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nsp_bert_classification.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# __author__ = "Sponge_sy"
# Date: 2021/6/30
import numpy
from tqdm import tqdm
from sklearn import metrics
from bert4keras.tokenizers import Tokenizer
from bert4keras.models import build_transformer_model
from bert4keras.snippets import sequence_padding, DataGenerator
from utils import *
from hyper_parameters import *
class data_generator(DataGenerator):
"""Data Generator"""
def __init__(self, pattern="", is_pre=True, *args, **kwargs):
super(data_generator, self).__init__(*args, **kwargs)
self.pattern = pattern
self.is_pre = is_pre
def __iter__(self, random=False):
batch_token_ids, batch_segment_ids, batch_output_ids = [], [], []
for is_end, (text, label) in self.sample(random):
if (self.is_pre):
token_ids, segment_ids = tokenizer.encode(first_text=self.pattern, second_text=text, maxlen=maxlen)
else:
token_ids, segment_ids = tokenizer.encode(first_text=text, second_text=self.pattern, maxlen=maxlen)
source_ids, target_ids = token_ids[:], token_ids[:]
batch_token_ids.append(source_ids)
batch_segment_ids.append(segment_ids)
batch_output_ids.append(target_ids)
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_output_ids = sequence_padding(batch_output_ids)
yield [batch_token_ids, batch_segment_ids, batch_output_ids], None
batch_token_ids, batch_segment_ids, batch_output_ids = [], [], []
def evaluate(data_generator_list, data, note=""):
print("\n*******************Start to Zero-Shot predict on 【{}】*******************".format(note), flush=True)
patterns_logits = [[] for _ in patterns]
for i in range(len(data_generator_list)):
print("\nPattern{}".format(i), flush=True)
data_generator = data_generator_list[i]
counter = 0
for (x, _) in tqdm(data_generator):
outputs = model.predict(x[:2])
for out in outputs:
logit_pos = out[0].T
patterns_logits[i].append(logit_pos)
counter += 1
# for i, logits in enumerate(patterns_logits):
# print("******************************Class {}******************************".format(i))
# sorted_logits = sorted(logits, reverse=True)
# for l in sorted_logits:
# print("{}".format(l))
# Evaluate the results
trues = [d[1] for d in data]
preds = []
for i in range(len(patterns_logits[0])):
pred = numpy.argmax([logits[i] for logits in patterns_logits])
preds.append(int(pred))
# for i in range(len(patterns_logits[0])):
# ls = [logits[i] for logits in patterns_logits]
# print("True:{}, Pred:{}, Neg:{:.6f}, Pos:{:.6f}, Text:{}".format(trues[i], preds[i], ls[0], ls[1], data[i]))
confusion_matrix = metrics.confusion_matrix(trues, preds, labels=None, sample_weight=None)
print("Confusion Matrix:\n{}".format(confusion_matrix), flush=True)
if (dataset.metric == 'Matthews'):
matthews_corrcoef = metrics.matthews_corrcoef(trues, preds)
print("Matthews Corrcoef:\n{}".format(matthews_corrcoef), flush=True)
else:
acc = metrics.accuracy_score(trues, preds, normalize=True, sample_weight=None)
print("Acc.:\t{:.4f}".format(acc), flush=True)
return acc
if __name__ == "__main__":
# Load the hyper-parameters-----------------------------------------------------------
maxlen = 256 # The max length 128 is used in our paper
batch_size = 40 # Will not influence the results
# Choose a dataset----------------------------------------------------------------------
# For Chinese datasets
# dataset_names = ['eprstmt', 'tnews', 'csldcp', 'iflytek']
# For English datasets in KPT
# dataset_names = ['AGNews', 'DBPedia', 'IMDB', 'Amazon']
# For GLUE benchmark
# dataset_names = ['CoLA', 'SST-2']
# Others in LM-BFF
# dataset_names = ['SST-5', 'MR', 'CR', 'MPQA', 'Subj', 'TREC']
dataset_name = 'eprstmt'
# Choose a model----------------------------------------------------------------------
# Recommend to use 'uer-mixed-bert-base' and 'google-bert-cased'
# model_names = ['google-bert-uncased', 'google-bert-small', 'google-bert-cased', 'google-bert-cased-large',
# 'google-bert-wwm-large', 'google-bert-cased-wwm-large',
# 'google-bert-zh', 'hfl-bert-wwm', 'hfl-bert-wwm-ext',
# 'uer-mixed-bert-tiny', 'uer-mixed-bert-small',
# 'uer-mixed-bert-base', 'uer-mixed-bert-large']
model_name = MODEL_NAME[dataset_name]
# Load model and dataset class
bert_model = Model(model_name=model_name)
dataset = Datasets(dataset_name=dataset_name)
# Choose a template [0, 1, 2]--------------------------------------------------------
patterns = dataset.patterns[PATTERN_INDEX[dataset_name]]
# Prefix or Suffix-------------------------------------------------------------------
is_pre = IS_PRE[dataset_name]
# Load the dev set--------------------------------------------------------------------
# -1 for all the samples
dev_data = dataset.load_data(dataset.dev_path, sample_num=-1, is_shuffle=True)
dev_data = sample_dataset(dev_data, K_SHOT[dataset_name])
dev_generator_list = []
for p in patterns:
dev_generator_list.append(data_generator(pattern=p, is_pre=is_pre, data=dev_data, batch_size=batch_size))
# Load the test set--------------------------------------------------------------------
# -1 for all the samples
test_data = dataset.load_data(dataset.test_path, sample_num=-1, is_shuffle=True)
test_generator_list = []
for p in patterns:
test_generator_list.append(data_generator(pattern=p, is_pre=is_pre, data=test_data, batch_size=batch_size))
# Build BERT model---------------------------------------------------------------------
tokenizer = Tokenizer(bert_model.dict_path, do_lower_case=True)
# Load BERT model with NSP head
model = build_transformer_model(
config_path=bert_model.config_path,
checkpoint_path=bert_model.checkpoint_path,
with_nsp=True,
)
# Zero-Shot predict and evaluate-------------------------------------------------------
evaluate(dev_generator_list, dev_data, note="Dev Set")
evaluate(test_generator_list, test_data, note="Test Set")