optimizer.SGD(lr=xxx);如果没有传入parameters，则在trainer中帮他加入parameter

# Conflicts:
#	fastNLP/core/trainer.py

* 添加Trainer参数metric_key，指明用来做模型选择的指标的名字
* 在Trainer添加处理tester返回的评价指标的逻辑，选择当前最好的模型

* 添加fast_load_embedding方法，用vocab的词索引pre-trained中的embedding
* 如果vocab有词没出现在pre-train中，从已有embedding中正态采样

Update embed_loader:
* add fast_load_embedding method, to index pre-trained embedding with words in Vocab
* If words in Vocab are not exist in pre-trained, sample them from normal distribution computed by current embeddings

* 添加fast_load_embedding方法，用vocab的词索引pre-trained中的embedding
* 如果vocab有词没出现在pre-train中，从已有embedding中正态采样

Update embed_loader:
* add fast_load_embedding method, to index pre-trained embedding with words in Vocab
* If words in Vocab are not exist in pre-trained, sample them from normal distribution computed by current embeddings

* 增量添加单词到词典中
* lazy update: 当用到词典的时候才重新build
* 当新添加的词导致词典大小超出限制时，打印一个warning

Update Vocabulary:
* More words can be added after the building.
* Lazy update: rebuild automatically when vocab is used.
* print warning when max size is reached

* In init, detect content type to be Python int, float, or str.
* In append(), check type consistence.
* In init & append(), int will be cast into float if they occur together.
* Map Python type into numpy dtype
* Raise error if type detection fails.

* refine interface of set_target & set_input
* rename DataSet.Instance into DataSet.DataSetIter
* remove unused methods in DataSet.DataSetIter
* remove __setattr__ in DataSet; It is dangerous.
* comment adjustment

* remove torchvision in requirements.txt

A brand new version update (0.1.1)

* refine code style
* set up unit tests for Batch, DataSet, FieldArray
* remove a lot of out-of-date unit tests, to get testing passed

- update CNNText model

* add more code comments
* fix tester
* refresh code styles

* remove and fix other unit tests
* add more code comments