# word参数默认为False,说明我们的中文文本是按单个字拆分训练的,True说明是按词语拆分 parser = argparse.ArgumentParser(description='Chinese Text Classification') parser.add_argument('--model', type=str, required=True, help='choose a model:
得到一个tensor :[batch_size, seq_length, embedding_size] 既 [?, 100, 128], 此处? 表示batch, 即不知道会有多少输入。 embedding_var的shape是(vocab_size,embedding_dim) 举例说明:我们有一个词典大小为3的词典,一共对应三个词 “今天”,“天气” “很好“,embedding_var =[[0,0,0,1],[0,0,...
引言短文本多分类 文本分类(text classification)是自然语言处理(NLP)中应用最广泛的任务之一。如新闻分类、垃圾邮件分类、情感分类等,这些是最常见的文本分类实际应用。更多的应用场景比如:电商评价分类、…
bigram = torch.LongTensor([_[3] for _ in datas]).to(self.device) trigram = torch.LongTensor([_[4] for _ in datas]).to(self.device) # pad前的长度(超过pad_size的设为pad_size) seq_len = torch.LongTensor([_[2] for _ in datas]).to(self.device) return (x, seq_len, bigram,...
"""A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence...
Tensors, a high-level generalization of vector and matrix, can be used for image classification, reducing data storage costs while maintaining spatial correlation between pixels. Tensor networks, formed by the contraction of several tensors, can be decomposed into related lower-order core tensors to...
A few other pre-trained models are implemented off-the-shelf inrun_classifier.py, so it should be straightforward to follow those examples to use BERT for any single-sentence or sentence-pair classification task. Note: You might see a messageRunning train on CPU. This really just means that...
4. Model Training and Optimization To train and optimize our proposed TCFLTformer cyber threat entity recognition model for ATM, we used the following steps: 4.1. Data Preprocessing Before model training, we need to preprocess the data of the air traffic management system cyber threat entity recog...
there are two essential differences between supervised learning and reinforcement learning: first, there is no complete access to the function, which requires optimization, meaning that it should be queried via interaction; second, the state being interacted with is founded on an environment, where th...
For image classification, we leveraged pre-trained models rooted in both Convolutional Neural Network (CNN) and Transformer deep learning architectures for transfer learning. Within the CNN framework, we opted for models including DenseNet-121, EfficientNetB2, ResNet50, and VGG19 (Visual Geometry Grou...