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- # 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 numpy as np
- import paddle
- import paddle.nn.functional as F
- from paddlenlp.data import Stack, Tuple, Pad
- from paddlenlp.datasets import load_dataset
- from paddlenlp.transformers import SkepCrfForTokenClassification, SkepModel, SkepTokenizer
-
- # yapf: disable
- parser = argparse.ArgumentParser()
- parser.add_argument("--params_path", type=str, required=True, help="The path to model parameters to be loaded.")
- parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. "
- "Sequences longer than this will be truncated, sequences shorter will be padded.")
- parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
- parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
- args = parser.parse_args()
- # yapf: enable
-
-
- def convert_example(example, tokenizer, max_seq_length=512, is_test=False):
- """
- Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
- by concatenating and adding special tokens. And creates a mask from the two sequences passed
- to be used in a sequence-pair classification task.
-
- A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence has the following format:
- ::
- - single sequence: ``[CLS] X [SEP]``
- - pair of sequences: ``[CLS] A [SEP] B [SEP]``
-
- A skep_ernie_1.0_large_ch/skep_ernie_2.0_large_en sequence pair mask has the following format:
- ::
-
- 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
- | first sequence | second sequence |
-
- If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
-
- Args:
- example(obj:`list[str]`): List of input data, containing text and label if it have label.
- tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
- which contains most of the methods. Users should refer to the superclass for more information regarding methods.
- max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
- Sequences longer than this will be truncated, sequences shorter will be padded.
-
- Returns:
- input_ids(obj:`list[int]`): The list of token ids.
- token_type_ids(obj: `list[int]`): List of sequence pair mask.
- """
- tokens = example["tokens"]
- encoded_inputs = tokenizer(
- tokens,
- return_length=True,
- is_split_into_words=True,
- max_seq_len=max_seq_length)
- input_ids = np.array(encoded_inputs["input_ids"], dtype="int64")
- token_type_ids = np.array(encoded_inputs["token_type_ids"], dtype="int64")
- seq_len = np.array(encoded_inputs["seq_len"], dtype="int64")
-
- return input_ids, token_type_ids, seq_len
-
-
- @paddle.no_grad()
- def predict(model, data_loader, label_map):
- """
- Given a prediction dataset, it gives the prediction results.
-
- Args:
- model(obj:`paddle.nn.Layer`): A model to classify texts.
- data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
- label_map(obj:`dict`): The label id (key) to label str (value) map.
- """
- model.eval()
- results = []
- for input_ids, token_type_ids, seq_lens in data_loader:
- preds = model(input_ids, token_type_ids, seq_lens=seq_lens)
- tags = parse_predict_result(preds.numpy(), seq_lens.numpy(), label_map)
- results.extend(tags)
- return results
-
-
- def parse_predict_result(predictions, seq_lens, label_map):
- """
- Parses the prediction results to the label tag.
- """
- pred_tag = []
- for idx, pred in enumerate(predictions):
- seq_len = seq_lens[idx]
- # drop the "[CLS]" and "[SEP]" token
- tag = [label_map[i] for i in pred[1:seq_len - 1]]
- pred_tag.append(tag)
- return pred_tag
-
-
- def create_dataloader(dataset,
- mode='train',
- batch_size=1,
- batchify_fn=None,
- trans_fn=None):
- if trans_fn:
- dataset = dataset.map(trans_fn)
-
- shuffle = True if mode == 'train' else False
- if mode == 'train':
- batch_sampler = paddle.io.DistributedBatchSampler(
- dataset, batch_size=batch_size, shuffle=shuffle)
- else:
- batch_sampler = paddle.io.BatchSampler(
- dataset, batch_size=batch_size, shuffle=shuffle)
-
- return paddle.io.DataLoader(
- dataset=dataset,
- batch_sampler=batch_sampler,
- collate_fn=batchify_fn,
- return_list=True)
-
-
- if __name__ == "__main__":
- paddle.set_device(args.device)
-
- test_ds = load_dataset("cote", "dp", splits=['test'])
- # The COTE_DP dataset labels with "BIO" schema.
- label_map = {0: "B", 1: "I", 2: "O"}
- # `no_entity_label` represents that the token isn't an entity.
- no_entity_label_idx = 2
-
- skep = SkepModel.from_pretrained('skep_ernie_1.0_large_ch')
- model = SkepCrfForTokenClassification(
- skep, num_classes=len(test_ds.label_list))
- tokenizer = SkepTokenizer.from_pretrained('skep_ernie_1.0_large_ch')
-
- if args.params_path and os.path.isfile(args.params_path):
- state_dict = paddle.load(args.params_path)
- model.set_dict(state_dict)
- print("Loaded parameters from %s" % args.params_path)
-
- trans_func = partial(
- convert_example,
- tokenizer=tokenizer,
- max_seq_length=args.max_seq_length)
- batchify_fn = lambda samples, fn=Tuple(
- Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # input ids
- Pad(axis=0, pad_val=tokenizer.vocab[tokenizer.pad_token]), # token type ids
- Stack(dtype='int64'), # sequence lens
- ): [data for data in fn(samples)]
-
- test_data_loader = create_dataloader(
- test_ds,
- mode='test',
- batch_size=args.batch_size,
- batchify_fn=batchify_fn,
- trans_fn=trans_func)
-
- results = predict(model, test_data_loader, label_map)
- for idx, example in enumerate(test_ds.data):
- print(len(example['tokens']), len(results[idx]))
- print('Data: {} \t Label: {}'.format(example, results[idx]))
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