# Construct transfer learning network
# Use token-level output.
query = outputs["emb"]
title = outputs['emb_2']
# Select fine-tune strategy
strategy = hub.DefaultStrategy(optimizer_name="sgd",
learning_rate=args.learning_rate)
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(eval_interval=300,
use_data_parallel=False,
use_cuda=False,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a text matching task by PaddleHub's API
# network choice: bow, cnn, gru, lstm (PaddleHub pre-defined network)
matching_task = hub.PointwiseTextMatchingTask(dataset=dataset,
query_feature=query,
title_feature=title,
tokenizer=tokenizer,
network=args.network,
config=config)
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
matching_task.finetune_and_eval()
def main(type, cnf):
class SouhuCompetition(TextMatchingDataset):
def __init__(self, tokenizer=None, max_seq_len=None):
base_path = './data'
if type in ['ssA', 'slA', 'llA']:
train_file = 'data78383/{}_train.tsv'.format(type)
dev_file = 'data78383/{}_valid.tsv'.format(type)
elif type in ['ssB', 'slB', 'llB']:
train_file = 'data78384/{}_train.tsv'.format(type)
dev_file = 'data78384/{}_valid.tsv'.format(type)
super(SouhuCompetition, self).__init__(
is_pair_wise=False, # 文本匹配类型，是否为pairwise
base_path=base_path,
train_file=train_file, # 相对于base_path的文件路径
dev_file=dev_file, # 相对于base_path的文件路径
train_file_with_header=True,
dev_file_with_header=True,
label_list=["0", "1"],
tokenizer=tokenizer,
max_seq_len=max_seq_len)
module = hub.Module(name="ernie")
# pointwise任务需要: query, title_left (2 slots)
inputs, outputs, program = module.context(trainable=True,
max_seq_len=cnf.max_seq_len,
num_slots=2)
tokenizer = hub.BertTokenizer(vocab_file=module.get_vocab_path(),
tokenize_chinese_chars=True)
dataset = SouhuCompetition(tokenizer=tokenizer,
max_seq_len=cnf.max_seq_len)
strategy = hub.AdamWeightDecayStrategy(weight_decay=0.01,
warmup_proportion=0.1,
learning_rate=1e-5)
config = hub.RunConfig(
eval_interval=300,
use_cuda=True,
num_epoch=10,
batch_size=cnf.train_and_eval_batch,
checkpoint_dir='./ckpt_ernie_pointwise_matching_{}'.format(type),
strategy=strategy)
# 构建迁移网络，使用ernie的token-level输出
query = outputs["sequence_output"]
title = outputs['sequence_output_2']
# 创建pointwise文本匹配任务
pointwise_matching_task = hub.PointwiseTextMatchingTask(
dataset=dataset,
query_feature=query,
title_feature=title,
tokenizer=tokenizer,
config=config)
run_states = pointwise_matching_task.finetune_and_eval()
# # 预测数据样例
# text_pairs = [
# [
# "小孩吃了百令胶囊能打预防针吗", # query
# "小孩吃了百令胶囊能不能打预防针", # title
# ],
# [
# "请问呕血与咯血有什么区别?", # query
# "请问呕血与咯血异同？", # title
# ]
# ]
save_df = pd.DataFrame(columns=['id', 'label'])
def predict(text_pairs):
results = pointwise_matching_task.predict(
data=text_pairs,
max_seq_len=cnf.max_seq_len,
label_list=dataset.get_labels(),
return_result=True,
accelerate_mode=False)
return results
if type in ['ssA', 'slA', 'llA']:
test_file = './data/data78383/{}_test.tsv'.format(type)
elif type in ['ssB', 'slB', 'llB']:
test_file = './data/data78384/{}_test.tsv'.format(type)
test_df = pd.read_csv(test_file, sep='\t')
test_df.columns = ['text_a', 'text_b', 'id']
text_pairs = []
ids = []
for index, row in test_df.iterrows():
text_pairs.append([row['text_a'], row['text_b']])
ids.append(row['id'])
if len(text_pairs) == cnf.test_batch:
results = predict(text_pairs)
for i in range(len(ids)):
new = pd.DataFrame({
'id': ids[i],
'label': results[i]
},
index=[0])
save_df = save_df.append(new, ignore_index=True)
text_pairs = []
ids = []
if len(text_pairs) != 0:
results = predict(text_pairs)
for i in range(len(ids)):
new = pd.DataFrame({'id': ids[i], 'label': results[i]}, index=[0])
save_df = save_df.append(new, ignore_index=True)
save_df.to_csv('./results/{}.csv'.format(type),
header=True,
sep=',',
index=False)
query = outputs["pooled_output"]
left = outputs['pooled_output_2']
# Select fine-tune strategy
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(eval_interval=300,
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# Define a pointwise text matching task by PaddleHub's API
# network choice: bow, cnn, gru, lstm (PaddleHub pre-defined network)
pointwise_matching_task = hub.PointwiseTextMatchingTask(
dataset=dataset,
query_feature=query,
title_feature=left,
tokenizer=tokenizer,
config=config)
# Fine-tune and evaluate by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
pointwise_matching_task.finetune_and_eval()