positional arguments:output Path indicating where to store generatedONNXmodel.options:-h,--help showthishelp message and exit-mMODEL,--modelMODELModelIDon huggingface.co or path on disk to load model from.--fea
from datasets import load_dataset # 下载的数据集名称, model_name = 'keremberke/plane-detection' # 数据集保存的路径 save_path = 'datasets' #name参数为full或mini,full表示下载全部数据,mini表示下载部分少量数据 dataset = load_dataset(model_name, name="full") dataset.save_to_disk(save_path) 1....
首先需要将自己需要使用的数据集zip文件解压到矩池云网盘或者机器中其他目录(存到网盘后下次可以直接使用),使用数据集时在代码抬头添加代码from datasets import load_from_disk,并将代码中加载数据集函数load_dataset更改为load_from_disk(数据集存放路径)即可。部分数据集需指定Subset。 以使用dbpedia_14数据集为例子...
from datasets import load_metric metric = load_metric('PATH/TO/MY/METRIC/SCRIPT') # Example of typical usage for batch in dataset: inputs, references = batch predictions = model(inputs) metric.add_batch(predictions=predictions, references=references) score = metric.compute() 1.5.2 Load conf...
dataset = load_from_disk("your_path") 1. 2. 3. 运行过程中遇到了 ConnectionError: Couldn't reach 'm-a-p/COIG-CQIA' on the Hub (SSLError)的报错,解决方法见第三部分。 2. python调用huggingface_hub library下载 这个方法下载的是整个项目的所有文件包括一些历史版本,和点进项目的files and version...
import datasetsdataset = datasets.load_dataset("stas/wmt16-en-ro-pre-processed", cache_dir="./wmt16-en_ro")在上图1中可以看到数据集内容。我们需要将其“压平”，这样可以更好的访问数据，让后将其保存到硬盘中。def flatten(batch): batch['en'] = batch['translation']['en'] batch['ro...
dataset = load_dataset(dataset_id,name=dataset_config) # Load tokenizer of FLAN-t5-base tokenizer = AutoTokenizer.from_pretrained(model_id) print(f"Train dataset size:{len(dataset['train'])}") print(f"Test dataset size:{len(dataset['test'])}") ...
from datasets import load_from_diskdataset = load_from_disk('./')3. 评价指标 Evaluate 安装Evaluate库：pip install evaluate （1）加载 import evaluateaccuracy = evaluate.load("accuracy")（2）从社区加载模块 element_count = evaluate.load("lvwerra/element_count", module_type="measurement")（3）...
model_path = "pretrained-bert" # make the directory if not already there if not os.path.isdir(model_path): os.mkdir(model_path) # save the tokenizer tokenizer.save_model(model_path) # dumping some of the tokenizer config to config file, ...
登陆后复制fromtransformersimportAutoTokenizer,AutoModelForCausalLM,DataCollatorForSeq2Seq,Trainer,TrainingArguments fromdatasetsimportload_dataset frompeftimportLoraConfig,TaskType,get_peft_model frompeftimportPeftModel tokenizer = AutoTokenizer.from_pretrained("Qwen2-0.5B-Instruct") ...