def build_model_layers(self):
''' builds the layers in the model '''
self.bert = BertForTokenClassification.from_pretrained(
"bert-base-cased",
num_labels=self.num_labels,
output_attentions=False,
output_hidden_states=False)
if self.use_crf:
self.crf = CRF(self.tag_pad_idx, self.pad_token, self.tag_names)
def build_model(self):
'''
build the embedding layer, lstm layer and CRF layer
'''
self.hidden2tag = nn.Linear(self.embedding_dim, self.n_tags)
crf_config = {
'n_tags': self.config['n_ent_tags'],
'start_idx': self.config['start_ent_idx'],
'end_idx': self.config['end_ent_idx'],
'use_cuda': self.use_cuda
}
self.crf = CRF(crf_config)
self.bert = transformers.BertModel.from_pretrained('bert-base-chinese')
def build_model_layers(self):
''' builds the layers in the model '''
# embedding layer
self.embedding = nn.Embedding(num_embeddings=self.input_dim, embedding_dim=self.embedding_dim,
padding_idx=self.text_pad_idx)
# dropout for embedding layer
self.embedding_dropout = nn.Dropout(self.embedding_dropout_ratio)
# character cnn
if self.char_embedding_dim:
self.char_embedding = nn.Embedding(num_embeddings=self.char_input_dim,
embedding_dim=self.char_embedding_dim,
padding_idx=self.char_pad_idx)
self.char_cnn = nn.Conv1d(in_channels=self.char_embedding_dim,
out_channels=self.char_embedding_dim*self.char_filter,
kernel_size=self.char_kernel,
groups=self.char_embedding_dim)
self.cnn_dropout = nn.Dropout(self.cnn_dropout_ratio)
all_embedding_dim = self.embedding_dim+(self.char_embedding_dim*self.char_filter)
# lstm layers with dropout
else:
all_embedding_dim = self.embedding_dim
# lstm layers with dropout
self.lstm = nn.LSTM(batch_first=True, input_size=all_embedding_dim,
hidden_size=self.hidden_dim, num_layers=self.lstm_layers,
bidirectional=True, dropout=self.lstm_dropout_ratio if self.lstm_layers > 1 else 0)
# use multihead attention if there are attention heads
if self.attn_heads:
self.attn = nn.MultiheadAttention(embed_dim=self.hidden_dim*2, num_heads=self.attn_heads, dropout=self.attn_dropout_ratio)
# dropout for fully connected layer
self.fc_dropout = nn.Dropout(self.fc_dropout_ratio)
# fully connected layer
self.fc = nn.Linear(self.hidden_dim*2, self.output_dim)
# use crf layer if it is switched on
if self.use_crf:
self.crf = CRF(self.tag_pad_idx, self.pad_token, self.tag_names)
def build_model(self):
'''
build the embedding layer, lstm layer and CRF layer
'''
self.word_embeds = nn.Embedding(self.n_words, self.embedding_dim)
self.lstm = nn.LSTM(self.embedding_dim,
self.hidden_dim // 2,
batch_first=True,
num_layers=self.lstm_layer_num,
dropout=self.dropout_prob,
bidirectional=True)
self.hidden2tag = nn.Linear(self.hidden_dim, self.n_tags)
crf_config = {
'n_tags': self.config['n_ent_tags'],
'start_idx': self.config['start_ent_idx'],
'end_idx': self.config['end_ent_idx'],
'use_cuda': self.use_cuda
}
self.crf = CRF(crf_config)
def build_model_layers(self):
''' builds the layers in the model '''
# embedding layer
self.embedding = nn.Embedding(num_embeddings=self.input_dim,
embedding_dim=self.embedding_dim,
padding_idx=self.text_pad_idx)
# dropout for embedding layer
self.embedding_dropout = nn.Dropout(self.embedding_dropout_ratio)
# character cnn
if self.char_embedding_dim:
self.char_embedding = nn.Embedding(
num_embeddings=self.char_input_dim,
embedding_dim=self.char_embedding_dim,
padding_idx=self.char_pad_idx)
self.char_cnn = nn.Conv1d(in_channels=self.char_embedding_dim,
out_channels=self.char_embedding_dim *
self.char_filter,
kernel_size=self.char_kernel,
groups=self.char_embedding_dim)
self.cnn_dropout = nn.Dropout(self.cnn_dropout_ratio)
# lstm layers with dropout
all_embedding_dim = self.embedding_dim + (self.char_embedding_dim *
self.char_filter)
else:
all_embedding_dim = self.embedding_dim
# transformer encoder layers with attention and dropout
self.position_encoder = PositionalEncoding(d_model=all_embedding_dim)
encoder_layers = nn.TransformerEncoderLayer(
d_model=all_embedding_dim,
nhead=self.attn_heads,
activation='relu',
dropout=self.trf_dropout_ratio)
self.encoder = nn.TransformerEncoder(encoder_layer=encoder_layers,
num_layers=self.trf_layers)
# fully connected layer with gelu activation
self.fc1 = nn.Linear(in_features=all_embedding_dim,
out_features=self.hidden_dim)
self.fc1_gelu = nn.GELU()
# layer norm
self.fc1_norm = nn.LayerNorm(self.hidden_dim)
# dropout for fully connected layer
self.fc2_dropout = nn.Dropout(self.fc_dropout_ratio)
# fully connected layer
self.fc2 = nn.Linear(self.hidden_dim, self.output_dim)
# use crf layer if it is switched on
if self.use_crf:
self.crf = CRF(self.tag_pad_idx, self.pad_token, self.tag_names)
class REL_BLSTM_CRF(MODEL_TEMP):
def __init__(self, config={}, show_param=False):
'''
:param - dict
param['embedding_dim']
param['hidden_dim']
***param['n_ent_tags']
param['n_rel_tags']
param['n_rels']
param['n_words']
param['start_idx'] int, <start> tag index for entity tag seq
param['end_idx'] int, <end> tag index for entity tag seq
param['use_cuda']
param['dropout_prob']
param['lstm_layer_num']
'''
super(REL_BLSTM_CRF, self).__init__()
self.config = config
self.embedding_dim = self.config.get('embedding_dim', 128)
self.hidden_dim = self.config.get('hidden_dim', 64)
assert self.hidden_dim % 2 == 0, 'hidden_dim for BLSTM must be even'
self.n_tags = self.config.get('n_rel_tags', 8)
self.n_rels = self.config.get('n_rels', 9)
self.n_words = self.config.get('n_words', 10000)
self.dropout_prob = self.config.get('dropout_prob', 0)
self.lstm_layer_num = self.config.get('lstm_layer_num', 1)
self.use_cuda = self.config.get('use_cuda', False)
self.model_type = 'REL_BLSTM_CRF'
self.build_model()
self.reset_parameters()
if show_param:
self.show_model_param()
def show_model_param(self):
log('=' * 80, 0)
log(f'model_type: {self.model_type}', 1)
log(f'embedding_dim: {self.embedding_dim}', 1)
log(f'hidden_dim: {self.hidden_dim}', 1)
log(f'use_cuda: {self.use_cuda}', 1)
log(f'lstm_layer_num: {self.lstm_layer_num}', 1)
log(f'dropout_prob: {self.dropout_prob}', 1)
log('=' * 80, 0)
def build_model(self):
'''
build the embedding layer, lstm layer and CRF layer
'''
self.word_embeds = nn.Embedding(self.n_words, self.embedding_dim)
self.rel_embeds = nn.Embedding(self.n_rels, self.embedding_dim)
self.embed2hidden = nn.Linear(self.embedding_dim * 2,
self.embedding_dim)
self.lstm = nn.LSTM(input_size=self.embedding_dim,
hidden_size=self.hidden_dim // 2,
batch_first=True,
num_layers=self.lstm_layer_num,
dropout=self.dropout_prob,
bidirectional=True)
self.hidden2tag = nn.Linear(self.hidden_dim, self.n_tags)
crf_config = {
'n_tags': self.n_tags,
'start_idx': self.config['start_rel_idx'],
'end_idx': self.config['end_rel_idx'],
'use_cuda': self.use_cuda
}
self.crf = CRF(crf_config)
self.relu_layer = nn.ReLU()
def reset_parameters(self):
I.xavier_normal_(self.word_embeds.weight.data)
I.xavier_normal_(self.rel_embeds.weight.data)
self.lstm.reset_parameters()
# stdv = 1.0 / math.sqrt(self.hidden_dim)
# for weight in self.lstm.parameters():
# I.uniform_(weight, -stdv, stdv)
I.xavier_normal_(self.embed2hidden.weight.data)
I.xavier_normal_(self.hidden2tag.weight.data)
self.crf.reset_parameters()
def _get_lstm_features(self, x, relation_type, use_cuda=None):
'''
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@relation_type: index之后的relation_type， (batch_size, 1), np.array
:return
@lstm_feature: (batch_size, T, n_tags) -- 类似于eject score, torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
batch_size, T = x.shape[0], x.shape[1]
##embedding layer
words_tensor = self._to_tensor(x, use_cuda) #(batch_size, T)
# print('words_tensor_shape', words_tensor.shape)
word_input_embeds = self.word_embeds(
words_tensor) #(batch_size, T, n_embed)
# print('word_input_embeds_shape', word_input_embeds.shape)
reltype_tensor = self._to_tensor(relation_type,
use_cuda) #(batch_size, 1)
# print('reltype_tensor', reltype_tensor.shape)
reltype_input_embeds = self.rel_embeds(
reltype_tensor) #(batch_size, 1, n_embed)
# print('reltype_input_embeds', reltype_input_embeds.shape)
reltype_input_embeds = reltype_input_embeds.repeat(
1, T, 1) #(batch_size, T, n_embed)
# print('reltype_input_embeds2', reltype_input_embeds.shape)
input_embeds_all = torch.cat([word_input_embeds, reltype_input_embeds],
-1) #(batch_size, T, n_embed*2)
# print('input_embeds_all.shape', input_embeds_all.shape)
embeds = self.embed2hidden(
input_embeds_all) #(batch_size, T, n_embeds)
# print('embeds.shape', embeds.shape)
# ##LSTM layer
if use_cuda:
h_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim //
2).cuda() #(n_layer*n_dir, N, n_hid)
c_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2).cuda()
else:
h_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2)
c_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2)
# c_0 = h_0.clone()
hidden = (h_0, c_0)
lstm_out, _hidden = self.lstm(
embeds, hidden) #(batch_size, T, n_dir*n_hid), (h, c)
##FC layer
lstm_feature = self.hidden2tag(lstm_out) #(batch_size, T, n_tags)
lstm_feature = torch.tanh(lstm_feature)
# print(lstm_feature.shape)
return lstm_feature
def _loss(self, x, relation_type, y_rel, lens, use_cuda=None):
'''
loss function: neg_log_likelihood
:param
@x: (batch_size, T), np.array, index之后的word, 每个字符按照字典对应到index,
@relation_type: (batch_size, 1), np.array, 关系类别
@y_rel: (batch_size, T), np.array, index之后的关系序列, 字符级别,
@lens: (batch_size), list, 具体每个句子的长度,
:return
@loss: (batch_size), torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_lstm_features(x, relation_type, use_cuda)
log_norm_score = self.crf.log_norm_score(logits, lens)
path_score = self.crf.path_score(logits, y_rel, lens)
loss = log_norm_score - path_score
loss = (loss / self._to_tensor(lens, use_cuda).float()).mean()
return loss
def _output(self, x, relation_type, lens, use_cuda=None):
'''
return the crf decode paths
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@relation_type: (batch_size, 1), np.array, 关系类别
@lens: (batch_size), list, 具体每个句子的长度,
:return
@paths: (batch_size, T), torch.tensor, 最佳句子路径
@scores: (batch_size), torch.tensor, 最佳句子路径上的得分
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_lstm_features(x, relation_type, use_cuda)
scores, paths = self.crf.viterbi_decode(logits, lens, use_cuda)
return paths
def train_model(self,
data_loader: KGDataLoader,
train_dataset=None,
eval_dataset=None,
hyper_param={},
use_cuda=None,
rebuild=False):
'''
:param
@data_loader: (KGDataLoader),
@result_dir: (str) path to save the trained model and extracted dictionary
@hyper_param: (dict)
@hyper_param['EPOCH']
@hyper_param['batch_size']
@hyper_param['learning_rate_upper']
@hyper_param['learning_rate_bert']
@hyper_param['bert_finetune']
@hyper_param['visualize_length'] #num of batches between two check points
@hyper_param['isshuffle']
@hyper_param['result_dir']
@hyper_param['model_name']
:return
@loss_record,
@score_record
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
if use_cuda:
print('use cuda=========================')
self.cuda()
EPOCH = hyper_param.get('EPOCH', 3)
BATCH_SIZE = hyper_param.get('batch_size', 4)
LEARNING_RATE_upper = hyper_param.get('learning_rate_upper', 1e-2)
LEARNING_RATE_bert = hyper_param.get('learning_rate_bert', 5e-5)
bert_finetune = hyper_param.get('bert_finetune', True)
visualize_length = hyper_param.get('visualize_length', 10)
result_dir = hyper_param.get('result_dir', './result/')
model_name = hyper_param.get('model_name', 'model.p')
is_shuffle = hyper_param.get('isshuffle', True)
DATA_TYPE = 'rel'
train_dataset = data_loader.dataset.train_dataset if train_dataset is None else train_dataset
if rebuild:
train_data_mat_dict = data_loader.transform(train_dataset,
data_type=DATA_TYPE)
## 保存预处理的文本，这样调参的时候可以直接读取，节约时间 *WARNING*
else:
old_train_dict_path = os.path.join(result_dir,
'train_data_mat_dict.pkl')
if os.path.exists(old_train_dict_path):
train_data_mat_dict = data_loader.load_preprocessed_data(
old_train_dict_path)
log('Reload preprocessed data successfully~')
else:
# train_data_mat_dict = data_loader.transform(train_dataset, data_type=DATA_TYPE)
train_data_mat_dict = data_loader.transform(
train_dataset, istest=False, data_type=DATA_TYPE, ratio=0)
data_loader.save_preprocessed_data(old_train_dict_path,
train_data_mat_dict)
## 保存预处理的文本，这样调参的时候可以直接读取，节约时间 *WARNING*
data_generator = Batch_Generator(train_data_mat_dict,
batch_size=BATCH_SIZE,
data_type=DATA_TYPE,
isshuffle=is_shuffle)
print('train_data_set_length:', len(train_dataset))
print('train_data_mat_dict_length:',
train_data_mat_dict['cha_matrix'].shape)
all_param = list(self.named_parameters())
bert_param = [p for n, p in all_param if 'bert' in n]
other_param = [p for n, p in all_param if 'bert' not in n]
if bert_finetune:
optimizer_group_paramters = [{
'params': other_param,
'lr': LEARNING_RATE_upper
}, {
'params': bert_param,
'lr': LEARNING_RATE_bert
}]
optimizer = torch.optim.Adam(optimizer_group_paramters)
log(
f'****BERT_finetune, learning_rate_upper: {LEARNING_RATE_upper}, learning_rate_bert: {LEARNING_RATE_bert}',
0)
else:
optimizer = torch.optim.Adam(other_param, lr=LEARNING_RATE_upper)
log(f'****BERT_fix, learning_rate_upper: {LEARNING_RATE_upper}', 0)
# ##TODO:
scheduler = LambdaLR(optimizer, lr_lambda=my_lr_lambda)
# # scheduler = transformers.optimization.get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=int(EPOCH*0.2), num_training_steps=EPOCH)
all_cnt = len(train_data_mat_dict['cha_matrix'])
log(f'{model_name} Training start!', 0)
loss_record = []
score_record = []
max_score = -1
evel_param = {
'batch_size': 100,
'issave': False,
'result_dir': result_dir
}
for epoch in range(EPOCH):
self.train()
log(f'EPOCH: {epoch+1}/{EPOCH}', 0)
loss = 0.0
for cnt, data_batch in enumerate(data_generator):
x, pos, reltype, y_rel, y_ent, lens, data_list = data_batch
loss_avg = self._loss(x, reltype, y_rel, lens)
optimizer.zero_grad()
loss_avg.backward()
optimizer.step()
loss += loss_avg
if use_cuda:
loss_record.append(loss_avg.cpu().item())
else:
loss_record.append(loss_avg.item())
if (cnt + 1) % visualize_length == 0:
loss_cur = loss / visualize_length
log(
f'[TRAIN] step: {(cnt+1)*BATCH_SIZE}/{all_cnt} | loss: {loss_cur:.4f}',
1)
loss = 0.0
# self.eval()
# print(data_list[0]['input'])
# pre_paths = self._output(x, reltype, lens)
# print('predict-path')
# print(pre_paths[0])
# print('target-path')
# print(y_rel[0])
# self.train()
temp_score = self.eval_model(data_loader,
data_set=eval_dataset,
hyper_param=evel_param,
use_cuda=use_cuda)
score_record.append(temp_score)
scheduler.step()
if temp_score[2] > max_score:
max_score = temp_score[2]
save_path = os.path.join(result_dir, model_name)
self.save_model(save_path)
print(
f'Checkpoint saved successfully, current best socre is {max_score}'
)
log(f'the best score of the model is {max_score}')
return loss_record, score_record
@torch.no_grad()
def predict(self,
data_loader,
data_set=None,
hyper_param={},
use_cuda=None,
rebuild=False):
'''
预测出 test_data_mat_dict['y_ent_matrix']中的内容，重新填写进该matrix, 未预测之前都是0
:param
@data_loader: (KGDataLoader),
@hyper_param: (dict)
@hyper_param['batch_size'] ##默认4
@hyper_param['issave'] ##默认False
@hyper_param['result_dir'] ##默认None
:return
@result: list, len(句子个数)
case = result[0]
case['input']
case['relation_list']
r = case['relation_list'][0]
r['relation']: 成立日期
r['head']: '百度'
r['tail']: '2016年04月08日'
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
if use_cuda:
print('use cuda=========================')
self.cuda()
BATCH_SIZE = hyper_param.get('batch_size', 100)
ISSAVE = hyper_param.get('issave', False)
result_dir = hyper_param.get('result_dir', './result/')
DATA_TYPE = 'rel'
test_dataset = data_loader.dataset.test_dataset if data_set is None else data_set
if rebuild:
test_data_mat_dict = data_loader.transform(test_dataset,
istest=True,
data_type=DATA_TYPE)
## 保存预处理的文本，这样调参的时候可以直接读取，节约时间 *WARNING*
else:
old_test_dict_path = os.path.join(result_dir,
'test_data_mat_dict.pkl')
if os.path.exists(old_test_dict_path):
test_data_mat_dict = data_loader.load_preprocessed_data(
old_test_dict_path)
log('Reload preprocessed data successfully~')
else:
test_data_mat_dict = data_loader.transform(test_dataset,
istest=True,
data_type=DATA_TYPE,
ratio=0)
data_loader.save_preprocessed_data(old_test_dict_path,
test_data_mat_dict)
## 保存预处理的文本，这样调参的时候可以直接读取，节约时间 *WARNING*
print('test_dataset_length:', len(test_dataset))
print('test_data_mat_dict_length:',
test_data_mat_dict['cha_matrix'].shape)
data_generator = Batch_Generator(test_data_mat_dict,
batch_size=BATCH_SIZE,
data_type=DATA_TYPE,
isshuffle=False)
self.eval() #disable dropout layer and the bn layer
total_output_rel = []
all_cnt = len(test_data_mat_dict['cha_matrix'])
log(f'Predict start!', 0)
for cnt, data_batch in enumerate(data_generator):
x, pos, reltype, y_rel, y_ent, lens, data_list = data_batch
pre_paths = self._output(
x, reltype, lens) ##pre_paths, (batch_size, T), torch.tensor
if use_cuda:
pre_paths = pre_paths.data.cpu().numpy().astype(np.int)
else:
pre_paths = pre_paths.data.numpy().astype(np.int)
total_output_rel.append(pre_paths)
if (cnt + 1) % 10 == 0:
log(f'[PREDICT] step {(cnt+1)*BATCH_SIZE}/{all_cnt}', 1)
## add mask when the ent seq idx larger than sentance length
pred_output = np.vstack(
total_output_rel) ###(N, max_length), numpy.array
len_list = test_data_mat_dict['sentence_length'] ###(N), list
pred_output = self._padding_mask(pred_output,
len_list[:len(pred_output)])
## transform back to the dict form
test_data_mat_dict['y_rel_matrix'] = pred_output
result = data_loader.transform_back(test_data_mat_dict,
data_type=DATA_TYPE)
## save the result
if ISSAVE and result_dir:
save_file = os.path.join(result_dir, 'predict.json')
with open(save_file, 'w') as f:
for data in result:
temps = json.dumps(data, ensure_ascii=False)
f.write(temps + '\n')
log(f'save the predict result in {save_file}')
print('final predict length:', len(result))
return result
@torch.no_grad()
def eval_model(self,
data_loader,
data_set=None,
hyper_param={},
use_cuda=None,
rebuild=False):
'''
:param
@data_loader: (KGDataLoader),
@hyper_param: (dict)
@hyper_param['batch_size'] #默认64
@hyper_param['issave'] ##默认False
@hyper_param['result_dir'] ##默认./result WARNING:可能报错如果result目录不存在的话
:return
@precision_s,
@recall_s,
@f1_s
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
if use_cuda:
print('use cuda=========================')
self.cuda()
def dict2str(d):
## 将entity 从字典形式转化为str形式方便比较
# res = d['entity']+':'+d['entity_type']+':'+str(d['entity_index']['begin'])+'-'+str(d['entity_index']['end'])
## 将relation 从字典形式转化为str形式方便比较
res = d['relation'] + '-' + d['head'] + '-' + d['tail']
return res
def calculate_f1(pred_cnt, tar_cnt, correct_cnt):
precision_s = round(correct_cnt / (pred_cnt + 1e-8), 3)
recall_s = round(correct_cnt / (tar_cnt + 1e-8), 3)
f1_s = round(
2 * precision_s * recall_s / (precision_s + recall_s + 1e-8),
3)
return precision_s, recall_s, f1_s
eva_data_set = data_loader.dataset.dev_dataset if data_set is None else data_set
pred_result = self.predict(
data_loader, eva_data_set, hyper_param, use_cuda,
rebuild=rebuild) ###list(dict), 预测结果 len=n_sentence
target = eva_data_set ###list(dict) AutoKGDataset, 真实结果
pred_cnt = 0
tar_cnt = 0
correct_cnt = 0
cnt_all = len(eva_data_set)
log('Eval start')
for idx in range(cnt_all):
sentence = pred_result[idx]['input']
pred_list = pred_result[idx]['relation_list']
tar_list = target[idx]['output']['relation_list']
str_pred_set = set(map(dict2str, pred_list))
str_tar_set = set(map(dict2str, tar_list))
common_set = str_pred_set.intersection(str_tar_set)
# print('target:')
# print(str_tar_set)
# print('predict:')
# print(str_pred_set)
pred_cnt += len(str_pred_set)
tar_cnt += len(str_tar_set)
correct_cnt += len(common_set)
if (idx + 1) % 1000 == 0:
precision_s, recall_s, f1_s = calculate_f1(
pred_cnt, tar_cnt, correct_cnt)
log(
f'[EVAL] step {idx+1}/{cnt_all} | precision: {precision_s} | recall: {recall_s} | f1 score: {f1_s}',
1)
precision_s, recall_s, f1_s = calculate_f1(pred_cnt, tar_cnt,
correct_cnt)
print('=' * 100)
log(
f'[FINAL] | precision: {precision_s} | recall: {recall_s} | f1 score: {f1_s}',
0)
print('=' * 100)
return (precision_s, recall_s, f1_s)
class BERT_CRF(MODEL_TEMP):
def __init__(self, config={}, show_param=False):
'''
:param - dict
param['embedding_dim']
param['hidden_dim']
param['n_ent_tags']
param['n_rel_tags']
param['n_words']
param['start_ent_idx'] int, <start> tag index for entity tag seq
param['end_ent_idx'] int, <end> tag index for entity tag seq
param['start_rel_idx']
param['end_rel_idx']
param['use_cuda']
param['dropout_prob']
param['lstm_layer_num']
'''
super(BERT_CRF, self).__init__()
self.config = config
self.embedding_dim = self.config.get('embedding_dim', 768)
self.n_tags = self.config['n_ent_tags']
# self.n_words = self.config['n_words']
# self.dropout_prob = self.config.get('dropout_prob', 0)
self.use_cuda = self.config['use_cuda']
self.model_type = 'BERT_CRF'
self.build_model()
self.reset_parameters()
if show_param:
self.show_model_param()
def show_model_param(self):
log('=' * 80, 0)
log(f'model_type: {self.model_type}', 1)
log(f'use_cuda: {self.use_cuda}', 1)
log(f'embedding_dim: {self.embedding_dim}', 1)
log(f'n_ent_tags: {self.n_tags}', 1)
log(f"crf_start_idx: {self.config['start_ent_idx']}", 1)
log(f"crf_end_idx: {self.config['end_ent_idx']}", 1)
# log(f'dropout_prob: {self.dropout_prob}', 1)
log('=' * 80, 0)
def build_model(self):
'''
build the embedding layer, lstm layer and CRF layer
'''
self.hidden2tag = nn.Linear(self.embedding_dim, self.n_tags)
crf_config = {
'n_tags': self.config['n_ent_tags'],
'start_idx': self.config['start_ent_idx'],
'end_idx': self.config['end_ent_idx'],
'use_cuda': self.use_cuda
}
self.crf = CRF(crf_config)
self.bert = transformers.BertModel.from_pretrained('bert-base-chinese')
def reset_parameters(self):
I.xavier_normal_(self.hidden2tag.weight.data)
self.crf.reset_parameters()
def _get_features(self, x, lens, use_cuda=None):
'''
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@lens: 每个句子的实际长度 (batch_size)
:return
@lstm_feature: (batch_size, T, n_tags) -- 类似于eject score, torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
batch_size, T = x.shape
##bert layer
words_tensor = self._to_tensor(x, use_cuda) #(batch_size, T)
lens = self._to_tensor(lens, use_cuda)
att_mask = self._generate_mask(lens, max_len=T)
embeds = self.bert(
words_tensor,
attention_mask=att_mask)[0] #(batch_size, T, n_embed)
##FC layer
feature = self.hidden2tag(embeds) #(batch_size, T, n_tags)
feature = torch.tanh(feature)
# print(feature.shape)
return feature
def _loss(self, x, y_ent, lens, use_cuda=None):
'''
loss function: neg_log_likelihood
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@y_ent: (batch_size, T), np.array, index之后的entity seq, 字符级别,
@lens: (batch_size), list, 具体每个句子的长度,
:return
@loss: (1), torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_features(x, lens)
log_norm_score = self.crf.log_norm_score(logits, lens)
path_score = self.crf.path_score(logits, y_ent, lens)
loss = log_norm_score - path_score ##(batch_size, )
loss = (loss / self._to_tensor(lens, use_cuda)).mean()
return loss
def _output(self, x, lens, use_cuda=None):
'''
return the crf decode paths
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@lens: (batch_size), list, 具体每个句子的长度,
:return
@paths: (batch_size, T+1), torch.tensor, 最佳句子路径
@scores: (batch_size), torch.tensor, 最佳句子路径上的得分
'''
# self.eval()
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_features(x, lens, use_cuda)
scores, paths = self.crf.viterbi_decode(logits, lens, use_cuda)
return paths
class BLSTM_CRF(MODEL_TEMP):
def __init__(self, config={}, show_param=False):
'''
:param - dict
param['embedding_dim']
param['hidden_dim']
param['n_tags']
param['n_words']
param['start_idx'] int, <start> tag index for entity tag seq
param['end_idx'] int, <end> tag index for entity tag seq
param['use_cuda']
param['dropout_prob']
param['lstm_layer_num']
'''
super(BLSTM_CRF, self).__init__()
self.config = config
self.embedding_dim = self.config.get('embedding_dim',
768) #TODO: 64, 768
self.hidden_dim = self.config.get('hidden_dim', 64) #TODO: 128*2, 64
assert self.hidden_dim % 2 == 0, 'hidden_dim for BLSTM must be even'
self.n_tags = self.config.get('n_ent_tags', 45)
self.n_words = self.config.get('n_words', 10000)
self.dropout_prob = self.config.get('dropout_prob', 0)
self.lstm_layer_num = self.config.get('lstm_layer_num', 1)
self.use_cuda = self.config.get('use_cuda', False)
self.model_type = 'BLSTM_CRF'
self.build_model()
self.reset_parameters()
if show_param:
self.show_model_param()
def show_model_param(self):
log('=' * 80, 0)
log(f'model_type: {self.model_type}', 1)
log(f'use_cuda: {self.use_cuda}', 1)
log(f'embedding_dim: {self.embedding_dim}', 1)
log(f'hidden_dim: {self.hidden_dim}', 1)
log(f'lstm_layer_num: {self.lstm_layer_num}', 1)
log(f'dropout_prob: {self.dropout_prob}', 1)
log(f'n_ent_tags: {self.n_tags}', 1)
log(f"crf_start_idx: {self.config['start_ent_idx']}", 1)
log(f"crf_end_idx: {self.config['end_ent_idx']}", 1)
log('=' * 80, 0)
def build_model(self):
'''
build the embedding layer, lstm layer and CRF layer
'''
self.word_embeds = nn.Embedding(self.n_words, self.embedding_dim)
self.lstm = nn.LSTM(self.embedding_dim,
self.hidden_dim // 2,
batch_first=True,
num_layers=self.lstm_layer_num,
dropout=self.dropout_prob,
bidirectional=True)
self.hidden2tag = nn.Linear(self.hidden_dim, self.n_tags)
crf_config = {
'n_tags': self.config['n_ent_tags'],
'start_idx': self.config['start_ent_idx'],
'end_idx': self.config['end_ent_idx'],
'use_cuda': self.use_cuda
}
self.crf = CRF(crf_config)
def reset_parameters(self):
I.xavier_normal_(self.word_embeds.weight.data)
self.lstm.reset_parameters()
# stdv = 1.0 / math.sqrt(self.hidden_dim)
# for weight in self.lstm.parameters():
# I.uniform_(weight, -stdv, stdv)
I.xavier_normal_(self.hidden2tag.weight.data)
self.crf.reset_parameters()
def _get_lstm_features(self, x, use_cuda=None):
'''
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
:return
@lstm_feature: (batch_size, T, n_tags) -- 类似于eject score, torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
batch_size = x.shape[0]
##embedding layer
words_tensor = self._to_tensor(x, use_cuda) #(batch_size, T)
embeds = self.word_embeds(words_tensor) #(batch_size, T, n_embed)
##LSTM layer
if use_cuda:
h_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim //
2).cuda() #(n_layer*n_dir, N, n_hid)
c_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2).cuda()
else:
h_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2)
c_0 = torch.randn(2 * self.lstm_layer_num, batch_size,
self.hidden_dim // 2)
# c_0 = h_0.clone()
hidden = (h_0, c_0)
lstm_out, _hidden = self.lstm(
embeds, hidden) #(batch_size, T, n_dir*n_hid), (h, c)
##FC layer
lstm_feature = self.hidden2tag(lstm_out) #(batch_size, T, n_tags)
lstm_feature = torch.tanh(lstm_feature)
return lstm_feature
def _loss(self, x, y_ent, lens, use_cuda=None):
'''
loss function: neg_log_likelihood
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@y_ent: (batch_size, T), np.array, index之后的entity seq, 字符级别,
@lens: (batch_size), list, 具体每个句子的长度,
:return
@loss: (batch_size), torch.tensor
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_lstm_features(x)
log_norm_score = self.crf.log_norm_score(logits, lens)
path_score = self.crf.path_score(logits, y_ent, lens)
loss = log_norm_score - path_score
loss = (loss / self._to_tensor(lens, use_cuda)).mean()
return loss
def _output(self, x, lens, use_cuda=None):
'''
return the crf decode paths
:param
@x: index之后的word, 每个字符按照字典对应到index, (batch_size, T), np.array
@lens: (batch_size), list, 具体每个句子的长度,
:return
@paths: (batch_size, T), torch.tensor, 最佳句子路径
@scores: (batch_size), torch.tensor, 最佳句子路径上的得分
'''
use_cuda = self.use_cuda if use_cuda is None else use_cuda
logits = self._get_lstm_features(x, use_cuda)
scores, paths = self.crf.viterbi_decode(logits, lens, use_cuda)
return paths
class BERT(nn.Module):
def __init__(self, num_labels, use_crf, tag_pad_idx, pad_token, tag_names):
'''
bert sequence classifier
num_labels: number of output classes
use_crf: switch for using conditional random field (reduces probability of invalid tagging sequences)
tag_pad_idx: index for tag padding token
pad_token: pad token
tag_names: the names of all of the tags in the tag field
'''
super().__init__()
self.num_labels = num_labels
self.use_crf = use_crf
self.tag_pad_idx, self.pad_token, self.tag_names = tag_pad_idx, pad_token, tag_names
self.build_model_layers()
self.init_weights()
def build_model_layers(self):
''' builds the layers in the model '''
self.bert = BertForTokenClassification.from_pretrained(
"bert-base-cased",
num_labels=self.num_labels,
output_attentions=False,
output_hidden_states=False)
if self.use_crf:
self.crf = CRF(self.tag_pad_idx, self.pad_token, self.tag_names)
def forward(self, sentence, attention_mask, tags):
''' forward operation for network '''
outputs = self.bert(sentence,
token_type_ids=None,
attention_mask=attention_mask,
labels=tags)
loss, logits = outputs[0], outputs[1]
if self.use_crf:
# remove first token id in each sentence (to make crf mask work)
# crf_out, crf_loss = self.crf(logits, tags)
crf_out, crf_loss = self.crf(logits[:, 1:], tags[:, 1:])
return crf_out, crf_loss
else:
return logits, loss
def init_weights(self):
''' initializes model weights '''
# param_initializer = list(self.bert.classifier.named_parameters())
# if self.crf:
# param_initializer += list(self.crf.named_parameters())
# for name, param in param_initializer:
# nn.init.normal_(param.data, mean=0, std=0.1)
# only initialize conditional random field weights
if self.crf:
for name, param in self.crf.named_parameters():
nn.init.normal_(param.data, mean=0, std=0.1)
def count_parameters(self):
''' counts model parameters '''
return sum(p.numel() for p in self.parameters() if p.requires_grad)
files_t = []
for i in range(10):
files_t.append('test'+str(i+1)+'.nn.ner')
for i in range(10):
print('file ' + str(i+1))
fw = open(dirs + files_t[i] + '.pipe_crf6', 'w')
print('load train')
doc, label, sparse, trans_tr = load_train_data_pipe(dirs+files[i], vocab_w2i, sen_len, sparse_len, crf_num, label_num, label_onehot)
print('load test')
doc_t, label_t, sparse_t, trans_t = load_train_data_pipe(dirs+files_t[i], vocab_w2i, sen_len, sparse_len, crf_num, label_num, label_onehot)
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
crf = CRF(sen_len, label_num, sparse_len, crf_num, learning_rate, label_m, trans_tr)
sess.run(tf.initialize_all_variables())
def train_step(input_, label_):
feed_dict = {
crf.input : input_,
crf.label : label_
}
_, lss = sess.run([crf.trains, crf.loss], feed_dict)
def test_step(input_, label_, fw, trans):
totals_ = 0
corrects_ = 0
feed_dict = {crf.input : input_, crf.label : label_}
unary_score, lens, _ = sess.run([crf.unary_score, crf.lens, crf.trains], feed_dict)
for unary_, l_, lens_ in zip(unary_score, label_, lens):
u = unary_[:lens_]
l = l_[:lens_]
# print information about datasets
print('train set: {} sentences'.format(len(corpus.train_set)))
print('valid set: {} sentences'.format(len(corpus.valid_set)))
print('test set: {} sentences'.format(len(corpus.valid_set)))
print(m * '-')
# parameters from corpus
text_pad_idx = corpus.text_pad_idx
text_unk_idx = corpus.text_unk_idx
char_pad_idx = corpus.char_pad_idx
tag_pad_idx = corpus.tag_pad_idx
pad_token = corpus.pad_token
pretrained_embeddings = corpus.text_field.vocab.vectors
try:
CRF(tag_pad_idx, pad_token, tag_names)
use_crf = True
print('using crf for models')
except:
use_crf = False
print('not using crf for models (incompatible tagging format)')
print(m * '-')
# shared cnn parameters
char_embedding_dim = 37
char_filter = 4
char_kernel = 3
# shared dropouts
embedding_dropout_ratio = 0.5
char_embedding_dropout_ratio = 0.25
cnn_dropout_ratio = 0.25