pred.dim()>2: - if pred.size(1)!=target.size(1): + if pred.dim() > 2: + if pred.size(1) != target.size(1): pred = pred.transpose(1, 2) pred = pred.reshape(-1, pred.size(-1)) target = target.reshape(-1) @@ -263,15 +245,18 @@ class L1Loss(LossBase): :param pred: 参数映射表中 `pred` 的映射关系，None表示映射关系为 `pred` -> `pred` :param target: 参数映射表中 `target` 的映射关系，None表示映射关系为 `target` >`target` + :param str reduction: 支持'mean'，'sum'和'none'. """ - def __init__(self, pred=None, target=None): + def __init__(self, pred=None, target=None, reduction='mean'): super(L1Loss, self).__init__() self._init_param_map(pred=pred, target=target) + assert reduction in ('mean', 'sum', 'none') + self.reduction = reduction def get_loss(self, pred, target): - return F.l1_loss(input=pred, target=target) + return F.l1_loss(input=pred, target=target, reduction=self.reduction) class BCELoss(LossBase): @@ -282,14 +267,17 @@ class BCELoss(LossBase): :param pred: 参数映射表中`pred`的映射关系，None表示映射关系为`pred`->`pred` :param target: 参数映射表中`target`的映射关系，None表示映射关系为`target`->`target` + :param str reduction: 支持'mean'，'sum'和'none'. """ - def __init__(self, pred=None, target=None): + def __init__(self, pred=None, target=None, reduction='mean'): super(BCELoss, self).__init__() self._init_param_map(pred=pred, target=target) + assert reduction in ('mean', 'sum', 'none') + self.reduction = reduction def get_loss(self, pred, target): - return F.binary_cross_entropy(input=pred, target=target) + return F.binary_cross_entropy(input=pred, target=target, reduction=self.reduction) class NLLLoss(LossBase): @@ -300,14 +288,20 @@ class NLLLoss(LossBase): :param pred: 参数映射表中`pred`的映射关系，None表示映射关系为`pred`->`pred` :param target: 参数映射表中`target`的映射关系，None表示映射关系为`target`->`target` + :param ignore_idx: ignore的index，在计算loss时将忽略target中标号为ignore_idx的内容, 可以通过该值代替 + 传入seq_len. + :param str reduction: 支持'mean'，'sum'和'none'. """ - def __init__(self, pred=None, target=None): + def __init__(self, pred=None, target=None, ignore_idx=-100, reduction='mean'): super(NLLLoss, self).__init__() self._init_param_map(pred=pred, target=target) + assert reduction in ('mean', 'sum', 'none') + self.reduction = reduction + self.ignore_idx = ignore_idx def get_loss(self, pred, target): - return F.nll_loss(input=pred, target=target) + return F.nll_loss(input=pred, target=target, ignore_index=self.ignore_idx, reduction=self.reduction) class LossInForward(LossBase): @@ -319,7 +313,7 @@ class LossInForward(LossBase): :param str loss_key: 在forward函数中loss的键名，默认为loss """ - def __init__(self, loss_key='loss'): + def __init__(self, loss_key=Const.LOSS): super().__init__() if not isinstance(loss_key, str): raise TypeError(f"Only str allowed for loss_key, got {type(loss_key)}.") diff --git a/fastNLP/io/base_loader.py b/fastNLP/io/base_loader.py index 465fb7e8..8cff1da1 100644 --- a/fastNLP/io/base_loader.py +++ b/fastNLP/io/base_loader.py @@ -10,6 +10,7 @@ from typing import Union, Dict import os from ..core.dataset import DataSet + class BaseLoader(object): """ 各个 Loader 的基类，提供了 API 的参考。 @@ -55,8 +56,6 @@ class BaseLoader(object): return obj - - def _download_from_url(url, path): try: from tqdm.auto import tqdm @@ -115,13 +114,11 @@ class DataInfo: 经过处理的数据信息，包括一系列数据集（比如：分开的训练集、验证集和测试集）及它们所用的词表和词嵌入。 :param vocabs: 从名称(字符串)到 :class:`~fastNLP.Vocabulary` 类型的dict - :param embeddings: 从名称(字符串)到一系列 embedding 的dict，参考 :class:`~fastNLP.io.EmbedLoader` :param datasets: 从名称(字符串)到 :class:`~fastNLP.DataSet` 类型的dict """ - def __init__(self, vocabs: dict = None, embeddings: dict = None, datasets: dict = None): + def __init__(self, vocabs: dict = None, datasets: dict = None): self.vocabs = vocabs or {} - self.embeddings = embeddings or {} self.datasets = datasets or {} def __repr__(self): @@ -133,6 +130,7 @@ class DataInfo: _str += '\t{} has {} entries.\n'.format(name, len(vocab)) return _str + class DataSetLoader: """ 别名：:class:`fastNLP.io.DataSetLoader` :class:`fastNLP.io.dataset_loader.DataSetLoader` @@ -213,7 +211,6 @@ class DataSetLoader: 返回的 :class:`DataInfo` 对象有如下属性： - vocabs: 由从数据集中获取的词表组成的字典，每个词表 - - embeddings: (可选) 数据集对应的词嵌入 - datasets: 一个dict，包含一系列 :class:`~fastNLP.DataSet` 类型的对象。其中 field 的命名参考 :mod:`~fastNLP.core.const` :param paths: 原始数据读取的路径 diff --git a/fastNLP/io/data_loader/__init__.py b/fastNLP/io/data_loader/__init__.py new file mode 100644 index 00000000..6f4dd973 --- /dev/null +++ b/fastNLP/io/data_loader/__init__.py @@ -0,0 +1,19 @@ +""" +用于读数据集的模块, 具体包括: + +这些模块的使用方法如下: +""" +__all__ = [ + 'SSTLoader', + + 'MatchingLoader', + 'SNLILoader', + 'MNLILoader', + 'QNLILoader', + 'QuoraLoader', + 'RTELoader', +] + +from .sst import SSTLoader +from .matching import MatchingLoader, SNLILoader, \ + MNLILoader, QNLILoader, QuoraLoader, RTELoader diff --git a/fastNLP/models/bert.py b/fastNLP/models/bert.py index 4846c7fa..fb186ce4 100644 --- a/fastNLP/models/bert.py +++ b/fastNLP/models/bert.py @@ -8,35 +8,7 @@ from torch import nn from .base_model import BaseModel from ..core.const import Const from ..modules.encoder import BertModel - - -class BertConfig: - - def __init__( - self, - vocab_size=30522, - hidden_size=768, - num_hidden_layers=12, - num_attention_heads=12, - intermediate_size=3072, - hidden_act="gelu", - hidden_dropout_prob=0.1, - attention_probs_dropout_prob=0.1, - max_position_embeddings=512, - type_vocab_size=2, - initializer_range=0.02 - ): - self.vocab_size = vocab_size - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.intermediate_size = intermediate_size - self.hidden_act = hidden_act - self.hidden_dropout_prob = hidden_dropout_prob - self.attention_probs_dropout_prob = attention_probs_dropout_prob - self.max_position_embeddings = max_position_embeddings - self.type_vocab_size = type_vocab_size - self.initializer_range = initializer_range +from ..modules.encoder._bert import BertConfig class BertForSequenceClassification(BaseModel): @@ -84,11 +56,17 @@ class BertForSequenceClassification(BaseModel): self.bert = BertModel.from_pretrained(bert_dir) else: if config is None: - config = BertConfig() - self.bert = BertModel(**config.__dict__) + config = BertConfig(30522) + self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, num_labels) + @classmethod + def from_pretrained(cls, num_labels, pretrained_model_dir): + config = BertConfig(pretrained_model_dir) + model = cls(num_labels=num_labels, config=config, bert_dir=pretrained_model_dir) + return model + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): _, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) pooled_output = self.dropout(pooled_output) @@ -151,11 +129,17 @@ class BertForMultipleChoice(BaseModel): self.bert = BertModel.from_pretrained(bert_dir) else: if config is None: - config = BertConfig() - self.bert = BertModel(**config.__dict__) + config = BertConfig(30522) + self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) + @classmethod + def from_pretrained(cls, num_choices, pretrained_model_dir): + config = BertConfig(pretrained_model_dir) + model = cls(num_choices=num_choices, config=config, bert_dir=pretrained_model_dir) + return model + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): flat_input_ids = input_ids.view(-1, input_ids.size(-1)) flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) @@ -224,11 +208,17 @@ class BertForTokenClassification(BaseModel): self.bert = BertModel.from_pretrained(bert_dir) else: if config is None: - config = BertConfig() - self.bert = BertModel(**config.__dict__) + config = BertConfig(30522) + self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, num_labels) + @classmethod + def from_pretrained(cls, num_labels, pretrained_model_dir): + config = BertConfig(pretrained_model_dir) + model = cls(num_labels=num_labels, config=config, bert_dir=pretrained_model_dir) + return model + def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None): sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) sequence_output = self.dropout(sequence_output) @@ -302,12 +292,18 @@ class BertForQuestionAnswering(BaseModel): self.bert = BertModel.from_pretrained(bert_dir) else: if config is None: - config = BertConfig() - self.bert = BertModel(**config.__dict__) + config = BertConfig(30522) + self.bert = BertModel(config) # TODO check with Google if it's normal there is no dropout on the token classifier of SQuAD in the TF version # self.dropout = nn.Dropout(config.hidden_dropout_prob) self.qa_outputs = nn.Linear(config.hidden_size, 2) + @classmethod + def from_pretrained(cls, pretrained_model_dir): + config = BertConfig(pretrained_model_dir) + model = cls(config=config, bert_dir=pretrained_model_dir) + return model + def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None): sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) logits = self.qa_outputs(sequence_output) diff --git a/fastNLP/modules/decoder/mlp.py b/fastNLP/modules/decoder/mlp.py index c1579224..418b3a77 100644 --- a/fastNLP/modules/decoder/mlp.py +++ b/fastNLP/modules/decoder/mlp.py @@ -15,7 +15,8 @@ class MLP(nn.Module): 多层感知器 :param List[int] size_layer: 一个int的列表，用来定义MLP的层数，列表中的数字为每一层是hidden数目。MLP的层数为 len(size_layer) - 1 - :param Union[str,func,List[str]] activation: 一个字符串或者函数的列表，用来定义每一个隐层的激活函数，字符串包括relu，tanh和sigmoid，默认值为relu + :param Union[str,func,List[str]] activation: 一个字符串或者函数的列表，用来定义每一个隐层的激活函数，字符串包括relu，tanh和 + sigmoid，默认值为relu :param Union[str,func] output_activation: 字符串或者函数，用来定义输出层的激活函数，默认值为None，表示输出层没有激活函数 :param str initial_method: 参数初始化方式 :param float dropout: dropout概率，默认值为0 diff --git a/fastNLP/modules/encoder/_bert.py b/fastNLP/modules/encoder/_bert.py index 4669b511..61a5d7d1 100644 --- a/fastNLP/modules/encoder/_bert.py +++ b/fastNLP/modules/encoder/_bert.py @@ -26,6 +26,7 @@ import sys CONFIG_FILE = 'bert_config.json' + class BertConfig(object): """Configuration class to store the configuration of a `BertModel`. """ @@ -339,13 +340,19 @@ class BertModel(nn.Module): 如果你想使用预训练好的权重矩阵，请在以下网址下载. sources:: - 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz", - 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz", - 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz", - 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz", - 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz", - 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz", - 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz", + 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin", + 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-pytorch_model.bin", + 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin", + 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-pytorch_model.bin", + 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-pytorch_model.bin", + 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-pytorch_model.bin", + 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-pytorch_model.bin", + 'bert-base-german-cased': "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-pytorch_model.bin", + 'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-pytorch_model.bin", + 'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-pytorch_model.bin", + 'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-pytorch_model.bin", + 'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-pytorch_model.bin", + 'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin" 用预训练权重矩阵来建立BERT模型:: @@ -562,6 +569,7 @@ class WordpieceTokenizer(object): output_tokens.extend(sub_tokens) return output_tokens + def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() @@ -692,6 +700,7 @@ class BasicTokenizer(object): output.append(char) return "".join(output) + def _is_whitespace(char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them diff --git a/reproduction/README.md b/reproduction/README.md index 92652fb4..b6f61903 100644 --- a/reproduction/README.md +++ b/reproduction/README.md @@ -3,6 +3,8 @@ 复现的模型有: - [Star-Transformer](Star_transformer/) +- [Biaffine](https://github.com/fastnlp/fastNLP/blob/999a14381747068e9e6a7cc370037b320197db00/fastNLP/models/biaffine_parser.py#L239) +- [CNNText](https://github.com/fastnlp/fastNLP/blob/999a14381747068e9e6a7cc370037b320197db00/fastNLP/models/cnn_text_classification.py#L12) - ... # 任务复现 @@ -11,11 +13,11 @@ ## Matching (自然语言推理/句子匹配) -- [Matching 任务复现](matching/) +- [Matching 任务复现](matching) ## Sequence Labeling (序列标注) -- still in progress +- [NER](seqence_labelling/ner) ## Coreference resolution (指代消解) diff --git a/reproduction/Star_transformer/datasets.py b/reproduction/Star_transformer/datasets.py index a9257fd4..1532a041 100644 --- a/reproduction/Star_transformer/datasets.py +++ b/reproduction/Star_transformer/datasets.py @@ -2,7 +2,8 @@ import torch import json import os from fastNLP import Vocabulary -from fastNLP.io.dataset_loader import ConllLoader, SSTLoader, SNLILoader +from fastNLP.io.dataset_loader import ConllLoader +from fastNLP.io.data_loader import SSTLoader, SNLILoader from fastNLP.core import Const as C import numpy as np diff --git a/requirements.txt b/requirements.txt index 7ea8fdac..f8f7a951 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -numpy -torch>=0.4.0 -tqdm -nltk +numpy>=1.14.2 +torch>=1.0.0 +tqdm>=4.28.1 +nltk>=3.4.1 requests diff --git a/test/io/test_dataset_loader.py b/test/io/test_dataset_loader.py index 7cff3c12..b091339e 100644 --- a/test/io/test_dataset_loader.py +++ b/test/io/test_dataset_loader.py @@ -1,7 +1,7 @@ import unittest import os -from fastNLP.io import Conll2003Loader, PeopleDailyCorpusLoader, CSVLoader, SNLILoader, JsonLoader -from fastNLP.io.dataset_loader import SSTLoader +from fastNLP.io import Conll2003Loader, PeopleDailyCorpusLoader, CSVLoader, JsonLoader +from fastNLP.io.dataset_loader import SSTLoader, SNLILoader from reproduction.text_classification.data.yelpLoader import yelpLoader diff --git a/test/models/test_bert.py b/test/models/test_bert.py index 7177f31b..38a16f9b 100644 --- a/test/models/test_bert.py +++ b/test/models/test_bert.py @@ -8,8 +8,9 @@ from fastNLP.models.bert import * class TestBert(unittest.TestCase): def test_bert_1(self): from fastNLP.core.const import Const + from fastNLP.modules.encoder._bert import BertConfig - model = BertForSequenceClassification(2) + model = BertForSequenceClassification(2, BertConfig(32000)) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) @@ -22,8 +23,9 @@ class TestBert(unittest.TestCase): def test_bert_2(self): from fastNLP.core.const import Const + from fastNLP.modules.encoder._bert import BertConfig - model = BertForMultipleChoice(2) + model = BertForMultipleChoice(2, BertConfig(32000)) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) @@ -36,8 +38,9 @@ class TestBert(unittest.TestCase): def test_bert_3(self): from fastNLP.core.const import Const + from fastNLP.modules.encoder._bert import BertConfig - model = BertForTokenClassification(7) + model = BertForTokenClassification(7, BertConfig(32000)) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) @@ -50,8 +53,9 @@ class TestBert(unittest.TestCase): def test_bert_4(self): from fastNLP.core.const import Const + from fastNLP.modules.encoder._bert import BertConfig - model = BertForQuestionAnswering() + model = BertForQuestionAnswering(BertConfig(32000)) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) diff --git a/test/modules/encoder/test_bert.py b/test/modules/encoder/test_bert.py index 78bcf633..2a799478 100644 --- a/test/modules/encoder/test_bert.py +++ b/test/modules/encoder/test_bert.py @@ -8,8 +8,9 @@ from fastNLP.models.bert import BertModel class TestBert(unittest.TestCase): def test_bert_1(self): - model = BertModel(vocab_size=32000, hidden_size=768, - num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072) + from fastNLP.modules.encoder._bert import BertConfig + config = BertConfig(32000) + model = BertModel(config) input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]]) input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]]) @@ -18,4 +19,4 @@ class TestBert(unittest.TestCase): all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask) for layer in all_encoder_layers: self.assertEqual(tuple(layer.shape), (2, 3, 768)) - self.assertEqual(tuple(pooled_output.shape), (2, 768)) \ No newline at end of file + self.assertEqual(tuple(pooled_output.shape), (2, 768))