处理完数据集后,您可以使用**save_to_disk()**保存并在以后重用它。 通过提供要保存到的目录的路径来保存数据集: >>> 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 = lo...
What should I do differently to get huggingface to use my local pretrained model? Update to address the comments YOURPATH = '/somewhere/on/disk/' name = 'transfo-xl-wt103' tokenizer = TransfoXLTokenizerFast(name) model = TransfoXLModel.from_pretrained(name) tokenizer.save_pretrained(YOURPATH)...
dataset = load_dataset('text', data_files='https://huggingface.co/datasets/lhoestq/test/resolve/main/some_text.txt') 1.2.4 Parquet 与基于行的文件(如 CSV)不同,Parquet 文件以柱状格式存储。大型数据集可以存储在 Parquet 文件中,因为它更高效,返回查询的速度更快。#加载 Parquet 文件,如下例所示...
Describe the bug load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one spli...
The pyarrow library is called by the load_from_disk() function from the HuggingFace datasets package : fromdatasetsimportload_from_diskclassTextEmbedder(ABC):def__init__(self, model_name, paragraphs_path, device, load_existing_index=False): ...
Feature request Support for streaming datasets stored in object stores in load_from_disk. Motivation The load_from_disk function supports fetching datasets stored in object stores such as s3. In many cases, the datasets that are stored i...
For HuggingFace, the transformer library was used to initialize the model from its pytorch.bin file. For SafeTensors, the transformers or SafeTensors library was used to load the ‘model.safetensors’ file. Smaller model metrics In the first test, we compared CoreWeave’s Tensorizer with ...
huggingface支持以下4种数据格式的数据集,只需要在load的时候设定格式就好了,这已经非常全面了,基本上覆盖了大部分数据格式; 1.1 加载本地数据集 本地数据集会先load,然后放到.cache文件夹下面去,示例代码如下: from datasets import load_dataset squad_it_dataset = load_dataset("json", data_files="./data/SQ...
structure-recognitionHuggingface model (and potentially its image processor) to my local disk in Python 3.10. The goal is to load the model inside a Docker container later on without having to pull the model weights and configs from HuggingFace each time the container and Python server boot...
Take a simple example in this website, https://huggingface.co/datasets/Dahoas/rm-static: if I want to load this dataset online, I just directly use, from datasets import load_dataset dataset = load_dataset("Dahoas/rm-static") What if I want to load dataset from local path, so I ...