classLlamaModel(LlamaPreTrainedModel):"""Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]Args:config: LlamaConfig"""def__init__(self,config:LlamaConfig):super().__init__(config)self.padding_idx=config.pad_token_idself.vocab_size=conf...
通过 RwkvPreTrainedModel 的子类,并包含了具体的模型结构和前向传播函数。 通过@add_start_docstrings 装饰器,给 RwkvModel 类添加了一个开头的文档字符串。这个文档字符串描述了 RwkvModel 的基本功能,以及该模型在输出上没有任何特定头部(head)的内容。 定义了 RwkvModel 的构造函数 __init__,其中调用了父类...
warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. ' 'If you really want to use it, please modify the corresponding codes.') from gfpgan import GFPGANer import towhee @towhee.register class GFPGANerOp: def __init__(self, model_path='/GFPGAN.pth', up...
This is how a pretrained model is normally used: from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn') tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn') AR...
"To create a model from a Google pretrained model use " "`model = {}.from_pretrained(PR...
I'm using symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli pretrained model from huggingface. My task requires to use it on pretty large texts, so it's essential to know maximum input length. The following code is supposed to load pretrained model and its tokenizer: encoding_m...
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) def tokenize_function(examples): return tokenizer(examples["text"]) tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"]) ...
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)deftokenize_function(examples):returntokenizer(examples["text"]) tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])defgroup_texts(examples):# Concatenate all texts.concatenat...
model.save_pretrained("directory_on_my_computer")# 会生成两个文件:config.json pytorch_model.bin Tokenizer transformer模型使用的分词方法,往往不是直接的word-level分词或者char-level分词。 前者会让词表过大,后者则表示能力很低。 因此主流的方式是进行subword-level的分词。例如对 "tokenization" 这个词,可能...
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) tokenizer(["Hello, this one sentence!"]) # {'input_ids': [[101, 7592, 1010, 2023, 2028, 6251, 999, 102]], 'attention_mask': # [[1, 1, 1, 1, 1, 1, 1, 1]]} ...