NLP命名体识别bilstm+crf
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参考技术A """
NLP命名体识别bilstm+crf
1、准备数据：origin_handle_entities()
读取源数据文件，把人名，地名，机构名合并起来
2、读取处理后的数据：origin_handle_mark()
把预处理后的的文本标注成BMO的格式，
B(begin)、M(middle)、E(end)、O(other)
3、句子切分：sentence_split()
按照指定的格式，比如标点等内容对数据完成切分
4、保存数据
a.将标注的句子拆分自成列表和对应的标注序列
b.创建词汇表和标签
c.文本的向量化表示
d.划分训练集和测试集
e.保存成二进制pkl文件
5、加载数据
6、训练模型BiLSTM&HMM
7、保存训练后的模型用于预测
8、预测
"""
import codecs
import re
import collections
import pickle
import TorchCRF as CRF
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences #使用tensorflow的pad_sequences进行数据对齐 tensorflow2.3.1
from sklearn.model_selection import train_test_split
def origin_handle_entities():
with open(\'renmin.txt\',\'r\',encoding=\'utf-8\') as inp,
open(\'middle/renmin2.txt\',\'w\',encoding=\'utf-8\')
as outp:
#读取源文件中的数据
for line in inp.readlines():
#按照空格切分
line = line.split(\' \')
i = 1
while i < len(line) - 1:
if line[i][0] == \'[\':
outp.write(line[i].split(\'/\')[0][1:])
i += 1
while i < len(line) - 1 and line[i].find(\']\') == -1:
if line[i] !=\'\':
#print(line[i].split(\'/\')[0])
outp.write(line[i].split(\'/\')[0])
i += 1
outp.write(line[i].split(\'/\')[0].strip()+\'/\'+line[i])
elif line[i].split(\'/\')[1] == \'nr\':
word = line[i].split(\'/\')[0]
i += 1
if i < len(line) - 1 and line[i].split(\'/\')[1] == \'nr\':
outp.write(word + line[i].split(\'/\')[0] + \'nr\')
else:
outp.write(word + \'/nr \')
continue
else:
outp.write(line[i] + \'/no \')
i += 1
outp.write(\'\\n\')
import codecs
def origin_handle_mark():
"""
1、读取数据预处理后的renmin2.txt
2、将标注好的数据写入renmin3.txt
a.打开输入和输出文件
b.遍历输入文件renmin2.txt
:return:
"""
with codecs.open(\'middle/renmin2.txt\',\'r\',encoding=\'utf-8\') as inp,
codecs.open(\'middle/renmin3.txt\',\'w\',encoding=\'utf-8\') as outp:
#########句子切分###################################
import re
def sentence_split():
with codecs.open(\'middel/renmin3.txt\',\'r\',encoding=\'utf-8\') as inp,
codecs.open(\'middle/renmin4.txt\',\'w\',encoding=\'utf-8\') as outp:
#文本文件的内容设置为对应的utf-8编码，python3：先encode，再decode
texts = inp.read().encode(\'utf-8\').decode(\'utf-8\')
#切分句子
sentences =
re.split(\'[，。！？、\'\'""：]/[0]\'.encode(\'utf-8\').decode(\'utf-8\'),
texts)
for sentence in sentences:
if sentence != \' \':
outp.write(sentence.strip() + \'\\n\')
def data_to_pkl():
"""
将文本数据保存成二进制pkl文件
:return:
"""
def main():
# 数据清洗
origin_handle_entities()
#数据标注（字）
origin_handle_mark()
# 句子切分
sentence_split()
# 数据转换
data_to_pkl()
if name == \' main \':
main()
##################################################################################################
def load_data():
pickle_path = \'../data_target_pkl/renmindata.pkl\'
with open(pickle_path,\'rb\') as inp:
word2id,id2word,tag2id,id2tag,x_train,y_train,x_test,y_test,x_valid,y_valid =pickle.load(inp)
def main():
word2id = load_data()
print(len(word2id))
if name == \' main \':
main()
#######################################################################################
import torch
import torch.nn as nn
from torch.utils.data import Dataset # 批量读取数据
class NERDataSet(Dataset):
"""
X:表示样本，Y:表示标签
"""
def init (self,X,Y, args, *kwargs):
"""
class Config():
embedding_dim = 100 #词向量的维度
hidden_dim = 200
config = Config()
class NERLSTM_CRF(nn.Module):
"""
1、输入层
2、词映射（Embedding（vocab_size，embedding_dim））
3、LSTM
4、全连接层
"""
def init (self):
super(NERLSTM_CRF,self). init ()
self.embeding_dim = config.embeding_dim
self.hidden_dim = config.hidden_dim
self.vocab_size = config.vocab_size
self.num_tags = config.num_tags
##################################################
from torch.utils.data import DataLoader #批量加载数据
import torch
import torch.optim as op
def utils_to_train():
device = torch.device(\'cpu\')
max_epoch = 1
batch_size = 32
num_workers =4 #开启几个线程取执行程序
def parse_tags(text,path):
id2tag = load_data()
tags = [id2tag[idx] for idx in path]
##################################################
from sklearn.metrics import classification_report,precision_score,recall_score,f1_score
word2id = load_data()[0]
max_epoch,device,train_data_loader,valid_data_loader,test_data_loader,model = utils_to_train()
class ChineseNER(object):
def train(self):
for epoch in range(max_epoch):
自然语言处理（NLP）基于BiLSTM+CRF的事件抽取
【自然语言处理（NLP）】基于BiLSTM+CRF的事件抽取
(文章目录)
前言
(一)、任务描述
事件抽取技术是从非结构化信息中抽取出用户感兴趣的事件，并以结构化呈现给用户。
事件抽取任务可分解为4个子任务：触发词识别、事件类型分类、论元识别和角色分类任务，其中，触发词识别和事件类型分类可合并成事件识别任务。
(二)、环境配置
本示例使用BiLSTM实现两个子任务中的分类，代码运行的环境配置如下：Python版本为3.7，PaddlePaddle版本为2.0.0，操作平台为AI Studio。
import paddle
import numpy as np
import matplotlib.pyplot as plt
print(paddle.__version__)
输出结果如下图1所示：
一、数据准备
(一)、导入相关包
import os
import json
import paddle.fluid as fluid
import ast
import hashlib
import warnings
import argparse
from functools import partial
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.transformers import ErnieTokenizer, ErnieForTokenClassification, LinearDecayWithWarmup
from paddlenlp.metrics import ChunkEvaluator
warnings.filterwarnings(ignore)
(二)、数据集加载
------------------ 1.数据预处理及加载 -------------------
def cal_md5(str):
"""calculate string md5"""
str = str.decode("utf-8", "ignore").encode("utf-8", "ignore")
return hashlib.md5(str).hexdigest()
def read_by_lines(path):
"""read the data by line"""
result = list()
with open(path, "r") as infile:
for line in infile:
result.append(line.strip())
return result
def write_by_lines(path, data):
"""write the data"""
with open(path, "w") as outfile:
[outfile.write(d + "\\n") for d in data]
def text_to_sents(text):
"""text_to_sents"""
deliniter_symbols = [u"。", u"？", u"！"]
paragraphs = text.split("\\n")
ret = []
for para in paragraphs:
if para == u"":
continue
sents = [u""]
for s in para:
sents[-1] += s
if s in deliniter_symbols:
sents.append(u"")
if sents[-1] == u"":
sents = sents[:-1]
ret.extend(sents)
return ret
def load_dict(dict_path):
"""load_dict"""
vocab =
for line in open(dict_path, r, encoding=utf-8):
value, key = line.strip(\\n).split(\\t)
vocab[key] = int(value)
return vocab
def extract_result(text, labels):
"""extract_result"""
ret, is_start, cur_type = [], False, None
if len(text) != len(labels):
labels = labels[:len(text)]
for i, label in enumerate(labels):
if label != u"O":
_type = label[2:]
if label.startswith(u"B-"):
is_start = True
cur_type = _type
ret.append("start": i, "text": [text[i]], "type": _type)
elif _type != cur_type:
"""
# 如果是没有B-开头的，则不要这部分数据
cur_type = None
is_start = False
"""
cur_type = _type
is_start = True
ret.append("start": i, "text": [text[i]], "type": _type)
elif is_start:
ret[-1]["text"].append(text[i])
else:
cur_type = None
is_start = False
else:
cur_type = None
is_start = False
return ret
def data_process(path, model="trigger", is_predict=False):
"""data_process"""
def label_data(data, start, l, _type):
"""label_data"""
for i in range(start, start + l):
suffix = "B-" if i == start else "I-"
data[i] = "".format(suffix, _type)
return data
sentences = []
output = ["text_a"] if is_predict else ["text_a\\tlabel"]
with open(path) as f:
for line in f:
d_json = json.loads(line.strip())
_id = d_json["id"]
text_a = [
"，" if t == " " or t == "\\n" or t == "\\t" else t
for t in list(d_json["text"].lower())
]
if is_predict:
sentences.append("text": d_json["text"], "id": _id)
output.append(\\002.join(text_a))
else:
if model == "trigger":
labels = ["O"] * len(text_a)
for event in d_json.get("event_list", []):
event_type = event["event_type"]
start = event["trigger_start_index"]
trigger = event["trigger"]
labels = label_data(labels, start,
len(trigger), event_type)
output.append("\\t".format(\\002.join(text_a),
\\002.join(labels)))
elif model == "role":
for event in d_json.get("event_list", []):
labels = ["O"] * len(text_a)
for arg in event["arguments"]:
role_type = arg["role"]
argument = arg["argument"]
start = arg["argument_start_index"]
labels = label_data(labels, start,
len(argument), role_type)
output.append("\\t".format(\\002.join(text_a),
\\002.join(labels)))
return output
def schema_process(path, model="trigger"):
"""schema_process"""
def label_add(labels, _type):
"""label_add"""
if "B-".format(_type) not in labels:
labels.extend(["B-".format(_type), "I-".format(_type)])
return labels
labels = []
for line in read_by_lines(path):
d_json = json.loads(line.strip())
if model == "trigger":
labels = label_add(labels, d_json["event_type"])
elif model == "role":
for role in d_json["role_list"]:
labels = label_add(labels, role["role"])
labels.append("O")
tags = []
for index, label in enumerate(labels):
tags.append("\\t".format(index, label))
return tags
def word2id_(lines,vocab,max_len=145):
# 144 21 0.9505796670630202
res = []
lens = []
for line in lines:
r = []
for c in line:
if c not in vocab:
r.append(vocab[<pad>])
else:
r.append(vocab[c])
r =r[:max_len]
lens.append(len(r))
r = r+[0]*(max_len-len(r))
res.append(r)
return r,lens
def get_vocab():
train_lines = open(data/DuEE_1_0/train.json,r,encoding=utf-8).readlines()
dev_lines = open(data/DuEE_1_0/dev.json,r,encoding=utf-8).readlines()
lines = train_lines + dev_lines
vocab = set()
# dic =
for line in lines:
ll = json.loads(line.strip())
for c in ll[text]:
vocab.add(c)
vocab = c:i+2 for i,c in enumerate(list(vocab))
vocab[<pad>],vocab[<unk>]=0,1
return vocab
print("\\n================= DUEE 1.0 DATASET ==============")
conf_dir = "./data/DuEE_1_0"
schema_path = "/event_schema.json".format(conf_dir)
tags_trigger_path = "/trigger_tag.dict".format(conf_dir)
tags_role_path = "/role_tag.dict".format(conf_dir)
print("\\n=================start schema process==============")
print(input path .format(schema_path))
tags_trigger = schema_process(schema_path, "trigger")
write_by_lines(tags_trigger_path, tags_trigger)
print("save trigger tag at ".format(
len(tags_trigger), tags_trigger_path))
tags_role = schema_process(schema_path, "role")
write_by_lines(tags_role_path, tags_role)
print("save trigger tag at ".format(len(tags_role), tags_role_path))
print("=================end schema process===============")
# data process
data_dir = "./data/DuEE_1_0"
trigger_save_dir = "/trigger".format(data_dir)
role_save_dir = "/role".format(data_dir)
print("\\n=================start schema process==============")
if not os.path.exists(trigger_save_dir):
os.makedirs(trigger_save_dir)
if not os.path.exists(role_save_dir):
os.makedirs(role_save_dir)
print("\\n----trigger------for dir to ".format(data_dir,
trigger_save_dir))
train_tri = data_process("/train.json".format(data_dir), "trigger")
write_by_lines("/train.tsv".format(trigger_save_dir), train_tri)
dev_tri = data_process("/dev.json".format(data_dir), "trigger")
write_by_lines("/dev.tsv".format(trigger_save_dir), dev_tri)
test_tri = data_process("/test.json".format(data_dir), "trigger")
write_by_lines("/test.tsv".format(trigger_save_dir), test_tri)
print("train dev test ".format(
len(train_tri), len(dev_tri), len(test_tri)))
print("\\n----role------for dir to ".format(data_dir, role_save_dir))
train_role = data_process("/train.json".format(data_dir), "role")
write_by_lines("/train.tsv".format(role_save_dir), train_role)
dev_role = data_process("/dev.json".format(data_dir), "role")
write_by_lines("/dev.tsv".format(role_save_dir), dev_role)
test_role = data_process("/test.json".format(data_dir), "role")
write_by_lines("/test.tsv".format(role_save_dir), test_role)
print("train dev test ".format(
len(train_role), len(dev_role), len(test_role)))
print("=================end schema process==============")
vocab = get_vocab()
vocab_size = len(list(vocab))
# print(vocab)
print(vocab_size)
输出结果如下图2所示：
二、定义LSTM模型
随着深度学习的发展，目前主流的序列化标注任务基于词向量（word embedding）进行表示学习。下面介绍模型的整体训练流程如下：
序列标注任务常用的模型是RNN+CRF。GRU和LSTM都是常用的RNN单元。这里我们以Bi-LSTM+CRF模型为例，介绍如何使用 PaddlePaddle 定义序列化标注任务的网络结构。如下图所示，LSTM的输出可以作为 CRF 的输入，最后 CRF 的输出作为模型整体的预测结果。
class LSTM_Model(nn.Layer):
def __init__(self,vocab_num, emb_size, hidden_size, num_layers, num_labels, dropout):
super(LSTM_Model, self).__init__()
self.embedding = nn.Embedding(vocab_num, emb_size)
self.lstm = nn.LSTM(emb_size, hidden_size, num_layers=num_layers, direction=bidirect, dropout=dropout)
self.attention_linear = nn.Linear(hidden_size * 2, hidden_size)
self.linear = nn.Linear(hidden_size * 2, num_labels)
self.dropout = nn.Dropout(dropout)
def forward(self,input_ids,target=None):
token_emb = self.embedding(input_ids)
sequence_output, (hidden, cell) = self.lstm(token_emb) # [batch_size,time_steps,num_directions * hidden_size]
sequence_output = self.dropout(sequence_output)
logits = self.linear(sequence_output)
# feature_out = fluid.layers.fc(input=hidden_1, size=len(label_dict), act=tanh)
# 调用内置 CRF 函数并做状态转换解码.
# if target is not None:
# crf_cost = fluid.layers.linear_chain_crf(
# input=paddle.reshape(logits,[-1,logits.shape[-1]]), label=paddle.reshape(target,[-1,1]),
# param_attr=fluid.ParamAttr(name=crfw1, learning_rate=0.0001))
# avg_cost = fluid.layers.mean(crf_cost)
# else:
# avg_cost = 0
avg_cost = 0
return logits, avg_cost
三、定义训练框架
定义网络结构后，需要配置优化器、损失函数、评价指标。
评价指标
针对每条序列样本的预测结果，序列标注任务将预测结果按照语块（chunk）进行结合并进行评价。评价指标通常有 Precision、Recall 和 F1。
- Precision，精确率，也叫查准率，由模型预测正确的个数除以模型总的预测的个数得到，关注模型预测出来的结果准不准
- Recall，召回率，又叫查全率， 由模型预测正确的个数除以真实标签的个数得到，关注模型漏了哪些东西
- F1，综合评价指标，计算公式如下，$F1 = \\frac2PrecisionRecallPrecision+Recall$，同时考虑 Precision 和 Recall ，是 Precision 和 Recall 的折中。
paddlenlp.metrics中集成了ChunkEvaluator评价指标，并逐步丰富中，
# 定义训练框架
num_epoch = 10
learning_rate=0.0001
tag_path = ./data/DuEE_1_0/
data_dir = ./data/DuEE_1_0/trigger
train_data = ./data/DuEE_1_0/trigger/train.tsv
dev_data = ./data/DuEE_1_0/trigger/dev.tsv
test_data = ./data/DuEE_1_0/trigger/test.tsv
predict_data = ./data/DuEE_1_0/duee_test.json
checkpoints = ./data/DuEE_1_0/trigger/
init_ckpt = ./data/DuEE_1_0/trigger/best.pdparams
weight_decay=0.01
warmup_proportion=0.1
max_seq_len=145
valid_step=100
skip_step=50
batch_size=32
predict_save_path=None
seed=1000
@paddle.no_grad()
def eval（model, criterion, metric, num_label, data_loader):
"""evaluate"""
model.eval（)
metric.reset()
losses = []
for input_ids, labels, seq_lens in data_loader:
logits,_ = model(input_ids,labels)
loss = paddle.mean(criterion(logits.reshape([-1, num_label]), labels.reshape([-1])))
losses.append(loss.numpy())
preds = paddle.argmax(logits, axis=-1)
n_infer, n_label, n_correct = metric.compute(None, seq_lens, preds, labels) # metric.compute(None, seq_lens, preds, labels)
metric.update(n_infer.numpy(), n_label.numpy(), n_correct.numpy())
precision, recall, f1_score = metric.accumulate()
avg_loss = np.mean(losses)
model.train()
return precision, recall, f1_score, avg_loss
def word2id(line,vocab,max_len=145):
r = []
for c in line:
if c not in vocab:
r.append(vocab[<unk>])
else:
r.append(vocab[c])
r =r[:max_len]
lens = len(r)
r = r+[0]*(max_len-len(r))
# print(----------------)
# print(line,r)
return r,lens
def convert_example_to_feature(example, label_vocab=None, max_seq_len=145, no_entity_label="O",
ignore_label=-1, is_test=False):
tokens, labels, seq_len = example
input_ids,seq_lens = word2id(tokens,vocab)
if is_test:
return input_ids,seq_lens
elif label_vocab is not None:
encoded_label = labels[:seq_lens]
encoded_label = [label_vocab[x] for x in encoded_label]
encoded_label = encoded_label + [-1]*(max_seq_len-min(seq_lens,145) )
# print(++++++++++++++++++++++++++++)
# print(labels,encoded_label)
# print(++++++++++++++++++++++++++++)
return input_ids, encoded_label, seq_lens
class DuEventExtraction(paddle.io.Dataset):
"""DuEventExtraction"""
def __init__(self, data_path, tag_path):
self.label_vocab = load_dict(tag_path)
self.word_ids = []
self.label_ids = []
self.seq_lens = []
with open(data_path, r, encoding=utf-8) as fp:
# skip the head line
next(fp)
for line in fp.readlines():
words, labels = line.strip(\\n).split(\\t)
words = words.split(\\002)
labels = labels.split(\\002)
self.word_ids.append(words)
self.label_ids.append(labels)
self.seq_lens.append(len(words[:145]))
self.label_num = max(self.label_vocab.values()) + 1
def __len__(self):
return len(self.word_ids)
def __getitem__(self, index):
return self.word_ids[index], self.label_ids[index], self.seq_lens[index]
def do_train():
paddle.set_device(cpu)
no_entity_label = "O"
ignore_label = -1
label_map = load_dict(tag_path)
id2label = val: key for key, val in label_map.items()
vocab_num, emb_size, hidden_size, num_layers, num_labels, dropout = \\
vocab_size,256,256,2,len(list(id2label)),0.1
model = LSTM_Model(vocab_num, emb_size, hidden_size, num_layers, num_labels, dropout)
print("============start train==========")
train_ds = DuEventExtraction(train_data, tag_path)
dev_ds = DuEventExtraction(dev_data, tag_path)
test_ds = DuEventExtraction(test_data, tag_path)
trans_func = partial(
convert_example_to_feature,
label_vocab=train_ds.label_vocab,
max_seq_len=max_seq_len,
no_entity_label=no_entity_label,
ignore_label=ignore_label,
is_test=False)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=0), # input ids
Pad(axis=0, pad_val=ignore_label), # labels
Stack() # seq_lens
): fn(list(map(trans_func, samples)))
batch_sampler = paddle.io.DistributedBatchSampler(train_ds, batch_size=batch_size, shuffle=True)
train_loader = paddle.io.DataLoader(
dataset=train_ds,
batch_sampler=batch_sampler,
collate_fn=batchify_fn)
dev_loader = paddle.io.DataLoader(
dataset=dev_ds,
batch_size=batch_size,
collate_fn=batchify_fn)
test_loader = paddle.io.DataLoader(
dataset=test_ds,
batch_size=batch_size,
collate_fn=batchify_fn)
num_training_steps = len(train_loader) * num_epoch
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=learning_rate,
parameters=model.parameters(),
weight_decay=weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
metric = ChunkEvaluator(label_list=train_ds.label_vocab.keys(), suffix=False)
criterion = paddle.nn.loss.CrossEntropyLoss(ignore_index=ignore_label)
# print(ignore_label)
step, best_f1 = 0, 0.0
model.train()
for epoch in range(num_epoch):
for idx, (input_ids,labels,seq_lens) in enumerate(train_loader):
# print(input_ids[0],labels[0])
logits,_= model(input_ids,labels)
probs_ids = paddle.argmax(logits, -1).numpy()
# print(logits.shape,labels.shape)
logits = logits.reshape([-1, train_ds.label_num])
loss = paddle.mean(criterion(logits, labels.reshape([-1])))
loss.backward()
optimizer.step()
optimizer.clear_grad()
loss_item = loss.numpy().item()
if step > 0 and step % skip_step == 0:
# print(probs_ids )
print(ftrain epoch: epoch - step: step (total: num_training_steps) - loss: loss_item:.6f)
if step > 0 and step % valid_step == 0:
p, r, f1, avg_loss = eval（label_map), dev_loader)
print(fdev step: step - loss: avg_loss:.5f, precision: p:.5f, recall: r:.5f, \\
ff1: f1:.5f current best best_f1:.5f)
if f1 > best_f1:
best_f1 = f1
print(f==============================================save best model \\
fbest performerence best_f1:5f)
paddle.save(model.state_dict(), /best.pdparams.format(checkpoints))
step += 1
# save the final model
paddle.save(model.state_dict(), /final.pdparams.format(checkpoints))
def do_predict():
paddle.set_device(cpu)
no_entity_label = "O"
ignore_label = -1
label_map = load_dict(tag_path)
id2label = val: key for key, val in label_map.items()
vocab_num, emb_size, hidden_size, num_layers, num_labels, dropout = \\
vocab_size,256,256,2,len(list(id2label)),0.1
model = LSTM_Model(vocab_num, emb_size, hidden_size, num_layers, num_labels, dropout)
print("============start predict==========")
if not init_ckpt or not os.path.isfile(init_ckpt):
raise Exception("init checkpoints not exist".format(init_ckpt))
else:
state_dict = paddle.load(init_ckpt)
model.set_dict(state_dict)
print("Loaded parameters from %s" % init_ckpt)
# load data from predict file
sentences = read_by_lines(predict_data) # origin data format
sentences = [json.loads(sent) for sent in sentences]
encoded_inputs_list = []
for sent in sentences:
sent = sent["text"].replace(" ", "\\002")
input_ids = convert_example_to_feature([list(sent), [],len(sent)], max_seq_len=max_seq_len, is_test=True)
encoded_inputs_list.append((input_ids))
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=0), # input_ids
Stack()
): fn(samples)
# Seperates data into some batches.
batch_encoded_inputs = [encoded_inputs_list[i: i + batch_size]
for i in range(0, len(encoded_inputs_list),batch_size)]
results = []
model.eval（)
for batch in batch_encoded_inputs:
input_ids,seq_lens= batchify_fn(batch)
input_ids = paddle.to_tensor(input_ids)
# token_type_ids = paddle.to_tensor(token_type_ids)
logits,_ = model(input_ids)
probs = F.softmax(logits, axis=-1)
probs_ids = paddle.argmax(probs, -1).numpy()
probs = probs.numpy()
for p_list, p_ids, seq_len in zip(probs.tolist(), probs_ids.tolist(), seq_lens.tolist()):
prob_one = [p_list[index][pid] for index, pid in enumerate(p_ids[1: seq_len - 1])]
label_one = [id2label[pid] for pid in p_ids[1: seq_len - 1]]
results.append("probs": prob_one, "labels": label_one)
assert len(results) == len(sentences)
print(results[:10])
for sent, ret in zip(sentences, results):
sent["pred"] = ret
sentences = [json.dumps(sent, ensure_ascii=False) for sent in sentences]
print(sentences[:10])
# write_by_lines(predict_save_path, sentences)
# print("save data to ".format(len(sentences), predict_save_path))
四、模型训练及预测1
## 训练ner
num_epoch = 10
base_dir = DuEE_1_0
tag_path = ./data//trigger_tag.dict.format(base_dir)
data_dir = ./data//trigger.format(base_dir)
train_data = ./data//trigger/train.tsv.format(base_dir)
dev_data = ./data//trigger/dev.tsv.format(base_dir)
test_data = ./data//trigger/test.tsv.format(base_dir)
predict_data = ./data//test.json.format(base_dir)
checkpoints = ./data//trigger/.format(base_dir)
init_ckpt = ./data//trigger/final.pdparams.format(base_dir)
do_train()
do_predict()
输出结果如下图4所示：
五、模型训练及预测2
## 训练ee
num_epoch = 10
tag_path = ./data//role_tag.dict.format(base_dir)
data_dir = ./data//role.format(base_dir)
train_data = ./data//role/train.tsv.format(base_dir)
dev_data = ./data//role/dev.tsv.format(base_dir)
test_data = ./data//role/test.tsv.format(base_dir)
predict_data = ./data//test.json.format(base_dir)
checkpoints = ./data//role/.format(base_dir)
init_ckpt = ./data//role/final.pdparams.format(base_dir)
do_train()
do_predict()
输出结果如下图5所示：
总结
本系列文章内容为根据清华社出版的《自然语言处理实践》所作的相关笔记和感悟，其中代码均为基于百度飞桨开发，若有任何侵权和不妥之处，请私信于我，定积极配合处理，看到必回！！！
最后，引用本次活动的一句话，来作为文章的结语～(￣▽￣～)~：
【**学习的最大理由是想摆脱平庸，早一天就多一份人生的精彩；迟一天就多一天平庸的困扰。**】
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