Add a LSTM-CRF model at Conlll2003 Dataset

reproduction/LSTM-CRF/README.md

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+# BiLSTM-CRF
+This project is based on idad of paper https://arxiv.org/pdf/1508.01991.pdf on EMNLP'16, and with the assistance of the fastNLP, (https://github.com/fastnlp/fastNLP), which can facilitate the development of deep learning project based on NLP. 
+
+
+## Requirement
+
+```pip install fastNLP```
+
+## Usage
+```
+python main.py -h
+usage: main.py [-h]
+               [rnn_hidden] [epoch] [word_emb] [batch_size] [op] [lr] [cuda]
+               [bilstm] [continue]
+
+CRF-LSTM Model
+
+python main.py -h
+usage: main.py [-h] [--epoch [EPOCH]] [--rnn_hidden [RNN_HIDDEN]]
+               [--word_emb [WORD_EMB]] [--batch_size [BATCH_SIZE]] [--op [OP]]
+               [--lr [LR]] [--cuda [CUDA]] [--bilstm [BILSTM]] [--cont [CONT]]
+               [--mode [MODE]] [--device [DEVICE]]
+
+CRF-LSTM Model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --epoch [EPOCH]       The epoch times of training
+  --rnn_hidden [RNN_HIDDEN]
+                        The hidden dimension of the LSTM
+  --word_emb [WORD_EMB]
+                        The embedding size of vocab
+  --batch_size [BATCH_SIZE]
+                        The batch_size of trainer
+  --op [OP]             The optimizer for trainer, 0 for Adam, 1 for SGD
+  --lr [LR]             The learning rate of optimizer
+  --cuda [CUDA]         Whether use cuda
+  --bilstm [BILSTM]     bilstm or lstm
+  --cont [CONT]         Whether continue from the saved model or from scratch
+  --mode [MODE]         Choose the mode: train&test
+  --device [DEVICE]     Choose the free device
+```
+
+## Pretrained Model
+The pretrained model is saved at the save/ directory, you can use it by:
+```python main.py --cont="save/```
+
+## Jupyter Tutorial
+The jupyter file will walk you through the whole process step by step
+
+