tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='left')分词器是什么?分词器负责将句子分割成更小的文本片段 (词元) 并为每个词元分配一个称为输入 id 的值。这么做是必需的,因为我们的模型只能理解数字,所以我们首先必须将文本转换 (也称为编码) 为模型可以理解的形...
tokenizer = AutoTokenizer.from_pretrained(model_name,trust_remote_code=True,padding_side="left",add_eos_token=True,add_bos_token=True,use_fast=False) tokenizer.pad_token = tokenizer.eos_token 7. 使用零样本推理测试模型 我们将使用一些示例输入来评估上面加载的基本模型。 %%time from transformers imp...
tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='left') 分词器是什么? 分词器负责将句子分割成更小的文本片段 (词元) 并为每个词元分配一个称为输入 id 的值。这么做是必需的,因为我们的模型只能理解数字,所以我们首先必须将文本转换 (也称为编码) 为模型可以理解的...
Should beLiteral['right', 'left']as mentioned in the docs. https://huggingface.co/docs/transformers/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__.padding_side
# 加载tokenizer from transformers import BertTokenizer token = BertTokenizer.from_pretrained('bert-base-chinese') print(token) PreTrainedTokenizer(name_or_path='bert-base-chinese', vocab_size=21128, model_max_len=512, is_fast=False, padding_side='right', special_tokens={'unk_token': '[UNK]...
tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True, padding_side='left') 1. 分词器是什么? 分词器负责将句子分割成更小的文本片段 (词元) 并为每个词元分配一个称为输入 id 的值。这么做是必需的,因为我们的模型只能理解数字,所以我们首先必须将文本转换 (也称为编码) 为模型可以...
padding_side = "left" # Define PAD Token = EOS Token tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id # use different length sentences to test batching # measure time start_time = time() sentences = [ "Hello, my dog is a little", "...
def plot_diffEdit(init_img, output, mask): ## Plotting side by side fig, axs = plt.subplots(1, 3, figsize=(12, 6)) ## Visualizing initial image axs[0].imshow(init_img) axs[0].set_title(f"Initial image") ## Visualizing initial image axs[2].imshow(output[0]) axs[2].set_tit...
tokenizer = AutoTokenizer.from_pretrained(base_model_name, padding_side="left") tokenizer.add_special_tokens({"pad_token":"[PAD]"}) iftokenizer.chat_templateisNone: tokenizer.chat_template = SIMPLE_QUERY_CHAT_TEMPLATE reward_model = AutoModelForSequenceClassification.from_pretrained(base_model_name...
inp = tokenizer(prompts, padding="max_length", max_length=maxlen, truncation=True, return_tensors="pt") return text_encoder(inp.input_ids.to("cuda"))[0].half() vae, unet, tokenizer, text_encoder, scheduler = load_artifacts()