add senta finetune code

demo/senta/predict.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import numpy as np
+import os
+import time
+
+import paddle
+import paddle.fluid as fluid
+import paddlehub as hub
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+parser.add_argument("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
+args = parser.parse_args()
+# yapf: enable.
+
+if __name__ == '__main__':
+    # loading Paddlehub senta pretrained model
+    module = hub.Module(name="senta")
+    inputs, outputs, program = module.context(trainable=True)
+
+    # Sentence classification  dataset reader
+    dataset = hub.dataset.ChnSentiCorp()
+    reader = hub.reader.TextClassificationReader(
+        dataset=dataset, vocab_path=module.get_vocab_path())
+
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    with fluid.program_guard(program):
+        # Use "sequence_output" for classification tasks on an entire sentence.
+        # Use "sequence_outputs" for token-level output.
+        sequence_output = outputs["sequence_output"]
+
+        # Define a classfication finetune task by PaddleHub's API
+        cls_task = hub.create_text_cls_task(
+            feature=sequence_output, num_classes=dataset.num_labels)
+
+        # Setup feed list for data feeder
+        # Must feed all the tensor of senta's module need
+        feed_list = [inputs["words"].name, cls_task.variable('label').name]
+
+        # classificatin probability tensor
+        probs = cls_task.variable("probs")
+
+        pred = fluid.layers.argmax(probs, axis=1)
+
+        # load best model checkpoint
+        fluid.io.load_persistables(exe, args.checkpoint_dir)
+
+        inference_program = program.clone(for_test=True)
+
+        data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place)
+        test_reader = reader.data_generator(phase='test', shuffle=False)
+        test_examples = dataset.get_test_examples()
+        total = 0
+        correct = 0
+        for index, batch in enumerate(test_reader()):
+            pred_v = exe.run(
+                feed=data_feeder.feed(batch),
+                fetch_list=[pred.name],
+                program=inference_program)
+            total += 1
+            if (pred_v[0][0] == int(test_examples[index].label)):
+                correct += 1
+                acc = 1.0 * correct / total
+            print("%s\tpredict=%s" % (test_examples[index], pred_v[0][0]))
+        print("accuracy = %f" % acc)

demo/senta/run_classifier.sh

+export CUDA_VISIBLE_DEVICES=0
+
+# User can select chnsenticorp, nlpcc_dbqa, lcqmc for different task
+DATASET="chnsenticorp"
+CKPT_DIR="./ckpt_${DATASET}"
+
+python -u text_classifier.py \
+                   --batch_size=24 \
+                   --use_gpu=True \
+                   --checkpoint_dir=${CKPT_DIR} \
+                   --num_epoch=3

demo/senta/run_predict.sh

+export CUDA_VISIBLE_DEVICES=0
+
+CKPT_DIR="./ckpt_chnsenticorp/best_model"
+python -u predict.py --checkpoint_dir $CKPT_DIR --use_gpu True

demo/senta/text_classifier.py

+import argparse
+import ast
+
+import paddle.fluid as fluid
+import paddlehub 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("--use_gpu", type=ast.literal_eval, default=False, help="Whether use GPU for finetuning, input should be True or False")
+parser.add_argument("--checkpoint_dir", type=str, default=None, help="Directory to model checkpoint")
+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__':
+    # Step1: load Paddlehub senta pretrained model
+    module = hub.Module(name="senta")
+    inputs, outputs, program = module.context(trainable=True)
+
+    # Step2: Download dataset and use TextClassificationReader to read dataset
+    dataset = hub.dataset.ChnSentiCorp()
+
+    reader = hub.reader.TextClassificationReader(
+        dataset=dataset, vocab_path=module.get_vocab_path())
+
+    # Step3: construct transfer learning network
+    # Use "sequence_output" for classification tasks on an entire sentence.
+    # Use "sequence_output" for token-level output.
+    sequence_output = outputs["sequence_output"]
+
+    # Define a classfication finetune task by PaddleHub's API
+    cls_task = hub.create_text_cls_task(
+        feature=sequence_output, num_classes=dataset.num_labels)
+
+    # Setup feed list for data feeder
+    # Must feed all the tensor of senta's module need
+    feed_list = [inputs["words"].name, cls_task.variable('label').name]
+
+    # Setup runing config for PaddleHub Finetune API
+    config = hub.RunConfig(
+        use_cuda=args.use_gpu,
+        num_epoch=args.num_epoch,
+        batch_size=args.batch_size,
+        checkpoint_dir=args.checkpoint_dir,
+        strategy=hub.finetune.strategy.DefaultFinetuneStrategy())
+
+    # 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)