wehub-resource-sync
109 lines
4.3 KiB
Python
109 lines
4.3 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import random
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from functools import partial
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import numpy as np
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import paddle
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from data import convert_example, create_dataloader, read_custom_data
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from model import TextCNNModel
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from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab
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from paddlenlp.datasets import load_dataset
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--epochs", type=int, default=10, help="Number of epoches for training.")
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parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--lr", type=float, default=5e-5, help="Learning rate used to train.")
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parser.add_argument("--save_dir", type=str, default='checkpoints/', help="Directory to save model checkpoint")
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parser.add_argument("--data_path", type=str, default='./RobotChat', help="The path of datasets to be loaded")
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parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number of a batch for training.")
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parser.add_argument("--vocab_path", type=str, default="./robot_chat_word_dict.txt", help="The directory to dataset.")
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parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
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args = parser.parse_args()
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# yapf: enable
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def set_seed(seed=1000):
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"""Sets random seed."""
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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if __name__ == "__main__":
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paddle.set_device(args.device)
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set_seed()
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# Load vocab.
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if not os.path.exists(args.vocab_path):
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raise RuntimeError("The vocab_path can not be found in the path %s" % args.vocab_path)
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vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
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# Load datasets.
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dataset_names = ["train.tsv", "dev.tsv", "test.tsv"]
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train_ds, dev_ds, test_ds = [
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load_dataset(read_custom_data, filename=os.path.join(args.data_path, dataset_name), lazy=False)
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for dataset_name in dataset_names
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]
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tokenizer = JiebaTokenizer(vocab)
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trans_fn = partial(convert_example, tokenizer=tokenizer)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=vocab.token_to_idx.get("[PAD]", 0)), Stack(dtype="int64") # label
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): [data for data in fn(samples)]
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train_loader = create_dataloader(
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train_ds, batch_size=args.batch_size, mode="train", batchify_fn=batchify_fn, trans_fn=trans_fn
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)
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dev_loader = create_dataloader(
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dev_ds, batch_size=args.batch_size, mode="validation", batchify_fn=batchify_fn, trans_fn=trans_fn
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)
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test_loader = create_dataloader(
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test_ds, batch_size=args.batch_size, mode="test", batchify_fn=batchify_fn, trans_fn=trans_fn
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)
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label_map = {0: "negative", 1: "neutral", 2: "positive"}
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vocab_size = len(vocab)
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num_classes = len(label_map)
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pad_token_id = vocab.to_indices("[PAD]")
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model = TextCNNModel(vocab_size, num_classes, padding_idx=pad_token_id, ngram_filter_sizes=(1, 2, 3))
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if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
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state_dict = paddle.load(args.init_from_ckpt)
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model.set_dict(state_dict)
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model = paddle.Model(model)
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optimizer = paddle.optimizer.Adam(parameters=model.parameters(), learning_rate=args.lr)
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# Define loss and metric.
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criterion = paddle.nn.CrossEntropyLoss()
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metric = paddle.metric.Accuracy()
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model.prepare(optimizer, criterion, metric)
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# Start training and evaluating.
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callback = paddle.callbacks.ProgBarLogger(log_freq=10, verbose=3)
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model.fit(train_loader, dev_loader, epochs=args.epochs, save_dir=args.save_dir, callbacks=callback)
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# Evaluate on test dataset
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print("Start to evaluate on test dataset...")
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model.evaluate(test_loader, log_freq=len(test_loader))
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