Datasets:

lewtun

/

github-issues

like 9

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[ "Merging since the CI failure is unrelated to this PR" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/2853", "html_url": "https://github.com/huggingface/datasets/pull/2853", "diff_url": "https://github.com/huggingface/datasets/pull/2853.diff", "patch_url": "https://github.com/huggingface/datasets/pull/2853.patch" }

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[ "Merging since the CI error is unrelated this this PR" ]

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[ "Merging since the CI error is unrelated to this PR and fixed on master" ]

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[]

[ "Fixed by #2847." ]

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[ "The CI error is unrelated to this PR\r\n\r\n... merging !" ]

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[ "merging since the windows error is just a CircleCI issue", "It works, eg https://observablehq.com/@huggingface/datasets-preview-backend-client#{%22datasetId%22%3A%22discovery%22} and https://datasets-preview.huggingface.tech/rows?dataset=discovery&config=discovery&split=train", "Nice !" ]

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[ "From what I can understand, you want the saved arrow file directory to have username as well instead of just dataset name if it was downloaded with the user prefix?", "I don't think the user cares of how this is done, but the 2nd command should fail, IMHO, as its dataset name is invalid:\r\n```\r\n# first run\r\npython -c \"from datasets import load_dataset; load_dataset('stas/openwebtext-10k')\"\r\n# now run immediately\r\npython -c \"from datasets import load_dataset; load_dataset('openwebtext-10k')\"\r\n# the second command should fail, but it doesn't fail now.\r\n```\r\n\r\nMoreover, if someone were to create `openwebtext-10k` w/o the prefix, they will now get the wrong dataset, if they previously downloaded `stas/openwebtext-10k`.\r\n\r\nAnd if there are 2 users with the same dataset name `foo/ds` and `bar/ds` - currently this won't work to get the correct dataset.\r\n\r\nSo really there 3 unrelated issues hiding in the current behavior." ]

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[ "Thanks for reporting, I'm updating the verifications metadata", "I just regenerated the verifications metadata and noticed that nothing changed: the data file is fine (the checksum didn't change), and the number of examples is still 8013769. Not sure how you managed to get 7982430 examples.\r\n\r\nCan you try to delete your cache ( by default at `~/.cache/huggingface/datasets`) and try again please ?\r\nAlso, on which platform are you (linux/macos/windows) ?", "I'll try without deleting the whole cache (we have large datasets already stored). I was under the impression that `download_mode=\"force_redownload\"` would bypass cache.\r\nSorry plateform should be linux (Redhat version 8.1)", "Hi @thomasw21 , are you still having this issue after clearing your cache ?", "Sorry I haven't had time to work on this. I'll close and re-open if I can't figure out why I'm having this issue. Thanks for taking a look !" ]

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[ "Hello, I opened #2887 to fix this." ]

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[ "Maybe worth updating the docs here as well?", "Yup, will do !" ]

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[ "Hi ! It fails because of the feature type inference.\r\n\r\nBecause the first 1000 examples all have null values in the \"match\" field, then it infers that the type for this field is `null` type before writing the data on disk. But as soon as it tries to map an example with a non-null \"match\" field, then it fails.\r\n\r\nTo fix that you can either:\r\n- increase the writer_batch_size to >2000 (default is 1000) so that some non-null values will be in the first batch written to disk\r\n```python\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], writer_batch_size=2000)\r\n```\r\n- OR force the feature type with:\r\n```python\r\nfrom datasets import Features, Value\r\n\r\nfeatures = Features({\r\n 'conflict': Value('int64'),\r\n 'date': Value('string'),\r\n 'headline': Value('string'),\r\n 'match': Value('float64'),\r\n 'label': Value('float64')\r\n})\r\ndatasets = datasets.map(lambda e: {'labels': e['match']}, remove_columns=['id'], features=features)\r\n```" ]

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[ "@lhoestq @albertvillanova it would be super cool if we could get the Image Classification task to work with this. I'm not sure how to have the dataset find the unique label names _after_ the dataset has been loaded. Is that even possible? \r\n\r\nMy hacky community version [here](https://huggingface.co/datasets/nateraw/image-folder) does this, but it wouldn't pass the test suite here. Any thoughts?", "Hi ! Dataset builders that require some `data_files` like `csv` or `json` are handled differently that actual dataset scripts.\r\n\r\nIn particular:\r\n- they are placed directly in the `src` folder of the lib so that you can use it without internet connection (more exactly in `src/datasets/packaged_modules/<builder_name>.py`). So feel free to move the dataset python file there. You also need to register it in `src/datasets/packaked_modules.__init__.py`\r\n- they are handled a bit differently in our test suite (see the `PackagedDatasetTest` class in `test_dataset_common.py`). To be able to test the builder with your dummy data, you just need to modify `get_packaged_dataset_dummy_data_files` in `test_dataset_common.py` to return the right `data_files` for your builder. The dummy data can stay in `datasets/image_folder/dummy`\r\n\r\nLet me know if you have questions or if I can help !", "Hey @lhoestq , I actually already did both of those things. I'm trying to get the `image-classification` task to work now. \r\n\r\nFor example...When you run `ds = load_dataset('imagefolder', data_files='my_files')`, with a directory called `./my_files` that looks like this:\r\n\r\n```\r\nmy_files\r\n----| Cat\r\n--------| image1.jpg\r\n--------| ...\r\n----| Dog\r\n--------| image1.jpg\r\n--------| ...\r\n```\r\n\r\n...We should set the dataset's `labels` feature to `datasets.features.ClassLabel(names=['cat', 'dog'])` dynamically with class names we find by getting a list of directories in `my_files` (via `data_files`). Otherwise the `datasets.tasks.ImageClassification` task will break, as the `labels` feature is not a `ClassLabel`.\r\n\r\nI couldn't figure out how to access the `data_files` in the builder's `_info` function in a way that would pass in the test suite. ", "Nice ! Then maybe you can use `self.config.data_files` in `_info()` ?\r\nWhat error are you getting in the test suite ?\r\n\r\nAlso note that `data_files` was first developed to accept paths to actual files, not directories. In particular, it fetches the metadata of all the data_files to get a unique hash for the caching mechanism. So we may need to do a few changes first." ]

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[ "Hi ! In your script it looks like you're trying to load the dataset `bn_hate_speech,`, not KanHope.\r\n\r\nMoreover the error `KeyError: ' '` means that you have a feature of type ClassLabel, but for a certain example of the dataset, it looks like the label is empty (it's just a string with a space). Can you make sure that the data don't have missing labels, and that your dataset script parses the labels correctly ?" ]

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[ "This also happened to me on COLAB.\r\nDetails:\r\nI ran the `run_mlm.py` in two different notebooks. \r\nIn the first notebook, I do tokenization since I can get 4 CPU cores without any GPUs, and save the cache into a folder which I copy to drive.\r\nIn the second notebook, I copy the cache folder from drive and re-run the run_mlm.py script (this time I uncomment the trainer code which happens after the tokenization)\r\n\r\nNote: I didn't change anything in the arguments, not even the preprocessing_num_workers\r\n ", "Thanks for reporting ! This is indeed a bug, I'm looking into it", "#2854 fixed the issue :)\r\n\r\nWe'll do a new release of `datasets` soon to make the fix available.\r\nIn the meantime, feel free to try it out by installing `datasets` from source\r\n\r\nIf you have other issues or any question, feel free to re-open the issue :)" ]

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[ "Agh - I'm a muppet. No quote marks are needed.\r\nset HF_DATASETS_CACHE = C:\\Datasets\r\nworks as intended." ]

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[ "Thanks for the feedback :) I will also complete the documentation to explain this convention", "I just added some documentation about how streaming works with chained URLs.\r\n\r\nI will also add some docs about how to use chained URLs directly in `load_dataset` in #2662, since #2662 does change the documentation already and to avoid having to resolve conflicts.", "Merging this one now, next step is resolve the conflicts in #2662 and update the docs for URL chaining :)\r\n\r\nThere is also the glob feature of zip files that I need to add, to be able to do this for example:\r\n```python\r\nload_dataset(\"json\", data_files=\"zip://*::https://foo.bar/archive.zip\")\r\n```" ]

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[ "Thanks for reporting ! #2852 fixed this error\r\n\r\nWe'll do a new release of `datasets` soon :)" ]

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[ "```\r\nUsing custom data configuration default\r\nDownloading and preparing dataset reddit/default (download: 2.93 GiB, generated: 17.64 GiB, post-processed: Unknown size, total: 20.57 GiB) to /Volumes/My Passport for Mac/og-chat-data/reddit/default/1.0.0/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969...\r\nDownloading: 13%\r\n403M/3.14G [44:39<2:27:09, 310kB/s]\r\n---------------------------------------------------------------------------\r\ntimeout Traceback (most recent call last)\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in _error_catcher(self)\r\n 437 try:\r\n--> 438 yield\r\n 439 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in read(self, amt, decode_content, cache_content)\r\n 518 cache_content = False\r\n--> 519 data = self._fp.read(amt) if not fp_closed else b\"\"\r\n 520 if (\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/http/client.py in read(self, amt)\r\n 458 b = bytearray(amt)\r\n--> 459 n = self.readinto(b)\r\n 460 return memoryview(b)[:n].tobytes()\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/http/client.py in readinto(self, b)\r\n 502 # (for example, reading in 1k chunks)\r\n--> 503 n = self.fp.readinto(b)\r\n 504 if not n and b:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/socket.py in readinto(self, b)\r\n 703 try:\r\n--> 704 return self._sock.recv_into(b)\r\n 705 except timeout:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/ssl.py in recv_into(self, buffer, nbytes, flags)\r\n 1240 self.__class__)\r\n-> 1241 return self.read(nbytes, buffer)\r\n 1242 else:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/ssl.py in read(self, len, buffer)\r\n 1098 if buffer is not None:\r\n-> 1099 return self._sslobj.read(len, buffer)\r\n 1100 else:\r\n\r\ntimeout: The read operation timed out\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nReadTimeoutError Traceback (most recent call last)\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/requests/models.py in generate()\r\n 757 try:\r\n--> 758 for chunk in self.raw.stream(chunk_size, decode_content=True):\r\n 759 yield chunk\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in stream(self, amt, decode_content)\r\n 575 while not is_fp_closed(self._fp):\r\n--> 576 data = self.read(amt=amt, decode_content=decode_content)\r\n 577 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in read(self, amt, decode_content, cache_content)\r\n 540 # Content-Length are caught.\r\n--> 541 raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n 542 \r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/contextlib.py in __exit__(self, type, value, traceback)\r\n 134 try:\r\n--> 135 self.gen.throw(type, value, traceback)\r\n 136 except StopIteration as exc:\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/urllib3/response.py in _error_catcher(self)\r\n 442 # there is yet no clean way to get at it from this context.\r\n--> 443 raise ReadTimeoutError(self._pool, None, \"Read timed out.\")\r\n 444 \r\n\r\nReadTimeoutError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nConnectionError Traceback (most recent call last)\r\n/var/folders/3f/md0t9sgj6rz8xy01fskttqdc0000gn/T/ipykernel_89016/1133441872.py in <module>\r\n 1 from datasets import load_dataset\r\n 2 \r\n----> 3 dataset = load_dataset(\"reddit\", ignore_verifications=True, cache_dir=\"/Volumes/My Passport for Mac/og-chat-data\")\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 845 \r\n 846 # Download and prepare data\r\n--> 847 builder_instance.download_and_prepare(\r\n 848 download_config=download_config,\r\n 849 download_mode=download_mode,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)\r\n 613 logger.warning(\"HF google storage unreachable. Downloading and preparing it from source\")\r\n 614 if not downloaded_from_gcs:\r\n--> 615 self._download_and_prepare(\r\n 616 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 617 )\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 669 split_dict = SplitDict(dataset_name=self.name)\r\n 670 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)\r\n--> 671 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n 672 \r\n 673 # Checksums verification\r\n\r\n~/.cache/huggingface/modules/datasets_modules/datasets/reddit/98ba5abea674d3178f7588aa6518a5510dc0c6fa8176d9653a3546d5afcb3969/reddit.py in _split_generators(self, dl_manager)\r\n 73 def _split_generators(self, dl_manager):\r\n 74 \"\"\"Returns SplitGenerators.\"\"\"\r\n---> 75 dl_path = dl_manager.download_and_extract(_URL)\r\n 76 return [\r\n 77 datasets.SplitGenerator(\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in download_and_extract(self, url_or_urls)\r\n 287 extracted_path(s): `str`, extracted paths of given URL(s).\r\n 288 \"\"\"\r\n--> 289 return self.extract(self.download(url_or_urls))\r\n 290 \r\n 291 def get_recorded_sizes_checksums(self):\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in download(self, url_or_urls)\r\n 195 \r\n 196 start_time = datetime.now()\r\n--> 197 downloaded_path_or_paths = map_nested(\r\n 198 download_func,\r\n 199 url_or_urls,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 194 # Singleton\r\n 195 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 196 return function(data_struct)\r\n 197 \r\n 198 disable_tqdm = bool(logger.getEffectiveLevel() > logging.INFO) or not utils.is_progress_bar_enabled()\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/download_manager.py in _download(self, url_or_filename, download_config)\r\n 218 # append the relative path to the base_path\r\n 219 url_or_filename = url_or_path_join(self._base_path, url_or_filename)\r\n--> 220 return cached_path(url_or_filename, download_config=download_config)\r\n 221 \r\n 222 def iter_archive(self, path):\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n 286 if is_remote_url(url_or_filename):\r\n 287 # URL, so get it from the cache (downloading if necessary)\r\n--> 288 output_path = get_from_cache(\r\n 289 url_or_filename,\r\n 290 cache_dir=cache_dir,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/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, max_retries, use_auth_token)\r\n 643 ftp_get(url, temp_file)\r\n 644 else:\r\n--> 645 http_get(\r\n 646 url,\r\n 647 temp_file,\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)\r\n 451 disable=bool(logging.get_verbosity() == logging.NOTSET),\r\n 452 )\r\n--> 453 for chunk in response.iter_content(chunk_size=1024):\r\n 454 if chunk: # filter out keep-alive new chunks\r\n 455 progress.update(len(chunk))\r\n\r\n/usr/local/anaconda3/envs/og-data-env/lib/python3.9/site-packages/requests/models.py in generate()\r\n 763 raise ContentDecodingError(e)\r\n 764 except ReadTimeoutError as e:\r\n--> 765 raise ConnectionError(e)\r\n 766 else:\r\n 767 # Standard file-like object.\r\n\r\nConnectionError: HTTPSConnectionPool(host='zenodo.org', port=443): Read timed out.\r\n```", "Hey @lhoestq should I try to fix this issue ?", "It also doesn't seem to be \"smart caching\" and I received an error about a file not being found...", "To be clear, the error I get when I try to \"re-instantiate\" the download after failure is: \r\n```\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 20] Not a directory: <HOME>/.cache/huggingface/datasets/downloads/1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c/corpus-webis-tldr-17.json'\r\n```", "Here is a new error:\r\n```\r\nConnectionError: Couldn't reach https://zenodo.org/record/1043504/files/corpus-webis-tldr-17.zip?download=1\r\n```", "Hi ! Since https://github.com/huggingface/datasets/pull/2803 we've changed the time out from 10sec to 100sec.\r\nThis should prevent the `ReadTimeoutError`. Feel free to try it out by installing `datasets` from source\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```\r\n\r\nWhen re-running your code you said you get a `OSError`, could you try deleting the file at the path returned by the error ? (the one after `[Errno 20] Not a directory:`). Ideally when a download fails you should be able to re-run it without error; there might be an issue here.\r\n\r\nFinally not sure what we can do about `ConnectionError`, this must be an issue from zenodo. If it happens you simply need to try again\r\n", "@lhoestq thanks for the update. The directory specified by the OSError ie. \r\n```\r\n1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c/corpus-webis-tldr-17.json \r\n```\r\n was not actually in that directory so I can't delete it. ", "Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?\r\nThis way the download manager will know that it has to uncompress the data again", "It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. ", "Great ! The timeout change will be available in the next release of `datasets` :)" ]

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[ "Thanks for adding this one ! I just did some minor changes and set the timeout back to 100sec instead of 1000", "The CI failure is unrelated to this PR - let me take a look", "> Thanks for adding this one! I just did some minor changes and set the timeout back to 100sec instead of 1000\r\n\r\nThank you for updating the language tags. I tried timeout values up to 300 sec on my local machine, but some of the larger files still get timed out. Although this could have been a network issue on my end, have you verified that 100 sec works for all files?", "Well the main issue with google drive - even before the time out issues - is that it has a daily quota of downloads per file.\r\nTherefore if many people start downloading this dataset, it will be unavailable until the quota is reset the next day.\r\n\r\nSo ideally it would be nice if the data were hosted elsewhere than Google drive, to avoid the quota and time out issue.\r\nHF can probably help with hosting the data if needed", "> Well the main issue with google drive - even before the time out issues - is that it has a daily quota of downloads per file.\r\n> Therefore if many people start downloading this dataset, it will be unavailable until the quota is reset the next day.\r\n> \r\n> So ideally it would be nice if the data were hosted elsewhere than Google drive, to avoid the quota and time out issue.\r\n> HF can probably help with hosting the data if needed\r\n\r\nIt'd be great if the dataset can be hosted in HF. How should I proceed here though? Upload the dataset files as a community dataset and update the links in this pull request or is there a more straightforward way?", "Hi ! Ideally everything should be in the same place, so feel free to create a community dataset on the Hub and upload your data files as well as you dataset script (and also the readme.md and dataset_infos.json).\r\n\r\nThe only change you have to do in your dataset script is use a relative path to your data files instead of urls.\r\nFor example if your repository looks like this:\r\n```\r\nxlsum/\r\n├── data/\r\n│ ├── amharic_XLSum_v2.0.tar.bz2\r\n│ ├── ...\r\n│ └── yoruba_XLSum_v2.0.tar.bz2\r\n├── xlsum.py\r\n├── README.md\r\n└── dataset_infos.json\r\n```\r\nThen you just need to pass `\"data/amharic_XLSum_v2.0.tar.bz2\"` to `dl_manager.download_and_extract(...)`, instead of an url.\r\n\r\nLocally you can test that it's working as expected with\r\n```python\r\nload_dataset(\"path/to/my/directory/named/xlsum\")\r\n```\r\n\r\nThen once it's on the Hub, you can load it with\r\n```python\r\nload_dataset(\"username/xlsum\")\r\n```\r\n\r\nLet me know if you have questions :)", "Thank you for your detailed response regarding the community dataset building process. However, will this pull request be merged into the main branch?", "If XL-sum is available via the Hub we don't need to add it again in the `datasets` github repo ;)" ]

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[ "Hi ! The `data_files` parameter must be a string, a list/tuple or a python dict.\r\n\r\nCan you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ?" ]

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[ "Sounds good. Should we also have a “the_pile” dataset with the subsets as configuration?", "I think the main `the_pile` datasets will be the one that is the mix of all the subsets: https://the-eye.eu/public/AI/pile/\r\n\r\nWe can also add configurations for each subset, and even allow users to specify the subsets they want:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"the_pile\", subsets=[\"openwebtext2\", \"books3\", \"hn\"])\r\n```\r\n\r\nWe're alrady doing something similar for mC4, where users can specify the list of languages they want to load." ]

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[ "I started working on that." ]

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[ "After discussing with @lhoestq, a reasonable alternative:\r\n- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats: \r\n `bz2::http://domain.org/filename.bz2`\r\n- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:\r\n `fsspec.open(\"http://domain.org/filename.bz2\", compression=\"bz2\")`\r\n\r\nPros:\r\n- clean solution that continues giving support to all compression formats\r\n- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)\r\n\r\nCons:\r\n- we create a \"private\" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` argument" ]

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[ "One additional note: if we can try to not change the code of oscar.py too often, I'm sure users that have it in their cache directory will be happy to not have to redownload it every time they update the library ;)\r\n\r\n(since changing the code changes the cache directory of the dataset)", "I don't think this is confusing for users because users don't even know we have patched `open`. The only thing users care is that if the pass `streaming=True`, they want to be able to load the dataset in streaming mode.\r\n\r\nI don't see any other dataset where patching `open` with `fsspec.open`+`compression` is an \"underlying issue\". Are there other datasets where this is an issue?\r\n\r\nThe only dataset where this was an issue is in oscar and the issue is indeed due to the additional `open` you added inside `zip.open`.", "Closing this one since https://github.com/huggingface/datasets/pull/2822 reverted the change of behavior of `open`" ]

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[ "In case it's relevant for this PR, I'm finding that I cannot stream the `bookcorpus` dataset (using the `master` branch of `datasets`), which is a `.tar.bz2` file:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nbooks_dataset_streamed = load_dataset(\"bookcorpus\", split=\"train\", streaming=True)\r\n# Throws a 404 HTTP error\r\nnext(iter(books_dataset_streamed))\r\n```\r\n\r\nThe full stack trace is:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\n<ipython-input-11-5ebbbe110b13> in <module>()\r\n----> 1 next(iter(books_dataset_streamed))\r\n\r\n11 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in __iter__(self)\r\n 339 \r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339 \r\n 340 def __iter__(self):\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in __iter__(self)\r\n 76 \r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80 \r\n\r\n/root/.cache/huggingface/modules/datasets_modules/datasets/bookcorpus/44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700/bookcorpus.py in _generate_examples(self, directory)\r\n 98 for txt_file in files:\r\n 99 with open(txt_file, mode=\"r\", encoding=\"utf-8\") as f:\r\n--> 100 for line in f:\r\n 101 yield _id, {\"text\": line.strip()}\r\n 102 _id += 1\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/implementations/http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499 \r\n 500 async def async_fetch_all(self):\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/caching.py in _fetch(self, start, end)\r\n 374 ):\r\n 375 # First read, or extending both before and after\r\n--> 376 self.cache = self.fetcher(start, bend)\r\n 377 self.start = start\r\n 378 elif start < self.start:\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89 \r\n 90 return wrapper\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71 \r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/implementations/http.py in async_fetch_range(self, start, end)\r\n 535 # range request outside file\r\n 536 return b\"\"\r\n--> 537 r.raise_for_status()\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n\r\n/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)\r\n 1003 status=self.status,\r\n 1004 message=self.reason,\r\n-> 1005 headers=self.headers,\r\n 1006 )\r\n 1007 \r\n\r\nClientResponseError: 404, message='Not Found', url=URL('https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2/books_large_p1.txt')\r\n```\r\n\r\nLet me know if this is unrelated and I'll open a separate issue :)\r\n\r\nEnvironment info:\r\n\r\n```\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n```", "@lewtun: `.tar.compression-extension` files are not supported yet. That is the objective of this PR.", "> @lewtun: `.tar.compression-extension` files are not supported yet. That is the objective of this PR.\r\n\r\nthanks for the context and the great work on the streaming features (right now i'm writing the streaming section of the HF course, so am acting like a beta tester 😄)", "@lewtun this PR fixes previous issue with xjoin:\r\n\r\nGiven:\r\n```python\r\nxjoin(\r\n \"https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2\",\r\n \"books_large_p1.txt\"\r\n)\r\n```\r\n\r\n- Before it gave: \r\n `\"https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2/books_large_p1.txt\"`\r\n thus raising the 404 error\r\n\r\n- Now it gives:\r\n `tar://books_large_p1.txt::https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2`\r\n (this is the expected format for `fsspec`) and additionally passes the parameter `compression=\"bz2\"`.\r\n See: https://github.com/huggingface/datasets/pull/2806/files#diff-97bb2d08db65ce3b679aefc43cadad76d053c1e58ecc315e49b80873d0fbdabeR15" ]

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[ "Hi ! Merging this one since it's all good :)\r\n\r\nHowever I think it would also be better to actually rename it `the_pile_stack_exchange` to make things clearer and to avoid name collisions in the future. I would like to do the same for `books3` as well.\r\n\r\nIf you don't mind I'll open a PR to do the renaming", "\r\n> If you don't mind I'll open a PR to do the renaming\r\n\r\n@lhoestq That will be nice !!\r\n" ]

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[ "It seems we need to `pip install jsonlines` to pass the checks ?", "Hi ! Do you really need `jsonlines` ? I think it simply uses `json.loads` under the hood.\r\n\r\nCurrently the test are failing because `jsonlines` is not part of the extra requirements `TESTS_REQUIRE` in setup.py\r\n\r\nSo either you can replace `jsonlines` with a simple for loop on the lines of the files and use `json.loads`, or you can add `TESTS_REQUIRE` to the test requirements (but in this case users will have to install it as well).", "Thanks for your suggestion. I now know `io` and json lines format better and has changed `jsonlines` to just `readlines`." ]

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[ "> When I was creating dataset card. I found there is room for creating / editing dataset card. I've made it an issue. #2797\r\n\r\nThanks for the message, we'll definitely improve this\r\n\r\n> Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675\r\n\r\nWell currently no, but I think @lewtun was about to do it (though he's currently on vacations)", "> > Also I am wondering whether the import of The Pile dataset is actively undertaken (because I may need it recently)? #1675\r\n> \r\n> Well currently no, but I think @lewtun was about to do it (though he's currently on vacations)\r\n\r\nyes i plan to start working on this next week #2185 \r\n\r\none question for @richarddwang - do you know if eleutherai happened to also release the \"existing\" datasets like enron emails and opensubtitles? \r\n\r\nin appendix c of their paper, they provide details on how they extracted these datasets, but it would be nice if we could just point to a url so we can be as close as possible to original implementation.", "@lewtun \r\n\r\n> yes i plan to start working on this next week\r\n\r\nNice! Looking forward to it.\r\n\r\n> one question for @richarddwang - do you know if eleutherai happened to also release the \"existing\" datasets like enron emails and opensubtitles?\r\n\r\nSadly, I don't know any existing dataset of enron emails, but I believe opensubtitles dataset is hosted at here. https://the-eye.eu/public/AI/pile_preliminary_components/\r\n\r\n", "thanks for the link @richarddwang! i think that corpus is actually the youtube subtitles one and my impression is that eleutherai have only uploaded the 14 new datasets they created. i've contacted one of the authors so hopefully they can share some additional info for us :)\r\n\r\nbtw it might take a while to put together all the corpora if i also need to preprocess them (e.g. the open subtitles / enron email etc), but i expect no longer than a few weeks." ]

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[ "Hi ! Why do we need the custom `readline` for exactly ? feel free to add a comment to say why it's needed" ]

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[ "Hi @lewtun, thanks for reporting.\r\n\r\nApparently, `pyarrow.json` tries to cast timestamp-like fields in your JSON file to pyarrow timestamp type, and it fails with `ArrowNotImplementedError`.\r\n\r\nI will investigate if there is a way to tell pyarrow not to try that timestamp casting.", "I think the issue is more complex than that...\r\n\r\nI just took one of your JSON lines and pyarrow.json read it without problem.", "> I just took one of your JSON lines an pyarrow.json read it without problem.\r\n\r\nyes, and for some peculiar reason the error is non-deterministic (i was eventually able to load the whole dataset by just re-running the `load_dataset` cell multiple times 🤔)\r\n\r\nthanks for looking into this 🙏 !", "I think the error is generated by the `pyarrow.json.read()` option: `read_options=paj.ReadOptions(block_size=block_size)`...\r\ncc: @lhoestq ", "The code works fine on my side.\r\nNot sure what's going on here :/\r\n\r\nI remember we did a few changes in the JSON loader in #2638 , did you do an update `datasets` when debugging this ?\r\n", "OK after upgrading `datasets` to v1.12.1 the issue seems to have gone away. Closing this now :)", "Oops, I spoke too soon 😓 \r\n\r\nAfter deleting the cache and trying the above code snippet again I am hitting the same error. You can also reproduce it in the Colab notebook I linked to in the issue description. " ]

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[ "Hi ! I don't fully understand this change @albertvillanova \r\nThe `_extract` method used to return the compound URL that points to the root of the inside of the archive.\r\nThis way users can use the usual os.path.join or other functions to point to the relevant files. I don't see why you're using a glob pattern ?", "This change is to allow this:\r\n```python\r\ndata_files = f\"https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip\"\r\nds = load_dataset(\"json\", split=\"train\", data_files=data_files, streaming=True)\r\nassert isinstance(ds, IterableDataset)\r\n```\r\nNote that in this case the user will not call os.path.join.\r\n\r\nBefore this PR it gave error because pointing to the root, without any subsequent join, gives error:\r\n```python\r\nfsspec.open(\"zip://::https://huggingface.co/datasets/albertvillanova/datasets-tests-compression/resolve/main/sample.zip\")\r\n```" ]

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[ "> Hi ! Thanks a lot for adding this one :)\r\n> \r\n> Good job with the dataset card and the dataset script !\r\n> \r\n> I left a few suggestions\r\n\r\nThank you very much for your helpful suggestions. I have tried to carry them all out." ]

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[ "@albertvillanova my tests are failing here:\r\n```\r\ndataset_name = 'gooaq'\r\n\r\n def test_load_dataset(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\r\n\r\ntests/test_dataset_common.py:234: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_dataset_common.py:187: in check_load_dataset\r\n self.parent.assertTrue(len(dataset[split]) > 0)\r\nE AssertionError: False is not true\r\n```\r\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?", "Thanks for the help, @albertvillanova! All tests are passing now." ]

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[ "Thanks for finishing it @albertvillanova " ]

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[ "Hi ! This is not possible just with `load_dataset`.\r\n\r\nYou can do something like this instead:\r\n```python\r\nseed=42\r\ndata_files_dict = {\r\n \"train\": [train_file1, train_file2],\r\n \"test\": [test_file1, test_file2],\r\n \"val\": [val_file1, val_file2]\r\n}\r\ndataset = datasets.load_dataset(\r\n \"csv\",\r\n data_files=data_files_dict,\r\n).shuffle(seed=seed)\r\n\r\nsample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}\r\n```\r\n\r\nAnother alternative is loading each file separately with `split=\"train[:8]\"` and then use `concatenate_datasets` to merge the sample of each file." ]

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[ "the bug code locate in ：\r\n if data_args.task_name is not None:\r\n # Downloading and loading a dataset from the hub.\r\n datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)", "Hi @jinec,\r\n\r\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\r\n\r\nNormally, it should work if you wait a little and then retry.\r\n\r\nCould you please confirm if the problem persists?", "cannot connect，even by Web browser，please check that there is some problems。", "I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...", "> I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...\r\n\r\nI can not access https://raw.githubusercontent.com/huggingface/datasets either, I am in China", "Finally i can access it, by the superfast software. Thanks" ]

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[ "thanks a lot for implementing this @anton-l !!\r\n\r\ni won't have time to review this while i'm away, so happy for @albertvillanova and @patrickvonplaten to decide when to merge :)", "@albertvillanova thanks! Everything should be ready now :)", "> The _background_noise_/_silence_ audio files are much longer than others, so they require some sort of slicing for downstream training. I decided to leave the implementation of that up to the users, since TFDS and s3prl take different approaches (either slicing wavs deterministically, or subsampling randomly at runtime)\r\n\r\n@anton-l I was thinking that maybe we could give some hints in the dataset card (in a Usage section); something similar as for diarization: https://github.com/huggingface/datasets/blob/master/datasets/superb/README.md#example-of-usage\r\nNote that for diarization it is not yet finished: we have to test it and then provide an end-to-end example: https://github.com/huggingface/datasets/pull/2661/files#r680224909 ", "@albertvillanova yeah, I'm not sure how to best implement it in pure `datasets` yet. It's something like this, where `sample_noise()` needs to be called from a pytorch batch collator or other framework-specific variant:\r\n\r\n```python\r\ndef map_to_array(example):\r\n import soundfile as sf\r\n\r\n speech_array, sample_rate = sf.read(example[\"file\"])\r\n example[\"speech\"] = speech_array\r\n example[\"sample_rate\"] = sample_rate\r\n return example\r\n\r\n\r\ndef sample_noise(example):\r\n # Use a version of this function in a stateless way to extract random 1 sec slices of background noise\r\n # on each epoch\r\n from random import randint\r\n\r\n # _silence_ audios are longer than 1 sec\r\n if example[\"label\"] == \"_silence_\":\r\n random_offset = randint(0, len(example[\"speech\"]) - example[\"sample_rate\"] - 1)\r\n example[\"speech\"] = example[\"speech\"][random_offset : random_offset + example[\"sample_rate\"]]\r\n\r\n return example\r\n```", "I see... Yes, not trivial indeed. Maybe for the moment you could add those functions above to the README (as it is the case for now in diarization)? What do you think?" ]

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[ "I dug into what I believe is the root of this issue and added a repro in my comment. If this is better addressed as a cross-team issue, let me know and I can open an issue in the Transformers repo", "Hi !\r\n\r\nIMO we shouldn't try to modify `set_seed` from transformers but maybe make `datasets` have its own RNG just to generate random fingerprints.\r\n\r\nAny opinion on this @LysandreJik ?", "Yes, this sounds good @lhoestq " ]

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[ "I'm guessing tests are failling, because this was pushed before https://github.com/huggingface/datasets/pull/2779 was merged? cc @albertvillanova ", "Hi @thomasw21, yes you are right: those failing tests were fixed with #2779.\r\n\r\nWould you mind to merge current upstream master branch and push again?\r\n```\r\ngit checkout sequential_map_when_cached\r\ngit fetch upstream master\r\ngit merge upstream/master\r\ngit push -u origin sequential_map_when_cached\r\n```", "Thanks for working on this ! I'm sure we can figure something out ;)\r\n\r\nCurrently `map` starts a process to apply the map function on each shard. If the shard has already been processed, then the process that has been spawned loads the processed shard from the cache and returns it.\r\n\r\nI think we should be able to simply not start a process if a shard is already processed and cached.\r\nThis way:\r\n- you won't need to specify `sequential=True`\r\n- it won't create new processes if the dataset is already processed and cached\r\n- it will properly reload each processed shard that is cached\r\n\r\nTo know if we have to start a new process for a shard you can use the function `update_fingerprint` from fingerprint.py to know the expected fingerprint of the processed shard.\r\nThen, if the shard has already been processed, there will be a cache file named `cached-<new_fingerprint>.arrow` and you can load it with\r\n```\r\nDataset.from_file(path_to_cache_file, info=self.info, split=self.split)\r\n```\r\n\r\nLet me know if that makes sense !", "Yes that makes total sense, I tried to initially do that, except the way fingerprint is handled doesn't allow to easily manipulate such a field. Typically the fingerprinting is handled in `@fingerprint_transform` which has a bunch of params that aren't quite easy to extract. Those params are used to manipulate args, kwargs in fancy ways in order to finally obtain a dictionary used for fingerprint. I could duplicate everything, but this look like a very risky thing to do. I'll take a look if I can make something work with `inspect` if I can make a very simple wrapper.\r\n\r\nA much more simpler solution I think is adding an optional `shard: Optional[int] = None` parameter. If None, we use the number of proc as the number of shards, otherwise we pass down the expected number of shards and use either sequential/multiprocessing (with arbitrary number of workers) to load the shards? This would allow the weird case where one wants a large number of shards with a limited amount of processes. Not the smartest thing to do, but it's not an absurd behaviour. Would this be acceptable?", "@lhoestq friendly ping as I feel it's up for review.", "The CI error is unrelated to the changes of this PR - it looks like an SSL issue with conda" ]

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