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))