diff --git a/reproduction/coreference_resolution/__init__.py b/reproduction/coreference_resolution/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/coreference_resolution/data_load/__init__.py b/reproduction/coreference_resolution/data_load/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/coreference_resolution/data_load/cr_loader.py b/reproduction/coreference_resolution/data_load/cr_loader.py
new file mode 100644
index 00000000..986afcd5
--- /dev/null
+++ b/reproduction/coreference_resolution/data_load/cr_loader.py
@@ -0,0 +1,68 @@
+from fastNLP.io.dataset_loader import JsonLoader,DataSet,Instance
+from fastNLP.io.file_reader import _read_json
+from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.io.base_loader import DataInfo
+from reproduction.coreference_resolution.model.config import Config
+import reproduction.coreference_resolution.model.preprocess as preprocess
+
+
+class CRLoader(JsonLoader):
+    def __init__(self, fields=None, dropna=False):
+        super().__init__(fields, dropna)
+
+    def _load(self, path):
+        """
+        加载数据
+        :param path:
+        :return:
+        """
+        dataset = DataSet()
+        for idx, d in _read_json(path, fields=self.fields_list, dropna=self.dropna):
+            if self.fields:
+                ins = {self.fields[k]: v for k, v in d.items()}
+            else:
+                ins = d
+            dataset.append(Instance(**ins))
+        return dataset
+
+    def process(self, paths, **kwargs):
+        data_info = DataInfo()
+        for name in ['train', 'test', 'dev']:
+            data_info.datasets[name] = self.load(paths[name])
+
+        config = Config()
+        vocab = Vocabulary().from_dataset(*data_info.datasets.values(), field_name='sentences')
+        vocab.build_vocab()
+        word2id = vocab.word2idx
+
+        char_dict = preprocess.get_char_dict(config.char_path)
+        data_info.vocabs = vocab
+
+        genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])}
+
+        for name, ds in data_info.datasets.items():
+            ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter),
+                                                        config.max_sentences, is_train=name=='train')[0],
+                         new_field_name='doc_np')
+            ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter),
+                                                        config.max_sentences, is_train=name=='train')[1],
+                         new_field_name='char_index')
+            ds.apply(lambda x: preprocess.doc2numpy(x['sentences'], word2id, char_dict, max(config.filter),
+                                                        config.max_sentences, is_train=name=='train')[2],
+                         new_field_name='seq_len')
+            ds.apply(lambda x: preprocess.speaker2numpy(x["speakers"], config.max_sentences, is_train=name=='train'),
+                         new_field_name='speaker_ids_np')
+            ds.apply(lambda x: genres[x["doc_key"][:2]], new_field_name='genre')
+
+            ds.set_ignore_type('clusters')
+            ds.set_padder('clusters', None)
+            ds.set_input("sentences", "doc_np", "speaker_ids_np", "genre", "char_index", "seq_len")
+            ds.set_target("clusters")
+
+        # train_dev, test = self.ds.split(348 / (2802 + 343 + 348), shuffle=False)
+        # train, dev = train_dev.split(343 / (2802 + 343), shuffle=False)
+
+        return data_info
+
+
+
diff --git a/reproduction/coreference_resolution/model/__init__.py b/reproduction/coreference_resolution/model/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/coreference_resolution/model/config.py b/reproduction/coreference_resolution/model/config.py
new file mode 100644
index 00000000..6011257b
--- /dev/null
+++ b/reproduction/coreference_resolution/model/config.py
@@ -0,0 +1,54 @@
+class Config():
+    def __init__(self):
+        self.is_training = True
+        # path
+        self.glove = 'data/glove.840B.300d.txt.filtered'
+        self.turian = 'data/turian.50d.txt'
+        self.train_path = "data/train.english.jsonlines"
+        self.dev_path = "data/dev.english.jsonlines"
+        self.test_path = "data/test.english.jsonlines"
+        self.char_path = "data/char_vocab.english.txt"
+
+        self.cuda = "0"
+        self.max_word = 1500
+        self.epoch = 200
+
+        # config
+        # self.use_glove = True
+        # self.use_turian = True      #No
+        self.use_elmo = False
+        self.use_CNN = True
+        self.model_heads = True     #Yes
+        self.use_width = True       # Yes
+        self.use_distance = True    #Yes
+        self.use_metadata = True   #Yes
+
+        self.mention_ratio = 0.4
+        self.max_sentences = 50
+        self.span_width = 10
+        self.feature_size = 20          #宽度信息emb的size
+        self.lr = 0.001
+        self.lr_decay = 1e-3
+        self.max_antecedents = 100    # 这个参数在mention detection中没有用
+        self.atten_hidden_size = 150
+        self.mention_hidden_size = 150
+        self.sa_hidden_size = 150
+
+        self.char_emb_size = 8
+        self.filter = [3,4,5]
+
+
+    # decay = 1e-5
+
+    def __str__(self):
+        d = self.__dict__
+        out = 'config==============\n'
+        for i in list(d):
+            out += i+":"
+            out += str(d[i])+"\n"
+        out+="config==============\n"
+        return out
+
+if __name__=="__main__":
+    config = Config()
+    print(config)
diff --git a/reproduction/coreference_resolution/model/metric.py b/reproduction/coreference_resolution/model/metric.py
new file mode 100644
index 00000000..2c924660
--- /dev/null
+++ b/reproduction/coreference_resolution/model/metric.py
@@ -0,0 +1,163 @@
+from fastNLP.core.metrics import MetricBase
+
+import numpy as np
+
+from collections import Counter
+from sklearn.utils.linear_assignment_ import linear_assignment
+
+"""
+Mostly borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
+"""
+
+
+
+class CRMetric(MetricBase):
+    def __init__(self):
+        super().__init__()
+        self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]
+
+    # TODO 改名为evaluate，输入也
+    def evaluate(self, predicted, mention_to_predicted,clusters):
+        for e in self.evaluators:
+            e.update(predicted,mention_to_predicted, clusters)
+
+    def get_f1(self):
+        return sum(e.get_f1() for e in self.evaluators) / len(self.evaluators)
+
+    def get_recall(self):
+        return sum(e.get_recall() for e in self.evaluators) / len(self.evaluators)
+
+    def get_precision(self):
+        return sum(e.get_precision() for e in self.evaluators) / len(self.evaluators)
+
+    # TODO 原本的getprf
+    def get_metric(self,reset=False):
+        res = {"pre":self.get_precision(), "rec":self.get_recall(), "f":self.get_f1()}
+        self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]
+        return res
+
+
+
+
+
+
+class Evaluator():
+    def __init__(self, metric, beta=1):
+        self.p_num = 0
+        self.p_den = 0
+        self.r_num = 0
+        self.r_den = 0
+        self.metric = metric
+        self.beta = beta
+
+    def update(self, predicted,mention_to_predicted,gold):
+        gold = gold[0].tolist()
+        gold = [tuple(tuple(m) for m in gc) for gc in gold]
+        mention_to_gold = {}
+        for gc in gold:
+            for mention in gc:
+                mention_to_gold[mention] = gc
+
+        if self.metric == ceafe:
+            pn, pd, rn, rd = self.metric(predicted, gold)
+        else:
+            pn, pd = self.metric(predicted, mention_to_gold)
+            rn, rd = self.metric(gold, mention_to_predicted)
+        self.p_num += pn
+        self.p_den += pd
+        self.r_num += rn
+        self.r_den += rd
+
+    def get_f1(self):
+        return f1(self.p_num, self.p_den, self.r_num, self.r_den, beta=self.beta)
+
+    def get_recall(self):
+        return 0 if self.r_num == 0 else self.r_num / float(self.r_den)
+
+    def get_precision(self):
+        return 0 if self.p_num == 0 else self.p_num / float(self.p_den)
+
+    def get_prf(self):
+        return self.get_precision(), self.get_recall(), self.get_f1()
+
+    def get_counts(self):
+        return self.p_num, self.p_den, self.r_num, self.r_den
+
+
+
+def b_cubed(clusters, mention_to_gold):
+    num, dem = 0, 0
+
+    for c in clusters:
+        if len(c) == 1:
+            continue
+
+        gold_counts = Counter()
+        correct = 0
+        for m in c:
+            if m in mention_to_gold:
+                gold_counts[tuple(mention_to_gold[m])] += 1
+        for c2, count in gold_counts.items():
+            if len(c2) != 1:
+                correct += count * count
+
+        num += correct / float(len(c))
+        dem += len(c)
+
+    return num, dem
+
+
+def muc(clusters, mention_to_gold):
+    tp, p = 0, 0
+    for c in clusters:
+        p += len(c) - 1
+        tp += len(c)
+        linked = set()
+        for m in c:
+            if m in mention_to_gold:
+                linked.add(mention_to_gold[m])
+            else:
+                tp -= 1
+        tp -= len(linked)
+    return tp, p
+
+
+def phi4(c1, c2):
+    return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2))
+
+
+def ceafe(clusters, gold_clusters):
+    clusters = [c for c in clusters if len(c) != 1]
+    scores = np.zeros((len(gold_clusters), len(clusters)))
+    for i in range(len(gold_clusters)):
+        for j in range(len(clusters)):
+            scores[i, j] = phi4(gold_clusters[i], clusters[j])
+    matching = linear_assignment(-scores)
+    similarity = sum(scores[matching[:, 0], matching[:, 1]])
+    return similarity, len(clusters), similarity, len(gold_clusters)
+
+
+def lea(clusters, mention_to_gold):
+    num, dem = 0, 0
+
+    for c in clusters:
+        if len(c) == 1:
+            continue
+
+        common_links = 0
+        all_links = len(c) * (len(c) - 1) / 2.0
+        for i, m in enumerate(c):
+            if m in mention_to_gold:
+                for m2 in c[i + 1:]:
+                    if m2 in mention_to_gold and mention_to_gold[m] == mention_to_gold[m2]:
+                        common_links += 1
+
+        num += len(c) * common_links / float(all_links)
+        dem += len(c)
+
+    return num, dem
+
+def f1(p_num, p_den, r_num, r_den, beta=1):
+    p = 0 if p_den == 0 else p_num / float(p_den)
+    r = 0 if r_den == 0 else r_num / float(r_den)
+    return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r)
diff --git a/reproduction/coreference_resolution/model/model_re.py b/reproduction/coreference_resolution/model/model_re.py
new file mode 100644
index 00000000..9dd90ec4
--- /dev/null
+++ b/reproduction/coreference_resolution/model/model_re.py
@@ -0,0 +1,576 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+
+from allennlp.commands.elmo import ElmoEmbedder
+from fastNLP.models.base_model import BaseModel
+from fastNLP.modules.encoder.variational_rnn import VarLSTM
+from reproduction.coreference_resolution.model import preprocess
+from fastNLP.io.embed_loader import EmbedLoader
+import random
+
+# 设置seed
+torch.manual_seed(0)  # cpu
+torch.cuda.manual_seed(0)  # gpu
+np.random.seed(0) # numpy
+random.seed(0)
+
+
+class ffnn(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(ffnn, self).__init__()
+
+        self.f = nn.Sequential(
+            # 多少层数
+            nn.Linear(input_size, hidden_size),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.2),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.2),
+            nn.Linear(hidden_size, output_size)
+        )
+        self.reset_param()
+
+    def reset_param(self):
+        for name, param in self.named_parameters():
+            if param.dim() > 1:
+                nn.init.xavier_normal_(param)
+                # param.data = torch.tensor(np.random.randn(*param.shape)).float()
+            else:
+                nn.init.zeros_(param)
+
+    def forward(self, input):
+        return self.f(input).squeeze()
+
+
+class Model(BaseModel):
+    def __init__(self, vocab, config):
+        word2id = vocab.word2idx
+        super(Model, self).__init__()
+        vocab_num = len(word2id)
+        self.word2id = word2id
+        self.config = config
+        self.char_dict = preprocess.get_char_dict('data/char_vocab.english.txt')
+        self.genres = {g: i for i, g in enumerate(["bc", "bn", "mz", "nw", "pt", "tc", "wb"])}
+        self.device = torch.device("cuda:" + config.cuda)
+
+        self.emb = nn.Embedding(vocab_num, 350)
+
+        emb1 = EmbedLoader().load_with_vocab(config.glove, vocab,normalize=False)
+        emb2 = EmbedLoader().load_with_vocab(config.turian,  vocab ,normalize=False)
+        pre_emb = np.concatenate((emb1, emb2), axis=1)
+        pre_emb /= (np.linalg.norm(pre_emb, axis=1, keepdims=True) + 1e-12)
+
+        if pre_emb is not None:
+            self.emb.weight = nn.Parameter(torch.from_numpy(pre_emb).float())
+            for param in self.emb.parameters():
+                param.requires_grad = False
+        self.emb_dropout = nn.Dropout(inplace=True)
+
+
+        if config.use_elmo:
+            self.elmo = ElmoEmbedder(options_file='data/elmo/elmo_2x4096_512_2048cnn_2xhighway_options.json',
+                                     weight_file='data/elmo/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5',
+                                     cuda_device=int(config.cuda))
+            print("elmo load over.")
+            self.elmo_args = torch.randn((3), requires_grad=True).to(self.device)
+
+        self.char_emb = nn.Embedding(len(self.char_dict), config.char_emb_size)
+        self.conv1 = nn.Conv1d(config.char_emb_size, 50, 3)
+        self.conv2 = nn.Conv1d(config.char_emb_size, 50, 4)
+        self.conv3 = nn.Conv1d(config.char_emb_size, 50, 5)
+
+        self.feature_emb = nn.Embedding(config.span_width, config.feature_size)
+        self.feature_emb_dropout = nn.Dropout(p=0.2, inplace=True)
+
+        self.mention_distance_emb = nn.Embedding(10, config.feature_size)
+        self.distance_drop = nn.Dropout(p=0.2, inplace=True)
+
+        self.genre_emb = nn.Embedding(7, config.feature_size)
+        self.speaker_emb = nn.Embedding(2, config.feature_size)
+
+        self.bilstm = VarLSTM(input_size=350+150*config.use_CNN+config.use_elmo*1024,hidden_size=200,bidirectional=True,batch_first=True,hidden_dropout=0.2)
+        # self.bilstm = nn.LSTM(input_size=500, hidden_size=200, bidirectional=True, batch_first=True)
+        self.h0 = nn.init.orthogonal_(torch.empty(2, 1, 200)).to(self.device)
+        self.c0 = nn.init.orthogonal_(torch.empty(2, 1, 200)).to(self.device)
+        self.bilstm_drop = nn.Dropout(p=0.2, inplace=True)
+
+        self.atten = ffnn(input_size=400, hidden_size=config.atten_hidden_size, output_size=1)
+        self.mention_score = ffnn(input_size=1320, hidden_size=config.mention_hidden_size, output_size=1)
+        self.sa = ffnn(input_size=3980+40*config.use_metadata, hidden_size=config.sa_hidden_size, output_size=1)
+        self.mention_start_np = None
+        self.mention_end_np = None
+
+    def _reorder_lstm(self, word_emb, seq_lens):
+        sort_ind = sorted(range(len(seq_lens)), key=lambda i: seq_lens[i], reverse=True)
+        seq_lens_re = [seq_lens[i] for i in sort_ind]
+        emb_seq = self.reorder_sequence(word_emb, sort_ind, batch_first=True)
+        packed_seq = nn.utils.rnn.pack_padded_sequence(emb_seq, seq_lens_re, batch_first=True)
+
+        h0 = self.h0.repeat(1, len(seq_lens), 1)
+        c0 = self.c0.repeat(1, len(seq_lens), 1)
+        packed_out, final_states = self.bilstm(packed_seq, (h0, c0))
+
+        lstm_out, _ = nn.utils.rnn.pad_packed_sequence(packed_out, batch_first=True)
+        back_map = {ind: i for i, ind in enumerate(sort_ind)}
+        reorder_ind = [back_map[i] for i in range(len(seq_lens_re))]
+        lstm_out = self.reorder_sequence(lstm_out, reorder_ind, batch_first=True)
+        return lstm_out
+
+    def reorder_sequence(self, sequence_emb, order, batch_first=True):
+        """
+        sequence_emb: [T, B, D] if not batch_first
+        order: list of sequence length
+        """
+        batch_dim = 0 if batch_first else 1
+        assert len(order) == sequence_emb.size()[batch_dim]
+
+        order = torch.LongTensor(order)
+        order = order.to(sequence_emb).long()
+
+        sorted_ = sequence_emb.index_select(index=order, dim=batch_dim)
+
+        del order
+        return sorted_
+
+    def flat_lstm(self, lstm_out, seq_lens):
+        batch = lstm_out.shape[0]
+        seq = lstm_out.shape[1]
+        dim = lstm_out.shape[2]
+        l = [j + i * seq for i, seq_len in enumerate(seq_lens) for j in range(seq_len)]
+        flatted = torch.index_select(lstm_out.view(batch * seq, dim), 0, torch.LongTensor(l).to(self.device))
+        return flatted
+
+    def potential_mention_index(self, word_index, max_sent_len):
+        # get mention index [3,2]:the first sentence is 3 and secend 2
+        # [0,0,0,1,1] --> [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [3, 3], [3, 4], [4, 4]] (max =2)
+        potential_mention = []
+        for i in range(len(word_index)):
+            for j in range(i, i + max_sent_len):
+                if (j < len(word_index) and word_index[i] == word_index[j]):
+                    potential_mention.append([i, j])
+        return potential_mention
+
+    def get_mention_start_end(self, seq_lens):
+        # 序列长度转换成mention
+        # [3,2] --> [0,0,0,1,1]
+        word_index = [0] * sum(seq_lens)
+        sent_index = 0
+        index = 0
+        for length in seq_lens:
+            for l in range(length):
+                word_index[index] = sent_index
+                index += 1
+            sent_index += 1
+
+        # [0,0,0,1,1]-->[[0,0],[0,1],[0,2]....]
+        mention_id = self.potential_mention_index(word_index, self.config.span_width)
+        mention_start = np.array(mention_id, dtype=int)[:, 0]
+        mention_end = np.array(mention_id, dtype=int)[:, 1]
+        return mention_start, mention_end
+
+    def get_mention_emb(self, flatten_lstm, mention_start, mention_end):
+        mention_start_tensor = torch.from_numpy(mention_start).to(self.device)
+        mention_end_tensor = torch.from_numpy(mention_end).to(self.device)
+        emb_start = flatten_lstm.index_select(dim=0, index=mention_start_tensor)  # [mention_num,embed]
+        emb_end = flatten_lstm.index_select(dim=0, index=mention_end_tensor)  # [mention_num,embed]
+        return emb_start, emb_end
+
+    def get_mask(self, mention_start, mention_end):
+        # big mask for attention
+        mention_num = mention_start.shape[0]
+        mask = np.zeros((mention_num, self.config.span_width))  # [mention_num,span_width]
+        for i in range(mention_num):
+            start = mention_start[i]
+            end = mention_end[i]
+            # 实际上是宽度
+            for j in range(end - start + 1):
+                mask[i][j] = 1
+        mask = torch.from_numpy(mask)  # [mention_num,max_mention]
+        # 0-->-inf  1-->0
+        log_mask = torch.log(mask)
+        return log_mask
+
+    def get_mention_index(self, mention_start, max_mention):
+        # TODO 后面可能要改
+        assert len(mention_start.shape) == 1
+        mention_start_tensor = torch.from_numpy(mention_start)
+        num_mention = mention_start_tensor.shape[0]
+        mention_index = mention_start_tensor.expand(max_mention, num_mention).transpose(0,
+                                                                                        1)  # [num_mention,max_mention]
+        assert mention_index.shape[0] == num_mention
+        assert mention_index.shape[1] == max_mention
+        range_add = torch.arange(0, max_mention).expand(num_mention, max_mention).long()  # [num_mention,max_mention]
+        mention_index = mention_index + range_add
+        mention_index = torch.min(mention_index, torch.LongTensor([mention_start[-1]]).expand(num_mention, max_mention))
+        return mention_index.to(self.device)
+
+    def sort_mention(self, mention_start, mention_end, candidate_mention_emb, candidate_mention_score, seq_lens):
+        # 排序记录,高分段在前面
+        mention_score, mention_ids = torch.sort(candidate_mention_score, descending=True)
+        preserve_mention_num = int(self.config.mention_ratio * sum(seq_lens))
+        mention_ids = mention_ids[0:preserve_mention_num]
+        mention_score = mention_score[0:preserve_mention_num]
+
+        mention_start_tensor = torch.from_numpy(mention_start).to(self.device).index_select(dim=0,
+                                                                                            index=mention_ids)  # [lamda*word_num]
+        mention_end_tensor = torch.from_numpy(mention_end).to(self.device).index_select(dim=0,
+                                                                                        index=mention_ids)  # [lamda*word_num]
+        mention_emb = candidate_mention_emb.index_select(index=mention_ids, dim=0)  # [lamda*word_num,emb]
+        assert mention_score.shape[0] == preserve_mention_num
+        assert mention_start_tensor.shape[0] == preserve_mention_num
+        assert mention_end_tensor.shape[0] == preserve_mention_num
+        assert mention_emb.shape[0] == preserve_mention_num
+        # TODO 不交叉没做处理
+
+        # 对start进行再排序，实际位置在前面
+        # TODO 这里只考虑了start没有考虑end
+        mention_start_tensor, temp_index = torch.sort(mention_start_tensor)
+        mention_end_tensor = mention_end_tensor.index_select(dim=0, index=temp_index)
+        mention_emb = mention_emb.index_select(dim=0, index=temp_index)
+        mention_score = mention_score.index_select(dim=0, index=temp_index)
+        return mention_start_tensor, mention_end_tensor, mention_score, mention_emb
+
+    def get_antecedents(self, mention_starts, max_antecedents):
+        num_mention = mention_starts.shape[0]
+        max_antecedents = min(max_antecedents, num_mention)
+        # mention和它是第几个mention之间的对应关系
+        antecedents = np.zeros((num_mention, max_antecedents), dtype=int)  # [num_mention,max_an]
+        # 记录长度
+        antecedents_len = [0] * num_mention
+        for i in range(num_mention):
+            ante_count = 0
+            for j in range(max(0, i - max_antecedents), i):
+                antecedents[i, ante_count] = j
+                ante_count += 1
+            # 补位操作
+            for j in range(ante_count, max_antecedents):
+                antecedents[i, j] = 0
+            antecedents_len[i] = ante_count
+        assert antecedents.shape[1] == max_antecedents
+        return antecedents, antecedents_len
+
+    def get_antecedents_score(self, span_represent, mention_score, antecedents, antecedents_len, mention_speakers_ids,
+                              genre):
+        num_mention = mention_score.shape[0]
+        max_antecedent = antecedents.shape[1]
+
+        pair_emb = self.get_pair_emb(span_represent, antecedents, mention_speakers_ids, genre)  # [span_num,max_ant,emb]
+        antecedent_scores = self.sa(pair_emb)
+        mask01 = self.sequence_mask(antecedents_len, max_antecedent)
+        maskinf = torch.log(mask01).to(self.device)
+        assert maskinf.shape[1] <= max_antecedent
+        assert antecedent_scores.shape[0] == num_mention
+        antecedent_scores = antecedent_scores + maskinf
+        antecedents = torch.from_numpy(antecedents).to(self.device)
+        mention_scoreij = mention_score.unsqueeze(1) + torch.gather(
+            mention_score.unsqueeze(0).expand(num_mention, num_mention), dim=1, index=antecedents)
+        antecedent_scores += mention_scoreij
+
+        antecedent_scores = torch.cat([torch.zeros([mention_score.shape[0], 1]).to(self.device), antecedent_scores],
+                                      1)  # [num_mentions, max_ant + 1]
+        return antecedent_scores
+
+    ##############################
+    def distance_bin(self, mention_distance):
+        bins = torch.zeros(mention_distance.size()).byte().to(self.device)
+        rg = [[1, 1], [2, 2], [3, 3], [4, 4], [5, 7], [8, 15], [16, 31], [32, 63], [64, 300]]
+        for t, k in enumerate(rg):
+            i, j = k[0], k[1]
+            b = torch.LongTensor([i]).unsqueeze(-1).expand(mention_distance.size()).to(self.device)
+            m1 = torch.ge(mention_distance, b)
+            e = torch.LongTensor([j]).unsqueeze(-1).expand(mention_distance.size()).to(self.device)
+            m2 = torch.le(mention_distance, e)
+            bins = bins + (t + 1) * (m1 & m2)
+        return bins.long()
+
+    def get_distance_emb(self, antecedents_tensor):
+        num_mention = antecedents_tensor.shape[0]
+        max_ant = antecedents_tensor.shape[1]
+
+        assert max_ant <= self.config.max_antecedents
+        source = torch.arange(0, num_mention).expand(max_ant, num_mention).transpose(0,1).to(self.device)  # [num_mention,max_ant]
+        mention_distance = source - antecedents_tensor
+        mention_distance_bin = self.distance_bin(mention_distance)
+        distance_emb = self.mention_distance_emb(mention_distance_bin)
+        distance_emb = self.distance_drop(distance_emb)
+        return distance_emb
+
+    def get_pair_emb(self, span_emb, antecedents, mention_speakers_ids, genre):
+        emb_dim = span_emb.shape[1]
+        num_span = span_emb.shape[0]
+        max_ant = antecedents.shape[1]
+        assert span_emb.shape[0] == antecedents.shape[0]
+        antecedents = torch.from_numpy(antecedents).to(self.device)
+
+        # [num_span,max_ant,emb]
+        antecedent_emb = torch.gather(span_emb.unsqueeze(0).expand(num_span, num_span, emb_dim), dim=1,
+                                      index=antecedents.unsqueeze(2).expand(num_span, max_ant, emb_dim))
+        # [num_span,max_ant,emb]
+        target_emb_tiled = span_emb.expand((max_ant, num_span, emb_dim))
+        target_emb_tiled = target_emb_tiled.transpose(0, 1)
+
+        similarity_emb = antecedent_emb * target_emb_tiled
+
+        pair_emb_list = [target_emb_tiled, antecedent_emb, similarity_emb]
+
+        # get speakers and genre
+        if self.config.use_metadata:
+            antecedent_speaker_ids = mention_speakers_ids.unsqueeze(0).expand(num_span, num_span).gather(dim=1,
+                                                                                                         index=antecedents)
+            same_speaker = torch.eq(mention_speakers_ids.unsqueeze(1).expand(num_span, max_ant),
+                                    antecedent_speaker_ids)  # [num_mention,max_ant]
+            speaker_embedding = self.speaker_emb(same_speaker.long().to(self.device))  # [mention_num.max_ant,emb]
+            genre_embedding = self.genre_emb(
+                torch.LongTensor([genre]).expand(num_span, max_ant).to(self.device))  # [mention_num,max_ant,emb]
+            pair_emb_list.append(speaker_embedding)
+            pair_emb_list.append(genre_embedding)
+
+        # get distance emb
+        if self.config.use_distance:
+            distance_emb = self.get_distance_emb(antecedents)
+            pair_emb_list.append(distance_emb)
+
+        pair_emb = torch.cat(pair_emb_list, 2)
+        return pair_emb
+
+    def sequence_mask(self, len_list, max_len):
+        x = np.zeros((len(len_list), max_len))
+        for i in range(len(len_list)):
+            l = len_list[i]
+            for j in range(l):
+                x[i][j] = 1
+        return torch.from_numpy(x).float()
+
+    def logsumexp(self, value, dim=None, keepdim=False):
+        """Numerically stable implementation of the operation
+
+        value.exp().sum(dim, keepdim).log()
+        """
+        # TODO: torch.max(value, dim=None) threw an error at time of writing
+        if dim is not None:
+            m, _ = torch.max(value, dim=dim, keepdim=True)
+            value0 = value - m
+            if keepdim is False:
+                m = m.squeeze(dim)
+            return m + torch.log(torch.sum(torch.exp(value0),
+                                           dim=dim, keepdim=keepdim))
+        else:
+            m = torch.max(value)
+            sum_exp = torch.sum(torch.exp(value - m))
+
+            return m + torch.log(sum_exp)
+
+    def softmax_loss(self, antecedent_scores, antecedent_labels):
+        antecedent_labels = torch.from_numpy(antecedent_labels * 1).to(self.device)
+        gold_scores = antecedent_scores + torch.log(antecedent_labels.float())  # [num_mentions, max_ant + 1]
+        marginalized_gold_scores = self.logsumexp(gold_scores, 1)  # [num_mentions]
+        log_norm = self.logsumexp(antecedent_scores, 1)  # [num_mentions]
+        return torch.sum(log_norm - marginalized_gold_scores)  # [num_mentions]reduce_logsumexp
+
+    def get_predicted_antecedents(self, antecedents, antecedent_scores):
+        predicted_antecedents = []
+        for i, index in enumerate(np.argmax(antecedent_scores.detach(), axis=1) - 1):
+            if index < 0:
+                predicted_antecedents.append(-1)
+            else:
+                predicted_antecedents.append(antecedents[i, index])
+        return predicted_antecedents
+
+    def get_predicted_clusters(self, mention_starts, mention_ends, predicted_antecedents):
+        mention_to_predicted = {}
+        predicted_clusters = []
+        for i, predicted_index in enumerate(predicted_antecedents):
+            if predicted_index < 0:
+                continue
+            assert i > predicted_index
+            predicted_antecedent = (int(mention_starts[predicted_index]), int(mention_ends[predicted_index]))
+            if predicted_antecedent in mention_to_predicted:
+                predicted_cluster = mention_to_predicted[predicted_antecedent]
+            else:
+                predicted_cluster = len(predicted_clusters)
+                predicted_clusters.append([predicted_antecedent])
+                mention_to_predicted[predicted_antecedent] = predicted_cluster
+
+            mention = (int(mention_starts[i]), int(mention_ends[i]))
+            predicted_clusters[predicted_cluster].append(mention)
+            mention_to_predicted[mention] = predicted_cluster
+
+        predicted_clusters = [tuple(pc) for pc in predicted_clusters]
+        mention_to_predicted = {m: predicted_clusters[i] for m, i in mention_to_predicted.items()}
+
+        return predicted_clusters, mention_to_predicted
+
+    def evaluate_coref(self, mention_starts, mention_ends, predicted_antecedents, gold_clusters, evaluator):
+        gold_clusters = [tuple(tuple(m) for m in gc) for gc in gold_clusters]
+        mention_to_gold = {}
+        for gc in gold_clusters:
+            for mention in gc:
+                mention_to_gold[mention] = gc
+        predicted_clusters, mention_to_predicted = self.get_predicted_clusters(mention_starts, mention_ends,
+                                                                               predicted_antecedents)
+        evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold)
+        return predicted_clusters
+
+
+    def forward(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len):
+        """
+        实际输入都是tensor
+        :param sentences: 句子，被fastNLP转化成了numpy，
+        :param doc_np: 被fastNLP转化成了Tensor
+        :param speaker_ids_np: 被fastNLP转化成了Tensor
+        :param genre: 被fastNLP转化成了Tensor
+        :param char_index: 被fastNLP转化成了Tensor
+        :param seq_len: 被fastNLP转化成了Tensor
+        :return:
+        """
+        # change for fastNLP
+        sentences = sentences[0].tolist()
+        doc_tensor = doc_np[0]
+        speakers_tensor = speaker_ids_np[0]
+        genre = genre[0].item()
+        char_index = char_index[0]
+        seq_len = seq_len[0].cpu().numpy()
+
+        # 类型
+
+        # doc_tensor = torch.from_numpy(doc_np).to(self.device)
+        # speakers_tensor = torch.from_numpy(speaker_ids_np).to(self.device)
+        mention_emb_list = []
+
+        word_emb = self.emb(doc_tensor)
+        word_emb_list = [word_emb]
+        if self.config.use_CNN:
+            # [batch, length, char_length, char_dim]
+            char = self.char_emb(char_index)
+            char_size = char.size()
+            # first transform to [batch *length, char_length, char_dim]
+            # then transpose to [batch * length, char_dim, char_length]
+            char = char.view(char_size[0] * char_size[1], char_size[2], char_size[3]).transpose(1, 2)
+
+            # put into cnn [batch*length, char_filters, char_length]
+            # then put into maxpooling [batch * length, char_filters]
+            char_over_cnn, _ = self.conv1(char).max(dim=2)
+            # reshape to [batch, length, char_filters]
+            char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1)
+            word_emb_list.append(char_over_cnn)
+
+            char_over_cnn, _ = self.conv2(char).max(dim=2)
+            char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1)
+            word_emb_list.append(char_over_cnn)
+
+            char_over_cnn, _ = self.conv3(char).max(dim=2)
+            char_over_cnn = torch.tanh(char_over_cnn).view(char_size[0], char_size[1], -1)
+            word_emb_list.append(char_over_cnn)
+
+        # word_emb = torch.cat(word_emb_list, dim=2)
+
+        # use elmo or not
+        if self.config.use_elmo:
+            # 如果确实被截断了
+            if doc_tensor.shape[0] == 50 and len(sentences) > 50:
+                sentences = sentences[0:50]
+            elmo_embedding, elmo_mask = self.elmo.batch_to_embeddings(sentences)
+            elmo_embedding = elmo_embedding.to(
+                self.device)  # [sentence_num,max_sent_len,3,1024]--[sentence_num,max_sent,1024]
+            elmo_embedding = elmo_embedding[:, 0, :, :] * self.elmo_args[0] + elmo_embedding[:, 1, :, :] * \
+                             self.elmo_args[1] + elmo_embedding[:, 2, :, :] * self.elmo_args[2]
+            word_emb_list.append(elmo_embedding)
+        # print(word_emb_list[0].shape)
+        # print(word_emb_list[1].shape)
+        # print(word_emb_list[2].shape)
+        # print(word_emb_list[3].shape)
+        # print(word_emb_list[4].shape)
+
+        word_emb = torch.cat(word_emb_list, dim=2)
+
+        word_emb = self.emb_dropout(word_emb)
+        # word_emb_elmo = self.emb_dropout(word_emb_elmo)
+        lstm_out = self._reorder_lstm(word_emb, seq_len)
+        flatten_lstm = self.flat_lstm(lstm_out, seq_len)  # [word_num,emb]
+        flatten_lstm = self.bilstm_drop(flatten_lstm)
+        # TODO 没有按照论文写
+        flatten_word_emb = self.flat_lstm(word_emb, seq_len)  # [word_num,emb]
+
+        mention_start, mention_end = self.get_mention_start_end(seq_len)  # [mention_num]
+        self.mention_start_np = mention_start  # [mention_num] np
+        self.mention_end_np = mention_end
+        mention_num = mention_start.shape[0]
+        emb_start, emb_end = self.get_mention_emb(flatten_lstm, mention_start, mention_end)  # [mention_num,emb]
+
+        # list
+        mention_emb_list.append(emb_start)
+        mention_emb_list.append(emb_end)
+
+        if self.config.use_width:
+            mention_width_index = mention_end - mention_start
+            mention_width_tensor = torch.from_numpy(mention_width_index).to(self.device)  # [mention_num]
+            mention_width_emb = self.feature_emb(mention_width_tensor)
+            mention_width_emb = self.feature_emb_dropout(mention_width_emb)
+            mention_emb_list.append(mention_width_emb)
+
+        if self.config.model_heads:
+            mention_index = self.get_mention_index(mention_start, self.config.span_width)  # [mention_num,max_mention]
+            log_mask_tensor = self.get_mask(mention_start, mention_end).float().to(
+                self.device)  # [mention_num,max_mention]
+            alpha = self.atten(flatten_lstm).to(self.device)  # [word_num]
+
+            # 得到attention
+            mention_head_score = torch.gather(alpha.expand(mention_num, -1), 1,
+                                              mention_index).float().to(self.device)  # [mention_num,max_mention]
+            mention_attention = F.softmax(mention_head_score + log_mask_tensor, dim=1)  # [mention_num,max_mention]
+
+            # TODO flatte lstm
+            word_num = flatten_lstm.shape[0]
+            lstm_emb = flatten_lstm.shape[1]
+            emb_num = flatten_word_emb.shape[1]
+
+            # [num_mentions, max_mention_width, emb]
+            mention_text_emb = torch.gather(
+                flatten_word_emb.unsqueeze(1).expand(word_num, self.config.span_width, emb_num),
+                0, mention_index.unsqueeze(2).expand(mention_num, self.config.span_width,
+                                                     emb_num))
+            # [mention_num,emb]
+            mention_head_emb = torch.sum(
+                mention_attention.unsqueeze(2).expand(mention_num, self.config.span_width, emb_num) * mention_text_emb,
+                dim=1)
+            mention_emb_list.append(mention_head_emb)
+
+        candidate_mention_emb = torch.cat(mention_emb_list, 1)  # [candidate_mention_num,emb]
+        candidate_mention_score = self.mention_score(candidate_mention_emb)  # [candidate_mention_num]
+
+        antecedent_scores, antecedents, mention_start_tensor, mention_end_tensor = (None, None, None, None)
+        mention_start_tensor, mention_end_tensor, mention_score, mention_emb = \
+            self.sort_mention(mention_start, mention_end, candidate_mention_emb, candidate_mention_score, seq_len)
+        mention_speakers_ids = speakers_tensor.index_select(dim=0, index=mention_start_tensor)  # num_mention
+
+        antecedents, antecedents_len = self.get_antecedents(mention_start_tensor, self.config.max_antecedents)
+        antecedent_scores = self.get_antecedents_score(mention_emb, mention_score, antecedents, antecedents_len,
+                                                       mention_speakers_ids, genre)
+
+        ans = {"candidate_mention_score": candidate_mention_score, "antecedent_scores": antecedent_scores,
+               "antecedents": antecedents, "mention_start_tensor": mention_start_tensor,
+               "mention_end_tensor": mention_end_tensor}
+
+        return ans
+
+    def predict(self, sentences, doc_np, speaker_ids_np, genre, char_index, seq_len):
+        ans = self(sentences,
+                   doc_np,
+                   speaker_ids_np,
+                   genre,
+                   char_index,
+                   seq_len)
+
+        predicted_antecedents = self.get_predicted_antecedents(ans["antecedents"], ans["antecedent_scores"])
+        predicted_clusters, mention_to_predicted = self.get_predicted_clusters(ans["mention_start_tensor"],
+                                                                               ans["mention_end_tensor"],
+                                                                               predicted_antecedents)
+
+        return {'predicted':predicted_clusters,"mention_to_predicted":mention_to_predicted}
+
+
+if __name__ == '__main__':
+    pass
diff --git a/reproduction/coreference_resolution/model/preprocess.py b/reproduction/coreference_resolution/model/preprocess.py
new file mode 100644
index 00000000..d97fcb4d
--- /dev/null
+++ b/reproduction/coreference_resolution/model/preprocess.py
@@ -0,0 +1,225 @@
+import json
+import numpy as np
+from . import util
+import collections
+
+def load(path):
+    """
+    load the file from jsonline
+    :param path:
+    :return: examples with many example(dict): {"clusters":[[[mention],[mention]],[another cluster]],
+    "doc_key":"str","speakers":[[,,,],[]...],"sentence":[[][]]}
+    """
+    with open(path) as f:
+        train_examples = [json.loads(jsonline) for jsonline in f.readlines()]
+        return train_examples
+
+def get_vocab():
+    """
+    从所有的句子中得到最终的字典,被main调用,不止是train，还有dev和test
+    :param examples:
+    :return: word2id & id2word
+    """
+    word2id = {'PAD':0,'UNK':1}
+    id2word = {0:'PAD',1:'UNK'}
+    index = 2
+    data = [load("../data/train.english.jsonlines"),load("../data/dev.english.jsonlines"),load("../data/test.english.jsonlines")]
+    for examples in data:
+        for example in examples:
+            for sent in example["sentences"]:
+                for word in sent:
+                    if(word not in word2id):
+                        word2id[word]=index
+                        id2word[index] = word
+                        index += 1
+    return word2id,id2word
+
+def normalize(v):
+    norm = np.linalg.norm(v)
+    if norm > 0:
+        return v / norm
+    else:
+        return v
+
+# 加载glove得到embedding
+def get_emb(id2word,embedding_size):
+    glove_oov = 0
+    turian_oov = 0
+    both = 0
+    glove_emb_path = "../data/glove.840B.300d.txt.filtered"
+    turian_emb_path = "../data/turian.50d.txt"
+    word_num = len(id2word)
+    emb = np.zeros((word_num,embedding_size))
+    glove_emb_dict = util.load_embedding_dict(glove_emb_path,300,"txt")
+    turian_emb_dict = util.load_embedding_dict(turian_emb_path,50,"txt")
+    for i in range(word_num):
+        if id2word[i] in glove_emb_dict:
+            word_embedding = glove_emb_dict.get(id2word[i])
+            emb[i][0:300] = np.array(word_embedding)
+        else:
+            # print(id2word[i])
+            glove_oov += 1
+        if id2word[i] in turian_emb_dict:
+            word_embedding = turian_emb_dict.get(id2word[i])
+            emb[i][300:350] = np.array(word_embedding)
+        else:
+            # print(id2word[i])
+            turian_oov += 1
+        if id2word[i] not in glove_emb_dict and id2word[i] not in turian_emb_dict:
+            both += 1
+        emb[i] = normalize(emb[i])
+    print("embedding num:"+str(word_num))
+    print("glove num:"+str(glove_oov))
+    print("glove oov rate:"+str(glove_oov/word_num))
+    print("turian num:"+str(turian_oov))
+    print("turian oov rate:"+str(turian_oov/word_num))
+    print("both num:"+str(both))
+    return emb
+
+
+def _doc2vec(doc,word2id,char_dict,max_filter,max_sentences,is_train):
+    max_len = 0
+    max_word_length = 0
+    docvex = []
+    length = []
+    if is_train:
+        sent_num = min(max_sentences,len(doc))
+    else:
+        sent_num = len(doc)
+
+    for i in range(sent_num):
+        sent = doc[i]
+        length.append(len(sent))
+        if (len(sent) > max_len):
+            max_len = len(sent)
+        sent_vec =[]
+        for j,word in enumerate(sent):
+            if len(word)>max_word_length:
+                max_word_length = len(word)
+            if word in word2id:
+                sent_vec.append(word2id[word])
+            else:
+                sent_vec.append(word2id["UNK"])
+        docvex.append(sent_vec)
+
+    char_index = np.zeros((sent_num, max_len, max_word_length),dtype=int)
+    for i in range(sent_num):
+        sent = doc[i]
+        for j,word in enumerate(sent):
+            char_index[i, j, :len(word)] = [char_dict[c] for c in word]
+
+    return docvex,char_index,length,max_len
+
+# TODO 修改了接口，确认所有该修改的地方都修改好
+def doc2numpy(doc,word2id,chardict,max_filter,max_sentences,is_train):
+    docvec, char_index, length, max_len = _doc2vec(doc,word2id,chardict,max_filter,max_sentences,is_train)
+    assert max(length) == max_len
+    assert char_index.shape[0]==len(length)
+    assert char_index.shape[1]==max_len
+    doc_np = np.zeros((len(docvec), max_len), int)
+    for i in range(len(docvec)):
+        for j in range(len(docvec[i])):
+            doc_np[i][j] = docvec[i][j]
+    return doc_np,char_index,length
+
+# TODO 没有测试
+def speaker2numpy(speakers_raw,max_sentences,is_train):
+    if is_train and len(speakers_raw)> max_sentences:
+        speakers_raw = speakers_raw[0:max_sentences]
+    speakers = flatten(speakers_raw)
+    speaker_dict = {s: i for i, s in enumerate(set(speakers))}
+    speaker_ids = np.array([speaker_dict[s] for s in speakers])
+    return speaker_ids
+
+
+def flat_cluster(clusters):
+    flatted = []
+    for cluster in clusters:
+        for item in cluster:
+            flatted.append(item)
+    return flatted
+
+def get_right_mention(clusters,mention_start_np,mention_end_np):
+    flatted = flat_cluster(clusters)
+    cluster_num = len(flatted)
+    mention_num = mention_start_np.shape[0]
+    right_mention = np.zeros(mention_num,dtype=int)
+    for i in range(mention_num):
+        if [mention_start_np[i],mention_end_np[i]] in flatted:
+            right_mention[i]=1
+    return right_mention,cluster_num
+
+def handle_cluster(clusters):
+    gold_mentions = sorted(tuple(m) for m in flatten(clusters))
+    gold_mention_map = {m: i for i, m in enumerate(gold_mentions)}
+    cluster_ids = np.zeros(len(gold_mentions), dtype=int)
+    for cluster_id, cluster in enumerate(clusters):
+        for mention in cluster:
+            cluster_ids[gold_mention_map[tuple(mention)]] = cluster_id
+    gold_starts, gold_ends = tensorize_mentions(gold_mentions)
+    return cluster_ids, gold_starts, gold_ends
+
+# 展平
+def flatten(l):
+    return [item for sublist in l for item in sublist]
+
+# 把mention分成start end
+def tensorize_mentions(mentions):
+    if len(mentions) > 0:
+        starts, ends = zip(*mentions)
+    else:
+        starts, ends = [], []
+    return np.array(starts), np.array(ends)
+
+def get_char_dict(path):
+    vocab = ["<UNK>"]
+    with open(path) as f:
+        vocab.extend(c.strip() for c in f.readlines())
+    char_dict = collections.defaultdict(int)
+    char_dict.update({c: i for i, c in enumerate(vocab)})
+    return char_dict
+
+def get_labels(clusters,mention_starts,mention_ends,max_antecedents):
+    cluster_ids, gold_starts, gold_ends = handle_cluster(clusters)
+    num_mention = mention_starts.shape[0]
+    num_gold = gold_starts.shape[0]
+    max_antecedents = min(max_antecedents, num_mention)
+    mention_indices = {}
+
+    for i in range(num_mention):
+        mention_indices[(mention_starts[i].detach().item(), mention_ends[i].detach().item())] = i
+    # 用来记录哪些mention是对的，-1表示错误，正数代表这个mention实际上对应哪个gold cluster的id
+    mention_cluster_ids = [-1] * num_mention
+    # test
+    right_mention_count = 0
+    for i in range(num_gold):
+        right_mention = mention_indices.get((gold_starts[i], gold_ends[i]))
+        if (right_mention != None):
+            right_mention_count += 1
+            mention_cluster_ids[right_mention] = cluster_ids[i]
+
+    # i j 是否属于同一个cluster
+    labels = np.zeros((num_mention, max_antecedents + 1), dtype=bool)  # [num_mention,max_an+1]
+    for i in range(num_mention):
+        ante_count = 0
+        null_label = True
+        for j in range(max(0, i - max_antecedents), i):
+            if (mention_cluster_ids[i] >= 0 and mention_cluster_ids[i] == mention_cluster_ids[j]):
+                labels[i, ante_count + 1] = True
+                null_label = False
+            else:
+                labels[i, ante_count + 1] = False
+            ante_count += 1
+        for j in range(ante_count, max_antecedents):
+            labels[i, j + 1] = False
+        labels[i, 0] = null_label
+    return labels
+
+# test===========================
+
+
+if __name__=="__main__":
+    word2id,id2word = get_vocab()
+    get_emb(id2word,350)
+
+
diff --git a/reproduction/coreference_resolution/model/softmax_loss.py b/reproduction/coreference_resolution/model/softmax_loss.py
new file mode 100644
index 00000000..c75a31d6
--- /dev/null
+++ b/reproduction/coreference_resolution/model/softmax_loss.py
@@ -0,0 +1,32 @@
+from fastNLP.core.losses import LossBase
+
+from reproduction.coreference_resolution.model.preprocess import get_labels
+from reproduction.coreference_resolution.model.config import Config
+import torch
+
+
+class SoftmaxLoss(LossBase):
+    """
+    交叉熵loss
+    允许多标签分类
+    """
+
+    def __init__(self, antecedent_scores=None, clusters=None, mention_start_tensor=None, mention_end_tensor=None):
+        """
+
+        :param pred:
+        :param target:
+        """
+        super().__init__()
+        self._init_param_map(antecedent_scores=antecedent_scores, clusters=clusters,
+                             mention_start_tensor=mention_start_tensor, mention_end_tensor=mention_end_tensor)
+
+    def get_loss(self, antecedent_scores, clusters, mention_start_tensor, mention_end_tensor):
+        antecedent_labels = get_labels(clusters[0], mention_start_tensor, mention_end_tensor,
+                                       Config().max_antecedents)
+
+        antecedent_labels = torch.from_numpy(antecedent_labels*1).to(torch.device("cuda:" + Config().cuda))
+        gold_scores = antecedent_scores + torch.log(antecedent_labels.float()).to(torch.device("cuda:" + Config().cuda))  # [num_mentions, max_ant + 1]
+        marginalized_gold_scores = gold_scores.logsumexp(dim=1)  # [num_mentions]
+        log_norm = antecedent_scores.logsumexp(dim=1)  # [num_mentions]
+        return torch.sum(log_norm - marginalized_gold_scores)
diff --git a/reproduction/coreference_resolution/model/util.py b/reproduction/coreference_resolution/model/util.py
new file mode 100644
index 00000000..42cd09fe
--- /dev/null
+++ b/reproduction/coreference_resolution/model/util.py
@@ -0,0 +1,101 @@
+import os
+import errno
+import collections
+import torch
+import numpy as np
+import pyhocon
+
+
+
+# flatten the list
+def flatten(l):
+    return [item for sublist in l for item in sublist]
+
+
+def get_config(filename):
+    return pyhocon.ConfigFactory.parse_file(filename)
+
+
+# safe make directions
+def mkdirs(path):
+    try:
+        os.makedirs(path)
+    except OSError as exception:
+        if exception.errno != errno.EEXIST:
+            raise
+    return path
+
+
+def load_char_dict(char_vocab_path):
+    vocab = ["<unk>"]
+    with open(char_vocab_path) as f:
+        vocab.extend(c.strip() for c in f.readlines())
+    char_dict = collections.defaultdict(int)
+    char_dict.update({c: i for i, c in enumerate(vocab)})
+    return char_dict
+
+# 加载embedding
+def load_embedding_dict(embedding_path, embedding_size, embedding_format):
+    print("Loading word embeddings from {}...".format(embedding_path))
+    default_embedding = np.zeros(embedding_size)
+    embedding_dict = collections.defaultdict(lambda: default_embedding)
+    skip_first = embedding_format == "vec"
+    with open(embedding_path) as f:
+        for i, line in enumerate(f.readlines()):
+            if skip_first and i == 0:
+                continue
+            splits = line.split()
+            assert len(splits) == embedding_size + 1
+            word = splits[0]
+            embedding = np.array([float(s) for s in splits[1:]])
+            embedding_dict[word] = embedding
+    print("Done loading word embeddings.")
+    return embedding_dict
+
+
+# safe devide
+def maybe_divide(x, y):
+    return 0 if y == 0 else x / float(y)
+
+
+def shape(x, dim):
+    return x.get_shape()[dim].value or torch.shape(x)[dim]
+
+
+def normalize(v):
+    norm = np.linalg.norm(v)
+    if norm > 0:
+        return v / norm
+    else:
+        return v
+
+
+class RetrievalEvaluator(object):
+    def __init__(self):
+        self._num_correct = 0
+        self._num_gold = 0
+        self._num_predicted = 0
+
+    def update(self, gold_set, predicted_set):
+        self._num_correct += len(gold_set & predicted_set)
+        self._num_gold += len(gold_set)
+        self._num_predicted += len(predicted_set)
+
+    def recall(self):
+        return maybe_divide(self._num_correct, self._num_gold)
+
+    def precision(self):
+        return maybe_divide(self._num_correct, self._num_predicted)
+
+    def metrics(self):
+        recall = self.recall()
+        precision = self.precision()
+        f1 = maybe_divide(2 * recall * precision, precision + recall)
+        return recall, precision, f1
+
+
+
+if __name__=="__main__":
+    print(load_char_dict("../data/char_vocab.english.txt"))
+    embedding_dict = load_embedding_dict("../data/glove.840B.300d.txt.filtered",300,"txt")
+    print("hello")
diff --git a/reproduction/coreference_resolution/readme.md b/reproduction/coreference_resolution/readme.md
new file mode 100644
index 00000000..67d8cdc7
--- /dev/null
+++ b/reproduction/coreference_resolution/readme.md
@@ -0,0 +1,49 @@
+# 共指消解复现
+## 介绍
+Coreference resolution是查找文本中指向同一现实实体的所有表达式的任务。
+对于涉及自然语言理解的许多更高级别的NLP任务来说，
+这是一个重要的步骤，例如文档摘要，问题回答和信息提取。
+代码的实现主要基于[ End-to-End Coreference Resolution (Lee et al, 2017)](https://arxiv.org/pdf/1707.07045).
+
+
+## 数据获取与预处理
+论文在[OntoNote5.0](https://allennlp.org/models)数据集上取得了当时的sota结果。
+由于版权问题，本文无法提供数据集的下载，请自行下载。
+原始数据集的格式为conll格式，详细介绍参考数据集给出的官方介绍页面。
+
+代码实现采用了论文作者Lee的预处理方法，具体细节参加[链接](https://github.com/kentonl/e2e-coref/blob/e2e/setup_training.sh)。
+处理之后的数据集为json格式，例子：
+```
+{
+  "clusters": [],
+  "doc_key": "nw",
+  "sentences": [["This", "is", "the", "first", "sentence", "."], ["This", "is", "the", "second", "."]],
+  "speakers": [["spk1", "spk1", "spk1", "spk1", "spk1", "spk1"], ["spk2", "spk2", "spk2", "spk2", "spk2"]]
+}
+```
+
+### embedding 数据集下载
+[turian emdedding](https://lil.cs.washington.edu/coref/turian.50d.txt)
+
+[glove embedding]( https://nlp.stanford.edu/data/glove.840B.300d.zip)
+
+
+
+## 运行
+```python
+# 训练代码
+CUDA_VISIBLE_DEVICES=0 python train.py
+# 测试代码
+CUDA_VISIBLE_DEVICES=0 python valid.py
+```
+
+## 结果
+原论文作者在测试集上取得了67.2%的结果，AllenNLP复现的结果为 [63.0%](https://allennlp.org/models)。
+其中allenNLP训练时没有加入speaker信息，没有variational dropout以及只使用了100的antecedents而不是250。
+
+在与allenNLP使用同样的超参和配置时，本代码复现取得了63.6%的F1值。
+
+
+## 问题
+如果您有什么问题或者反馈，请提issue或者邮件联系我：
+yexu_i@qq.com
diff --git a/reproduction/coreference_resolution/test/__init__.py b/reproduction/coreference_resolution/test/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/coreference_resolution/test/test_dataloader.py b/reproduction/coreference_resolution/test/test_dataloader.py
new file mode 100644
index 00000000..0d9dae52
--- /dev/null
+++ b/reproduction/coreference_resolution/test/test_dataloader.py
@@ -0,0 +1,14 @@
+import unittest
+from ..data_load.cr_loader import CRLoader
+
+class Test_CRLoader(unittest.TestCase):
+    def test_cr_loader(self):
+        train_path = 'data/train.english.jsonlines.mini'
+        dev_path = 'data/dev.english.jsonlines.minid'
+        test_path = 'data/test.english.jsonlines'
+        cr = CRLoader()
+        data_info = cr.process({'train':train_path,'dev':dev_path,'test':test_path})
+
+        print(data_info.datasets['train'][0])
+        print(data_info.datasets['dev'][0])
+        print(data_info.datasets['test'][0])
diff --git a/reproduction/coreference_resolution/train.py b/reproduction/coreference_resolution/train.py
new file mode 100644
index 00000000..a231a575
--- /dev/null
+++ b/reproduction/coreference_resolution/train.py
@@ -0,0 +1,69 @@
+import sys
+sys.path.append('../..')
+
+import torch
+from torch.optim import Adam
+
+from fastNLP.core.callback import Callback, GradientClipCallback
+from fastNLP.core.trainer import Trainer
+
+from reproduction.coreference_resolution.data_load.cr_loader import CRLoader
+from reproduction.coreference_resolution.model.config import Config
+from reproduction.coreference_resolution.model.model_re import Model
+from reproduction.coreference_resolution.model.softmax_loss import SoftmaxLoss
+from reproduction.coreference_resolution.model.metric import CRMetric
+from fastNLP import SequentialSampler
+from fastNLP import cache_results
+
+
+# torch.backends.cudnn.benchmark = False
+# torch.backends.cudnn.deterministic = True
+
+class LRCallback(Callback):
+    def __init__(self, parameters, decay_rate=1e-3):
+        super().__init__()
+        self.paras = parameters
+        self.decay_rate = decay_rate
+
+    def on_step_end(self):
+        if self.step % 100 == 0:
+            for para in self.paras:
+                para['lr'] = para['lr'] * (1 - self.decay_rate)
+
+
+if __name__ == "__main__":
+    config = Config()
+
+    print(config)
+
+    @cache_results('cache.pkl')
+    def cache():
+        cr_train_dev_test = CRLoader()
+
+        data_info = cr_train_dev_test.process({'train': config.train_path, 'dev': config.dev_path,
+                                               'test': config.test_path})
+        return data_info
+    data_info = cache()
+    print("数据集划分：\ntrain:", str(len(data_info.datasets["train"])),
+          "\ndev:" + str(len(data_info.datasets["dev"])) + "\ntest:" + str(len(data_info.datasets["test"])))
+    # print(data_info)
+    model = Model(data_info.vocabs, config)
+    print(model)
+
+    loss = SoftmaxLoss()
+
+    metric = CRMetric()
+
+    optim = Adam(model.parameters(), lr=config.lr)
+
+    lr_decay_callback = LRCallback(optim.param_groups, config.lr_decay)
+
+    trainer = Trainer(model=model, train_data=data_info.datasets["train"], dev_data=data_info.datasets["dev"],
+                      loss=loss, metrics=metric, check_code_level=-1,sampler=None,
+                      batch_size=1, device=torch.device("cuda:" + config.cuda), metric_key='f', n_epochs=config.epoch,
+                      optimizer=optim,
+                      save_path='/remote-home/xxliu/pycharm/fastNLP/fastNLP/reproduction/coreference_resolution/save',
+                      callbacks=[lr_decay_callback, GradientClipCallback(clip_value=5)])
+    print()
+
+    trainer.train()
diff --git a/reproduction/coreference_resolution/valid.py b/reproduction/coreference_resolution/valid.py
new file mode 100644
index 00000000..826332c6
--- /dev/null
+++ b/reproduction/coreference_resolution/valid.py
@@ -0,0 +1,24 @@
+import torch
+from reproduction.coreference_resolution.model.config import Config
+from reproduction.coreference_resolution.model.metric import CRMetric
+from reproduction.coreference_resolution.data_load.cr_loader import CRLoader
+from fastNLP import Tester
+import argparse
+
+
+if __name__=='__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--path')
+    args = parser.parse_args()
+    
+    cr_loader = CRLoader()
+    config = Config()
+    data_info = cr_loader.process({'train': config.train_path, 'dev': config.dev_path,
+                                               'test': config.test_path})
+    metirc = CRMetric()
+    model = torch.load(args.path)
+    tester = Tester(data_info.datasets['test'],model,metirc,batch_size=1,device="cuda:0")
+    tester.test()
+    print('test over')
+
+
diff --git a/reproduction/text_classification/data/IMDBLoader.py b/reproduction/text_classification/data/IMDBLoader.py
index 22841c4d..30daf233 100644
--- a/reproduction/text_classification/data/IMDBLoader.py
+++ b/reproduction/text_classification/data/IMDBLoader.py
@@ -32,7 +32,7 @@ class IMDBLoader(DataSetLoader):
                     continue
                 parts = line.split('\t')
                 target = parts[0]
-                words = parts[1].split()
+                words = parts[1].lower().split()
                 dataset.append(Instance(words=words, target=target))
 
         if len(dataset)==0:
@@ -46,7 +46,7 @@ class IMDBLoader(DataSetLoader):
                 tgt_vocab_opt: VocabularyOption = None,
                 src_embed_opt: EmbeddingOption = None,
                 char_level_op=False):
-       
+      
         datasets = {}
         info = DataInfo()
         for name, path in paths.items():
@@ -69,7 +69,7 @@ class IMDBLoader(DataSetLoader):
 
         src_vocab = Vocabulary() if src_vocab_opt is None else Vocabulary(**src_vocab_opt)
         src_vocab.from_dataset(datasets['train'], field_name='words')
-        # src_vocab.from_dataset(datasets['train'], datasets["dev"], datasets["test"], field_name='words')
+
         src_vocab.index_dataset(*datasets.values(), field_name='words')
 
         tgt_vocab = Vocabulary(unknown=None, padding=None) \
@@ -95,6 +95,7 @@ class IMDBLoader(DataSetLoader):
         return info
 
 
+
 if __name__=="__main__":
     datapath = {"train": "/remote-home/ygwang/IMDB_data/train.csv",
                 "test": "/remote-home/ygwang/IMDB_data/test.csv"}
@@ -106,3 +107,4 @@ if __name__=="__main__":
 
     ave_len = len_count / len(datainfo.datasets["train"])
     print(ave_len)
+
diff --git a/reproduction/text_classification/data/MTL16Loader.py b/reproduction/text_classification/data/MTL16Loader.py
index 1b3e6245..066b53b4 100644
--- a/reproduction/text_classification/data/MTL16Loader.py
+++ b/reproduction/text_classification/data/MTL16Loader.py
@@ -32,7 +32,7 @@ class MTL16Loader(DataSetLoader):
                     continue
                 parts = line.split('\t')
                 target = parts[0]
-                words = parts[1].split()
+                words = parts[1].lower().split()
                 dataset.append(Instance(words=words, target=target))
         if len(dataset)==0:
             raise RuntimeError(f"{path} has no valid data.")
@@ -72,4 +72,8 @@ class MTL16Loader(DataSetLoader):
             embed = EmbedLoader.load_with_vocab(**src_embed_opt, vocab=src_vocab)
             info.embeddings['words'] = embed
 
+        for name, dataset in info.datasets.items():
+            dataset.set_input("words")
+            dataset.set_target("target")
+
         return info
diff --git a/reproduction/text_classification/data/SSTLoader.py b/reproduction/text_classification/data/SSTLoader.py
new file mode 100644
index 00000000..b570994e
--- /dev/null
+++ b/reproduction/text_classification/data/SSTLoader.py
@@ -0,0 +1,99 @@
+from typing import Iterable
+from nltk import Tree
+from fastNLP.io.base_loader import DataInfo, DataSetLoader
+from fastNLP.core.vocabulary import VocabularyOption, Vocabulary
+from fastNLP import DataSet
+from fastNLP import Instance
+from fastNLP.io.embed_loader import EmbeddingOption, EmbedLoader
+
+
+class SSTLoader(DataSetLoader):
+    URL = 'https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'
+    DATA_DIR = 'sst/'
+
+    """
+    别名：:class:`fastNLP.io.SSTLoader` :class:`fastNLP.io.dataset_loader.SSTLoader`
+
+    读取SST数据集, DataSet包含fields::
+
+        words: list(str) 需要分类的文本
+        target: str 文本的标签
+
+    数据来源: https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip
+
+    :param subtree: 是否将数据展开为子树，扩充数据量. Default: ``False``
+    :param fine_grained: 是否使用SST-5标准，若 ``False`` , 使用SST-2。Default: ``False``
+    """
+
+    def __init__(self, subtree=False, fine_grained=False):
+        self.subtree = subtree
+
+        tag_v = {'0': 'very negative', '1': 'negative', '2': 'neutral',
+                 '3': 'positive', '4': 'very positive'}
+        if not fine_grained:
+            tag_v['0'] = tag_v['1']
+            tag_v['4'] = tag_v['3']
+        self.tag_v = tag_v
+
+    def _load(self, path):
+        """
+
+        :param str path: 存储数据的路径
+        :return: 一个 :class:`~fastNLP.DataSet` 类型的对象
+        """
+        datalist = []
+        with open(path, 'r', encoding='utf-8') as f:
+            datas = []
+            for l in f:
+                datas.extend([(s, self.tag_v[t])
+                              for s, t in self._get_one(l, self.subtree)])
+        ds = DataSet()
+        for words, tag in datas:
+            ds.append(Instance(words=words, target=tag))
+        return ds
+
+    @staticmethod
+    def _get_one(data, subtree):
+        tree = Tree.fromstring(data)
+        if subtree:
+            return [(t.leaves(), t.label()) for t in tree.subtrees()]
+        return [(tree.leaves(), tree.label())]
+
+    def process(self,
+                paths,
+                train_ds: Iterable[str] = None,
+                src_vocab_op: VocabularyOption = None,
+                tgt_vocab_op: VocabularyOption = None,
+                src_embed_op: EmbeddingOption = None):
+        input_name, target_name = 'words', 'target'
+        src_vocab = Vocabulary() if src_vocab_op is None else Vocabulary(**src_vocab_op)
+        tgt_vocab = Vocabulary(unknown=None, padding=None) \
+            if tgt_vocab_op is None else Vocabulary(**tgt_vocab_op)
+
+        info = DataInfo(datasets=self.load(paths))
+        _train_ds = [info.datasets[name]
+                     for name in train_ds] if train_ds else info.datasets.values()
+        src_vocab.from_dataset(*_train_ds, field_name=input_name)
+        tgt_vocab.from_dataset(*_train_ds, field_name=target_name)
+        src_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=input_name, new_field_name=input_name)
+        tgt_vocab.index_dataset(
+            *info.datasets.values(),
+            field_name=target_name, new_field_name=target_name)
+        info.vocabs = {
+            input_name: src_vocab,
+            target_name: tgt_vocab
+        }
+
+        if src_embed_op is not None:
+            src_embed_op.vocab = src_vocab
+            init_emb = EmbedLoader.load_with_vocab(**src_embed_op)
+            info.embeddings[input_name] = init_emb
+
+        for name, dataset in info.datasets.items():
+            dataset.set_input(input_name)
+            dataset.set_target(target_name)
+
+        return info
+
diff --git a/reproduction/text_classification/data/yelpLoader.py b/reproduction/text_classification/data/yelpLoader.py
index 90a80301..0e65fb20 100644
--- a/reproduction/text_classification/data/yelpLoader.py
+++ b/reproduction/text_classification/data/yelpLoader.py
@@ -200,4 +200,4 @@ if __name__=="__main__":
         len_count+=len(instance["chars"])
 
     ave_len=len_count/len(datainfo.datasets["train"])
-    print(ave_len)
\ No newline at end of file
+    print(ave_len)
diff --git a/reproduction/text_classification/model/awd_lstm.py b/reproduction/text_classification/model/awd_lstm.py
new file mode 100644
index 00000000..0d8f711a
--- /dev/null
+++ b/reproduction/text_classification/model/awd_lstm.py
@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from .awdlstm_module import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class AWDLSTMSentiment(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 nfc=128,
+                 wdrop=0.5):
+        super(AWDLSTMSentiment,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True, wdrop=wdrop)
+        self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        output = self.mlp(output[:,-1,:])
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}
+
diff --git a/reproduction/text_classification/model/awdlstm_module.py b/reproduction/text_classification/model/awdlstm_module.py
new file mode 100644
index 00000000..87bfe730
--- /dev/null
+++ b/reproduction/text_classification/model/awdlstm_module.py
@@ -0,0 +1,86 @@
+"""
+轻量封装的 Pytorch LSTM 模块.
+可在 forward 时传入序列的长度, 自动对padding做合适的处理.
+"""
+__all__ = [
+    "LSTM"
+]
+
+import torch
+import torch.nn as nn
+import torch.nn.utils.rnn as rnn
+
+from fastNLP.modules.utils import initial_parameter
+from torch import autograd
+from .weight_drop import WeightDrop
+
+
+class LSTM(nn.Module):
+    """
+    别名：:class:`fastNLP.modules.LSTM`  :class:`fastNLP.modules.encoder.lstm.LSTM`
+
+    LSTM 模块, 轻量封装的Pytorch LSTM. 在提供seq_len的情况下，将自动使用pack_padded_sequence; 同时默认将forget gate的bias初始化
+        为1; 且可以应对DataParallel中LSTM的使用问题。
+
+    :param input_size:  输入 `x` 的特征维度
+    :param hidden_size: 隐状态 `h` 的特征维度.
+    :param num_layers: rnn的层数. Default: 1
+    :param dropout: 层间dropout概率. Default: 0
+    :param bidirectional: 若为 ``True``, 使用双向的RNN. Default: ``False``
+    :param batch_first: 若为 ``True``, 输入和输出 ``Tensor`` 形状为
+        :(batch, seq, feature). Default: ``False``
+    :param bias: 如果为 ``False``, 模型将不会使用bias. Default: ``True``
+    """
+    
+    def __init__(self, input_size, hidden_size=100, num_layers=1, dropout=0.0, batch_first=True,
+                 bidirectional=False, bias=True, wdrop=0.5):
+        super(LSTM, self).__init__()
+        self.batch_first = batch_first
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bias=bias, batch_first=batch_first,
+                            dropout=dropout, bidirectional=bidirectional)
+        self.lstm = WeightDrop(self.lstm, ['weight_hh_l0'], dropout=wdrop)
+        self.init_param()
+
+    def init_param(self):
+        for name, param in self.named_parameters():
+            if 'bias' in name:
+                # based on https://github.com/pytorch/pytorch/issues/750#issuecomment-280671871
+                param.data.fill_(0)
+                n = param.size(0)
+                start, end = n // 4, n // 2
+                param.data[start:end].fill_(1)
+            else:
+                nn.init.xavier_uniform_(param)
+
+    def forward(self, x, seq_len=None, h0=None, c0=None):
+        """
+
+        :param x: [batch, seq_len, input_size] 输入序列
+        :param seq_len: [batch, ] 序列长度, 若为 ``None``, 所有输入看做一样长. Default: ``None``
+        :param h0: [batch, hidden_size] 初始隐状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :param c0: [batch, hidden_size] 初始Cell状态, 若为 ``None`` , 设为全0向量. Default: ``None``
+        :return (output, ht) 或 output: 若 ``get_hidden=True`` [batch, seq_len, hidden_size*num_direction] 输出序列
+            和 [batch, hidden_size*num_direction] 最后时刻隐状态.
+        """
+        batch_size, max_len, _ = x.size()
+        if h0 is not None and c0 is not None:
+            hx = (h0, c0)
+        else:
+            hx = None
+        if seq_len is not None and not isinstance(x, rnn.PackedSequence):
+            sort_lens, sort_idx = torch.sort(seq_len, dim=0, descending=True)
+            if self.batch_first:
+                x = x[sort_idx]
+            else:
+                x = x[:, sort_idx]
+            x = rnn.pack_padded_sequence(x, sort_lens, batch_first=self.batch_first)
+            output, hx = self.lstm(x, hx)  # -> [N,L,C]
+            output, _ = rnn.pad_packed_sequence(output, batch_first=self.batch_first, total_length=max_len)
+            _, unsort_idx = torch.sort(sort_idx, dim=0, descending=False)
+            if self.batch_first:
+                output = output[unsort_idx]
+            else:
+                output = output[:, unsort_idx]
+        else:
+            output, hx = self.lstm(x, hx)
+        return output, hx
diff --git a/reproduction/text_classification/model/lstm.py b/reproduction/text_classification/model/lstm.py
new file mode 100644
index 00000000..388f3f1c
--- /dev/null
+++ b/reproduction/text_classification/model/lstm.py
@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from fastNLP.modules.encoder.lstm import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class BiLSTMSentiment(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 nfc=128):
+        super(BiLSTMSentiment,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
+        self.mlp = MLP(size_layer=[hidden_dim* 2, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        output = self.mlp(output[:,-1,:])
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}
+
diff --git a/reproduction/text_classification/model/lstm_self_attention.py b/reproduction/text_classification/model/lstm_self_attention.py
new file mode 100644
index 00000000..239635fe
--- /dev/null
+++ b/reproduction/text_classification/model/lstm_self_attention.py
@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+from fastNLP.core.const import Const as C
+from fastNLP.modules.encoder.lstm import LSTM
+from fastNLP.modules import encoder
+from fastNLP.modules.aggregator.attention import SelfAttention
+from fastNLP.modules.decoder.mlp import MLP
+
+
+class BiLSTM_SELF_ATTENTION(nn.Module):
+    def __init__(self, init_embed,
+                 num_classes,
+                 hidden_dim=256,
+                 num_layers=1,
+                 attention_unit=256,
+                 attention_hops=1,
+                 nfc=128):
+        super(BiLSTM_SELF_ATTENTION,self).__init__()
+        self.embed = encoder.Embedding(init_embed)
+        self.lstm = LSTM(input_size=self.embed.embedding_dim, hidden_size=hidden_dim, num_layers=num_layers, bidirectional=True)
+        self.attention = SelfAttention(input_size=hidden_dim * 2 , attention_unit=attention_unit, attention_hops=attention_hops)
+        self.mlp = MLP(size_layer=[hidden_dim* 2*attention_hops, nfc, num_classes])
+
+    def forward(self, words):
+        x_emb = self.embed(words)
+        output, _ = self.lstm(x_emb)
+        after_attention, penalty = self.attention(output,words)
+        after_attention =after_attention.view(after_attention.size(0),-1)
+        output = self.mlp(after_attention)
+        return {C.OUTPUT: output}
+
+    def predict(self, words):
+        output = self(words)
+        _, predict = output[C.OUTPUT].max(dim=1)
+        return {C.OUTPUT: predict}
diff --git a/reproduction/text_classification/model/weight_drop.py b/reproduction/text_classification/model/weight_drop.py
new file mode 100644
index 00000000..60fda179
--- /dev/null
+++ b/reproduction/text_classification/model/weight_drop.py
@@ -0,0 +1,99 @@
+import torch
+from torch.nn import Parameter
+from functools import wraps
+
+class WeightDrop(torch.nn.Module):
+    def __init__(self, module, weights, dropout=0, variational=False):
+        super(WeightDrop, self).__init__()
+        self.module = module
+        self.weights = weights
+        self.dropout = dropout
+        self.variational = variational
+        self._setup()
+
+    def widget_demagnetizer_y2k_edition(*args, **kwargs):
+        # We need to replace flatten_parameters with a nothing function
+        # It must be a function rather than a lambda as otherwise pickling explodes
+        # We can't write boring code though, so ... WIDGET DEMAGNETIZER Y2K EDITION!
+        # (╯°□°）╯︵ ┻━┻
+        return
+
+    def _setup(self):
+        # Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN
+        if issubclass(type(self.module), torch.nn.RNNBase):
+            self.module.flatten_parameters = self.widget_demagnetizer_y2k_edition
+
+        for name_w in self.weights:
+            print('Applying weight drop of {} to {}'.format(self.dropout, name_w))
+            w = getattr(self.module, name_w)
+            del self.module._parameters[name_w]
+            self.module.register_parameter(name_w + '_raw', Parameter(w.data))
+
+    def _setweights(self):
+        for name_w in self.weights:
+            raw_w = getattr(self.module, name_w + '_raw')
+            w = None
+            if self.variational:
+                mask = torch.autograd.Variable(torch.ones(raw_w.size(0), 1))
+                if raw_w.is_cuda: mask = mask.cuda()
+                mask = torch.nn.functional.dropout(mask, p=self.dropout, training=True)
+                w = mask.expand_as(raw_w) * raw_w
+            else:
+                w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training)
+            setattr(self.module, name_w, w)
+
+    def forward(self, *args):
+        self._setweights()
+        return self.module.forward(*args)
+
+if __name__ == '__main__':
+    import torch
+    from weight_drop import WeightDrop
+
+    # Input is (seq, batch, input)
+    x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda()
+    h0 = None
+
+    ###
+
+    print('Testing WeightDrop')
+    print('=-=-=-=-=-=-=-=-=-=')
+
+    ###
+
+    print('Testing WeightDrop with Linear')
+
+    lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9)
+    lin.cuda()
+    run1 = [x.sum() for x in lin(x).data]
+    run2 = [x.sum() for x in lin(x).data]
+
+    print('All items should be different')
+    print('Run 1:', run1)
+    print('Run 2:', run2)
+
+    assert run1[0] != run2[0]
+    assert run1[1] != run2[1]
+
+    print('---')
+
+    ###
+
+    print('Testing WeightDrop with LSTM')
+
+    wdrnn = WeightDrop(torch.nn.LSTM(10, 10), ['weight_hh_l0'], dropout=0.9)
+    wdrnn.cuda()
+
+    run1 = [x.sum() for x in wdrnn(x, h0)[0].data]
+    run2 = [x.sum() for x in wdrnn(x, h0)[0].data]
+
+    print('First timesteps should be equal, all others should differ')
+    print('Run 1:', run1)
+    print('Run 2:', run2)
+
+    # First time step, not influenced by hidden to hidden weights, should be equal
+    assert run1[0] == run2[0]
+    # Second step should not
+    assert run1[1] != run2[1]
+
+    print('---')
diff --git a/reproduction/text_classification/results_LSTM.xlsx b/reproduction/text_classification/results_LSTM.xlsx
new file mode 100644
index 00000000..0d7b841b
Binary files /dev/null and b/reproduction/text_classification/results_LSTM.xlsx differ
diff --git a/reproduction/text_classification/train_awdlstm.py b/reproduction/text_classification/train_awdlstm.py
new file mode 100644
index 00000000..ce3e52bc
--- /dev/null
+++ b/reproduction/text_classification/train_awdlstm.py
@@ -0,0 +1,102 @@
+# 这个模型需要在pytorch=0.4下运行，weight_drop不支持1.0
+
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.awd_lstm import AWDLSTMSentiment
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    nfc=128
+    wdrop=0.5
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=AWDLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc, wdrop=opt.wdrop)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()
diff --git a/reproduction/text_classification/train_lstm.py b/reproduction/text_classification/train_lstm.py
new file mode 100644
index 00000000..b320e79c
--- /dev/null
+++ b/reproduction/text_classification/train_lstm.py
@@ -0,0 +1,99 @@
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.lstm import BiLSTMSentiment
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    nfc=128
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=BiLSTMSentiment(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, nfc=opt.nfc)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()
diff --git a/reproduction/text_classification/train_lstm_att.py b/reproduction/text_classification/train_lstm_att.py
new file mode 100644
index 00000000..8db27d09
--- /dev/null
+++ b/reproduction/text_classification/train_lstm_att.py
@@ -0,0 +1,101 @@
+# 首先需要加入以下的路径到环境变量，因为当前只对内部测试开放，所以需要手动申明一下路径
+import os
+os.environ['FASTNLP_BASE_URL'] = 'http://10.141.222.118:8888/file/download/'
+os.environ['FASTNLP_CACHE_DIR'] = '/remote-home/hyan01/fastnlp_caches'
+
+
+import torch.nn as nn
+
+from data.SSTLoader import SSTLoader
+from data.IMDBLoader import IMDBLoader
+from data.yelpLoader import yelpLoader
+from fastNLP.modules.encoder.embedding import StaticEmbedding
+from model.lstm_self_attention import BiLSTM_SELF_ATTENTION
+
+from fastNLP.core.const import Const as C
+from fastNLP import CrossEntropyLoss, AccuracyMetric
+from fastNLP import Trainer, Tester
+from torch.optim import Adam
+from fastNLP.io.model_io import ModelLoader, ModelSaver
+
+import argparse
+
+
+class Config():
+    train_epoch= 10
+    lr=0.001
+
+    num_classes=2
+    hidden_dim=256
+    num_layers=1
+    attention_unit=256
+    attention_hops=1
+    nfc=128
+
+    task_name = "IMDB"
+    datapath={"train":"IMDB_data/train.csv", "test":"IMDB_data/test.csv"}
+    load_model_path="./result_IMDB/best_BiLSTM_SELF_ATTENTION_acc_2019-07-07-04-16-51"
+    save_model_path="./result_IMDB_test/"
+opt=Config
+
+
+# load data
+dataloaders = {
+    "IMDB":IMDBLoader(),
+    "YELP":yelpLoader(),
+    "SST-5":SSTLoader(subtree=True,fine_grained=True),
+    "SST-3":SSTLoader(subtree=True,fine_grained=False)
+}
+
+if opt.task_name not in ["IMDB", "YELP", "SST-5", "SST-3"]:
+    raise ValueError("task name must in ['IMDB', 'YELP, 'SST-5', 'SST-3']")
+
+dataloader = dataloaders[opt.task_name]
+datainfo=dataloader.process(opt.datapath)
+# print(datainfo.datasets["train"])
+# print(datainfo)
+
+
+# define model
+vocab=datainfo.vocabs['words']
+embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-840b-300', requires_grad=True)
+model=BiLSTM_SELF_ATTENTION(init_embed=embed, num_classes=opt.num_classes, hidden_dim=opt.hidden_dim, num_layers=opt.num_layers, attention_unit=opt.attention_unit, attention_hops=opt.attention_hops, nfc=opt.nfc)
+
+
+# define loss_function and metrics
+loss=CrossEntropyLoss()
+metrics=AccuracyMetric()
+optimizer= Adam([param for param in model.parameters() if param.requires_grad==True], lr=opt.lr)
+
+
+def train(datainfo, model, optimizer, loss, metrics, opt):
+    trainer = Trainer(datainfo.datasets['train'], model, optimizer=optimizer, loss=loss,
+                        metrics=metrics, dev_data=datainfo.datasets['dev'], device=0, check_code_level=-1,
+                        n_epochs=opt.train_epoch, save_path=opt.save_model_path)
+    trainer.train()
+
+
+def test(datainfo, metrics, opt):
+    # load model
+    model = ModelLoader.load_pytorch_model(opt.load_model_path)
+    print("model loaded!")
+
+    # Tester
+    tester = Tester(datainfo.datasets['test'], model, metrics, batch_size=4, device=0)
+    acc = tester.test()
+    print("acc=",acc)
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mode', required=True, dest="mode",help='set the model\'s model')
+
+
+args = parser.parse_args()
+if args.mode == 'train':
+    train(datainfo, model, optimizer, loss, metrics, opt)
+elif args.mode == 'test':
+    test(datainfo, metrics, opt)
+else:
+    print('no mode specified for model!')
+    parser.print_help()