大幅度更新：

1、更新requirements以及README.md
2、更新DataLoader
3、更新loss
4、更新model/bert.py内容及适配的测试代码
5、更新reproduction/README.md
6、修复其他测试代码的报错的地方

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：
 </tr>
 <tr>
     <td><b> fastNLP.core </b></td>
-    <td> 实现了核心功能，包括数据处理组件、训练器、测速器等 </td>
+    <td> 实现了核心功能，包括数据处理组件、训练器、测试器等 </td>
 </tr>
 <tr>
     <td><b> fastNLP.models </b></td>

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)}.")

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

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

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)

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

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

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)

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
 

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

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
 
 

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]])

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))
+        self.assertEqual(tuple(pooled_output.shape), (2, 768))