一、Load dataset本节参考官方文档: Load数据集存储在各种位置,比如 Hub 、本地计算机的磁盘上、Github 存储库中以及内存中的数据结构(如 Python 词典和 Pandas DataFrames)中。无论您的数据集存储在何处, Da…
from datasets import load_dataset dataset = load_dataset("squad", split="train") dataset.features {'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None), 'context': Value(dtype='string', id=None...
logging.set_verbosity_error() from datasets import load_dataset, get_dataset_split_names # the following only finds train, validation and test splits correctly path = "./test_data1" print("###", get_dataset_split_names(path), "###") dataset_list = [] for spt in ["train", "test"...
load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is ...
import{downloadFileToCacheDir}from"@huggingface/hub";constfile=awaitdownloadFileToCacheDir({repo:'foo/bar',path:'README.md'});console.log(file); Note: this does not work in the browser snapshotDownload You can download an entire repository at a given revision in the cache directory using the...
kg = np.load( os.path.join(kg_output, 'wiki_kg.npz'), allow_pickle=True ) self.wiki5m_alias2qid, self.wiki5m_qid2alias, self.wiki5m_pid2alias, self.head_cluster = \ kg['wiki5m_alias2qid'][()], kg['wiki5m_qid2alias'][()], kg['wiki5m_pid2alias'][()], kg['head_clu...
jobs:build:uses:huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@my-test-branchwith:repo_owner:xenovacommit_sha:${{github.sha}}pr_number:${{github.event.number}}package:transformers.jspath_to_docs:transformers.js/docs/sourcepre_command:cdtransformers.js&&npminstall&&npmrundocs...
importtensorflowastfimporttensorflow_datasetsfromtransformersimport*# Load dataset, tokenizer, model from pretrained model/vocabularytokenizer = BertTokenizer.from_pretrained('bert-base-cased') model = TFBertForSequenceClassification.from_pretrained('bert-base-cased') data = tensorflow_datasets.load('glue/...
1 2 3 fromdatasetsimportload_dataset dataset = load_dataset("rotten_tomatoes", split="train") 当一个数据集由多个文件(我们称之为分片)组成时,可以显著加快数据集的下载和准备步骤 您可以使用num_proc参数选择并行准备数据集时要使用的进程数。在这种情况下,每个进程被分配了一部分分片来进行准备 1 2 3 ...
#加载数据集 from datasets import load_dataset dataset = load_dataset("rotten_tomatoes") # doctest: +IGNORE_RESULT #创建一个分词的函数,相当于词表,需要将文字映射到词表 def tokenize_dataset(dataset): return tokenizer(dataset["text"]) dataset = dataset.map(tokenize_dataset, batched=True) #创建...