233 lines
9.2 KiB
Python
233 lines
9.2 KiB
Python
#! -*- coding: utf-8 -*-
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# 10个epoch后在valid上能达到约0.77的分数
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# (Accuracy=0.7282149325820084 F1=0.8207266829447049 Final=0.7744708077633566)
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import json, os, re
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os.environ['TF_KERAS'] = '1'
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import numpy as np
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from bert4keras.backend import keras, K
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from bert4keras.models import build_transformer_model
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from bert4keras.tokenizers import Tokenizer, load_vocab
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from bert4keras.optimizers import Adam
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from bert4keras.snippets import sequence_padding, DataGenerator
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from bert4keras.snippets import open
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from keras.layers import Lambda
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from keras.models import Model
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from tqdm import tqdm
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import tensorflow as tf
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True # 按需分配显存
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tf_session = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=config)
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tf.compat.v1.keras.backend.set_session(tf_session)
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max_p_len = 256
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max_q_len = 64
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max_a_len = 32
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batch_size = 32
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epochs = 10
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# # bert配置
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# config_path = '../models/nezha_gpt/config.json'
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# checkpoint_path = '../models/nezha_gpt/gpt.ckpt'
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# dict_path = '../models/tokenizer/vocab.txt'
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# # 标注数据
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# webqa_data = json.load(open('../data/qa/WebQA.json'))
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# sogou_data = json.load(open('../data/qa/SogouQA.json'))
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# # 保存一个随机序（供划分valid用）
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# if not os.path.exists('../random_order.json'):
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# random_order = list(range(len(sogou_data)))
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# np.random.shuffle(random_order)
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# json.dump(random_order, open('../random_order.json', 'w'), indent=4)
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# else:
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# random_order = json.load(open('../random_order.json'))
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# # 划分valid
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# train_data = [sogou_data[j] for i, j in enumerate(random_order) if i % 3 != 0]
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# valid_data = [sogou_data[j] for i, j in enumerate(random_order) if i % 3 == 0]
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# train_data.extend(train_data)
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# train_data.extend(webqa_data) # 将SogouQA和WebQA按2:1的比例混合
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# # 加载并精简词表，建立分词器
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# token_dict, keep_tokens = load_vocab(
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# dict_path=dict_path,
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# simplified=True,
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# startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]', '[MASK]'],
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# )
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# tokenizer = Tokenizer(token_dict, do_lower_case=True)
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# class data_generator(DataGenerator):
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# """数据生成器
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# """
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# def __iter__(self, random=False):
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# """单条样本格式为
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# 输入：[CLS][MASK][MASK][SEP]问题[SEP]篇章[SEP]
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# 输出：答案
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# """
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# batch_token_ids, batch_segment_ids, batch_a_token_ids = [], [], []
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# for is_end, D in self.sample(random):
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# question = D['question']
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# answers = [p['answer'] for p in D['passages'] if p['answer']]
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# passage = np.random.choice(D['passages'])['passage']
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# passage = re.sub(u' |、|；|，', ',', passage)
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# final_answer = ''
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# for answer in answers:
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# if all([
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# a in passage[:max_p_len - 2] for a in answer.split(' ')
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# ]):
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# final_answer = answer.replace(' ', ',')
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# break
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# a_token_ids, _ = tokenizer.encode(
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# final_answer, maxlen=max_a_len + 1
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# )
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# q_token_ids, _ = tokenizer.encode(question, maxlen=max_q_len + 1)
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# p_token_ids, _ = tokenizer.encode(passage, maxlen=max_p_len + 1)
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# token_ids = [tokenizer._token_start_id]
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# token_ids += ([tokenizer._token_mask_id] * max_a_len)
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# token_ids += [tokenizer._token_end_id]
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# token_ids += (q_token_ids[1:] + p_token_ids[1:])
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# segment_ids = [0] * len(token_ids)
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# batch_token_ids.append(token_ids)
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# batch_segment_ids.append(segment_ids)
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# batch_a_token_ids.append(a_token_ids[1:])
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# if len(batch_token_ids) == self.batch_size or is_end:
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# batch_token_ids = sequence_padding(batch_token_ids)
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# batch_segment_ids = sequence_padding(batch_segment_ids)
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# batch_a_token_ids = sequence_padding(
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# batch_a_token_ids, max_a_len
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# )
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# yield [batch_token_ids, batch_segment_ids], batch_a_token_ids
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# batch_token_ids, batch_segment_ids, batch_a_token_ids = [], [], []
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def masked_cross_entropy(y_true, y_pred):
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"""交叉熵作为loss，并mask掉padding部分的预测
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"""
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y_true = K.reshape(y_true, [K.shape(y_true)[0], -1])
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y_mask = K.cast(K.not_equal(y_true, 0), K.floatx())
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cross_entropy = K.sparse_categorical_crossentropy(y_true, y_pred)
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cross_entropy = K.sum(cross_entropy * y_mask) / K.sum(y_mask)
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return cross_entropy
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def build_reading_model(config_path: str, ckpt_path: str, keep_tokens: str, weight_path: str):
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model = build_transformer_model(
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config_path,
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ckpt_path,
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with_mlm=True,
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keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表
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)
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output = Lambda(lambda x: x[:, 1:max_a_len + 1])(model.output)
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model = Model(model.input, output)
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model.compile(loss=masked_cross_entropy, optimizer=Adam(1e-5))
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model.load_weights(weight_path)
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return model
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def get_ngram_set(x, n):
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"""生成ngram合集，返回结果格式是:
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{(n-1)-gram: set([n-gram的第n个字集合])}
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"""
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result = {}
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for i in range(len(x) - n + 1):
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k = tuple(x[i:i + n])
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if k[:-1] not in result:
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result[k[:-1]] = set()
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result[k[:-1]].add(k[-1])
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return result
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def gen_answer(question, passages, model, tokenizer):
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"""由于是MLM模型，所以可以直接argmax解码。
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"""
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all_p_token_ids, token_ids, segment_ids = [], [], []
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for passage in passages:
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passage = re.sub(u' |、|；|，', ',', passage)
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p_token_ids, _ = tokenizer.encode(passage, maxlen=max_p_len + 1)
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q_token_ids, _ = tokenizer.encode(question, maxlen=max_q_len + 1)
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all_p_token_ids.append(p_token_ids[1:])
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token_ids.append([tokenizer._token_start_id])
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token_ids[-1] += ([tokenizer._token_mask_id] * max_a_len)
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token_ids[-1] += [tokenizer._token_end_id]
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token_ids[-1] += (q_token_ids[1:] + p_token_ids[1:])
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segment_ids.append([0] * len(token_ids[-1]))
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token_ids = sequence_padding(token_ids)
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segment_ids = sequence_padding(segment_ids)
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probas = model.predict([token_ids, segment_ids])
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results = {}
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for t, p in zip(all_p_token_ids, probas):
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a, score = tuple(), 0.
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for i in range(max_a_len):
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idxs = list(get_ngram_set(t, i + 1)[a])
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if tokenizer._token_end_id not in idxs:
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idxs.append(tokenizer._token_end_id)
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# pi是将passage以外的token的概率置零
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pi = np.zeros_like(p[i])
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pi[idxs] = p[i, idxs]
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a = a + (pi.argmax(),)
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score += pi.max()
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if a[-1] == tokenizer._token_end_id:
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break
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score = score / (i + 1)
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a = tokenizer.decode(a)
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if a:
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results[a] = results.get(a, []) + [score]
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results = {
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k: (np.array(v) ** 2).sum() / (sum(v) + 1)
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for k, v in results.items()
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}
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return results
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def max_in_dict(d):
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if d:
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return sorted(d.items(), key=lambda s: -s[1])[0][0]
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# def predict_to_file(data, filename):
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# """将预测结果输出到文件，方便评估
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# """
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# with open(filename, 'w', encoding='utf-8') as f:
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# for d in tqdm(iter(data), desc=u'正在预测(共%s条样本)' % len(data)):
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# q_text = d['question']
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# p_texts = [p['passage'] for p in d['passages']]
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# a = gen_answer(q_text, p_texts)
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# a = max_in_dict(a)
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# if a:
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# s = u'%s\t%s\n' % (d['id'], a)
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# else:
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# s = u'%s\t\n' % (d['id'])
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# f.write(s)
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# f.flush()
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# class Evaluator(keras.callbacks.Callback):
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# """评估与保存
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# """
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# def __init__(self):
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# self.lowest = 1e10
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# def on_epoch_end(self, epoch, logs=None):
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# # 保存最优
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# if logs['loss'] <= self.lowest:
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# self.lowest = logs['loss']
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# model.save_weights('../models/qa/best_model.weights')
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if __name__ == '__main__':
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model = build_reading_model()
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model.load_weights('../models/qa/best_model.weights')
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questions = "嬴政出生在哪里？"
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passages = [
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"秦始皇嬴政（前259年—前210年），嬴姓，赵氏 ，名政（一说名“正”），又称赵政 、祖龙 ，也有吕政一说（详见“人物争议-姓名之争”目录）。秦庄襄王和赵姬之子。中国古代杰出的政治家、战略家、改革家，首次完成中国大一统的政治人物，也是中国第一个称皇帝的君主。",
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"公元前221年，秦统一六国之后，秦王嬴政认为自己“德兼三皇，功过五帝”，遂采用三皇之“皇”、五帝之“帝”构成“皇帝”的称号，是中国历史上第一个使用“皇帝”称号的君主，所以自称“始皇帝”。",
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"秦始皇有二十余子。长子扶苏，少子胡亥。",
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"嬴政出生在当时赵国的邯郸廓城（在今城内中街以东，丛台西南的朱家巷一带），是当时的秦国王孙异人之子。"]
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print(gen_answer(questions, passages))
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