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Cannot get the config names for the dataset.
Error code: ConfigNamesError Exception: DataFilesNotFoundError Message: No (supported) data files found in ACCC1380/private-model Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 72, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1904, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1270, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 597, in infer_module_for_data_files raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else "")) datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in ACCC1380/private-model
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此huggingface库主要存储本人电脑的一些重要文件
如果无法下载文件,把下载链接的huggingface.co改成hf-mirror.com 即可
如果你也想要在此处永久备份文件,可以参考我的上传代码:
# 功能函数,清理打包上传
from pathlib import Path
from huggingface_hub import HfApi, login
repo_id = 'ACCC1380/private-model'
yun_folders = ['/kaggle/input']
def hugface_upload(yun_folders, repo_id):
if 5 == 5:
hugToken = '********************' #改成你的huggingface_token
if hugToken != '':
login(token=hugToken)
api = HfApi()
print("HfApi 类已实例化")
print("开始上传文件...")
for yun_folder in yun_folders:
folder_path = Path(yun_folder)
if folder_path.exists() and folder_path.is_dir():
for file_in_folder in folder_path.glob('**/*'):
if file_in_folder.is_file():
try:
response = api.upload_file(
path_or_fileobj=file_in_folder,
path_in_repo=str(file_in_folder.relative_to(folder_path.parent)),
repo_id=repo_id,
repo_type="dataset"
)
print("文件上传完成")
print(f"响应: {response}")
except Exception as e:
print(f"文件 {file_in_folder} 上传失败: {e}")
continue
else:
print(f'Error: Folder {yun_folder} does not exist')
else:
print(f'Error: File {huggingface_token_file} does not exist')
hugface_upload(yun_folders, repo_id)
本地电脑需要梯子环境,上传可能很慢。可以使用kaggle等中转服务器上传,下载速率400MB/s,上传速率60MB/s。
在kaggle上面转存模型:
- 第一步:下载文件
!apt install -y aria2
!aria2c -x 16 -s 16 -c -k 1M "把下载链接填到这双引号里" -o "保存的文件名称.safetensors"
- 第二步:使用上述代码的API上传
# 功能函数,清理打包上传
from pathlib import Path
from huggingface_hub import HfApi, login
repo_id = 'ACCC1380/private-model'
yun_folders = ['/kaggle/working'] #kaggle的output路径
def hugface_upload(yun_folders, repo_id):
if 5 == 5:
hugToken = '********************' #改成你的huggingface_token
if hugToken != '':
login(token=hugToken)
api = HfApi()
print("HfApi 类已实例化")
print("开始上传文件...")
for yun_folder in yun_folders:
folder_path = Path(yun_folder)
if folder_path.exists() and folder_path.is_dir():
for file_in_folder in folder_path.glob('**/*'):
if file_in_folder.is_file():
try:
response = api.upload_file(
path_or_fileobj=file_in_folder,
path_in_repo=str(file_in_folder.relative_to(folder_path.parent)),
repo_id=repo_id,
repo_type="dataset"
)
print("文件上传完成")
print(f"响应: {response}")
except Exception as e:
print(f"文件 {file_in_folder} 上传失败: {e}")
continue
else:
print(f'Error: Folder {yun_folder} does not exist')
else:
print(f'Error: File {huggingface_token_file} does not exist')
hugface_upload(yun_folders, repo_id)
- 第三步:等待上传完成:
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