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https://github.com/huggingface/datasets/issues/807
load_dataset for LOCAL CSV files report CONNECTION ERROR
> > hello, how did you solve this problems? > > > > > Hi ! > > > > > The url works on my side. > > > > > Is the url working in your navigator ? > > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ? > > > > > > > > > > > > I tried another server, it's working now. Thanks a lot. > > > > And I'm curious about why download things from "github" when I load dataset from local files ? Dose datasets work if my network crashed? > > > > > > > > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`? > > > Thanks :D > > > > > > hello, I tried this. but it still failed. how do you fix this error? > > 你把那个脚本下载到你本地安装目录下,然后 `load_dataset(csv_script_path, data_fiels)` 好的好的!解决了,感谢感谢!!!
## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ```
174
load_dataset for LOCAL CSV files report CONNECTION ERROR ## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ``` > > hello, how did you solve this problems? > > > > > Hi ! > > > > > The url works on my side. > > > > > Is the url working in your navigator ? > > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ? > > > > > > > > > > > > I tried another server, it's working now. Thanks a lot. > > > > And I'm curious about why download things from "github" when I load dataset from local files ? Dose datasets work if my network crashed? > > > > > > > > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`? > > > Thanks :D > > > > > > hello, I tried this. but it still failed. how do you fix this error? > > 你把那个脚本下载到你本地安装目录下,然后 `load_dataset(csv_script_path, data_fiels)` 好的好的!解决了,感谢感谢!!!
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https://github.com/huggingface/datasets/issues/807
load_dataset for LOCAL CSV files report CONNECTION ERROR
> > > > hello, how did you solve this problems? > > > > > Hi ! > > > > > The url works on my side. > > > > > Is the url working in your navigator ? > > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ? > > > > > > > > > > > > I tried another server, it's working now. Thanks a lot. > > > > And I'm curious about why download things from "github" when I load dataset from local files ? Dose datasets work if my network crashed? > > > > > > > > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`? > > > Thanks :D > > > > > > hello, I tried this. but it still failed. how do you fix this error? > > 你把那个脚本下载到你本地安装目录下,然后 `load_dataset(csv_script_path, data_fiels)` 我照着做了,然后报错。 ValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\dataset_infos.json as a URL or as a local path `--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-fd2106a3f053> in <module> ----> 1 dataset = load_dataset('C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets/csv.py', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 296 local_dataset_infos_path = cached_path( 297 dataset_infos, --> 298 download_config=download_config, 299 ) 300 except (FileNotFoundError, ConnectionError): C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 316 else: 317 # Something unknown --> 318 raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) 319 320 if download_config.extract_compressed_file and output_path is not None: ValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\dataset_infos.json as a URL or as a local path `
## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ```
316
load_dataset for LOCAL CSV files report CONNECTION ERROR ## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ``` > > > > hello, how did you solve this problems? > > > > > Hi ! > > > > > The url works on my side. > > > > > Is the url working in your navigator ? > > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ? > > > > > > > > > > > > I tried another server, it's working now. Thanks a lot. > > > > And I'm curious about why download things from "github" when I load dataset from local files ? Dose datasets work if my network crashed? > > > > > > > > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`? > > > Thanks :D > > > > > > hello, I tried this. but it still failed. how do you fix this error? > > 你把那个脚本下载到你本地安装目录下,然后 `load_dataset(csv_script_path, data_fiels)` 我照着做了,然后报错。 ValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\dataset_infos.json as a URL or as a local path `--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-fd2106a3f053> in <module> ----> 1 dataset = load_dataset('C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets/csv.py', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 296 local_dataset_infos_path = cached_path( 297 dataset_infos, --> 298 download_config=download_config, 299 ) 300 except (FileNotFoundError, ConnectionError): C:\Software\Anaconda\envs\ptk_gpu2\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 316 else: 317 # Something unknown --> 318 raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) 319 320 if download_config.extract_compressed_file and output_path is not None: ValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\dataset_infos.json as a URL or as a local path `
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0.0190695263, 0.2123968005, -0.0396619216, 0.4695009887, 0.1008591875, -0.242797181, -0.1551300734, -0.2195902616 ]
https://github.com/huggingface/datasets/issues/807
load_dataset for LOCAL CSV files report CONNECTION ERROR
I also experienced this issue this morning. Looks like something specific to windows. I'm working on a fix
## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ```
18
load_dataset for LOCAL CSV files report CONNECTION ERROR ## load_dataset for LOCAL CSV files report CONNECTION ERROR - **Description:** A local demo csv file: ``` import pandas as pd import numpy as np from datasets import load_dataset import torch import transformers df = pd.DataFrame(np.arange(1200).reshape(300,4)) df.to_csv('test.csv', header=False, index=False) print('datasets version: ', datasets.__version__) print('pytorch version: ', torch.__version__) print('transformers version: ', transformers.__version__) # output: datasets version: 1.1.2 pytorch version: 1.5.0 transformers version: 3.2.0 ``` when I load data through `dataset`: ``` dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ``` Error infos: ``` ConnectionError Traceback (most recent call last) <ipython-input-17-bbdadb9a0c78> in <module> ----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False) ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs) 588 # Download/copy dataset processing script 589 module_path, hash = prepare_module( --> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True 591 ) 592 ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs) 266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version) 267 try: --> 268 local_path = cached_path(file_path, download_config=download_config) 269 except FileNotFoundError: 270 if script_version is not None: ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs) 306 user_agent=download_config.user_agent, 307 local_files_only=download_config.local_files_only, --> 308 use_etag=download_config.use_etag, 309 ) 310 elif os.path.exists(url_or_filename): ~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag) 473 elif response is not None and response.status_code == 404: 474 raise FileNotFoundError("Couldn't find file at {}".format(url)) --> 475 raise ConnectionError("Couldn't reach {}".format(url)) 476 477 # Try a second time ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py ``` And I try to connect to the site with requests: ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ``` Similarly Error occurs: ``` --------------------------------------------------------------------------- ConnectionRefusedError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 159 conn = connection.create_connection( --> 160 (self._dns_host, self.port), self.timeout, **extra_kw 161 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 83 if err is not None: ---> 84 raise err 85 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options) 73 sock.bind(source_address) ---> 74 sock.connect(sa) 75 return sock ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: NewConnectionError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 676 headers=headers, --> 677 chunked=chunked, 678 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 380 try: --> 381 self._validate_conn(conn) 382 except (SocketTimeout, BaseSSLError) as e: ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn) 975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect() 977 ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self) 307 # Add certificate verification --> 308 conn = self._new_conn() 309 hostname = self.host ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self) 171 raise NewConnectionError( --> 172 self, "Failed to establish a new connection: %s" % e 173 ) NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused During handling of the above exception, another exception occurred: MaxRetryError Traceback (most recent call last) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 724 retries = retries.increment( --> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2] 726 ) ~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace) 438 if new_retry.is_exhausted(): --> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause)) 440 MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) During handling of the above exception, another exception occurred: ConnectionError Traceback (most recent call last) <ipython-input-20-18cc3eb4a049> in <module> 1 import requests 2 ----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py") ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs) 102 103 kwargs.setdefault('allow_redirects', False) --> 104 return request('head', url, **kwargs) 105 106 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs) 59 # cases, and look like a memory leak in others. 60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs) 62 63 ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 514 raise SSLError(e, request=request) 515 --> 516 raise ConnectionError(e, request=request) 517 518 except ClosedPoolError as e: ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',)) ``` I also experienced this issue this morning. Looks like something specific to windows. I'm working on a fix
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https://github.com/huggingface/datasets/issues/806
Quail dataset urls are out of date
Hi ! Thanks for reporting. We should fix the urls and use quail 1.3. If you want to contribute feel free to fix the urls and open a PR :)
<h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.
30
Quail dataset urls are out of date <h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. Hi ! Thanks for reporting. We should fix the urls and use quail 1.3. If you want to contribute feel free to fix the urls and open a PR :)
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https://github.com/huggingface/datasets/issues/806
Quail dataset urls are out of date
Done! PR [https://github.com/huggingface/datasets/pull/820](https://github.com/huggingface/datasets/pull/820) Updated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset).
<h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore.
24
Quail dataset urls are out of date <h3>Code</h3> ``` from datasets import load_dataset quail = load_dataset('quail') ``` <h3>Error</h3> ``` FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml ``` As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. Done! PR [https://github.com/huggingface/datasets/pull/820](https://github.com/huggingface/datasets/pull/820) Updated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset).
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0.0053986604, 0.505484283, 0.0244075824, -0.2426119745, 0.2663947344, -0.0170518793 ]
https://github.com/huggingface/datasets/issues/805
On loading a metric from datasets, I get the following error
Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object. Could you update pyarrow and try again ? ``` pip install --upgrade pyarrow ```
`from datasets import load_metric` `metric = load_metric('bleurt')` Traceback: 210 class _ArrayXDExtensionType(pa.PyExtensionType): 211 212 ndims: int = None AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' Any help will be appreciated. Thank you.
31
On loading a metric from datasets, I get the following error `from datasets import load_metric` `metric = load_metric('bleurt')` Traceback: 210 class _ArrayXDExtensionType(pa.PyExtensionType): 211 212 ndims: int = None AttributeError: module 'pyarrow' has no attribute 'PyExtensionType' Any help will be appreciated. Thank you. Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object. Could you update pyarrow and try again ? ``` pip install --upgrade pyarrow ```
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https://github.com/huggingface/datasets/issues/804
Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')
Yes: TriviaQA has a private test set for the leaderboard [here](https://competitions.codalab.org/competitions/17208) For the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here: https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md
# The issue It's all in the title, it appears to be fine on the train and validation sets. Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ? # How to reproduce ```py from datasets import load_dataset kilt_tasks = load_dataset("kilt_tasks") trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # both in "kilt_tasks" In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']]) Out[18]: False # and "trivia_qa" In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']]) Out[13]: True # appears to be fine on the train and validation sets. In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']]) Out[14]: False In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']]) Out[15]: False In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']]) Out[16]: True In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']]) Out[17]: True ```
32
Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') # The issue It's all in the title, it appears to be fine on the train and validation sets. Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ? # How to reproduce ```py from datasets import load_dataset kilt_tasks = load_dataset("kilt_tasks") trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # both in "kilt_tasks" In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']]) Out[18]: False # and "trivia_qa" In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']]) Out[13]: True # appears to be fine on the train and validation sets. In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']]) Out[14]: False In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']]) Out[15]: False In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']]) Out[16]: True In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']]) Out[17]: True ``` Yes: TriviaQA has a private test set for the leaderboard [here](https://competitions.codalab.org/competitions/17208) For the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here: https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md
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https://github.com/huggingface/datasets/issues/804
Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')
Oh ok, I guess I read the paper too fast 😅, thank you for your answer!
# The issue It's all in the title, it appears to be fine on the train and validation sets. Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ? # How to reproduce ```py from datasets import load_dataset kilt_tasks = load_dataset("kilt_tasks") trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # both in "kilt_tasks" In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']]) Out[18]: False # and "trivia_qa" In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']]) Out[13]: True # appears to be fine on the train and validation sets. In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']]) Out[14]: False In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']]) Out[15]: False In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']]) Out[16]: True In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']]) Out[17]: True ```
16
Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') # The issue It's all in the title, it appears to be fine on the train and validation sets. Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ? # How to reproduce ```py from datasets import load_dataset kilt_tasks = load_dataset("kilt_tasks") trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext') # both in "kilt_tasks" In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']]) Out[18]: False # and "trivia_qa" In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']]) Out[13]: True # appears to be fine on the train and validation sets. In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']]) Out[14]: False In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']]) Out[15]: False In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']]) Out[16]: True In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']]) Out[17]: True ``` Oh ok, I guess I read the paper too fast 😅, thank you for your answer!
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https://github.com/huggingface/datasets/issues/801
How to join two datasets?
Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset
Hi, I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence. Thanks!
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How to join two datasets? Hi, I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence. Thanks! Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset
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https://github.com/huggingface/datasets/issues/801
How to join two datasets?
Closing this one. Feel free to re-open if you have other questions about this issue. Also linking another discussion about joining datasets: #853
Hi, I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence. Thanks!
23
How to join two datasets? Hi, I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels? I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence. Thanks! Closing this one. Feel free to re-open if you have other questions about this issue. Also linking another discussion about joining datasets: #853
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Hi ! Indeed there's an issue with those links. We should probably use the target urls of the redirections instead
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
20
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Hi ! Indeed there's an issue with those links. We should probably use the target urls of the redirections instead
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Hi, the same issue here, could you tell me how to download it through datasets? thanks
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
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Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Hi, the same issue here, could you tell me how to download it through datasets? thanks
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Actually it's already fixed on the master branch since #740 I'll do the 1.1.3 release soon
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
16
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Actually it's already fixed on the master branch since #740 I'll do the 1.1.3 release soon
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Hi thanks, but I did tried to install from the pip install git+... and it does not work for me,. thanks for the help. I have the same issue with wmt16, "ro-en" thanks. Best Rabeeh On Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com> wrote: > Actually it's already fixed on the master branch since #740 > <https://github.com/huggingface/datasets/pull/740> > I'll do the 1.1.3 release soon > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/798#issuecomment-727854736>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA> > . >
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
98
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Hi thanks, but I did tried to install from the pip install git+... and it does not work for me,. thanks for the help. I have the same issue with wmt16, "ro-en" thanks. Best Rabeeh On Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com> wrote: > Actually it's already fixed on the master branch since #740 > <https://github.com/huggingface/datasets/pull/740> > I'll do the 1.1.3 release soon > > — > You are receiving this because you commented. > Reply to this email directly, view it on GitHub > <https://github.com/huggingface/datasets/issues/798#issuecomment-727854736>, > or unsubscribe > <https://github.com/notifications/unsubscribe-auth/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA> > . >
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
I just tested on google colab using ```python !pip install git+https://github.com/huggingface/datasets.git from datasets import load_dataset load_dataset("trec") ``` and it works. Can you detail how you got the issue even when using the latest version on master ? Also about wmt we'll look into it, thanks for reporting !
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
48
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. I just tested on google colab using ```python !pip install git+https://github.com/huggingface/datasets.git from datasets import load_dataset load_dataset("trec") ``` and it works. Can you detail how you got the issue even when using the latest version on master ? Also about wmt we'll look into it, thanks for reporting !
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
I think the new url with .edu is also broken: ``` ConnectionError: Couldn't reach https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label ``` Cant download the dataset anymore.
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
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Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. I think the new url with .edu is also broken: ``` ConnectionError: Couldn't reach https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label ``` Cant download the dataset anymore.
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Hi ! The URL seems to work fine on my side, can you try again ?
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
16
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Hi ! The URL seems to work fine on my side, can you try again ?
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https://github.com/huggingface/datasets/issues/798
Cannot load TREC dataset: ConnectionError
Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer: ``` Thank you for your report. The server was offline for maintenance and is now available again. ``` Guess all back to normal now 🙂
## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here.
50
Cannot load TREC dataset: ConnectionError ## Problem I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>. * `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.` * Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address * Increasing max_redirects to 100 doesn't help Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant. * datasets.__version__ == '1.1.2' * requests.__version__ == '2.24.0' ## Error trace ``` >>> import datasets >>> datasets.__version__ '1.1.2' >>> dataset = load_dataset("trec", split="train") Using custom data configuration default Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset ignore_verifications=ignore_verifications, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators dl_files = dl_manager.download_and_extract(_URLs) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download num_proc=download_config.num_proc, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested return function(data_struct) File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label ``` I would appreciate some suggestions here. Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer: ``` Thank you for your report. The server was offline for maintenance and is now available again. ``` Guess all back to normal now 🙂
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https://github.com/huggingface/datasets/issues/796
Seq2Seq Metrics QOL: Bleu, Rouge
Hi ! Thanks for letting us know your experience :) We should at least improve the error messages indeed
Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore.
19
Seq2Seq Metrics QOL: Bleu, Rouge Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore. Hi ! Thanks for letting us know your experience :) We should at least improve the error messages indeed
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https://github.com/huggingface/datasets/issues/796
Seq2Seq Metrics QOL: Bleu, Rouge
prediction = [['Hey', 'how', 'are', 'you', '?']] reference=[['Hey', 'how', 'are', 'you', '?']] bleu.compute(predictions=prediction,references=reference) also tried this kind of things lol I definitely need help too
Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore.
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Seq2Seq Metrics QOL: Bleu, Rouge Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore. prediction = [['Hey', 'how', 'are', 'you', '?']] reference=[['Hey', 'how', 'are', 'you', '?']] bleu.compute(predictions=prediction,references=reference) also tried this kind of things lol I definitely need help too
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https://github.com/huggingface/datasets/issues/796
Seq2Seq Metrics QOL: Bleu, Rouge
Hi ! As described in the documentation for `bleu`: ``` Args: predictions: list of translations to score. Each translation should be tokenized into a list of tokens. references: list of lists of references for each translation. Each reference should be tokenized into a list of tokens. ``` Therefore you can use this metric this way: ```python from datasets import load_metric predictions = [ ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample ["foo", "bar", "foobar"] # tokenized prediction of the second sample ] references = [ [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references) [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference) ] bleu = load_metric("bleu") bleu.compute(predictions=predictions, references=references) # Or you can also add batches before calling compute() # bleu.add_batch(predictions=predictions, references=references) # bleu.compute() ``` Hope this helps :)
Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore.
142
Seq2Seq Metrics QOL: Bleu, Rouge Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand: + Bleu expects tokenization, can I just kwarg it like sacrebleu? + different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61 #### What I tried Rouge experience: ```python rouge = load_metric('rouge') rouge.add_batch(['hi im sam'], ['im daniel']) # fails rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing. ``` BLEU experience: ```python bleu = load_metric('bleu') bleu.add_batch(predictions=['hi im sam'], references=['im daniel']) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']]) ``` All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'` #### Doc Typo This says `dataset=load_metric(...)` which seems wrong, will cause `NameError` ![image](https://user-images.githubusercontent.com/6045025/98004483-ff0d0580-1dbd-11eb-9f35-6f35904611bb.png) cc @lhoestq, feel free to ignore. Hi ! As described in the documentation for `bleu`: ``` Args: predictions: list of translations to score. Each translation should be tokenized into a list of tokens. references: list of lists of references for each translation. Each reference should be tokenized into a list of tokens. ``` Therefore you can use this metric this way: ```python from datasets import load_metric predictions = [ ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample ["foo", "bar", "foobar"] # tokenized prediction of the second sample ] references = [ [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references) [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference) ] bleu = load_metric("bleu") bleu.compute(predictions=predictions, references=references) # Or you can also add batches before calling compute() # bleu.add_batch(predictions=predictions, references=references) # bleu.compute() ``` Hope this helps :)
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https://github.com/huggingface/datasets/issues/792
KILT dataset: empty string in triviaqa input field
Just found out about https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md (Not very clear in https://huggingface.co/datasets/kilt_tasks links to http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md which is dead, closing the issue though :))
# What happened Both train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark) # Versions KILT version is `1.0.0` `datasets` version is `1.1.2` [more here](https://gist.github.com/PaulLerner/3768c8d25f723edbac20d99b6a4056c1) # How to reproduce ```py In [1]: from datasets import load_dataset In [4]: dataset = load_dataset("kilt_tasks") # everything works fine, removed output for a better readibility Dataset kilt_tasks downloaded and prepared to /people/lerner/.cache/huggingface/datasets/kilt_tasks/all_tasks/1.0.0/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data. # empty string in triviaqa input field In [36]: dataset['train_triviaqa'][0] Out[36]: {'id': 'dpql_5197', 'input': '', 'meta': {'left_context': '', 'mention': '', 'obj_surface': {'text': []}, 'partial_evidence': {'end_paragraph_id': [], 'meta': [], 'section': [], 'start_paragraph_id': [], 'title': [], 'wikipedia_id': []}, 'right_context': '', 'sub_surface': {'text': []}, 'subj_aliases': {'text': []}, 'template_questions': {'text': []}}, 'output': {'answer': ['five £', '5 £', '£5', 'five £'], 'meta': [], 'provenance': [{'bleu_score': [1.0], 'end_character': [248], 'end_paragraph_id': [30], 'meta': [], 'section': ['Section::::Question of legal tender.\n'], 'start_character': [246], 'start_paragraph_id': [30], 'title': ['Banknotes of the pound sterling'], 'wikipedia_id': ['270680']}]}} In [35]: dataset['train_triviaqa']['input'][:10] Out[35]: ['', '', '', '', '', '', '', '', '', ''] # same with test set In [37]: dataset['test_triviaqa']['input'][:10] Out[37]: ['', '', '', '', '', '', '', '', '', ''] # works fine with natural questions In [34]: dataset['train_nq']['input'][:10] Out[34]: ['how i.met your mother who is the mother', 'who had the most wins in the nfl', 'who played mantis guardians of the galaxy 2', 'what channel is the premier league on in france', "god's not dead a light in the darkness release date", 'who is the current president of un general assembly', 'when do the eclipse supposed to take place', 'what is the name of the sea surrounding dubai', 'who holds the nba record for most points in a career', 'when did the new maze runner movie come out'] ``` Stay safe :)
21
KILT dataset: empty string in triviaqa input field # What happened Both train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark) # Versions KILT version is `1.0.0` `datasets` version is `1.1.2` [more here](https://gist.github.com/PaulLerner/3768c8d25f723edbac20d99b6a4056c1) # How to reproduce ```py In [1]: from datasets import load_dataset In [4]: dataset = load_dataset("kilt_tasks") # everything works fine, removed output for a better readibility Dataset kilt_tasks downloaded and prepared to /people/lerner/.cache/huggingface/datasets/kilt_tasks/all_tasks/1.0.0/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data. # empty string in triviaqa input field In [36]: dataset['train_triviaqa'][0] Out[36]: {'id': 'dpql_5197', 'input': '', 'meta': {'left_context': '', 'mention': '', 'obj_surface': {'text': []}, 'partial_evidence': {'end_paragraph_id': [], 'meta': [], 'section': [], 'start_paragraph_id': [], 'title': [], 'wikipedia_id': []}, 'right_context': '', 'sub_surface': {'text': []}, 'subj_aliases': {'text': []}, 'template_questions': {'text': []}}, 'output': {'answer': ['five £', '5 £', '£5', 'five £'], 'meta': [], 'provenance': [{'bleu_score': [1.0], 'end_character': [248], 'end_paragraph_id': [30], 'meta': [], 'section': ['Section::::Question of legal tender.\n'], 'start_character': [246], 'start_paragraph_id': [30], 'title': ['Banknotes of the pound sterling'], 'wikipedia_id': ['270680']}]}} In [35]: dataset['train_triviaqa']['input'][:10] Out[35]: ['', '', '', '', '', '', '', '', '', ''] # same with test set In [37]: dataset['test_triviaqa']['input'][:10] Out[37]: ['', '', '', '', '', '', '', '', '', ''] # works fine with natural questions In [34]: dataset['train_nq']['input'][:10] Out[34]: ['how i.met your mother who is the mother', 'who had the most wins in the nfl', 'who played mantis guardians of the galaxy 2', 'what channel is the premier league on in france', "god's not dead a light in the darkness release date", 'who is the current president of un general assembly', 'when do the eclipse supposed to take place', 'what is the name of the sea surrounding dubai', 'who holds the nba record for most points in a career', 'when did the new maze runner movie come out'] ``` Stay safe :) Just found out about https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md (Not very clear in https://huggingface.co/datasets/kilt_tasks links to http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md which is dead, closing the issue though :))
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-0.2654485106, -0.1597660929, 0.0733580217, 0.0531112589, 0.0430420749, -0.2308626026 ]
https://github.com/huggingface/datasets/issues/790
Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist
I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now
I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error. ```sh git clone https://github.com/huggingface/datasets cd datasets virtualenv venv -p python3 --system-site-packages source venv/bin/activate pip install -e ".[dev]" ``` ![image](https://user-images.githubusercontent.com/59632/97868518-72871800-1cd5-11eb-9cd2-37d4e9d20b39.png) ![image](https://user-images.githubusercontent.com/59632/97868592-977b8b00-1cd5-11eb-8f3c-0c409616149c.png) Python 3.7.7
18
Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error. ```sh git clone https://github.com/huggingface/datasets cd datasets virtualenv venv -p python3 --system-site-packages source venv/bin/activate pip install -e ".[dev]" ``` ![image](https://user-images.githubusercontent.com/59632/97868518-72871800-1cd5-11eb-9cd2-37d4e9d20b39.png) ![image](https://user-images.githubusercontent.com/59632/97868592-977b8b00-1cd5-11eb-8f3c-0c409616149c.png) Python 3.7.7 I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now
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https://github.com/huggingface/datasets/issues/786
feat(dataset): multiprocessing _generate_examples
I agree that would be cool :) Right now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik
forking this out of #741, this issue is only regarding multiprocessing I'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool. In my use case, I would instead of: ```python for datum in data: yield self.load_datum(datum) ``` do: ```python return pool.map(self.load_datum, data) ``` As the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset. If this was a larger dataset (and many such datasets exist), it would take multiple days to complete. Using multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour.
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feat(dataset): multiprocessing _generate_examples forking this out of #741, this issue is only regarding multiprocessing I'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool. In my use case, I would instead of: ```python for datum in data: yield self.load_datum(datum) ``` do: ```python return pool.map(self.load_datum, data) ``` As the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset. If this was a larger dataset (and many such datasets exist), it would take multiple days to complete. Using multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour. I agree that would be cool :) Right now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik
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https://github.com/huggingface/datasets/issues/784
Issue with downloading Wikipedia data for low resource language
Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https://dumps.wikimedia.org/jvwiki) here for `jv`) ?
Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks!
21
Issue with downloading Wikipedia data for low resource language Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks! Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https://dumps.wikimedia.org/jvwiki) here for `jv`) ?
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https://github.com/huggingface/datasets/issues/784
Issue with downloading Wikipedia data for low resource language
@lhoestq I've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya. Also, using another date (e.g. `load_dataset('wikipedia', '20201120.zh', beam_runner='DirectRunner')`) will give the following error message. ``` ValueError: BuilderConfig 20201120.zh not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` I am pretty sure that `https://dumps.wikimedia.org/enwiki/20201120/dumpstatus.json` exists.
Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks!
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Issue with downloading Wikipedia data for low resource language Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks! @lhoestq I've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya. Also, using another date (e.g. `load_dataset('wikipedia', '20201120.zh', beam_runner='DirectRunner')`) will give the following error message. ``` ValueError: BuilderConfig 20201120.zh not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu'] ``` I am pretty sure that `https://dumps.wikimedia.org/enwiki/20201120/dumpstatus.json` exists.
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https://github.com/huggingface/datasets/issues/784
Issue with downloading Wikipedia data for low resource language
For posterity, here's how I got the data I needed: I needed Bengali, so I had to check which dumps are available here: https://dumps.wikimedia.org/bnwiki/ , then I ran: ``` load_dataset("wikipedia", language="bn", date="20211101", beam_runner="DirectRunner") ```
Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks!
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Issue with downloading Wikipedia data for low resource language Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet ``` jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner') su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner') ``` And I get the following error for these two languages: Javanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json ``` Sundanese ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json ``` I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid. Any suggestions on how to handle this issue? Thanks! For posterity, here's how I got the data I needed: I needed Bengali, so I had to check which dumps are available here: https://dumps.wikimedia.org/bnwiki/ , then I ran: ``` load_dataset("wikipedia", language="bn", date="20211101", beam_runner="DirectRunner") ```
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https://github.com/huggingface/datasets/issues/778
Unexpected behavior when loading cached csv file?
Hi ! Thanks for reporting. The same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 . The fix will be available in the next release :)
I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset. Small snippet to reproduce the behavior: ```python import datasets with open("dummy_data.csv", "w") as file: file.write("test,this;text\n") print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names) # ["test", "this;text"] print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names) # still ["test", "this;text"] ``` By the way, thanks a lot for this amazing library! :)
36
Unexpected behavior when loading cached csv file? I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset. Small snippet to reproduce the behavior: ```python import datasets with open("dummy_data.csv", "w") as file: file.write("test,this;text\n") print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names) # ["test", "this;text"] print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names) # still ["test", "this;text"] ``` By the way, thanks a lot for this amazing library! :) Hi ! Thanks for reporting. The same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 . The fix will be available in the next release :)
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https://github.com/huggingface/datasets/issues/778
Unexpected behavior when loading cached csv file?
Thanks for the prompt reply and terribly sorry for the spam! Looking forward to the new release!
I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset. Small snippet to reproduce the behavior: ```python import datasets with open("dummy_data.csv", "w") as file: file.write("test,this;text\n") print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names) # ["test", "this;text"] print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names) # still ["test", "this;text"] ``` By the way, thanks a lot for this amazing library! :)
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Unexpected behavior when loading cached csv file? I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset. Small snippet to reproduce the behavior: ```python import datasets with open("dummy_data.csv", "w") as file: file.write("test,this;text\n") print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names) # ["test", "this;text"] print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names) # still ["test", "this;text"] ``` By the way, thanks a lot for this amazing library! :) Thanks for the prompt reply and terribly sorry for the spam! Looking forward to the new release!
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https://github.com/huggingface/datasets/issues/773
Adding CC-100: Monolingual Datasets from Web Crawl Data
These dataset files are no longer available. https://data.statmt.org/cc-100/ files provided in this link are no longer available. Can anybody fix that issue? @abhishekkrthakur @yjernite
## Adding a Dataset - **Name:** CC-100: Monolingual Datasets from Web Crawl Data - **Description:** https://twitter.com/alex_conneau/status/1321507120848625665 - **Paper:** https://arxiv.org/abs/1911.02116 - **Data:** http://data.statmt.org/cc-100/ - **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
24
Adding CC-100: Monolingual Datasets from Web Crawl Data ## Adding a Dataset - **Name:** CC-100: Monolingual Datasets from Web Crawl Data - **Description:** https://twitter.com/alex_conneau/status/1321507120848625665 - **Paper:** https://arxiv.org/abs/1911.02116 - **Data:** http://data.statmt.org/cc-100/ - **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). These dataset files are no longer available. https://data.statmt.org/cc-100/ files provided in this link are no longer available. Can anybody fix that issue? @abhishekkrthakur @yjernite
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https://github.com/huggingface/datasets/issues/773
Adding CC-100: Monolingual Datasets from Web Crawl Data
Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)
## Adding a Dataset - **Name:** CC-100: Monolingual Datasets from Web Crawl Data - **Description:** https://twitter.com/alex_conneau/status/1321507120848625665 - **Paper:** https://arxiv.org/abs/1911.02116 - **Data:** http://data.statmt.org/cc-100/ - **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
21
Adding CC-100: Monolingual Datasets from Web Crawl Data ## Adding a Dataset - **Name:** CC-100: Monolingual Datasets from Web Crawl Data - **Description:** https://twitter.com/alex_conneau/status/1321507120848625665 - **Paper:** https://arxiv.org/abs/1911.02116 - **Data:** http://data.statmt.org/cc-100/ - **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl. Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)
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https://github.com/huggingface/datasets/issues/771
Using `Dataset.map` with `n_proc>1` print multiple progress bars
Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset. At one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar
When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed.
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Using `Dataset.map` with `n_proc>1` print multiple progress bars When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed. Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset. At one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar
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https://github.com/huggingface/datasets/issues/769
How to choose proper download_mode in function load_dataset?
`download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work. This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing
Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?
17
How to choose proper download_mode in function load_dataset? Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue? `download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work. This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing
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https://github.com/huggingface/datasets/issues/769
How to choose proper download_mode in function load_dataset?
Indeed you should use `features` in this case. ```python features = Features({'text': Value('string'), 'label': Value('float32')}) dataset = load_dataset('csv', data_files=['sst_test.csv'], features=features) ``` Note that because of an issue with the caching when you change the features (see #750 ) you still need to specify the `FORCE_REDOWNLOAD ` flag. I'm working on a fix for this one
Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?
55
How to choose proper download_mode in function load_dataset? Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue? Indeed you should use `features` in this case. ```python features = Features({'text': Value('string'), 'label': Value('float32')}) dataset = load_dataset('csv', data_files=['sst_test.csv'], features=features) ``` Note that because of an issue with the caching when you change the features (see #750 ) you still need to specify the `FORCE_REDOWNLOAD ` flag. I'm working on a fix for this one
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https://github.com/huggingface/datasets/issues/769
How to choose proper download_mode in function load_dataset?
https://github.com/huggingface/datasets/issues/769#issuecomment-717837832 > This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing @lhoestq do you still think we should rename it?
Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?
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How to choose proper download_mode in function load_dataset? Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue? https://github.com/huggingface/datasets/issues/769#issuecomment-717837832 > This makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing @lhoestq do you still think we should rename it?
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https://github.com/huggingface/datasets/issues/769
How to choose proper download_mode in function load_dataset?
It's no big deal, but since it can be confusing to users I think it's worth renaming it, and deprecate `GenerateMode` until `datasets` 2.0 at least. IMO it's confusing to have `download_mode=GenerateMode.something`
Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue?
32
How to choose proper download_mode in function load_dataset? Hi, I am a beginner to datasets and I try to use datasets to load my csv file. my csv file looks like this ``` text,label "Effective but too-tepid biopic",3 "If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4 "Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5 ``` First I try to use this command to load my csv file . ``` python dataset=load_dataset('csv', data_files=['sst_test.csv']) ``` It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this. ``` python import pyarrow as pa from pyarrow import csv read_options = csv.ReadOptions(block_size=1024*1024) parse_options = csv.ParseOptions() convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()}) dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options, parse_options=parse_options, convert_options=convert_options) ``` It keeps the same: ```shell Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210) ``` I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right. Is it a bug? How to choose proper download_mode to avoid this issue? It's no big deal, but since it can be confusing to users I think it's worth renaming it, and deprecate `GenerateMode` until `datasets` 2.0 at least. IMO it's confusing to have `download_mode=GenerateMode.something`
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https://github.com/huggingface/datasets/issues/768
Add a `lazy_map` method to `Dataset` and `DatasetDict`
This is cool! I think some aspects to think about and decide in terms of API are: - do we allow several methods (chained i guess) - how do we inspect the currently set method(s) - how do we control/reset them
The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases: 1. load image on the fly 2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives).
41
Add a `lazy_map` method to `Dataset` and `DatasetDict` The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases: 1. load image on the fly 2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives). This is cool! I think some aspects to think about and decide in terms of API are: - do we allow several methods (chained i guess) - how do we inspect the currently set method(s) - how do we control/reset them
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https://github.com/huggingface/datasets/issues/767
Add option for named splits when using ds.train_test_split
Yes definitely we should give more flexibility to control the name of the splits outputted by `train_test_split`. Related is the very interesting feedback from @bramvanroy on how we should improve this method: https://discuss.huggingface.co/t/how-to-split-main-dataset-into-train-dev-test-as-datasetdict/1090/5 And in particular that it should advantageously be able to split in 3 splits as well instead of just 2 like we copied from sklearn.
### Feature Request 🚀 Can we add a way to name your splits when using the `.train_test_split` function? In almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep. ### Workaround this is my hack for dealin with this, for now :slightly_smiling_face: ```python from datasets import load_dataset ​ ​ ds = load_dataset('imdb') ds['train'], ds['validation'] = ds['train'].train_test_split(.1).values() ```
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Add option for named splits when using ds.train_test_split ### Feature Request 🚀 Can we add a way to name your splits when using the `.train_test_split` function? In almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep. ### Workaround this is my hack for dealin with this, for now :slightly_smiling_face: ```python from datasets import load_dataset ​ ​ ds = load_dataset('imdb') ds['train'], ds['validation'] = ds['train'].train_test_split(.1).values() ``` Yes definitely we should give more flexibility to control the name of the splits outputted by `train_test_split`. Related is the very interesting feedback from @bramvanroy on how we should improve this method: https://discuss.huggingface.co/t/how-to-split-main-dataset-into-train-dev-test-as-datasetdict/1090/5 And in particular that it should advantageously be able to split in 3 splits as well instead of just 2 like we copied from sklearn.
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https://github.com/huggingface/datasets/issues/761
Downloaded datasets are not usable offline
Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already. If we add a way to store the etag/hash locally after the first download, it would allow users to first download the dataset with an internet connection, and still have it working without an internet connection. I'll let you know when we add this feature.
I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset. Is this the intended behavior ? (Sorry, I wrote the the first version of this issue while still on nlp 0.3.0).
75
Downloaded datasets are not usable offline I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset. Is this the intended behavior ? (Sorry, I wrote the the first version of this issue while still on nlp 0.3.0). Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already. If we add a way to store the etag/hash locally after the first download, it would allow users to first download the dataset with an internet connection, and still have it working without an internet connection. I'll let you know when we add this feature.
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Does this HEAD request return 200 on your machine ? ```python import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py") ``` If it returns 200, could you try again to load the dataset ?
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
28
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Does this HEAD request return 200 on your machine ? ```python import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py") ``` If it returns 200, could you try again to load the dataset ?
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Thank you very much for your response. When I run ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py") ``` It returns 200. And I try again to load the dataset. I got the following errors again. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py", line 608, in load_dataset builder_instance.download_and_prepare( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\builder.py", line 475, in download_and_prepare self._download_and_prepare( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "C:\Users\666666\.cache\huggingface\modules\datasets_modules\datasets\cnn_dailymail\0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\cnn_dailymail.py", line 253, in _split_generators dl_paths = dl_manager.download_and_extract(_DL_URLS) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\download_manager.py", line 175, in download downloaded_path_or_paths = map_nested( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 224, in map_nested mapped = [ File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 163, in _single_map_nested return function(data_struct) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py", line 300, in cached_path output_path = get_from_cache( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ Connection error happened but the url was different. I add the following code. ``` requests.head("https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ") ``` This didn't return 200 It returned like this: Traceback (most recent call last): File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 159, in _new_conn conn = connection.create_connection( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\util\connection.py", line 84, in create_connection raise err File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\util\connection.py", line 74, in create_connection sock.connect(sa) TimeoutError: [WinError 10060] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 670, in urlopen httplib_response = self._make_request( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 381, in _make_request self._validate_conn(conn) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 978, in _validate_conn conn.connect() File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 309, in connect conn = self._new_conn() File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 171, in _new_conn raise NewConnectionError( urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001F6060618E0>: Failed to establish a new connection: [WinError 10060]
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
272
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Thank you very much for your response. When I run ``` import requests requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py") ``` It returns 200. And I try again to load the dataset. I got the following errors again. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py", line 608, in load_dataset builder_instance.download_and_prepare( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\builder.py", line 475, in download_and_prepare self._download_and_prepare( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\builder.py", line 531, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "C:\Users\666666\.cache\huggingface\modules\datasets_modules\datasets\cnn_dailymail\0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602\cnn_dailymail.py", line 253, in _split_generators dl_paths = dl_manager.download_and_extract(_DL_URLS) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\download_manager.py", line 254, in download_and_extract return self.extract(self.download(url_or_urls)) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\download_manager.py", line 175, in download downloaded_path_or_paths = map_nested( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 224, in map_nested mapped = [ File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 225, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\py_utils.py", line 163, in _single_map_nested return function(data_struct) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py", line 300, in cached_path output_path = get_from_cache( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py", line 475, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ Connection error happened but the url was different. I add the following code. ``` requests.head("https://drive.google.com/uc?export=download&id=0BwmD_VLjROrfTHk4NFg2SndKcjQ") ``` This didn't return 200 It returned like this: Traceback (most recent call last): File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 159, in _new_conn conn = connection.create_connection( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\util\connection.py", line 84, in create_connection raise err File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\util\connection.py", line 74, in create_connection sock.connect(sa) TimeoutError: [WinError 10060] During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 670, in urlopen httplib_response = self._make_request( File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 381, in _make_request self._validate_conn(conn) File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connectionpool.py", line 978, in _validate_conn conn.connect() File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 309, in connect conn = self._new_conn() File "C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\urllib3\connection.py", line 171, in _new_conn raise NewConnectionError( urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001F6060618E0>: Failed to establish a new connection: [WinError 10060]
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
I can browse the google drive through google chrome. It's weird. I can download the dataset through google drive manually.
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
20
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? I can browse the google drive through google chrome. It's weird. I can download the dataset through google drive manually.
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Is it possible I download the dataset manually from google drive and use it for further test ? How can I do this ? I want to reproduce the model in this link https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16. But I can't download the dataset through load_dataset method . I have tried many times and the connection error always happens .
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
56
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Is it possible I download the dataset manually from google drive and use it for further test ? How can I do this ? I want to reproduce the model in this link https://huggingface.co/patrickvonplaten/bert2bert-cnn_dailymail-fp16. But I can't download the dataset through load_dataset method . I have tried many times and the connection error always happens .
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
The head request should definitely work, not sure what's going on on your side. If you find a way to make it work, please post it here since other users might encounter the same issue. If you don't manage to fix it you can use `load_dataset` on google colab and then save it using `dataset.save_to_disk("path/to/dataset")`. Then you can download the directory on your machine and do ```python from datasets import load_from_disk dataset = load_from_disk("path/to/local/dataset") ```
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
75
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? The head request should definitely work, not sure what's going on on your side. If you find a way to make it work, please post it here since other users might encounter the same issue. If you don't manage to fix it you can use `load_dataset` on google colab and then save it using `dataset.save_to_disk("path/to/dataset")`. Then you can download the directory on your machine and do ```python from datasets import load_from_disk dataset = load_from_disk("path/to/local/dataset") ```
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Hi I want to know if this problem has been solved because I encountered a similar issue. Thanks. `train_data = datasets.load_dataset("xsum", `split="train")` `ConnectionError:` Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/xsum/xsum.py`
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
26
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Hi I want to know if this problem has been solved because I encountered a similar issue. Thanks. `train_data = datasets.load_dataset("xsum", `split="train")` `ConnectionError:` Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/xsum/xsum.py`
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Hi @smile0925 ! Do you have an internet connection ? Are you using some kind of proxy that may block the access to this file ? Otherwise you can try to update `datasets` since we introduced retries for http requests in the 1.2.0 version ``` pip install --upgrade datasets ``` Let me know if that helps.
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
56
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Hi @smile0925 ! Do you have an internet connection ? Are you using some kind of proxy that may block the access to this file ? Otherwise you can try to update `datasets` since we introduced retries for http requests in the 1.2.0 version ``` pip install --upgrade datasets ``` Let me know if that helps.
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Hi @lhoestq Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file. ![image](https://user-images.githubusercontent.com/46243662/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
26
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Hi @lhoestq Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file. ![image](https://user-images.githubusercontent.com/46243662/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png)
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
> Hi @lhoestq > Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file. > ![image](https://user-images.githubusercontent.com/46243662/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png) I have the same problem, have you solved it? Many thanks
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
40
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? > Hi @lhoestq > Oh, may be you are right. I find that my server uses some kind of proxy that block the access to this file. > ![image](https://user-images.githubusercontent.com/46243662/106456211-2ca24180-64c8-11eb-831e-47e9b40e7da4.png) I have the same problem, have you solved it? Many thanks
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https://github.com/huggingface/datasets/issues/759
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
Hi @ZhengxiangShi You can first try whether your network can access these files. I need to use VPN to access these files, so I download the files that cannot be accessed to the local in advance, and then use them in the code. Like this, `train_data = datasets.load_dataset("xsum.py", split="train")`
Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ?
49
(Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py Hey, I want to load the cnn-dailymail dataset for fine-tune. I write the code like this from datasets import load_dataset test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”) And I got the following errors. Traceback (most recent call last): File “test.py”, line 7, in test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset module_path, hash = prepare_module( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module local_path = cached_path(file_path, download_config=download_config) File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path output_path = get_from_cache( File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache raise ConnectionError(“Couldn’t reach {}”.format(url)) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py How can I fix this ? Hi @ZhengxiangShi You can first try whether your network can access these files. I need to use VPN to access these files, so I download the files that cannot be accessed to the local in advance, and then use them in the code. Like this, `train_data = datasets.load_dataset("xsum.py", split="train")`
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https://github.com/huggingface/datasets/issues/758
Process 0 very slow when using num_procs with map to tokenizer
Hi ! Thanks for reporting. Is the distribution of text length of your data evenly distributed across your dataset ? I mean, could it be because the examples in the first part of your dataset are slower to process ? Also could how many CPUs can you use for multiprocessing ? ```python import multiprocessing print(multiprocessing.cpu_count()) ``` Which tokenizer are you using ?
<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```
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Process 0 very slow when using num_procs with map to tokenizer <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ``` Hi ! Thanks for reporting. Is the distribution of text length of your data evenly distributed across your dataset ? I mean, could it be because the examples in the first part of your dataset are slower to process ? Also could how many CPUs can you use for multiprocessing ? ```python import multiprocessing print(multiprocessing.cpu_count()) ``` Which tokenizer are you using ?
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https://github.com/huggingface/datasets/issues/758
Process 0 very slow when using num_procs with map to tokenizer
Using pre trained HF tokenizer. The result is the same with tokenizer multiprocessing off and on. I have (absolutely) no idea about the distribution, but since this issue occurs on all of my datasets(regardless of files), I don't think distribution is the problems. I can use up to 16 cores.
<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```
50
Process 0 very slow when using num_procs with map to tokenizer <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ``` Using pre trained HF tokenizer. The result is the same with tokenizer multiprocessing off and on. I have (absolutely) no idea about the distribution, but since this issue occurs on all of my datasets(regardless of files), I don't think distribution is the problems. I can use up to 16 cores.
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https://github.com/huggingface/datasets/issues/758
Process 0 very slow when using num_procs with map to tokenizer
Ok weird, I don't manage to reproduce this issue on my side. Does it happen even with `num_proc=2` for example ? Also could you provide more details about your OS and the versions of tokenizers/datasets/multiprocess that you're using ?
<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```
39
Process 0 very slow when using num_procs with map to tokenizer <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ``` Ok weird, I don't manage to reproduce this issue on my side. Does it happen even with `num_proc=2` for example ? Also could you provide more details about your OS and the versions of tokenizers/datasets/multiprocess that you're using ?
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https://github.com/huggingface/datasets/issues/758
Process 0 very slow when using num_procs with map to tokenizer
Yes, I can confirm it also happens with ```num_proc=2```. ``` tokenizers 0.9.2 datasets 1.1.2 multiprocess 0.70.10 ``` ``` Linux nipa2020-0629 4.4.0-178-generic #208-Ubuntu SMP Sun Apr 5 23:45:10 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ```
<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```
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Process 0 very slow when using num_procs with map to tokenizer <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ``` Yes, I can confirm it also happens with ```num_proc=2```. ``` tokenizers 0.9.2 datasets 1.1.2 multiprocess 0.70.10 ``` ``` Linux nipa2020-0629 4.4.0-178-generic #208-Ubuntu SMP Sun Apr 5 23:45:10 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ```
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https://github.com/huggingface/datasets/issues/758
Process 0 very slow when using num_procs with map to tokenizer
I can't reproduce on my side unfortunately with the same versions. Do you have issues when doing multiprocessing with python ? ```python from tqdm.auto import tqdm from multiprocess import Pool, RLock def process_data(shard): # implement num_proc = 8 shards = [] # implement, this must be a list of size num_proc with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: results = [pool.apply_async(process_data, shard=shard) for shard in shards] transformed_shards = [r.get() for r in results] ```
<img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ```
73
Process 0 very slow when using num_procs with map to tokenizer <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png"> The code I am using is ``` dataset = load_dataset("text", data_files=[file_path], split='train') dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), num_proc=8) dataset.set_format(type='torch', columns=['input_ids']) dataset.save_to_disk(file_path+'.arrow') ``` I can't reproduce on my side unfortunately with the same versions. Do you have issues when doing multiprocessing with python ? ```python from tqdm.auto import tqdm from multiprocess import Pool, RLock def process_data(shard): # implement num_proc = 8 shards = [] # implement, this must be a list of size num_proc with Pool(num_proc, initargs=(RLock(),), initializer=tqdm.set_lock) as pool: results = [pool.apply_async(process_data, shard=shard) for shard in shards] transformed_shards = [r.get() for r in results] ```
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https://github.com/huggingface/datasets/issues/757
CUDA out of memory
```python tokenizer = FunnelTokenizer.from_pretrained('funnel-transformer/small') def tokenize(batch): return tokenizer(batch['text'], padding='max_length', truncation=True,max_length=512) dataset = load_dataset("bookcorpus",split='train[:1000]').shuffle() dataset = dataset.map(tokenize, batched=True, batch_size=512) # dataset = LineByLineTextDataset( # tokenizer=tokenizer, # file_path="./wiki1000.txt", # block_size=128 # ) data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm=True, mlm_probability=0.15 ) config=FunnelConfig( return_dict=True ) model= FunnelForMaskedLM(config=config) training_args = TrainingArguments( output_dir="./checkpoints", overwrite_output_dir=True, do_train=True, num_train_epochs=1, per_device_train_batch_size=16, per_device_eval_batch_size=16, save_steps=10000, logging_dir='./ptlogs' ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=dataset, ) trainer.train() ```
In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.
64
CUDA out of memory In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK. ```python tokenizer = FunnelTokenizer.from_pretrained('funnel-transformer/small') def tokenize(batch): return tokenizer(batch['text'], padding='max_length', truncation=True,max_length=512) dataset = load_dataset("bookcorpus",split='train[:1000]').shuffle() dataset = dataset.map(tokenize, batched=True, batch_size=512) # dataset = LineByLineTextDataset( # tokenizer=tokenizer, # file_path="./wiki1000.txt", # block_size=128 # ) data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm=True, mlm_probability=0.15 ) config=FunnelConfig( return_dict=True ) model= FunnelForMaskedLM(config=config) training_args = TrainingArguments( output_dir="./checkpoints", overwrite_output_dir=True, do_train=True, num_train_epochs=1, per_device_train_batch_size=16, per_device_eval_batch_size=16, save_steps=10000, logging_dir='./ptlogs' ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=dataset, ) trainer.train() ```
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https://github.com/huggingface/datasets/issues/757
CUDA out of memory
`RuntimeError: CUDA out of memory. Tried to allocate 954.00 MiB (GPU 0; 15.90 GiB total capacity; 14.35 GiB already allocated; 753.75 MiB free; 14.39 GiB reserved in total by PyTorch) Exception raised from malloc at /pytorch/c10/cuda/CUDACachingAllocator.cpp:272 (most recent call first):` part of error output
In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.
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CUDA out of memory In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK. `RuntimeError: CUDA out of memory. Tried to allocate 954.00 MiB (GPU 0; 15.90 GiB total capacity; 14.35 GiB already allocated; 753.75 MiB free; 14.39 GiB reserved in total by PyTorch) Exception raised from malloc at /pytorch/c10/cuda/CUDACachingAllocator.cpp:272 (most recent call first):` part of error output
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https://github.com/huggingface/datasets/issues/757
CUDA out of memory
from funnel model to bert model : error still happened from your dataset to LineByLineTextDataset : error disapeared
In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.
18
CUDA out of memory In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK. from funnel model to bert model : error still happened from your dataset to LineByLineTextDataset : error disapeared
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https://github.com/huggingface/datasets/issues/757
CUDA out of memory
Since you're using a data collator you don't need to tokenizer the dataset using `map`. Could you try not to use `map` and only the data collator instead ? The data collator is supposed to pad to the longest sequence in each batch afaik, instead of padding to 512. Also cc @sgugger
In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.
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CUDA out of memory In your dataset ,cuda run out of memory as long as the trainer begins: however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK. Since you're using a data collator you don't need to tokenizer the dataset using `map`. Could you try not to use `map` and only the data collator instead ? The data collator is supposed to pad to the longest sequence in each batch afaik, instead of padding to 512. Also cc @sgugger
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https://github.com/huggingface/datasets/issues/751
Error loading ms_marco v2.1 using load_dataset()
There was a similar issue in #294 Clearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?
Code: `dataset = load_dataset('ms_marco', 'v2.1')` Error: ``` `--------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) <ipython-input-16-34378c057212> in <module>() 9 10 # Downloading and loading a dataset ---> 11 dataset = load_dataset('ms_marco', 'v2.1') 10 frames /usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx) 353 """ 354 try: --> 355 obj, end = self.scan_once(s, idx) 356 except StopIteration as err: 357 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660) ` ```
31
Error loading ms_marco v2.1 using load_dataset() Code: `dataset = load_dataset('ms_marco', 'v2.1')` Error: ``` `--------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) <ipython-input-16-34378c057212> in <module>() 9 10 # Downloading and loading a dataset ---> 11 dataset = load_dataset('ms_marco', 'v2.1') 10 frames /usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx) 353 """ 354 try: --> 355 obj, end = self.scan_once(s, idx) 356 except StopIteration as err: 357 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660) ` ``` There was a similar issue in #294 Clearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?
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0.2618827224, 0.065392293, -0.001106308, 0.3464151621, 0.1690858454, -0.1502878368, 0.0528866388, -0.2060887367 ]
https://github.com/huggingface/datasets/issues/751
Error loading ms_marco v2.1 using load_dataset()
I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have. Let me know if clearing your cache fixes the problem
Code: `dataset = load_dataset('ms_marco', 'v2.1')` Error: ``` `--------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) <ipython-input-16-34378c057212> in <module>() 9 10 # Downloading and loading a dataset ---> 11 dataset = load_dataset('ms_marco', 'v2.1') 10 frames /usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx) 353 """ 354 try: --> 355 obj, end = self.scan_once(s, idx) 356 except StopIteration as err: 357 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660) ` ```
29
Error loading ms_marco v2.1 using load_dataset() Code: `dataset = load_dataset('ms_marco', 'v2.1')` Error: ``` `--------------------------------------------------------------------------- JSONDecodeError Traceback (most recent call last) <ipython-input-16-34378c057212> in <module>() 9 10 # Downloading and loading a dataset ---> 11 dataset = load_dataset('ms_marco', 'v2.1') 10 frames /usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx) 353 """ 354 try: --> 355 obj, end = self.scan_once(s, idx) 356 except StopIteration as err: 357 raise JSONDecodeError("Expecting value", s, err.value) from None JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660) ` ``` I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have. Let me know if clearing your cache fixes the problem
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0.2241238505, 0.1163618937, -0.0124492049, 0.3833835125, 0.1507354677, -0.1480349153, 0.0213338025, -0.1592855006 ]
https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
Small poll @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen . As stated in the XGLUE paper: https://arxiv.org/pdf/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: ![Screenshot from 2020-11-04 15-02-17](https://user-images.githubusercontent.com/23423619/98120893-d7499a80-1eae-11eb-9d0b-57dfe5d4ee68.png) So, I'd suggest to have exactly 11 "language-independent" configs: "ner", "pos", ... and give the sample in each dataset in the config a "language" label being one of "ar", "bg", .... => To me this makes more sense than making languaga specific config, *e.g.* "ner-de", ...especially because training data is only available in English. Do you guys agree?
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
105
[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance Small poll @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen . As stated in the XGLUE paper: https://arxiv.org/pdf/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language *cf.* here: ![Screenshot from 2020-11-04 15-02-17](https://user-images.githubusercontent.com/23423619/98120893-d7499a80-1eae-11eb-9d0b-57dfe5d4ee68.png) So, I'd suggest to have exactly 11 "language-independent" configs: "ner", "pos", ... and give the sample in each dataset in the config a "language" label being one of "ar", "bg", .... => To me this makes more sense than making languaga specific config, *e.g.* "ner-de", ...especially because training data is only available in English. Do you guys agree?
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc... This is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
50
[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance In this case we should have named splits, so config `ner` has splits `train`, `validation`, `test-en`, `test-ar`, `test-bg`, etc... This is more in the spirit of the task afaiu, and will avoid making users do the filtering step themselves when testing different models or different configurations of the same model.
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
I see your point! I think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. Good for me @yjernite ! What do the others think? @lhoestq
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
49
[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance I see your point! I think this would be quite feasible to do and makes sense to me as well! In the paper results are reported per language, so it seems more natural to do it this way. Good for me @yjernite ! What do the others think? @lhoestq
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this. See: https://github.com/huggingface/datasets/pull/802
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
24
[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance Okey actually not that easy to add things like `test-de` to `datasets` => this would be the first dataset to have this. See: https://github.com/huggingface/datasets/pull/802
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
IMO we should have one config per language. That's what we're doing for xnli, xtreme etc. Having split names that depend on the language seems wrong. We should try to avoid split names that are not train/val/test. Sorry for late response on this one
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
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[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance IMO we should have one config per language. That's what we're doing for xnli, xtreme etc. Having split names that depend on the language seems wrong. We should try to avoid split names that are not train/val/test. Sorry for late response on this one
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0.0073491619, -0.1698816121, 0.2085607499, -0.316673696, 0.2862682045, -0.0598952882 ]
https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
@lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https://www.tau-nlp.org/commonsenseqa with their train-sanity or dev-sanity splits
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
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[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance @lhoestq agreed on having one config per language, but we also need to be able to have different split names and people are going to want to use hyphens, so we should at the very least warn them why it's failing :) E.g. for ANLI with different stages of data (currently using underscores) or https://www.tau-nlp.org/commonsenseqa with their train-sanity or dev-sanity splits
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0.0192545634, 0.0247732587, 0.2521754205, -0.3240346313, 0.2067229897, -0.2375119179 ]
https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
Really cool dataset 👍 btw. does Transformers support all 11 tasks 🤔 would be awesome to have a xglue script (like the "normal" glue one)
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
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[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance Really cool dataset 👍 btw. does Transformers support all 11 tasks 🤔 would be awesome to have a xglue script (like the "normal" glue one)
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
Just to make sure this is what we want here. If we add one config per language, this means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. I think it could be quite confusing for the user to have ```python train_dataset = load_dataset("xglue", "ner-de", split="train") ``` in English even though it's `ner-de`. To be honest, I'd prefer: ```python train_dataset = load_dataset("xglue", "ner", split="train") test_dataset_de = load_dataset("xglue", "ner", split="test-de") test_dataset_fr = load_dataset("xglue", "ner", split="test-fr") ``` here
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
107
[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance Just to make sure this is what we want here. If we add one config per language, this means that this dataset ends up with well over 100 different configs most of which will have the same `train` split. The train split is always in English. Also, I'm not sure whether it's better for the user to be honest. I think it could be quite confusing for the user to have ```python train_dataset = load_dataset("xglue", "ner-de", split="train") ``` in English even though it's `ner-de`. To be honest, I'd prefer: ```python train_dataset = load_dataset("xglue", "ner", split="train") test_dataset_de = load_dataset("xglue", "ner", split="test-de") test_dataset_fr = load_dataset("xglue", "ner", split="test-fr") ``` here
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https://github.com/huggingface/datasets/issues/749
[XGLUE] Adding new dataset
Oh yes right I didn't notice the train set was always in english sorry. Moreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language). So to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. Something like your latest example patrick is fine imo : ```python train_dataset = load_dataset("xglue", "ner", split="train") test_dataset_de = load_dataset("xglue", "ner", split="test.de") ``` I just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\w+` regex), and usually we specify the language after a point.
XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance
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[XGLUE] Adding new dataset XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf). I'm planning on adding the dataset to the library myself in a couple of weeks. Also tagging @JetRunner @qiweizhen in case I need some guidance Oh yes right I didn't notice the train set was always in english sorry. Moreover it seems that the way this dataset is used is to pick a pretrained multilingual model, fine-tune it on the english train set and then evaluate on each test set (one per language). So to better fit the usual usage of this dataset, I agree that it's better to have one test split per language. Something like your latest example patrick is fine imo : ```python train_dataset = load_dataset("xglue", "ner", split="train") test_dataset_de = load_dataset("xglue", "ner", split="test.de") ``` I just replace test-de with test.de since `-` is not allowed for split names (it has to follow the `\w+` regex), and usually we specify the language after a point.
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Thank you ! Could you provide a csv file that reproduces the error ? It doesn't have to be one of your dataset. As long as it reproduces the error That would help a lot !
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
36
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Thank you ! Could you provide a csv file that reproduces the error ? It doesn't have to be one of your dataset. As long as it reproduces the error That would help a lot !
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
I think another good example is the following: ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sts-dev.csv"], delimiter="\t", column_names=["one", "two", "three", "four", "score", "sentence1", "sentence2"], script_version="master")` ` Displayed error `CSV parse error: Expected 7 columns, got 6` even tough I put 7 columns. First four columns from the csv don't have a name, so I've named them by default. The csv file is the .dev file from STSb benchmark dataset.
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
72
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you I think another good example is the following: ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sts-dev.csv"], delimiter="\t", column_names=["one", "two", "three", "four", "score", "sentence1", "sentence2"], script_version="master")` ` Displayed error `CSV parse error: Expected 7 columns, got 6` even tough I put 7 columns. First four columns from the csv don't have a name, so I've named them by default. The csv file is the .dev file from STSb benchmark dataset.
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi, seems I also can't read csv file. I was trying with a dummy csv with only three rows. ``` text,label I hate google,negative I love Microsoft,positive I don't like you,negative ``` I was using the HuggingFace image in Paperspace Gradient (datasets==1.1.3). The following code doesn't work: ``` from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",") ``` It outputs the following: ``` Using custom data configuration default Downloading and preparing dataset csv/default-3b6254ff4dd403e5 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data. ``` But `len(dataset)` gives `1` and I can't access rows with indexing `dataset[0]` (it gives `KeyError: 0`). However, loading from pandas dataframe is working. ``` from datasets import Dataset import pandas as pd df = pd.read_csv('test_data.csv') dataset = Dataset.from_pandas(df) ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
141
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi, seems I also can't read csv file. I was trying with a dummy csv with only three rows. ``` text,label I hate google,negative I love Microsoft,positive I don't like you,negative ``` I was using the HuggingFace image in Paperspace Gradient (datasets==1.1.3). The following code doesn't work: ``` from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",") ``` It outputs the following: ``` Using custom data configuration default Downloading and preparing dataset csv/default-3b6254ff4dd403e5 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-3b6254ff4dd403e5/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2. Subsequent calls will reuse this data. ``` But `len(dataset)` gives `1` and I can't access rows with indexing `dataset[0]` (it gives `KeyError: 0`). However, loading from pandas dataframe is working. ``` from datasets import Dataset import pandas as pd df = pd.read_csv('test_data.csv') dataset = Dataset.from_pandas(df) ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
This is because load_dataset without `split=` returns a dictionary of split names (train/validation/test) to dataset. You can do ```python from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",") print(dataset["train"][0]) ``` Or if you want to directly get the train split: ```python from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",", split="train") print(dataset[0]) ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
55
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you This is because load_dataset without `split=` returns a dictionary of split names (train/validation/test) to dataset. You can do ```python from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",") print(dataset["train"][0]) ``` Or if you want to directly get the train split: ```python from datasets import load_dataset dataset = load_dataset('csv', script_version="master", data_files=['test_data.csv'], delimiter=",", split="train") print(dataset[0]) ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Good point Design question for us, though: should `load_dataset` when no split is specified and only one split is present in the dataset (common use case with CSV/text/JSON datasets) return a `Dataset` instead of a `DatsetDict`? I feel like it's often what the user is expecting. I break a bit the paradigm of a unique return type but since this library is designed for widespread DS people more than CS people usage I would tend to think that UX should take precedence over CS reasons. What do you think?
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
89
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Good point Design question for us, though: should `load_dataset` when no split is specified and only one split is present in the dataset (common use case with CSV/text/JSON datasets) return a `Dataset` instead of a `DatsetDict`? I feel like it's often what the user is expecting. I break a bit the paradigm of a unique return type but since this library is designed for widespread DS people more than CS people usage I would tend to think that UX should take precedence over CS reasons. What do you think?
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications. I'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas. For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
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load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications. I'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas. For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ?
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Thanks for your quick response! I'm fine with specifying the split as @lhoestq suggested. My only concern is when I'm loading from python dict or pandas, the library returns a dataset instead of a dictionary of datasets when no split is specified. I know that they use a different function `Dataset.from_dict` or `Dataset.from_pandas` but the text/csv files use `load_dataset()`. However, to the user, they do the same task and we probably expect them to have the same behavior.
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
78
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Thanks for your quick response! I'm fine with specifying the split as @lhoestq suggested. My only concern is when I'm loading from python dict or pandas, the library returns a dataset instead of a dictionary of datasets when no split is specified. I know that they use a different function `Dataset.from_dict` or `Dataset.from_pandas` but the text/csv files use `load_dataset()`. However, to the user, they do the same task and we probably expect them to have the same behavior.
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
``` from datasets import load_dataset dataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=",", split=['train', 'test']) ``` I was running the above line, but got this error. ```ValueError: Unknown split "test". Should be one of ['train'].``` The data is amazon product data. I load the Video_Games_5.json.gz data into pandas and save it as csv file. and then load the csv file using the above code. I thought, ```split=['train', 'test']``` would split the data into train and test. did I misunderstood? Thank you!
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
78
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you ``` from datasets import load_dataset dataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=",", split=['train', 'test']) ``` I was running the above line, but got this error. ```ValueError: Unknown split "test". Should be one of ['train'].``` The data is amazon product data. I load the Video_Games_5.json.gz data into pandas and save it as csv file. and then load the csv file using the above code. I thought, ```split=['train', 'test']``` would split the data into train and test. did I misunderstood? Thank you!
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi ! the `split` argument in `load_dataset` is used to select the splits you want among the available splits. However when loading a csv with a single file as you did, only a `train` split is available by default. Indeed since `data_files='./amazon_data/Video_Games_5.csv'` is equivalent to `data_files={"train": './amazon_data/Video_Games_5.csv'}`, you can get a dataset with ```python from datasets import load_dataset dataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=",", split="train") ``` And then to get both a train and test split you can do ```python dataset = dataset.train_test_split() print(dataset.keys()) # ['train', 'test'] ``` Also note that a csv dataset may have several available splits if it is defined this way: ```python from datasets import load_dataset dataset = load_dataset('csv', data_files={ "train": './amazon_data/Video_Games_5_train.csv', "test": './amazon_data/Video_Games_5_test.csv' }) print(dataset.keys()) # ['train', 'test'] ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
123
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi ! the `split` argument in `load_dataset` is used to select the splits you want among the available splits. However when loading a csv with a single file as you did, only a `train` split is available by default. Indeed since `data_files='./amazon_data/Video_Games_5.csv'` is equivalent to `data_files={"train": './amazon_data/Video_Games_5.csv'}`, you can get a dataset with ```python from datasets import load_dataset dataset = load_dataset('csv', data_files='./amazon_data/Video_Games_5.csv', delimiter=",", split="train") ``` And then to get both a train and test split you can do ```python dataset = dataset.train_test_split() print(dataset.keys()) # ['train', 'test'] ``` Also note that a csv dataset may have several available splits if it is defined this way: ```python from datasets import load_dataset dataset = load_dataset('csv', data_files={ "train": './amazon_data/Video_Games_5_train.csv', "test": './amazon_data/Video_Games_5_test.csv' }) print(dataset.keys()) # ['train', 'test'] ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
> In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications. > I'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas. > > For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ? Yes maybe this would be good. I think having to select 'train' from the resulting object why the user gave no split information is a confusing and not intuitive behavior.
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
107
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you > In this case the user expects to get only one dataset object instead of the dictionary of datasets since only one csv file was specified without any split specifications. > I'm ok with returning the dataset object if no split specifications are given for text/json/csv/pandas. > > For the other datasets ton the other hand the user doesn't know in advance the splits so I would keep the dictionary by default. What do you think ? Yes maybe this would be good. I think having to select 'train' from the resulting object why the user gave no split information is a confusing and not intuitive behavior.
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
> Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. > > `from datasets import load_dataset` > `dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master")` > > Displayed error: > `... ArrowInvalid: CSV parse error: Expected 2 columns, got 1` I'm also facing the same issue when trying to load from a csv file locally: ```python from nlp import load_dataset dataset = load_dataset('csv', data_files='sample_data.csv') ``` Error when executed from Google Colab: ```python ArrowInvalid Traceback (most recent call last) <ipython-input-34-79a8d4f65ed6> in <module>() 1 from nlp import load_dataset ----> 2 dataset = load_dataset('csv', data_files='sample_data.csv') 9 frames /usr/local/lib/python3.7/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 547 # Download and prepare data 548 builder_instance.download_and_prepare( --> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, 550 ) 551 /usr/local/lib/python3.7/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 461 if not downloaded_from_gcs: 462 self._download_and_prepare( --> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 464 ) 465 # Sync info /usr/local/lib/python3.7/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 535 try: 536 # Prepare split will record examples associated to the split --> 537 self._prepare_split(split_generator, **prepare_split_kwargs) 538 except OSError: 539 raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) /usr/local/lib/python3.7/dist-packages/nlp/builder.py in _prepare_split(self, split_generator) 863 864 generator = self._generate_tables(**split_generator.gen_kwargs) --> 865 for key, table in utils.tqdm(generator, unit=" tables", leave=False): 866 writer.write_table(table) 867 num_examples, num_bytes = writer.finalize() /usr/local/lib/python3.7/dist-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs) 213 def __iter__(self, *args, **kwargs): 214 try: --> 215 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs): 216 # return super(tqdm...) will not catch exception 217 yield obj /usr/local/lib/python3.7/dist-packages/tqdm/std.py in __iter__(self) 1102 fp_write=getattr(self.fp, 'write', sys.stderr.write)) 1103 -> 1104 for obj in iterable: 1105 yield obj 1106 # Update and possibly print the progressbar. /usr/local/lib/python3.7/dist-packages/nlp/datasets/csv/ede98314803c971fef04bcee45d660c62f3332e8a74491e0b876106f3d99bd9b/csv.py in _generate_tables(self, files) 78 read_options=self.config.pa_read_options, 79 parse_options=self.config.pa_parse_options, ---> 80 convert_options=self.config.convert_options, 81 ) 82 yield i, pa_table /usr/local/lib/python3.7/dist-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: CSV parse error: Expected 1 columns, got 8 ``` Version: ``` nlp==0.4.0 ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
319
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you > Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. > > `from datasets import load_dataset` > `dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master")` > > Displayed error: > `... ArrowInvalid: CSV parse error: Expected 2 columns, got 1` I'm also facing the same issue when trying to load from a csv file locally: ```python from nlp import load_dataset dataset = load_dataset('csv', data_files='sample_data.csv') ``` Error when executed from Google Colab: ```python ArrowInvalid Traceback (most recent call last) <ipython-input-34-79a8d4f65ed6> in <module>() 1 from nlp import load_dataset ----> 2 dataset = load_dataset('csv', data_files='sample_data.csv') 9 frames /usr/local/lib/python3.7/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 547 # Download and prepare data 548 builder_instance.download_and_prepare( --> 549 download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, 550 ) 551 /usr/local/lib/python3.7/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs) 461 if not downloaded_from_gcs: 462 self._download_and_prepare( --> 463 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 464 ) 465 # Sync info /usr/local/lib/python3.7/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 535 try: 536 # Prepare split will record examples associated to the split --> 537 self._prepare_split(split_generator, **prepare_split_kwargs) 538 except OSError: 539 raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) /usr/local/lib/python3.7/dist-packages/nlp/builder.py in _prepare_split(self, split_generator) 863 864 generator = self._generate_tables(**split_generator.gen_kwargs) --> 865 for key, table in utils.tqdm(generator, unit=" tables", leave=False): 866 writer.write_table(table) 867 num_examples, num_bytes = writer.finalize() /usr/local/lib/python3.7/dist-packages/tqdm/notebook.py in __iter__(self, *args, **kwargs) 213 def __iter__(self, *args, **kwargs): 214 try: --> 215 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs): 216 # return super(tqdm...) will not catch exception 217 yield obj /usr/local/lib/python3.7/dist-packages/tqdm/std.py in __iter__(self) 1102 fp_write=getattr(self.fp, 'write', sys.stderr.write)) 1103 -> 1104 for obj in iterable: 1105 yield obj 1106 # Update and possibly print the progressbar. /usr/local/lib/python3.7/dist-packages/nlp/datasets/csv/ede98314803c971fef04bcee45d660c62f3332e8a74491e0b876106f3d99bd9b/csv.py in _generate_tables(self, files) 78 read_options=self.config.pa_read_options, 79 parse_options=self.config.pa_parse_options, ---> 80 convert_options=self.config.convert_options, 81 ) 82 yield i, pa_table /usr/local/lib/python3.7/dist-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: CSV parse error: Expected 1 columns, got 8 ``` Version: ``` nlp==0.4.0 ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi @kauvinlucas You can use the latest versions of `datasets` to do this. To do so, just `pip install datasets` instead of `nlp` (the library was renamed) and then ```python from datasets import load_dataset dataset = load_dataset('csv', data_files='sample_data.csv')
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
38
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi @kauvinlucas You can use the latest versions of `datasets` to do this. To do so, just `pip install datasets` instead of `nlp` (the library was renamed) and then ```python from datasets import load_dataset dataset = load_dataset('csv', data_files='sample_data.csv')
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi I'm having a different problem with loading local csv. ```Python from datasets import load_dataset dataset = load_dataset('csv', data_files='sample.csv') ``` gives `ValueError: Specified named and prefix; you can only specify one.` error versions: - datasets: 1.1.3 - python: 3.9.6 - pyarrow: 2.0.0
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
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load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi I'm having a different problem with loading local csv. ```Python from datasets import load_dataset dataset = load_dataset('csv', data_files='sample.csv') ``` gives `ValueError: Specified named and prefix; you can only specify one.` error versions: - datasets: 1.1.3 - python: 3.9.6 - pyarrow: 2.0.0
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Oh.. I figured it out. According to issue #[42387](https://github.com/pandas-dev/pandas/issues/42387) from pandas, this new version does not accept None for both parameters (which was being done by the repo I'm testing). Dowgrading Pandas==1.0.4 and Python==3.8 worked
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
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load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Oh.. I figured it out. According to issue #[42387](https://github.com/pandas-dev/pandas/issues/42387) from pandas, this new version does not accept None for both parameters (which was being done by the repo I'm testing). Dowgrading Pandas==1.0.4 and Python==3.8 worked
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi, I got an `OSError: Cannot find data file. ` when I tried to use load_dataset with tsv files. I have checked the paths, and they are correct. versions - python: 3.7.9 - datasets: 1.1.3 - pyarrow: 2.0.0 - transformers: 4.2.2 ~~~ data_files = {"train": "train.tsv", "test",: "test.tsv"} datasets = load_dataset("csv", data_files=data_files, delimiter="\t") ~~~ The entire Error message is on below: ```08/14/2021 16:55:44 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/data/0/val/label1.tsv 08/14/2021 16:55:44 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/data/unlabel/test.tsv Using custom data configuration default Downloading and preparing dataset csv/default-00a4200ae8507533 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-00a4200ae8507533/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 592, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 944, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 307, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "run_glue.py", line 484, in <module> main() File "run_glue.py", line 243, in main datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset ignore_verifications=ignore_verifications, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 594, in _download_and_prepare raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) OSError: Cannot find data file. ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
229
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi, I got an `OSError: Cannot find data file. ` when I tried to use load_dataset with tsv files. I have checked the paths, and they are correct. versions - python: 3.7.9 - datasets: 1.1.3 - pyarrow: 2.0.0 - transformers: 4.2.2 ~~~ data_files = {"train": "train.tsv", "test",: "test.tsv"} datasets = load_dataset("csv", data_files=data_files, delimiter="\t") ~~~ The entire Error message is on below: ```08/14/2021 16:55:44 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/data/0/val/label1.tsv 08/14/2021 16:55:44 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/data/unlabel/test.tsv Using custom data configuration default Downloading and preparing dataset csv/default-00a4200ae8507533 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-00a4200ae8507533/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 592, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 944, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 307, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "run_glue.py", line 484, in <module> main() File "run_glue.py", line 243, in main datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 610, in load_dataset ignore_verifications=ignore_verifications, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 515, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 594, in _download_and_prepare raise OSError("Cannot find data file. " + (self.manual_download_instructions or "")) OSError: Cannot find data file. ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi ! It looks like the error stacktrace doesn't match with your code snippet. What error do you get when running this ? ``` data_files = {"train": "train.tsv", "test",: "test.tsv"} datasets = load_dataset("csv", data_files=data_files, delimiter="\t") ``` can you check that both tsv files are in the same folder as the current working directory of your shell ?
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
57
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi ! It looks like the error stacktrace doesn't match with your code snippet. What error do you get when running this ? ``` data_files = {"train": "train.tsv", "test",: "test.tsv"} datasets = load_dataset("csv", data_files=data_files, delimiter="\t") ``` can you check that both tsv files are in the same folder as the current working directory of your shell ?
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi @lhoestq, Below is the entire error message after I move both tsv files to the same directory. It's the same with I got before. ``` /projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.) return torch._C._cuda_getDeviceCount() > 0 08/29/2021 22:56:43 - WARNING - __main__ - Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False 08/29/2021 22:56:43 - INFO - __main__ - Training/evaluation parameters TrainingArguments(output_dir=/projectnb/media-framing/pred_result/label1/, overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=True, evaluation_strategy=EvaluationStrategy.NO, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=8.0, max_steps=-1, lr_scheduler_type=SchedulerType.LINEAR, warmup_steps=0, logging_dir=runs/Aug29_22-56-43_scc1, logging_first_step=False, logging_steps=500, save_steps=3000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level=O1, fp16_backend=auto, local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name=/projectnb/media-framing/pred_result/label1/, disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, _n_gpu=0) 08/29/2021 22:56:43 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/temp_train.tsv 08/29/2021 22:56:43 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/temp_test.tsv 08/29/2021 22:56:43 - WARNING - datasets.builder - Using custom data configuration default-df627c23ac0e98ec Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 1166, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 428, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "run_glue.py", line 487, in <module> main() File "run_glue.py", line 244, in main datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset use_auth_token=use_auth_token, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 699, in _download_and_prepare + str(e) OSError: Cannot find data file. Original error: error closing file ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
311
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi @lhoestq, Below is the entire error message after I move both tsv files to the same directory. It's the same with I got before. ``` /projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.) return torch._C._cuda_getDeviceCount() > 0 08/29/2021 22:56:43 - WARNING - __main__ - Process rank: -1, device: cpu, n_gpu: 0distributed training: False, 16-bits training: False 08/29/2021 22:56:43 - INFO - __main__ - Training/evaluation parameters TrainingArguments(output_dir=/projectnb/media-framing/pred_result/label1/, overwrite_output_dir=True, do_train=True, do_eval=False, do_predict=True, evaluation_strategy=EvaluationStrategy.NO, prediction_loss_only=False, per_device_train_batch_size=8, per_device_eval_batch_size=8, gradient_accumulation_steps=1, eval_accumulation_steps=None, learning_rate=5e-05, weight_decay=0.0, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=8.0, max_steps=-1, lr_scheduler_type=SchedulerType.LINEAR, warmup_steps=0, logging_dir=runs/Aug29_22-56-43_scc1, logging_first_step=False, logging_steps=500, save_steps=3000, save_total_limit=None, no_cuda=False, seed=42, fp16=False, fp16_opt_level=O1, fp16_backend=auto, local_rank=-1, tpu_num_cores=None, tpu_metrics_debug=False, debug=False, dataloader_drop_last=False, eval_steps=500, dataloader_num_workers=0, past_index=-1, run_name=/projectnb/media-framing/pred_result/label1/, disable_tqdm=False, remove_unused_columns=True, label_names=None, load_best_model_at_end=False, metric_for_best_model=None, greater_is_better=None, ignore_data_skip=False, sharded_ddp=False, deepspeed=None, label_smoothing_factor=0.0, adafactor=False, _n_gpu=0) 08/29/2021 22:56:43 - INFO - __main__ - load a local file for train: /project/media-framing/transformer4/temp_train.tsv 08/29/2021 22:56:43 - INFO - __main__ - load a local file for test: /project/media-framing/transformer4/temp_test.tsv 08/29/2021 22:56:43 - WARNING - datasets.builder - Using custom data configuration default-df627c23ac0e98ec Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 1166, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 428, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "run_glue.py", line 487, in <module> main() File "run_glue.py", line 244, in main datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset use_auth_token=use_auth_token, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 699, in _download_and_prepare + str(e) OSError: Cannot find data file. Original error: error closing file ```
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0.2264323384, 0.2564281225, -0.1877182424, -0.20467861, -0.3282541037 ]
https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi ! Can you try running this into a python shell directly ? ```python import os from datasets import load_dataset data_files = {"train": "train.tsv", "test": "test.tsv"} assert all(os.path.isfile(data_file) for data_file in data_files.values()), "Couln't find files" datasets = load_dataset("csv", data_files=data_files, delimiter="\t") print("success !") ``` This way all the code from `run_glue.py` doesn't interfere with our tests :)
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
56
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi ! Can you try running this into a python shell directly ? ```python import os from datasets import load_dataset data_files = {"train": "train.tsv", "test": "test.tsv"} assert all(os.path.isfile(data_file) for data_file in data_files.values()), "Couln't find files" datasets = load_dataset("csv", data_files=data_files, delimiter="\t") print("success !") ``` This way all the code from `run_glue.py` doesn't interfere with our tests :)
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi @lhoestq, Below is what I got from terminal after I copied and run your code. I think the files themselves are good since there is no assertion error. ``` Using custom data configuration default-df627c23ac0e98ec Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 1166, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 428, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "test.py", line 7, in <module> datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset use_auth_token=use_auth_token, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 699, in _download_and_prepare + str(e) OSError: Cannot find data file. Original error: error closing file ```
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
160
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi @lhoestq, Below is what I got from terminal after I copied and run your code. I think the files themselves are good since there is no assertion error. ``` Using custom data configuration default-df627c23ac0e98ec Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /usr4/cs542sp/hey1/.cache/huggingface/datasets/csv/default-df627c23ac0e98ec/0.0.0/9144e0a4e8435090117cea53e6c7537173ef2304525df4a077c435d8ee7828ff... Traceback (most recent call last): File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 1166, in _prepare_split num_examples, num_bytes = writer.finalize() File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/arrow_writer.py", line 428, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file During handling of the above exception, another exception occurred: Traceback (most recent call last): File "test.py", line 7, in <module> datasets = load_dataset("csv", data_files=data_files, delimiter="\t") File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/load.py", line 852, in load_dataset use_auth_token=use_auth_token, File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 616, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/projectnb2/media-framing/env-trans4/lib/python3.7/site-packages/datasets/builder.py", line 699, in _download_and_prepare + str(e) OSError: Cannot find data file. Original error: error closing file ```
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Hi, could this be a permission error ? I think it fails to close the arrow file that contains the data from your CSVs in the cache. By default datasets are cached in `~/.cache/huggingface/datasets`, could you check that you have the right permissions ? You can also try to change the cache directory by passing `cache_dir="path/to/my/cache/dir"` to `load_dataset`.
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
58
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Hi, could this be a permission error ? I think it fails to close the arrow file that contains the data from your CSVs in the cache. By default datasets are cached in `~/.cache/huggingface/datasets`, could you check that you have the right permissions ? You can also try to change the cache directory by passing `cache_dir="path/to/my/cache/dir"` to `load_dataset`.
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https://github.com/huggingface/datasets/issues/743
load_dataset for CSV files not working
Thank you!! @lhoestq For some reason, I don't have the default path for datasets to cache, maybe because I work from a remote system. The issue solved after I pass the `cache_dir` argument to the function. Thank you very much!!
Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you
40
load_dataset for CSV files not working Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets. ` from datasets import load_dataset ` ` dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master") ` Displayed error: ` ... ArrowInvalid: CSV parse error: Expected 2 columns, got 1 ` I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains. I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset. https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing Are there any work around for it ? Thank you Thank you!! @lhoestq For some reason, I don't have the default path for datasets to cache, maybe because I work from a remote system. The issue solved after I pass the `cache_dir` argument to the function. Thank you very much!!
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
Thanks for reporting. In theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues. Could you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ? You can just copy paste what's inside `_generate_examples` and remove all the code for `datasets` (remove yield). If the RAM usage stays low after 600 examples it means that it comes from some sort of memory leak in the library, or with pyarrow.
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
96
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) Thanks for reporting. In theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues. Could you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ? You can just copy paste what's inside `_generate_examples` and remove all the code for `datasets` (remove yield). If the RAM usage stays low after 600 examples it means that it comes from some sort of memory leak in the library, or with pyarrow.
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
Here's an equivalent loading code: ```python images_path = "PHOENIX-2014-T-release-v3/PHOENIX-2014-T/features/fullFrame-210x260px/train" for dir_path in tqdm(os.listdir(images_path)): frames_path = os.path.join(images_path, dir_path) np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() ``` The process takes 0.3% of memory, even after 1000 examples on the small machine with 120GB RAM. I guess something in the datasets library doesn't release the reference to the objects I'm yielding, but no idea how to test for this
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
75
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) Here's an equivalent loading code: ```python images_path = "PHOENIX-2014-T-release-v3/PHOENIX-2014-T/features/fullFrame-210x260px/train" for dir_path in tqdm(os.listdir(images_path)): frames_path = os.path.join(images_path, dir_path) np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() ``` The process takes 0.3% of memory, even after 1000 examples on the small machine with 120GB RAM. I guess something in the datasets library doesn't release the reference to the objects I'm yielding, but no idea how to test for this
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
I've had similar issues with Arrow once. I'll investigate... For now maybe we can simply use the images paths in the dataset you want to add. I don't expect to fix this memory issue until 1-2 weeks unfortunately. Then we can just update the dataset with the images. What do you think ?
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
53
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) I've had similar issues with Arrow once. I'll investigate... For now maybe we can simply use the images paths in the dataset you want to add. I don't expect to fix this memory issue until 1-2 weeks unfortunately. Then we can just update the dataset with the images. What do you think ?
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
If it's just 1-2 weeks, I think it's best if we wait. I don't think it is very urgent to add it, and it will be much more useful with the images loaded rather than not (the images are low resolution, and thus papers using this dataset actually fit the entire video into memory anyway) I'll keep working on other datasets in the meanwhile :)
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
65
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) If it's just 1-2 weeks, I think it's best if we wait. I don't think it is very urgent to add it, and it will be much more useful with the images loaded rather than not (the images are low resolution, and thus papers using this dataset actually fit the entire video into memory anyway) I'll keep working on other datasets in the meanwhile :)
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
Ok found the issue. This is because the batch size used by the writer is set to 10 000 elements by default so it would load your full dataset in memory (the writer has a buffer that flushes only after each batch). Moreover to write in Apache Arrow we have to use python objects so what's stored inside the ArrowWriter's buffer is actually python integers (32 bits). Lowering the batch size to 10 should do the job. I will add a flag to the DatasetBuilder class of dataset scripts, so that we can customize the batch size.
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
97
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) Ok found the issue. This is because the batch size used by the writer is set to 10 000 elements by default so it would load your full dataset in memory (the writer has a buffer that flushes only after each batch). Moreover to write in Apache Arrow we have to use python objects so what's stored inside the ArrowWriter's buffer is actually python integers (32 bits). Lowering the batch size to 10 should do the job. I will add a flag to the DatasetBuilder class of dataset scripts, so that we can customize the batch size.
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
Thanks, that's awesome you managed to find the problem. About the 32 bits - really? there isn't a way to serialize the numpy array somehow? 32 bits would take 4 times the memory / disk space needed to store these videos. Please let me know when the batch size is customizable and I'll try again!
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
55
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) Thanks, that's awesome you managed to find the problem. About the 32 bits - really? there isn't a way to serialize the numpy array somehow? 32 bits would take 4 times the memory / disk space needed to store these videos. Please let me know when the batch size is customizable and I'll try again!
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
The 32 bit integrers are only used in the writer's buffer because Arrow doesn't take numpy arrays correctly as input. On disk it's stored as uint8 in arrow format ;)
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
30
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) The 32 bit integrers are only used in the writer's buffer because Arrow doesn't take numpy arrays correctly as input. On disk it's stored as uint8 in arrow format ;)
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
> I don't expect to fix this memory issue until 1-2 weeks unfortunately. Hi @lhoestq not to rush of course, but I was wondering if you have a new timeline so I know how to plan my work around this :)
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
41
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) > I don't expect to fix this memory issue until 1-2 weeks unfortunately. Hi @lhoestq not to rush of course, but I was wondering if you have a new timeline so I know how to plan my work around this :)
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
Alright it should be good now. You just have to specify `_writer_batch_size = 10` for example as a class attribute of the dataset builder class.
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
25
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) Alright it should be good now. You just have to specify `_writer_batch_size = 10` for example as a class attribute of the dataset builder class.
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https://github.com/huggingface/datasets/issues/741
Creating dataset consumes too much memory
I added it, but still it consumes as much memory https://github.com/huggingface/datasets/pull/722/files#diff-2e0d865dd4a60dedd1861d6f8c5ed281ded71508467908e1e0b1dbe7d2d420b1R66 Did I not do it correctly?
Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png)
17
Creating dataset consumes too much memory Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue. Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400): ```python def _generate_examples(self, base_path, split): """ Yields examples. """ filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv") images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split) with open(filepath, "r", encoding="utf-8") as f: data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE) for row in data: frames_path = os.path.join(images_path, row["video"])[:-7] np_frames = [] for frame_name in os.listdir(frames_path): frame_path = os.path.join(frames_path, frame_name) im = Image.open(frame_path) np_frames.append(np.asarray(im)) im.close() yield row["name"], {"video": np_frames} ``` The dataset creation process goes out of memory on a machine with 500GB RAM. I was under the impression that the "generator" here is exactly for that, to avoid memory constraints. However, even if you want the entire dataset in memory, it would be in the worst case `260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes So I'm not sure why it's taking more than 500GB. And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM. --- ## Info that might help: Iterating over examples is extremely slow. ![image](https://user-images.githubusercontent.com/5757359/96359590-3c666780-111d-11eb-9347-1f833ad982a9.png) If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec And you can see at this state it is using 94% of the memory: ![image](https://user-images.githubusercontent.com/5757359/96359606-7afc2200-111d-11eb-8c11-0afbdba1a6a3.png) And it is only using one CPU core, which is probably why it's so slow: ![image](https://user-images.githubusercontent.com/5757359/96359630-a3841c00-111d-11eb-9ba0-7fd3cdf51d26.png) I added it, but still it consumes as much memory https://github.com/huggingface/datasets/pull/722/files#diff-2e0d865dd4a60dedd1861d6f8c5ed281ded71508467908e1e0b1dbe7d2d420b1R66 Did I not do it correctly?
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