tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased') # 示例输入文本 text = "This is an example sentence for classification." # 分词和编码输入文本 inputs = tokenizer(text, return_tensors='pt') # 通过模型进行前...
batch_size, shuffle=False) dev_data_loader = DataLoader( dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) [2021-06-15 11:11:36,717] [ INFO] - Found /home/aistudio/.paddlenlp/models/xlnet-base-cased/xlnet-base-cased-spiece.mo...
下面的代码就是上述标签修改的具体实现。 frompytorch_transformersimportXLNetTokenizer# 使用 XLNet 的分词器tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') input_ids = [] input_labels = []fortext, ori_labelsinzip(train_samples, train_labels): l = text.split(' ') labels = [] te...
(self): self.task_name = "sst-2" self.model_name_or_path = "xlnet-base-cased" self.output_dir = "./tmp" self.max_seq_length = 128 self.batch_size = 32 self.learning_rate = 2e-5 self.weight_decay = 0.0 self.adam_epsilon = 1e-8 self.max_grad_norm = 1.0 self.num_train_...
('xlnet-base-cased')# 预处理文本train_inputs=tokenizer.batch_encode_plus(X_train,add_special_tokens=True,pad_to_max_length=True,return_tensors='pt')test_inputs=tokenizer.batch_encode_plus(X_test,add_special_tokens=True,pad_to_max_length=True,return_tensors='pt')# 微调模型model.train_...
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') best_score = 0 batch_size = 32 classes_list = [] output_dir = './models/' output_model_file = os.path.join(output_dir, WEIGHTS_NAME) ou...
model_name = 'xlnet-base-cased' tokenizer = XLNetTokenizer.from_pretrained(model_name) model = XLNetForSequenceClassification.from_pretrained(model_name) 现在,我们可以加载我们的数据集。假设我们有一个名为data的列表,其中包含输入文本和相应的标签: inputs = tokenizer(data['text'], padding='max_length...
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased-spiece.model') Run Code Online (Sandbox Code Playgroud) 提前谢谢你的帮助。 在 !pip install transformers 和 !pip install sentencepiece 之后, 请重新启动您的运行时,然后执行所有其他代码。
# xlnet-base-cased input_ids = torch.tensor(tokenizer.encode("I love <mask> .", add_special_tokens=False)).unsqueeze(0) # 输入"I love <mask> ." 与 "I love you ." 最后的预测的结果一致,因为perm_mask 指定you或者<mask>不可见 print(tokenizer.tokenize("I love <mask> .")) print(tok...
基本模型——将在2019年6月底发布一个XLNet-Base。 Uncased模型——目前, Cased XLNet-Large比Uncased XLNet-Large性能更好。开发者仍在观察与研究,当得出结论时,他们将马上发布Uncased模型。(预计时间不会太久) 在维基百科上进行微调的预训练模型,这可用于维基百科文本的任务,如SQuAD和HotpotQA。