graphrag火了，但是graph部分似乎有待提高，这里有个保姆教程，专业提取konwledge

{"text": "雀巢裁员4000人：时代抛弃你时，连招呼都不会打！", "id": "409389c96efe78d6af1c86e0450fd2d7", "event_list": [{"event_type": "组织关系-裁员", "trigger": "裁员", "trigger_start_index": 2, "arguments": [{"argument_start_index": 0, "role": "裁员方", "argument": "雀巢", "alias": []}, {"argument_start_index": 4, "role": "裁员人数", "argument": "4000人", "alias": []}], "class": "组织关系"}]}{"text": "美国“未来为”子公司大幅度裁员，这是为什么呢？任正非正式回应", "id": "5aec2b5b759c5f8f42f9c0156eb3c924", "event_list": [{"event_type": "组织关系-裁员", "trigger": "裁员", "trigger_start_index": 13, "arguments": [{"argument_start_index": 0, "role": "裁员方", "argument": "美国“未来为”子公司", "alias": []}], "class": "组织关系"}]}

#! -*- coding: utf-8 -*-
import jsonimport numpy as npfrom bert4keras.backend import keras, K, search_layerfrom bert4keras.models import build_transformer_modelfrom bert4keras.tokenizers import Tokenizerfrom bert4keras.optimizers import Adamfrom bert4keras.snippets import sequence_padding, DataGeneratorfrom bert4keras.snippets import openfrom bert4keras.layers import ConditionalRandomFieldfrom keras.layers import Densefrom keras.models import Modelfrom tqdm import tqdmimport pylcsfrom keras.layers import Input, Dense, Embedding, LSTM, Bidirectional,GRUfrom keras.optimizers import Optimizerfrom tensorflow.keras.callbacks import LearningRateSchedulerimport tensorflow as tfimport osos.environ["CUDA_VISIBLE_DEVICES"] = "0"os.environ['TF_AUTO_MIXED_PRECISION_GRAPH_REWRITE_IGNORE_PERFORMANCE'] = '1'# 基本信息train=True #参数为True代表训练 参数为False代表为预测maxlen = 220epochs = 10batch_size = 16learning_rate = 1e-5crf_lr_multiplier = 100# 必要时扩大CRF层的学习率model_save='best_model_v4.weights'#最好的成绩是模型2# bert配置config_path = 'data/chinese_L-12_H-768_A-12/bert_config.json'checkpoint_path = 'data/chinese_L-12_H-768_A-12/bert_model.ckpt'dict_path = 'data/chinese_L-12_H-768_A-12/vocab.txt'

def load_data(filename):D = []with open(filename) as f:for l in f:l = json.loads(l)arguments = {}for event in l['event_list']:for argument in event['arguments']:key = argument['argument']value = (event['event_type'], argument['role'])arguments[key] = valueD.append((l['text'], arguments))print(D)return D

# 读取数据train_data = load_data('data/duee_train.json')valid_data = load_data('data/duee_dev.json')
# 读取schemawith open('data/duee_event_schema.json') as f:id2label, label2id, n = {}, {}, 0for l in f:l = json.loads(l)for role in l['role_list']:key = (l['event_type'], role['role'])id2label[n] = keylabel2id[key] = nn += 1num_labels = len(id2label) * 2 + 1
# 建立分词器tokenizer = Tokenizer(dict_path, do_lower_case=True)

def search(pattern, sequence):"""从sequence中寻找子串pattern如果找到，返回第一个下标；否则返回-1。"""n = len(pattern)for i in range(len(sequence)):if sequence[i:i + n] == pattern:return ireturn -1

class data_generator(DataGenerator):"""数据生成器"""def __iter__(self, random=False):batch_token_ids, batch_segment_ids, batch_labels = [], [], []for is_end, (text, arguments) in self.sample(random):token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)labels = [0] * len(token_ids)for argument in arguments.items():a_token_ids = tokenizer.encode(argument[0])[0][1:-1]start_index = search(a_token_ids, token_ids)if start_index != -1:labels[start_index] = label2id[argument[1]] * 2 + 1for i in range(1, len(a_token_ids)):labels[start_index + i] = label2id[argument[1]] * 2 + 2batch_token_ids.append(token_ids)batch_segment_ids.append(segment_ids)batch_labels.append(labels)#print(batch_labels)if len(batch_token_ids) == self.batch_size or is_end:batch_token_ids = sequence_padding(batch_token_ids)batch_segment_ids = sequence_padding(batch_segment_ids)batch_labels = sequence_padding(batch_labels)yield [batch_token_ids, batch_segment_ids], batch_labelsbatch_token_ids, batch_segment_ids, batch_labels = [], [], []

model = build_transformer_model(config_path,checkpoint_path,model='bert')#lstm_output = Bidirectional(LSTM(num_labels//2, dropout=0.2, return_sequences=True))(model.output)output = Dense(num_labels)(model.output)CRF = ConditionalRandomField(lr_multiplier=crf_lr_multiplier)output = CRF(output)
model = Model(model.input, output)model.summary()opt =Adam(learning_rate)#AccumOptimizer(Adam(learning_rate), 5) # 10是累积步数#opt = tf.keras.optimizers.Adam(learning_rate)#opt = tf.train.experimental.enable_mixed_precision_graph_rewrite(#opt,#loss_scale='dynamic')model.compile(loss=CRF.sparse_loss,optimizer=opt,metrics=[CRF.sparse_accuracy])

def viterbi_decode(nodes, trans):"""Viterbi算法求最优路径其中nodes.shape=[seq_len, num_labels],trans.shape=[num_labels, num_labels]."""labels = np.arange(num_labels).reshape((1, -1))scores = nodes[0].reshape((-1, 1))scores[1:] -= np.inf# 第一个标签必然是0paths = labelsfor l in range(1, len(nodes)):M = scores + trans + nodes[l].reshape((1, -1))idxs = M.argmax(0)scores = M.max(0).reshape((-1, 1))paths = np.concatenate([paths[:, idxs], labels], 0)return paths[:, scores[:, 0].argmax()]

def extract_arguments(text):"""arguments抽取函数"""tokens = tokenizer.tokenize(text)while len(tokens) > 510:tokens.pop(-2)mapping = tokenizer.rematch(text, tokens)token_ids = tokenizer.tokens_to_ids(tokens)segment_ids = [0] * len(token_ids)nodes = model.predict([[token_ids], [segment_ids]])[0]trans = K.eval(CRF.trans)labels = viterbi_decode(nodes, trans)arguments, starting = [], Falsefor i, label in enumerate(labels):if label > 0:if label % 2 == 1:starting = Truearguments.append([[i], id2label[(label - 1) // 2]])elif starting:arguments[-1][0].append(i)else:starting = Falseelse:starting = False
return {text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1]: lfor w, l in arguments}

def evaluate(data):"""评测函数（跟官方评测结果不一定相同，但很接近）"""X, Y, Z = 1e-10, 1e-10, 1e-10for text, arguments in tqdm(data):inv_arguments = {v: k for k, v in arguments.items()}pred_arguments = extract_arguments(text)pred_inv_arguments = {v: k for k, v in pred_arguments.items()}Y += len(pred_inv_arguments)Z += len(inv_arguments)for k, v in pred_inv_arguments.items():if k in inv_arguments:# 用最长公共子串作为匹配程度度量l = pylcs.lcs(v, inv_arguments[k])X += 2. * l / (len(v) + len(inv_arguments[k]))f1, precision, recall = 2 * X / (Y + Z), X / Y, X / Zreturn f1, precision, recall

def predict_to_file(in_file, out_file):"""预测结果到文件，方便提交"""fw = open(out_file, 'w', encoding='utf-8')with open("/home/maxin/baidu_data/baidu_ee/event_schema.json", 'r', encoding='UTF-8') as x:event_dict = json.load(x)x.close()with open(in_file) as fr:for l in tqdm(fr):l = json.loads(l)arguments = extract_arguments(l['text'])event_list = []for k, v in arguments.items():event_list.append({'event_type': v[0],'trigger':v[0].split('-')[-1],'trigger_start_index':0,'arguments': [{'argument_start_index':0,'role': v[1],'argument': k,'class':event_dict.get(v[0])}]})l['event_list'] = event_listl = json.dumps(l, ensure_ascii=False)fw.write(l + '\n')fw.close()

class Evaluator(keras.callbacks.Callback):"""评估和保存模型"""def __init__(self):self.best_val_f1 = 0.
def on_epoch_end(self, epoch, logs=None):f1, precision, recall = evaluate(valid_data)if f1 >= self.best_val_f1:self.best_val_f1 = f1model.save_weights(model_save)print('f1: %.5f, precision: %.5f, recall: %.5f, best f1: %.5f\n' %(f1, precision, recall, self.best_val_f1))