使用huggingface datasets高效处理数据 chon zhang NLP炼丹师 7 人赞同了该文章 数据集处理指南 Datasets提供了许多工具来修改数据集的结构和内容。这些工具对于整理数据集、创建附加列、在特征和格式之间进行转换以及更多操作非常重要。 本指南将向您展示如何: 重新排列行并拆分数据集。 重命名和删除列以及其他常见的列...
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): self.dataset = load_from_disk(para...
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 li...
Support for streaming datasets stored in object stores inload_from_disk. Motivation Theload_from_diskfunction supports fetching datasets stored in object stores such ass3. In many cases, the datasets that are stored in object stores are very large and being able to stream the data from the buc...
I went to this site here which shows the directory tree for the specific huggingface model I wanted. I happened to want the uncased model, but these steps should be similar for your cased version. Also note that my link is to a very specific commit of this model, just for the sake of...
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...
I am trying to use Huggingface transformer api to load a locally downloaded M-BERT model but it is throwing an exception. I clone this repo: https://huggingface.co/bert-base-multilingual-cased bert = TFBertModel.from_pretrained("input/bert-base-multilingual-cased") The directory structure is...
Now I want to load the vectorstore from the persistent directory into a new script. This script is stored in the same folder as the vectorstore. I have done this using the following code: embeddings = HuggingFaceEmbeddings() persist_directory = './chroma' ...