tokenizer = old_tokenizer.train_new_from_iterator(datasets_sample, 52000) tokens = tokenizer.tokenize(example) # 打印出来看看,略有不同 print(tokens) #新训练的分词器可以保存起来,注意这里用的是AutoTokenizer tokenizer.save_pretrained( "code-search-net-tokenizer" ) Tokenizer的其他功能 第一个是编码相...
tokenizer = AutoTokenizer.from_pretrained(checkpoint) # 加载检查点对应的标记器 def tokenize_function(example): # 用于对数据集中的字符串转化为ID(即标记)时用的辅助函数 return tokenizer(example["sentence1"], example["sentence2"], truncation=True) tokenized_datasets = raw_datasets.map(tokenize_functio...
AutoModel >>>tokenizer= AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=Tru...
save_pretrained 4-bit models with bitsandbytes westnopened this issueMay 31, 2023· 11 comments huggingfacedeleted a comment fromgithub-actionsbotJul 6, 2023 Contributor younesbelkadacommentedAug 17, 2023 👀3nlpcat, majid999, and kbulutozler reacted with eyes emoji...
现在可以使用 AutoModelForSequenceClassification 类及其 from_pretrained 方法加载预训练的 BERT。这里要使用num_label = 2 参数,因为现在需要在是二进制分类任务上微调 BERT,我们将重新生成的head部分,用一个随机初始化的带有两个标签的分类头替换原始层(其权重将在训练期间学习) . ...
I will first share a clean, simple example: from transformers import AutoTokenizer, BartForConditionalGeneration model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn") tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") ARTICLE_TO_SUMMARIZE =...
现在可以使用 AutoModelForSequenceClassification 类及其 from_pretrained 方法加载预训练的 BERT。这里要使用num_label = 2 参数,因为现在需要在是二进制分类任务上微调 BERT,我们将重新生成的head部分,用一个随机初始化的带有两个标签的分类头替换原始层(其权重将在训练期间学习) . ...
I recently found that when fine-tuning using alpaca-lora, model.save_pretrained() will save a adapter_model.bin that is only 443 B. This seems to be happening after peft@75808eb2a6e7b4c3ed8aec003b6eeb30a2db1495. Normally adapter_model.bi...
I tried to save the model withpipe.save_pretrained("./local_model_directory")and then load the model in the second run with `pipe("object-detection", model="./local_model_directory"). This throws an error and doesn't work at all. ...
processor = AutoProcessor.from_pretrained(model_id) processor.tokenizer = tokenizer model = LlavaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16) 建一个数据整理器来组合文本和图像对。 classLLavaDataCollator: def__init__(self, processor): ...