wehub-resource-sync
161 lines
7.1 KiB
Python
161 lines
7.1 KiB
Python
# Copyright (c) 2020 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 paddle
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from model import SentenceTransformer
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from paddlenlp.data import Pad, Tuple
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from paddlenlp.transformers import AutoModel, AutoTokenizer
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--params_path", type=str, default='./checkpoint/model_2700/model_state.pdparams', help="The path to model parameters to be loaded.")
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parser.add_argument("--max_seq_length", default=50, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
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parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
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parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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args = parser.parse_args()
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# fmt: on
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def convert_example(example, tokenizer, max_seq_length=512):
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"""
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Builds model inputs from a sequence or a pair of sequence for sequence classification tasks
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by concatenating and adding special tokens. And creates a mask from the two sequences passed
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to be used in a sequence-pair classification task.
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A BERT sequence has the following format:
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- single sequence: ``[CLS] X [SEP]``
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- pair of sequences: ``[CLS] A [SEP] B [SEP]``
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A BERT sequence pair mask has the following format:
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::
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0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
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| first sequence | second sequence |
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If only one sequence, only returns the first portion of the mask (0's).
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Args:
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example(obj:`list[str]`): List of input data, containing query, title and label if it have label.
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tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
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which contains most of the methods. Users should refer to the superclass for more information regarding methods.
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max_seq_len(obj:`int`): The maximum total input sequence length after tokenization.
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Sequences longer than this will be truncated, sequences shorter will be padded.
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Returns:
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query_input_ids(obj:`list[int]`): The list of query token ids.
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query_token_type_ids(obj: `list[int]`): List of query sequence pair mask.
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title_input_ids(obj:`list[int]`): The list of title token ids.
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title_token_type_ids(obj: `list[int]`): List of title sequence pair mask.
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label(obj:`numpy.array`, data type of int64, optional): The input label if not is_test.
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"""
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query, title = example[0], example[1]
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query_encoded_inputs = tokenizer(text=query, max_seq_len=max_seq_length)
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query_input_ids = query_encoded_inputs["input_ids"]
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query_token_type_ids = query_encoded_inputs["token_type_ids"]
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title_encoded_inputs = tokenizer(text=title, max_seq_len=max_seq_length)
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title_input_ids = title_encoded_inputs["input_ids"]
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title_token_type_ids = title_encoded_inputs["token_type_ids"]
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return query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids
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def predict(model, data, tokenizer, label_map, batch_size=1):
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"""
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Predicts the data labels.
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Args:
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model (obj:`paddle.nn.Layer`): A model to classify texts.
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data (obj:`List(Example)`): The processed data whose each element is a Example (numedtuple) object.
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A Example object contains `text`(word_ids) and `se_len`(sequence length).
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tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
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which contains most of the methods. Users should refer to the superclass for more information regarding methods.
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label_map(obj:`dict`): The label id (key) to label str (value) map.
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batch_size(obj:`int`, defaults to 1): The number of batch.
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Returns:
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results(obj:`dict`): All the predictions labels.
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"""
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examples = []
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for text_pair in data:
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query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = convert_example(
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text_pair, tokenizer, max_seq_length=args.max_seq_length
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)
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examples.append((query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids))
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# Separates data into some batches.
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batches = [examples[idx : idx + batch_size] for idx in range(0, len(examples), batch_size)]
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # title_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # title_segment
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): [data for data in fn(samples)]
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results = []
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model.eval()
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for batch in batches:
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query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batchify_fn(batch)
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query_input_ids = paddle.to_tensor(query_input_ids)
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query_token_type_ids = paddle.to_tensor(query_token_type_ids)
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title_input_ids = paddle.to_tensor(title_input_ids)
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title_token_type_ids = paddle.to_tensor(title_token_type_ids)
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probs = model(
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query_input_ids,
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title_input_ids,
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query_token_type_ids=query_token_type_ids,
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title_token_type_ids=title_token_type_ids,
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)
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idx = paddle.argmax(probs, axis=1).numpy()
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idx = idx.tolist()
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labels = [label_map[i] for i in idx]
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results.extend(labels)
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return results
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if __name__ == "__main__":
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paddle.set_device(args.device)
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# ErnieTinyTokenizer is special for ernie-tiny pretained model.
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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data = [
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["世界上什么东西最小", "世界上什么东西最小？"],
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["光眼睛大就好看吗", "眼睛好看吗？"],
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["小蝌蚪找妈妈怎么样", "小蝌蚪找妈妈是谁画的"],
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]
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label_map = {0: "dissimilar", 1: "similar"}
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pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
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model = SentenceTransformer(pretrained_model)
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if args.params_path and os.path.isfile(args.params_path):
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state_dict = paddle.load(args.params_path)
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model.set_dict(state_dict)
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print("Loaded parameters from %s" % args.params_path)
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else:
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raise ValueError("Please set --params_path with correct pretrained model file")
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results = predict(model, data, tokenizer, label_map, batch_size=args.batch_size)
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for idx, text in enumerate(data):
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print("Data: {} \t Label: {}".format(text, results[idx]))
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