(2) Hugging Face - Documentation. Hugging Face - Documentation 访问时间 2023/4/12. (3) Hugging Face教程 - 5、huggingface的datasets库使用 - 知乎. bookname:Hugging Face教程 - 5、huggingface的datasets库使用 访问时间 2023/4/12. (
tokenized_datasets = dataset.map(tokenize_function, batched=True) 选择一个小的subset,尝试微调,选了1000个 small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000)) small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000)) 加载模型bert-b...
🤗 Datasets documentation: https://huggingface.co/docs/datasets/index dataset card: https://huggingface.co/docs/hub/datasets-cards main datasets page: https://huggingface.co/datasets 备注 打开指定数据集时,可以点击按钮``Use in dataset library``查看使用方法 Using Datasets Some datasets on the Hub...
Tokenize a Hugging Face datasetHugging Face Transformers models expect tokenized input, rather than the text in the downloaded data. To ensure compatibility with the base model, use an AutoTokenizer loaded from the base model. Hugging Face datasets allows you to directly apply the tokenizer ...
Before downloading a dataset from Hugging Face, it is possible to get the required disk space if the uploader provided the sizes in Hugging Face Hub.7 from datasets import load_dataset_builder from psutil._common import bytes2human def print_dataset_size_if_provided(*args, **kwargs): ...
Hugging Face 的 Transformers 模型需要分詞化的輸入,而不是下載資料中的原始文字。 若要確保與基本模型相容,請使用從基本模型載入的AutoTokenizer。 Hugging Facedatasets可讓您將權杖化工具一致地套用至訓練和測試資料。 例如: Python fromtransformersimportAutoTokenizer tokenizer = AutoTokenizer.fro...
Before downloading a dataset from Hugging Face, it is possible to get the required disk space if the uploader provided the sizes in Hugging Face Hub.7 from datasets import load_dataset_builder from psutil._common import bytes2human def print_dataset_size_if_provided(*args, **kwargs): ...
作为Stability AI 的 Stable Diffusion 家族最新的模型,Stable Diffusion 3(SD3) 现已登陆 Hugging Face Hub,并且可用在 🧨 Diffusers 中使用了。 当前放出的模型版本是 Stable Diffusion 3 Medium,有二十亿 (2B) 的参数量。 针对当前发布版本,我们提供了: ...
For more details on installation, check the installation page in the documentation: https://huggingface.co/docs/datasets/installation Installation to use with PyTorch/TensorFlow/pandas If you plan to use 🤗 Datasets with PyTorch (1.0+), TensorFlow (2.2+) or pandas, you should also install PyTor...
With Meilisearch already implemented for Hugging Face documentation, the expansion of the search solution to other use cases was smooth, once the ranking and internal rules were in place. Today, the Meilisearch engine powers the discovery of 220,000 model cards, 38,000 datasets, and 60,000 demo...