diff --git a/demo/multi-label-classification/multi_label_classifier.py b/demo/multi-label-classification/multi_label_classifier.py index a3e38cb92569e930eed4b9cc88a8dba51143f11f..57e47c6dc18f9ef15f841083aa788c89beaaafda 100644 --- a/demo/multi-label-classification/multi_label_classifier.py +++ b/demo/multi-label-classification/multi_label_classifier.py @@ -45,8 +45,7 @@ if __name__ == '__main__': # Setup feed list for data feeder feed_list = [ inputs["input_ids"].name, inputs["position_ids"].name, - inputs["segment_ids"].name, inputs["input_mask"].name, - inputs["task_ids"].name + inputs["segment_ids"].name, inputs["input_mask"].name ] else: module = hub.Module(name="bert_uncased_L-12_H-768_A-12") @@ -68,8 +67,7 @@ if __name__ == '__main__': reader = hub.reader.MultiLabelClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path(), - max_seq_len=args.max_seq_len, - use_task_id=args.use_taskid) + max_seq_len=args.max_seq_len) # Construct transfer learning network # Use "pooled_output" for classification tasks on an entire sentence. diff --git a/demo/multi-label-classification/predict.py b/demo/multi-label-classification/predict.py index 6a6ef240e0edfa69d44b1fe3bdbd3eceb5d0a602..2afcbd319271ab0712f79e2fe2386c963a2a71d0 100644 --- a/demo/multi-label-classification/predict.py +++ b/demo/multi-label-classification/predict.py @@ -50,9 +50,10 @@ if __name__ == '__main__': # Setup feed list for data feeder feed_list = [ - inputs["input_ids"].name, inputs["position_ids"].name, - inputs["segment_ids"].name, inputs["input_mask"].name, - inputs["task_ids"].name + inputs["input_ids"].name, + inputs["position_ids"].name, + inputs["segment_ids"].name, + inputs["input_mask"].name, ] else: module = hub.Module(name="bert_uncased_L-12_H-768_A-12") @@ -74,8 +75,7 @@ if __name__ == '__main__': reader = hub.reader.MultiLabelClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path(), - max_seq_len=args.max_seq_len, - use_task_id=args.use_taskid) + max_seq_len=args.max_seq_len) # Construct transfer learning network # Use "pooled_output" for classification tasks on an entire sentence. diff --git a/demo/regression/regression.py b/demo/regression/regression.py index 69808e178cdc8263a29cedd88bb612144ec62430..ddba625955f4151154f7376bda865974d0a314f1 100644 --- a/demo/regression/regression.py +++ b/demo/regression/regression.py @@ -55,8 +55,7 @@ if __name__ == '__main__': reader = hub.reader.RegressionReader( dataset=dataset, vocab_path=module.get_vocab_path(), - max_seq_len=args.max_seq_len, - use_task_id=args.use_taskid) + max_seq_len=args.max_seq_len) # Construct transfer learning network # Use "pooled_output" for classification tasks on an entire sentence. @@ -72,9 +71,6 @@ if __name__ == '__main__': inputs["input_mask"].name, ] - if args.use_taskid: - feed_list.append(inputs["task_ids"].name) - # Select finetune strategy, setup config and finetune strategy = hub.AdamWeightDecayStrategy( weight_decay=args.weight_decay, diff --git a/demo/sequence-labeling/sequence_label.py b/demo/sequence-labeling/sequence_label.py index 673f2ccf2f106a9f0fb488e05feb2aafdf1ab305..94dc27a2cf2570270b2196a5b91a371b37230ab5 100644 --- a/demo/sequence-labeling/sequence_label.py +++ b/demo/sequence-labeling/sequence_label.py @@ -51,7 +51,6 @@ if __name__ == '__main__': dataset=dataset, vocab_path=module.get_vocab_path(), max_seq_len=args.max_seq_len, - use_task_id=use_taskid, sp_model_path=module.get_spm_path(), word_dict_path=module.get_word_dict_path()) diff --git a/demo/text-classification/README.md b/demo/text-classification/README.md index a48a59ae86275ed28b1a8d8c16746a6f3f5efe68..bf8dd53024962d30f119b16c92c94a1b68697304 100644 --- a/demo/text-classification/README.md +++ b/demo/text-classification/README.md @@ -40,7 +40,6 @@ --max_seq_len: ERNIE/BERT模型使用的最大序列长度，最大不能超过512, 若出现显存不足，请适当调低这一参数 --use_data_parallel: 是否使用并行计算，默认False。打开该功能依赖nccl库。 --use_pyreader: 是否使用pyreader，默认False。 ---use_taskid: 是否使用taskid，taskid是ERNIE 2.0特有的，use_taskid=True表示使用ERNIE 2.0；如果想使用ERNIE 1.0 或者BERT等module，use_taskid应该设置为False。 # 任务相关 --checkpoint_dir: 模型保存路径，PaddleHub会自动保存验证集上表现最好的模型 @@ -86,8 +85,7 @@ dataset = hub.dataset.ChnSentiCorp() reader = hub.reader.ClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path(), - max_seq_len=128, - use_task_id=False) + max_seq_len=128) metrics_choices = ["acc"] ``` @@ -99,8 +97,6 @@ metrics_choices = ["acc"] `max_seq_len` 需要与Step1中context接口传入的序列长度保持一致 -`use_task_id` 表示是否使用ERNIR 2.0 module - ClassifyReader中的`data_generator`会自动按照模型对应词表对数据进行切词，以迭代器的方式返回ERNIE/BERT所需要的Tensor格式，包括`input_ids`，`position_ids`，`segment_id`与序列对应的mask `input_mask`. **NOTE**: Reader返回tensor的顺序是固定的，默认按照input_ids, position_ids, segment_id, input_mask这一顺序返回。 diff --git a/demo/text-classification/predict.py b/demo/text-classification/predict.py index 1732192043d6f48f9727622d4b0881123b0ab1e4..10f14be46aeeb23b991ea5118e6bcd299c75ce35 100644 --- a/demo/text-classification/predict.py +++ b/demo/text-classification/predict.py @@ -130,8 +130,7 @@ if __name__ == '__main__': reader = hub.reader.ClassifyReader( dataset=dataset, vocab_path=module.get_vocab_path(), - max_seq_len=args.max_seq_len, - use_task_id=args.use_taskid) + max_seq_len=args.max_seq_len) # Construct transfer learning network # Use "pooled_output" for classification tasks on an entire sentence. @@ -147,9 +146,6 @@ if __name__ == '__main__': inputs["input_mask"].name, ] - if args.use_taskid: - feed_list.append(inputs["task_ids"].name) - # Setup runing config for PaddleHub Finetune API config = hub.RunConfig( use_data_parallel=False, diff --git a/demo/text-classification/text_classifier.py b/demo/text-classification/text_classifier.py index 0510f8b7819e097cb91b863dc438e550f26131f7..178efa9d67aa9e5e26f3570a8071347ca9db9204 100644 --- a/demo/text-classification/text_classifier.py +++ b/demo/text-classification/text_classifier.py @@ -121,27 +121,11 @@ if __name__ == '__main__': # Start preparing parameters for reader and task accoring to module # For ernie_v2, it has an addition embedding named task_id # For ernie_v2_chinese_tiny, it use an addition sentence_piece_vocab to tokenize - if module.name.startswith("ernie_v2"): - use_taskid = True - else: - use_taskid = False - inputs, outputs, program = module.context( trainable=True, max_seq_len=args.max_seq_len) # Construct transfer learning network # Use "pooled_output" for classification tasks on an entire sentence. # Use "sequence_output" for token-level output. - - check = [inputs["task_ids"].name] - global_block = program.global_block() - for op in global_block.ops: - for input_arg in op.input_arg_names: - for ch in check: - if ch in input_arg: - print(op) - check.append(input_arg) - break - exit(0) pooled_output = outputs["pooled_output"] # Setup feed list for data feeder @@ -152,8 +136,6 @@ if __name__ == '__main__': inputs["segment_ids"].name, inputs["input_mask"].name, ] - if use_taskid: - feed_list.append(inputs["task_ids"].name) # Finish preparing parameter for reader and task accoring to modul # Define reader @@ -161,7 +143,6 @@ if __name__ == '__main__': dataset=dataset, vocab_path=module.get_vocab_path(), max_seq_len=args.max_seq_len, - use_task_id=use_taskid, sp_model_path=module.get_spm_path(), word_dict_path=module.get_word_dict_path()) diff --git a/paddlehub/module/module.py b/paddlehub/module/module.py index 3b79d41dbd15bf3746cb9b1aa90b2e81618353e3..b54c67cb99382b8fa3a7ff863c7b59bbb85dae04 100644 --- a/paddlehub/module/module.py +++ b/paddlehub/module/module.py @@ -606,15 +606,11 @@ class Module(object): "task_ids" ] logger.warning( - "%s will exploite task_id, the arguement use_taskid of Reader class must be True." - % self.name) + "For %s, it's no necessary to feed task_ids." % self.name) else: feed_list = [ "input_ids", "position_ids", "segment_ids", "input_mask" ] - logger.warning( - "%s has no task_id, the arguement use_taskid of Reader class must be False." - % self.name) for tensor_name in feed_list: seq_tensor_shape = [-1, max_seq_len, 1] logger.info("The shape of input tensor[{}] set to {}".format(