Save 一旦你完成了对数据集的处理,你可以用save_to_disk()来保存并在以后重新使用它。 通过提供你想保存的目录的路径来保存你的数据集: encoded_dataset.save_to_disk("path/of/my/dataset/directory") 使用load_from_disk()函数重新加载数据集: from datasets import load_from_disk reloaded_dataset = load_...
如果你禁用了cache,但是还是想保存,你可以手动保存,否则临时路径里的数据集在运行结束后会被直接删除。 Dataset.save_to_disk() 数据集下载timeout处理 importdatasetsfromdatasetsimportDownloadMode# resume_download是断点续传,max_retries可以在短暂断连后等待一个足够长到恢复连接的间隔config=datasets.DownloadConfig(r...
(input_ids,attention_mask,token_type_ids,labels) in enumerate(loader): out = model(input_ids=input_ids, attention_mask = attention_mask, token_type_ids=token_type_ids) loss = criterion(out,labels) loss
(11)数据的保存和加载 dataset.save_to_disk('./') 1. from datasets import load_from_disk dataset = load_from_disk('./') 1. 2. 3. 评价指标 Evaluate 安装Evaluate库: pip install evaluate 1. (1)加载 import evaluate accuracy = evaluate.load("accuracy") 1. 2. (2)从社区加载模块 element...
return model_inputs tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=["dialogue", "summary", "id"]) print(f"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}") # save datasets to disk for later easy loading tokenized_dataset["train"]....
eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # delete for full training overwrite_output_dir=True, save_total_limit=3, fp16=False, # True if GPU)trainer = Seq2SeqTrainer( model=model, args=...
1. Run: Saving to Local Disk ✅ pipe = pipeline( task="object-detection", model="microsoft/table-transformer-structure-recognition", ) pipe.save_pretrained("./local_model_directory") The following files are saved to./local_model_directory: ...
test.save_to_disk("./dataset/test") validation.save_to_disk("./dataset/validation") 下图2可以看到,已经从数据集中删除了“translation”维度。 标记器 标记器提供了训练标记器所需的所有工作。它由四个基本组成部分:(但这四个部分不是所有的都是必要的) ...
setfit has this class method model._save_pretrained(save_directory) and to load it saved_model = SetFitModel._from_pretrained(save_directory) user18610139 106 answered Oct 26, 2022 at 15:10 9 votes Accepted Which HuggingFace summarization models support more than 1024 tokens? Which model is...
在执行tokenizer.save_pretrained("local-pt-checkpoint")时,输出如下: 接下来我们可以在本地磁盘上看到保存下来的模型文件及相关配置: 一旦checkpoint被保存,我们可以通过将transformers.ONNX包的--model参数指向所需的目录将其导出到ONNX: python -m transformers.onnx --model=local-pt-checkpoint onnx/ ...