本文主要是介绍pytorch bilstm crf NER，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
参考链接：https://blog.csdn.net/Jason__Liang/article/details/81772632
https://github.com/jayavardhanr/End-to-end-Sequence-Labeling-via-Bi-directional-LSTM-CNNs-CRF-Tutorial/blob/master/Named_Entity_Recognition-LSTM-CNN-CRF-Tutorial.ipynb
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optimtorch.manual_seed(1)def argmax(vec):# return the argmax as a python int_, idx = torch.max(vec, 1)return idx.item()def prepare_sequence(seq, to_ix):idxs = [to_ix[w] for w in seq]return torch.tensor(idxs, dtype=torch.long)# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec):max_score = vec[0, argmax(vec)]max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])return max_score + \torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))#####################################################################
# Create modelclass BiLSTM_CRF(nn.Module):def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):super(BiLSTM_CRF, self).__init__()self.embedding_dim = embedding_dimself.hidden_dim = hidden_dimself.vocab_size = vocab_sizeself.tag_to_ix = tag_to_ixself.tagset_size = len(tag_to_ix)self.word_embeds = nn.Embedding(vocab_size, embedding_dim)self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,num_layers=1, bidirectional=True)# Maps the output of the LSTM into tag space.self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)# Matrix of transition parameters. Entry i,j is the score of# transitioning *to* i *from* j.self.transitions = nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))# These two statements enforce the constraint that we never transfer# to the start tag and we never transfer from the stop tagself.transitions.data[tag_to_ix[START_TAG], :] = -10000self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000self.hidden = self.init_hidden()def init_hidden(self):return (torch.randn(2, 1, self.hidden_dim // 2),torch.randn(2, 1, self.hidden_dim // 2))def _forward_alg(self, feats):# Do the forward algorithm to compute the partition functioninit_alphas = torch.full((1, self.tagset_size), -10000.)# START_TAG has all of the score.init_alphas[0][self.tag_to_ix[START_TAG]] = 0.# Wrap in a variable so that we will get automatic backpropforward_var = init_alphas# Iterate through the sentencefor feat in feats:alphas_t = [] # The forward tensors at this timestepfor next_tag in range(self.tagset_size):# broadcast the emission score: it is the same regardless of# the previous tagemit_score = feat[next_tag].view(1, -1).expand(1, self.tagset_size)# the ith entry of trans_score is the score of transitioning to# next_tag from itrans_score = self.transitions[next_tag].view(1, -1)# The ith entry of next_tag_var is the value for the# edge (i -> next_tag) before we do log-sum-expnext_tag_var = forward_var + trans_score + emit_score# The forward variable for this tag is log-sum-exp of all the# scores.alphas_t.append(log_sum_exp(next_tag_var).view(1))forward_var = torch.cat(alphas_t).view(1, -1)terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]alpha = log_sum_exp(terminal_var)return alphadef _get_lstm_features(self, sentence):# 随机初始化 num_layers*num_directions = 2, batch = 1, hidden_size = self.hidden_dim // 2self.hidden = self.init_hidden()# 为了满足input输入格式 embedding_dim 是 5embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)# 输入 embeds -> 【11， 1， 5】、 self.hidden -> 是一个tuple ([], [])"""输入数据格式：input(seq_len, batch, input_size)h0(num_layers * num_directions, batch, hidden_size)c0(num_layers * num_directions, batch, hidden_size)输出数据格式：output(seq_len, batch, hidden_size * num_directions)hn(num_layers * num_directions, batch, hidden_size)cn(num_layers * num_directions, batch, hidden_size)"""lstm_out, self.hidden = self.lstm(embeds, self.hidden)lstm_out = lstm_out.view(len(sentence), self.hidden_dim) # 回到原来的格式, 去掉batch_size# 从 [11, 4] -> [11, 5] # 最后的类别是5个lstm_feats = self.hidden2tag(lstm_out)return lstm_featsdef _score_sentence(self, feats, tags):# Gives the score of a provided tag sequencescore = torch.zeros(1)tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])for i, feat in enumerate(feats):score = score + \self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]return scoredef _viterbi_decode(self, feats):backpointers = []# Initialize the viterbi variables in log spaceinit_vvars = torch.full((1, self.tagset_size), -10000.)init_vvars[0][self.tag_to_ix[START_TAG]] = 0# forward_var at step i holds the viterbi variables for step i-1forward_var = init_vvarsfor feat in feats: # 对每个word进行循环, feat是每个word的emission分数bptrs_t = [] # holds the backpointers for this stepviterbivars_t = [] # holds the viterbi variables for this stepfor next_tag in range(self.tagset_size): # 对每个tag进行循环# next_tag_var[i] holds the viterbi variable for tag i at the# previous step, plus the score of transitioning# from tag i to next_tag.# We don't include the emission scores here because the max# does not depend on them (we add them in below)next_tag_var = forward_var + self.transitions[next_tag] # 这里 加了转移矩阵 + 之前的 forward_varbest_tag_id = argmax(next_tag_var)bptrs_t.append(best_tag_id)viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))# Now add in the emission scores, and assign forward_var to the set# of viterbi variables we just computedforward_var = (torch.cat(viterbivars_t) + feat).view(1, -1) # 这个时候加上emission分数后 cat之后的格式转换， 然后再送入刚才 forward_varbackpointers.append(bptrs_t)# Transition to STOP_TAGterminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]best_tag_id = argmax(terminal_var)path_score = terminal_var[0][best_tag_id]# Follow the back pointers to decode the best path.best_path = [best_tag_id]for bptrs_t in reversed(backpointers):best_tag_id = bptrs_t[best_tag_id]best_path.append(best_tag_id)# Pop off the start tag (we dont want to return that to the caller)start = best_path.pop()assert start == self.tag_to_ix[START_TAG] # Sanity checkbest_path.reverse()return path_score, best_pathdef neg_log_likelihood(self, sentence, tags):feats = self._get_lstm_features(sentence)forward_score = self._forward_alg(feats)gold_score = self._score_sentence(feats, tags)return forward_score - gold_scoredef forward(self, sentence): # dont confuse this with _forward_alg above.# Get the emission scores from the BiLSTMlstm_feats = self._get_lstm_features(sentence)# Find the best path, given the features.score, tag_seq = self._viterbi_decode(lstm_feats)return score, tag_seq#####################################################################
# Run trainingSTART_TAG = "<START>"
STOP_TAG = "<STOP>"
EMBEDDING_DIM = 5
HIDDEN_DIM = 4# Make up some training data
training_data = [("the wall street journal reported today that apple corporation made money".split(),"B I I I O O O B I O O".split()
), ("georgia tech is a university in georgia".split(),"B I O O O O B".split()
)]word_to_ix = {}
for sentence, tags in training_data:for word in sentence:if word not in word_to_ix:word_to_ix[word] = len(word_to_ix)tag_to_ix = {"B": 0, "I": 1, "O": 2, START_TAG: 3, STOP_TAG: 4}model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)# Check predictions before training
with torch.no_grad():precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)print(precheck_tags)print(model(precheck_sent))# Make sure prepare_sequence from earlier in the LSTM section is loaded
for epoch in range(300): # again, normally you would NOT do 300 epochs, it is toy datafor sentence, tags in training_data:# Step 1. Remember that Pytorch accumulates gradients.# We need to clear them out before each instancelog_sum_expmodel.zero_grad()# Step 2. Get our inputs ready for the network, that is,# turn them into Tensors of word indices.sentence_in = prepare_sequence(sentence, word_to_ix)targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)# Step 3. Run our forward pass.loss = model.neg_log_likelihood(sentence_in, targets)print(loss)# Step 4. Compute the loss, gradients, and update the parameters by# calling optimizer.step()loss.backward()optimizer.step()# Check predictions after training
with torch.no_grad():precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)print(model(precheck_sent))
# We got it!
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