wehub-resource-sync
134 lines
5.4 KiB
Python
134 lines
5.4 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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from functools import partial
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import paddle
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import paddle.nn.functional as F
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from tqdm import tqdm
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from paddlenlp.data import DataCollatorWithPadding
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from paddlenlp.datasets import load_dataset
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from paddlenlp.transformers import SkepForSequenceClassification, SkepTokenizer
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_name",
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choices=["skep_ernie_1.0_large_ch", "skep_ernie_2.0_large_en"],
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default="skep_ernie_1.0_large_ch",
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help="Select which model to train, defaults to skep_ernie_1.0_large_ch.",
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)
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parser.add_argument("--ckpt_dir", type=str, default=None, help="The directory of saved model checkpoint.")
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parser.add_argument(
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"--max_seq_len",
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default=400,
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type=int,
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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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)
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parser.add_argument("--batch_size", default=6, type=int, help="Batch size per GPU/CPU for prediction.")
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parser.add_argument(
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"--device",
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choices=["cpu", "gpu", "xpu"],
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default="gpu",
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help="Select which device to train model, defaults to gpu.",
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)
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args = parser.parse_args()
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@paddle.no_grad()
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def predict(model, data_loader, label_map):
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"""
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Given a prediction dataset, it gives the prediction results.
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Args:
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model(obj:`paddle.nn.Layer`): A model to classify texts.
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data_loader(obj:`paddle.io.DataLoader`): The dataset loader which generates batches.
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label_map(obj:`dict`): The label id (key) to label str (value) map.
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"""
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model.eval()
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results = []
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for batch in tqdm(data_loader):
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input_ids, token_type_ids = batch["input_ids"], batch["token_type_ids"]
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logits = model(input_ids, token_type_ids)
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probs = F.softmax(logits, axis=1)
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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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def convert_example_to_feature(example, tokenizer, max_seq_len=512, is_test=False):
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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.
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Args:
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example(obj:`dict`): Dict of input data, containing text 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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is_test(obj:`False`, defaults to `False`): Whether the example contains label or not.
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Returns:
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input_ids(obj:`list[int]`): The list of token ids.
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token_type_ids(obj: `list[int]`): The list of token_type_ids.
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label(obj:`int`, optional): The input label if not is_test.
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"""
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encoded_inputs = tokenizer(text=example["text"], text_pair=example["text_pair"], max_seq_len=max_seq_len)
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input_ids = encoded_inputs["input_ids"]
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token_type_ids = encoded_inputs["token_type_ids"]
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if is_test:
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return {"input_ids": input_ids, "token_type_ids": token_type_ids}
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else:
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label = example["label"]
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return {"input_ids": input_ids, "token_type_ids": token_type_ids, "labels": label}
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def create_dataloader(dataset, mode="train", batch_size=1, batchify_fn=None, trans_fn=None):
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if trans_fn:
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dataset = dataset.map(trans_fn)
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shuffle = True if mode == "train" else False
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if mode == "train":
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batch_sampler = paddle.io.DistributedBatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
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else:
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batch_sampler = paddle.io.BatchSampler(dataset, batch_size=batch_size, shuffle=shuffle)
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return paddle.io.DataLoader(dataset=dataset, batch_sampler=batch_sampler, collate_fn=batchify_fn, return_list=True)
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if __name__ == "__main__":
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test_ds = load_dataset("seabsa16", "phns", splits=["test"])
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label_map = {0: "negative", 1: "positive"}
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tokenizer = SkepTokenizer.from_pretrained(args.model_name)
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model = SkepForSequenceClassification.from_pretrained(args.ckpt_dir, num_labels=len(label_map))
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trans_func = partial(convert_example_to_feature, tokenizer=tokenizer, max_seq_len=args.max_seq_len, is_test=True)
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data_collator = DataCollatorWithPadding(tokenizer, padding=True)
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test_data_loader = create_dataloader(
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test_ds, mode="test", batch_size=args.batch_size, batchify_fn=data_collator, trans_fn=trans_func
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)
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results = predict(model, test_data_loader, label_map)
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for idx, text in enumerate(test_ds.data):
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print("Data: {} \t Label: {}".format(text, results[idx]))
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