其实作为NLP模型的输入,对于一些长句子我们还需要对齐进行padding使得每个batch的句子长度应该是一致的,这个过程tokenizer也可以帮我们完成,下面我们看看tokenizer的其他参数,可以参见文档了解更多,常使用的参数如下: padding:给序列补全到一定长度,True or ‘longest’: 是补全到batch中的最长长度,max_length’:补到给定max...
fromtransformersimportBertTokenizertokenizer=BertTokenizer.from_pretrained('bert-base-cased')example_text='I will watch Memento tonight'bert_input=tokenizer(example_text,padding='max_length',max_length=10,truncation=True,return_tensors="pt")# --- bert_input ---print(bert_input['input_ids'])prin...
借助最新的TensorRT 8.2，英伟达针对大模型的实时推断这一需求，优化了T5和GPT-2。首先，从Hugging Face模型中心下载Hugging Face PyTorch T5模型及其相关的tokenizer。T5_VARIANT = 't5-small't5_model = T5ForConditionalGeneration.from_pretrained(T5_VARIANT)tokenizer = T5Tokenizer.from_pretrained(T5_VARIANT)conf...
从下可以看到,虽然encode直接使用tokenizer.tokenize()进行词拆分,会保留头尾特殊字符的完整性,但是自己也会额外添加特殊字符。 token = tokenizer.tokenize(sents[0]) print(token) ids = tokenizer.convert_tokens_to_ids(token) print(ids) ids_encode = tokenizer.encode(sents[0]) print(ids_encode) token_en...
batch_tokenized = self.tokenizer.batch_encode_plus(batch_sentences, add_special_tokens=True, max_length=66, pad_to_max_length=True) input_ids = torch.tensor(batch_tokenized['input_ids']) attention_mask = torch.tensor(batch_tokenized['attention_mask']) ...
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') 1. 2. 3. 4. 5. 6. 7. 8. 让我们创建一个称为“ CustomDataset”的通用类。 Class从我们的原始输入特征生成张量,并且Pytorch张量可以接受class的输出。 它期望具有上面定义的“ TITLE”,“ target_list”,max_len,并使用BERT toknizer.enco...
input_ids = tokenizer(text, return_tensors="pt").input_ids prompt_length = input_ids.size(1) max_length = 50 + prompt_length t0 = time.perf_counter() input_ids = input_ids.to(model.device) generated_ids = model.generate(input_ids, max_length=max_length, temperature=0.8, top_k=20...
texts = [tokenizer(text, padding='max_length', max_length = 512, truncation=True, return_tensors="pt") for text in df['text']] def classes(self): return self.labels def __len__(self): return len(self.labels) def get_batch_labels(self, idx): # Fetch a batch of labels return ...
from argminer.data import ArgumentMiningDatasettrainset = ArgumentMiningDataset(df_label_map, df_train, tokenizer, max_length)train_loader = DataLoader(trainset)for epoch in range(epochs): model.train() for i, (inputs, targets) in enumerate(train_loader): optimizer.zero_grad() ...
summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf") 步骤4:输入文本进行总结 现在,在我们准备好我们的模型之后,我们可以开始输入我们想要总结的文本。想象一下,我们想从MedicineNet的一篇文章中总结以下关于COVID-19疫苗的内容: One month after the United States bega...