move bert-cls to ernie-classification

demo/ernie-classification/finetune_with_hub.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Finetuning on classification tasks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import argparse
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+import paddle_hub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--num_epoch", type=int, default=3, help="Number of epoches for fine-tuning.")
+parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--hub_module_dir", type=str, default=None, help="PaddleHub module directory")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--data_dir", type=str, default=None, help="Path to training data.")
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--batch_size", type=int, default=32, help="Total examples' number in batch for training.")
+
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    strategy = hub.BERTFinetuneStrategy(
+        weight_decay=args.weight_decay,
+        learning_rate=args.learning_rate,
+        warmup_strategy="linear_warmup_decay",
+    )
+    config = hub.RunConfig(
+        use_cuda=True,
+        num_epoch=args.num_epoch,
+        batch_size=args.batch_size,
+        strategy=strategy)
+
+    # loading Paddlehub BERT
+    module = hub.Module(module_dir=args.hub_module_dir)
+
+    # Use BERTTokenizeReader to tokenize the dataset according to model's
+    # vocabulary
+    reader = hub.reader.BERTTokenizeReader(
+        dataset=hub.dataset.ChnSentiCorp(),  # download chnsenticorp dataset
+        vocab_path=module.get_vocab_path(),
+        max_seq_len=args.max_seq_len)
+
+    num_labels = len(reader.get_labels())
+
+    input_dict, output_dict, program = module.context(
+        sign_name="tokens", trainable=True, max_seq_len=args.max_seq_len)
+
+    with fluid.program_guard(program):
+        label = fluid.layers.data(name="label", shape=[1], dtype='int64')
+
+        # Use "pooled_output" for classification tasks on an entire sentence.
+        # Use "sequence_outputs" for token-level output.
+        pooled_output = output_dict["pooled_output"]
+
+        # Setup feed list for data feeder
+        # Must feed all the tensor of bert's module need
+        feed_list = [
+            input_dict["input_ids"].name, input_dict["position_ids"].name,
+            input_dict["segment_ids"].name, input_dict["input_mask"].name,
+            label.name
+        ]
+        # Define a classfication finetune task by PaddleHub's API
+        cls_task = hub.append_mlp_classifier(
+            pooled_output, label, num_classes=num_labels)
+
+        # Finetune and evaluate by PaddleHub's API
+        # will finish training, evaluation, testing, save model automatically
+        hub.finetune_and_eval(
+            task=cls_task,
+            data_reader=reader,
+            feed_list=feed_list,
+            config=config)

demo/ernie-classification/run_fintune_with_hub.sh

+export CUDA_VISIBLE_DEVICES=5
+
+DATA_PATH=./chnsenticorp_data
+
+
+HUB_MODULE_DIR="./hub_module/bert_chinese_L-12_H-768_A-12.hub_module"
+#HUB_MODULE_DIR="./hub_module/ernie_stable.hub_module"
+CKPT_DIR="./ckpt"
+#rm -rf $CKPT_DIR
+python -u finetune_with_hub.py \
+                   --batch_size 32 \
+                   --hub_module_dir=$HUB_MODULE_DIR \
+                   --data_dir ${DATA_PATH} \
+                   --weight_decay  0.01 \
+                   --checkpoint_dir $CKPT_DIR \
+                   --num_epoch 3 \
+                   --max_seq_len 128 \
+                   --learning_rate 5e-5