]
# Setup runing config for PaddleHub Finetune API
config = hub.RunConfig(
use_data_parallel=False,
use_pyreader=args.use_pyreader,
use_cuda=args.use_gpu,
batch_size=args.batch_size,
enable_memory_optim=False,
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a sequence labeling finetune task by PaddleHub's API
seq_label_task = hub.SequenceLabelTask(data_reader=reader,
feature=sequence_output,
feed_list=feed_list,
max_seq_len=args.max_seq_len,
num_classes=dataset.num_labels,
config=config)
# test data
data = [
["我们变而以书会友，以书结缘，把欧美、港台流行的食品类图谱、画册、工具书汇集一堂。"],
["为了跟踪国际最新食品工艺、流行趋势，大量搜集海外专业书刊资料是提高技艺的捷径。"],
["其中线装古籍逾千册；民国出版物几百种；珍本四册、稀见本四百余册，出版时间跨越三百余年。"],
["有的古木交柯，春机荣欣，从诗人句中得之，而入画中，观之令人心驰。"],
["不过重在晋趣，略增明人气息，妙在集古有道、不露痕迹罢了。"],
]
run_states = seq_label_task.predict(data=data)
results = [run_state.run_results for run_state in run_states]
# Use "sequence_output" for token-level output.
sequence_output = outputs["sequence_output"]
# Setup RunConfig for PaddleHub Fine-tune API
config = hub.RunConfig(
use_data_parallel=False,
use_cuda=args.use_gpu,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
# Define a sequence labeling fine-tune task by PaddleHub's API
# if add crf, the network use crf as decoder
seq_label_task = hub.SequenceLabelTask(
feature=sequence_output,
max_seq_len=args.max_seq_len,
num_classes=num_classes,
config=config,
add_crf=False)
# Data to be predicted
text_a = [
"我们变而以书会友，以书结缘，把欧美、港台流行的食品类图谱、画册、工具书汇集一堂。",
"为了跟踪国际最新食品工艺、流行趋势，大量搜集海外专业书刊资料是提高技艺的捷径。",
"其中线装古籍逾千册；民国出版物几百种；珍本四册、稀见本四百余册，出版时间跨越三百余年。",
"有的古木交柯，春机荣欣，从诗人句中得之，而入画中，观之令人心驰。",
"不过重在晋趣，略增明人气息，妙在集古有道、不露痕迹罢了。",
]
# Add 0x02 between characters to match the format of training data,
# otherwise the length of prediction results will not match the input string
# if the input string contains non-Chinese characters.
def main():
# Load Paddlehub pretrained model
# 更多预训练模型 https://www.paddlepaddle.org.cn/hublist?filter=en_category&value=SemanticModel
#model_name = "ernie_tiny"
model_name = "chinese-roberta-wwm-ext-large"
module = hub.Module(name=model_name)
inputs, outputs, program = module.context(trainable=True,
max_seq_len=args.max_seq_len)
# Download dataset and use SequenceLabelReader to read dataset
dataset = EEDataset(args.data_dir, schema_labels, model=args.do_model)
reader = hub.reader.SequenceLabelReader(
dataset=dataset,
vocab_path=module.get_vocab_path(),
max_seq_len=args.max_seq_len,
sp_model_path=module.get_spm_path(),
word_dict_path=module.get_word_dict_path())
# Construct transfer learning network
# Use "sequence_output" for token-level output.
sequence_output = outputs["sequence_output"]
# Setup feed list for data feeder
# Must feed all the tensor of module need
feed_list = [
inputs["input_ids"].name, inputs["position_ids"].name,
inputs["segment_ids"].name, inputs["input_mask"].name
]
# Select a finetune strategy
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# Setup runing config for PaddleHub Finetune API
config = hub.RunConfig(eval_interval=args.eval_step,
save_ckpt_interval=args.model_save_step,
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 sequence labeling finetune task by PaddleHub's API
# If add crf, the network use crf as decoder
seq_label_task = hub.SequenceLabelTask(data_reader=reader,
feature=sequence_output,
feed_list=feed_list,
max_seq_len=args.max_seq_len,
num_classes=dataset.num_labels,
config=config,
add_crf=args.add_crf)
# Finetune and evaluate model by PaddleHub's API
# will finish training, evaluation, testing, save model automatically
if args.do_train:
print("start finetune and eval process")
seq_label_task.finetune_and_eval()
if args.do_predict:
print("start predict process")
ret = []
id2label = {val: key for key, val in reader.label_map.items()}
input_data = [[d] for d in predict_data]
run_states = seq_label_task.predict(data=input_data[1:])
results = []
for batch_states in run_states:
batch_results = batch_states.run_results
batch_infers = batch_results[0].reshape([-1]).astype(
np.int32).tolist()
seq_lens = batch_results[1].reshape([-1]).astype(np.int32).tolist()
current_id = 0
for length in seq_lens:
seq_infers = batch_infers[current_id:current_id + length]
seq_result = list(map(id2label.get, seq_infers[1:-1]))
current_id += length if args.add_crf else args.max_seq_len
results.append(seq_result)
ret = []
for sent, r_label in zip(predict_sents, results):
sent["labels"] = r_label
ret.append(json.dumps(sent, ensure_ascii=False))
write_by_lines("{}.{}.pred".format(args.predict_data, args.do_model),
ret)
def get_task(args, schema_labels, id):
# 加载PaddleHub 预训练模型ERNIE Tiny/RoBERTa large
# 更多预训练模型 https://www.paddlepaddle.org.cn/hublist?filter=en_category&value=SemanticModel
# model_name = "ernie_tiny"
model_name = "chinese-roberta-wwm-ext-large"
module = hub.Module(name=model_name)
inputs, outputs, program = module.context(trainable=True,
max_seq_len=args.max_seq_len)
# 加载数据并通过SequenceLabelReader读取数据
dataset = EEDataset(args.data_dir, schema_labels, model=args.do_model)
reader = SequenceLabelReader(dataset=dataset,
vocab_path=module.get_vocab_path(),
max_seq_len=args.max_seq_len,
sp_model_path=module.get_spm_path(),
word_dict_path=module.get_word_dict_path())
# 构建序列标注任务迁移网络
# 使用ERNIE模型字级别的输出sequence_output作为迁移网络的输入
sequence_output = outputs["sequence_output"]
# sequence_output = fluid.layers.dropout(
# x=sequence_output ,
# dropout_prob=args.dropout,
# dropout_implementation="upscale_in_train")
# 设置模型program需要输入的变量feed_list
# 必须按照以下顺序设置
feed_list = [
inputs["input_ids"].name, inputs["position_ids"].name,
inputs["segment_ids"].name, inputs["input_mask"].name
]
# 选择优化策略
strategy = hub.AdamWeightDecayStrategy(
warmup_proportion=args.warmup_proportion,
weight_decay=args.weight_decay,
learning_rate=args.learning_rate)
# 配置运行设置
config = hub.RunConfig(
log_interval=100,
eval_interval=args.eval_step,
save_ckpt_interval=args.model_save_step,
use_data_parallel=args.use_data_parallel,
use_cuda=args.use_gpu,
# enable_memory_optim=True,
num_epoch=args.num_epoch,
batch_size=args.batch_size,
checkpoint_dir=args.checkpoint_dir,
strategy=strategy)
# 构建序列标注迁移任务
seq_label_task = hub.SequenceLabelTask(data_reader=reader,
feature=sequence_output,
feed_list=feed_list,
max_seq_len=args.max_seq_len,
num_classes=dataset.num_labels,
config=config,
add_crf=args.add_crf)
seq_label_task.main_program.random_seed = args.random_seed
add_hook(args, seq_label_task, id)
return seq_label_task, reader