run_ernie_crf.py

# Copyright (c) 2021 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.

import argparse
import os
from functools import partial

import paddle
from paddlenlp.data import Stack, Tuple, Pad
from paddlenlp.transformers import ErnieTokenizer, ErnieForTokenClassification
from paddlenlp.metrics import ChunkEvaluator

from model import ErnieCrfForTokenClassification
from data import load_dict, load_dataset, parse_decodes

parser = argparse.ArgumentParser()

# yapf: disable
parser.add_argument("--save_dir", default='./ernie_crf_ckpt', type=str, help="The output directory where the model checkpoints will be written.")
parser.add_argument("--epochs", default=10, type=int, help="Total number of training epochs to perform.")
parser.add_argument("--batch_size", default=200, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--device", default="gpu", type=str, choices=["cpu", "gpu"] ,help="The device to select to train the model, is must be cpu/gpu.")
parser.add_argument("--data_dir", default='./waybill_ie/data', type=str, help="The folder where the dataset is located.")

args = parser.parse_args()
# yapf: enable


def convert_to_features(example, tokenizer, label_vocab):
    tokens, labels = example
    tokenized_input = tokenizer(
        tokens, return_length=True, is_split_into_words=True)
    # Token '[CLS]' and '[SEP]' will get label 'O'
    labels = ['O'] + labels + ['O']
    tokenized_input['labels'] = [label_vocab[x] for x in labels]
    return tokenized_input['input_ids'], tokenized_input[
        'token_type_ids'], tokenized_input['seq_len'], tokenized_input['labels']


@paddle.no_grad()
def evaluate(model, metric, data_loader):
    model.eval()
    metric.reset()
    for input_ids, seg_ids, lens, labels in data_loader:
        preds = model(input_ids, seg_ids, lengths=lens)
        n_infer, n_label, n_correct = metric.compute(lens, preds, labels)
        metric.update(n_infer.numpy(), n_label.numpy(), n_correct.numpy())
        precision, recall, f1_score = metric.accumulate()
    print("[EVAL] Precision: %f - Recall: %f - F1: %f" %
          (precision, recall, f1_score))
    model.train()


@paddle.no_grad()
def predict(model, data_loader, ds, label_vocab):
    all_preds = []
    all_lens = []
    for input_ids, seg_ids, lens, labels in data_loader:
        preds = model(input_ids, seg_ids, lengths=lens)
        # Drop CLS prediction
        preds = [pred[1:] for pred in preds.numpy()]
        all_preds.append(preds)
        all_lens.append(lens)
    sentences = [example[0] for example in ds.data]
    results = parse_decodes(sentences, all_preds, all_lens, label_vocab)
    return results


if __name__ == '__main__':
    paddle.set_device(args.device)

    # Create dataset, tokenizer and dataloader.
    train_ds, dev_ds, test_ds = load_dataset(
        datafiles=(os.path.join(args.data_dir, 'train.txt'),
                   os.path.join(args.data_dir, 'dev.txt'),
                   os.path.join(args.data_dir, 'test.txt')))

    label_vocab = load_dict(os.path.join(args.data_dir, 'tag.dic'))
    tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')

    trans_func = partial(
        convert_to_features, tokenizer=tokenizer, label_vocab=label_vocab)

    train_ds.map(trans_func)
    dev_ds.map(trans_func)
    test_ds.map(trans_func)

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype='int32'),  # input_ids
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype='int32'),  # token_type_ids
        Stack(dtype='int64'),  # seq_len
        Pad(axis=0, pad_val=label_vocab.get("O", 0), dtype='int64')  # labels
    ): fn(samples)

    train_loader = paddle.io.DataLoader(
        dataset=train_ds,
        batch_size=args.batch_size,
        return_list=True,
        collate_fn=batchify_fn)
    dev_loader = paddle.io.DataLoader(
        dataset=dev_ds,
        batch_size=args.batch_size,
        return_list=True,
        collate_fn=batchify_fn)
    test_loader = paddle.io.DataLoader(
        dataset=test_ds,
        batch_size=args.batch_size,
        return_list=True,
        collate_fn=batchify_fn)

    # Define the model netword and its loss
    ernie = ErnieForTokenClassification.from_pretrained(
        "ernie-1.0", num_classes=len(label_vocab))
    model = ErnieCrfForTokenClassification(ernie)

    metric = ChunkEvaluator(label_list=label_vocab.keys(), suffix=True)
    optimizer = paddle.optimizer.AdamW(
        learning_rate=2e-5, parameters=model.parameters())

    step = 0
    for epoch in range(args.epochs):
        for input_ids, token_type_ids, lengths, labels in train_loader:
            loss = model(
                input_ids, token_type_ids, lengths=lengths, labels=labels)
            avg_loss = paddle.mean(loss)
            avg_loss.backward()
            optimizer.step()
            optimizer.clear_grad()
            step += 1
            print("[TRAIN] Epoch:%d - Step:%d - Loss: %f" %
                  (epoch, step, avg_loss))
        evaluate(model, metric, dev_loader)

        paddle.save(model.state_dict(),
                    os.path.join(args.save_dir, 'model_%d' % step))

    preds = predict(model, test_loader, test_ds, label_vocab)
    file_path = "ernie_crf_results.txt"
    with open(file_path, "w", encoding="utf8") as fout:
        fout.write("\n".join(preds))
    # Print some examples
    print(
        "The results have been saved in the file: %s, some examples are shown below: "
        % file_path)
    print("\n".join(preds[:10]))