RobertaTokenizerFast 的引用可以参考以下代码示例: python from transformers import RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') 在上述代码中,我们首先导入了 RobertaTokenizerFast 类,然后使用 from_pretrained 方法加载了一个预训练的 RoBERTa 分词器模型。你可以根据需要选择不...
这种形式将tokenizer保存成一个json文件,然后用RobertaTokenizerFast(RoBERTa)的init function中的tokenizer_file参数指定加载tokenizer文件。具体我举一个例子(省去了pretraining部分): fromtransformersimportRobertaTokenizerFast model_dir='Language-Model-Roberta' tokenizer1 = RobertaTokenizerFast.from_pretrained("./"+m...
This bug in offset mapping actually affectsallthe fast tokenizers converted from sentencepiece. During the pre-tokenization step, we first split everything on whitespaces (WhitespaceSplitpre-tokenizer), and in a second step, we add the▁character in front of each word (Metaspacepre-tokenizer). T...
The time disparity leads me to believe that when RobertaTokenizer.add_tokens() is called, a trie is either not created or is created extremely fast, whereas when RobertaTokenizer.from_pretrained() is called, a trie is created (slowly). Using RobertaTokenizerFast instead of RobertaTokenizer produ...
In order to plug fast tokenizer I’ve trained above into this script I had to modify theLineByLineTextDatasetthat’s provided there. The final version looks like this: class LineByLineTextDataset(Dataset): def __init__(self, t: PreTrainedTokenizer, args, file_path: str, block_size=512)...
正在施工中的代码库也接入了Roberta预训练模型，同时支持半监督，领域迁移，降噪loss，蒸馏等模型优化项，...
XL.net 是建立在 BERT 之上的示例之一，它在 20 种不同任务上的表现优于 BERT。在理解基于 BERT ...
In order to plug fast tokenizer I’ve trained above into this script I had to modify theLineByLineTextDatasetthat’s provided there. The final version looks like this: class LineByLineTextDataset(Dataset): def __init__(self, t: PreTrainedTokenizer, args, file_path: str, block_size=512)...
Can you update the name of arguments according to newer transformers library? Thank you for reading this issue :) 👍 1 Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Assignees No one assigned Labels None yet Projects None yet Mileston...
openedonOct 6, 2021 The code: it first loads Roberta base, prepares input and convert model to onnx and the load and run it, import torch import time from transformers import RobertaTokenizerFast,RobertaForMaskedLM,BertForMaskedLM the_model_rb = "roberta-base" tokenizer = RobertaTokenizerFast...