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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4110). All of your documentation changes will be reflected on that endpoint."
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"WRT the high values, can you add some unit tests with some [string, model] pairs and their resulting perplexity code, and @TristanThrush can run the same pairs through his version of the code?",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4108). All of your documentation changes will be reflected on that endpoint.",
"I thought that the perplexity metric should output the average perplexity value of all the strings that it gets as input (not a perplexity value per string, as the new version does).\r\n@lhoestq , @TristanThrush thoughts?"
] | 1,649,249,841,000 | 1,649,254,632,000 | null | CONTRIBUTOR | null | false | {
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} | This PR makes necessary changes to perplexity such that:
- it runs much faster (via batching)
- it throws an error when input is empty, or when input is one word without <BOS> token
- it adds the option to add a <BOS> token
Issues:
- The values returned are extremely high, and I'm worried they aren't correct. Even if they are correct, they are sometimes returned as `inf`, which is not very useful (see [comment below](https://github.com/huggingface/datasets/pull/4108#discussion_r843931094) for some of the output values).
- If the values are not correct, can you help me find the error?
- If the values are correct, it might be worth it to measure something like perplexity per word, which would allow us to get actual values for the larger perplexities, instead of just `inf`
Future:
- `stride` is not currently implemented here. I have some thoughts on how to make it happen with batching, but I think it would be better to get another set of eyes to look at any possible errors causing such large values now rather than later. | {
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https://api.github.com/repos/huggingface/datasets/issues/4107 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4107/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4107/comments | https://api.github.com/repos/huggingface/datasets/issues/4107/events | https://github.com/huggingface/datasets/issues/4107 | 1,194,484,885 | I_kwDODunzps5HMmSV | 4,107 | Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows | {
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"Thanks for reporting. I'm looking at it",
" It's not related to the dataset viewer in itself. I can replicate the error with:\r\n\r\n```\r\n>>> import datasets as ds\r\n>>> d = ds.load_dataset('Pavithree/explainLikeImFive')\r\nUsing custom data configuration Pavithree--explainLikeImFive-b68b6d8112cd8a51\r\nDownloading and preparing dataset json/Pavithree--explainLikeImFive to /home/slesage/.cache/huggingface/datasets/json/Pavithree--explainLikeImFive-b68b6d8112cd8a51/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 305M/305M [00:03<00:00, 98.6MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 17.9M/17.9M [00:00<00:00, 75.7MB/s]\r\nDownloading data: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.9M/11.9M [00:00<00:00, 70.6MB/s]\r\nDownloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:05<00:00, 1.92s/it]\r\nExtracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1948.42it/s]\r\nFailed to read file '/home/slesage/.cache/huggingface/datasets/downloads/5fee9c8819754df277aee6f252e4db6897d785231c21938407b8862ca871d246' with error <class 'pyarrow.lib.ArrowInvalid'>: Exceeded maximum rows\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 144, in _generate_tables\r\n dataset = json.load(f)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 293, in load\r\n return loads(fp.read(),\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/home/slesage/.pyenv/versions/3.8.11/lib/python3.8/json/decoder.py\", line 340, in decode\r\n raise JSONDecodeError(\"Extra data\", s, end)\r\njson.decoder.JSONDecodeError: Extra data: line 1 column 916 (char 915)\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets/src/datasets/load.py\", line 1691, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 605, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 694, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/slesage/hf/datasets/src/datasets/builder.py\", line 1151, in _prepare_split\r\n for key, table in logging.tqdm(\r\n File \"/home/slesage/.pyenv/versions/datasets/lib/python3.8/site-packages/tqdm/std.py\", line 1168, in __iter__\r\n for obj in iterable:\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 146, in _generate_tables\r\n raise e\r\n File \"/home/slesage/hf/datasets/src/datasets/packaged_modules/json/json.py\", line 122, in _generate_tables\r\n pa_table = paj.read_json(\r\n File \"pyarrow/_json.pyx\", line 246, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 143, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Exceeded maximum rows\r\n```\r\n\r\ncc @lhoestq @albertvillanova @mariosasko ",
"It seems that train.json is not a valid JSON Lines file: it has several JSON objects in the first line (the 915th character in the first line starts a new object, and there's no \"\\n\")\r\n\r\nYou need to have one JSON object per line",
"I'm closing this issue.\r\n\r\n@Pavithree, please, feel free to re-open it if fixing the JSON file does not solve it."
] | 1,649,245,035,000 | 1,649,255,995,000 | 1,649,255,995,000 | NONE | null | null | null | ## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows
**Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive*
*This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error:
Status code: 400
Exception: ArrowInvalid
Message: Exceeded maximum rows
When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error*
Am I the one who added this dataset ? Yes
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"Looks like GH actions is not able to resolve `huggingface_hub` 0.5.0, I'm investivating",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4106). All of your documentation changes will be reflected on that endpoint.",
"I'm glad to see changes in `huggingface_hub` are simplifying code here.",
"seems to supersede #4102, feel free to close mine :)",
"maybe just cherry-pick the docstring fix",
"I think I've found the issue:\r\n- https://github.com/huggingface/huggingface_hub/pull/790",
"Good catch, `huggingface_hub` doesn't support python 3.6 anymore indeed, therefore we should keep support for 0.4.0. I'm reverting the requirement version bump for now.\r\n\r\nWe can update the requirement once we drop support for python 3.6 in `datasets`",
"@lhoestq, I've opened this PR on `huggingface_hub`: \r\n- https://github.com/huggingface/huggingface_hub/pull/823\r\n\r\nIs there any strong reason why `huggingface_hub` no longer supports Python 3.6? ",
"I think `datasets` can drop support for 3.6 soon. But for now maybe let's keep support for 0.4.0, python 3.6 users are not affected by https://github.com/huggingface/datasets/issues/4105 anyway.\r\n\r\n`huggingface_hub` doesn't not have to support 3.6 again just for the CI IMO",
"@lhoestq I commented on the PR, that IMO it is not a good practice to drop support for Python 3.6 without a previous deprecation cycle.",
"Re-added support for older versions. I ended up checking `huggingface_hub` version to use the old, deprecated API for <0.5.0",
"I find it good practice to have all dependency version related code in a single file so that when you decide to remove support for an old version of a dependency it's easy to find and remove them, hence suggesting `utils/_fixes.py` in https://github.com/huggingface/datasets/issues/4105#issuecomment-1090041204",
"good idea, thanks !",
"I used your suggestion @adrinjalali , I just replace the try/except with a check on the version of `huggingface_hub`"
] | 1,649,240,125,000 | 1,649,254,125,000 | null | MEMBER | null | false | {
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`huggingface_hub` deprecated some parameters in `HfApi` in 0.5. This PR updates all the calls to HfApi to remove all the deprecations, <s>and I set the `hugginface_hub` requirement to `>=0.5.0`</s>
cc @adrinjalali @LysandreJik | {
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"Hi ! Indeed there was a breaking change in `huggingface_hub` 0.5.0 in `HfApi.create_repo`, which is called here in `datasets` by passing the org name in both the `repo_id` and the `organization` arguments:\r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nI think we should fix that in `huggingface_hub`, will keep you posted. In the meantime please use `huggingface_hub` 0.4.0",
"I'll be sending a fix for this later today on the `huggingface_hub` side.\r\n\r\nThe error would be converted to a `FutureWarning` if `datasets` uses kwargs instead of positional, for example here: \r\n\r\nhttps://github.com/huggingface/datasets/blob/2230f7f7d7fbaf102cff356f5a8f3bd1561bea43/src/datasets/arrow_dataset.py#L3363-L3369\r\n\r\nto be:\r\n\r\n``` python\r\n api.create_repo(\r\n name=dataset_name,\r\n token=token,\r\n repo_type=\"dataset\",\r\n organization=organization,\r\n private=private,\r\n )\r\n```\r\n\r\nBut `name` and `organization` are deprecated in `huggingface_hub=0.5`, and people should pass `repo_id='org/name` instead. Note that `repo_id` was introduced in 0.5 and if `datasets` wants to support older `huggingface_hub` versions (which I encourage it to do), there needs to be a helper function to do that. It can be something like:\r\n\r\n\r\n```python\r\ndef create_repo(\r\n client,\r\n name: str,\r\n token: Optional[str] = None,\r\n organization: Optional[str] = None,\r\n private: Optional[bool] = None,\r\n repo_type: Optional[str] = None,\r\n exist_ok: Optional[bool] = False,\r\n space_sdk: Optional[str] = None,\r\n) -> str:\r\n try:\r\n return client.create_repo(\r\n repo_id=f\"{organization}/{name}\",\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n except TypeError:\r\n return client.create_repo(\r\n name=name,\r\n organization=organization,\r\n token=token,\r\n private=private,\r\n repo_type=repo_type,\r\n exist_ok=exist_ok,\r\n space_sdk=space_sdk,\r\n )\r\n```\r\n\r\nin a `utils/_fixes.py` kinda file and and be used internally.\r\n\r\nI'll be sending a patch to `huggingface_hub` to convert the error reported in this issue to a `FutureWarning`.",
"PR with the hotfix on the `huggingface_hub` side: https://github.com/huggingface/huggingface_hub/pull/822",
"We can definitely change `push_to_hub` to use `repo_id` in `datasets` and require `huggingface_hub>=0.5.0`.\r\n\r\nLet me open a PR :)",
"`huggingface_hub` 0.5.1 just got released with a fix, feel free to update `huggingface_hub` ;)"
] | 1,649,235,597,000 | 1,649,247,704,000 | null | NONE | null | null | null | ## Describe the bug
`ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id"
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("rubrix/news_test")
ds.push_to_hub("<your-user>/news_test", token="<your-token>")
```
## Expected results
The dataset is successfully uploaded
## Actual results
An error validation is raised:
```bash
if repo_id and (name or organization):
> raise ValueError(
"Only pass `repo_id` and leave deprecated `name` and "
"`organization` to be None."
E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.1
- `huggingface-hub`: 0.5
- Platform: macOS
- Python version: 3.8.12
- PyArrow version: 6.0.0
cc @adrinjalali
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"Can I use instructions present in below link for time series dataset as well? \r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md ",
"cc'ing @kashif and @NielsRogge for visibility!",
"@INF800 happy to add this dataset! I will try to set a PR by the end of the day... if you can kindly point me to the dataset? Also, note we have a bunch of time series datasets checked in e.g. `electricity_load_diagrams` or `monash_tsf`, and ideally this dataset could also be in a similar format. ",
"Thankyou. This is how raw data looks like before cleaning for an individual stocks:\r\n\r\n1. https://github.com/INF800/marktech/tree/raw-data/f/data/raw\r\n2. https://github.com/INF800/marktech/tree/raw-data/t/data/raw\r\n3. https://github.com/INF800/marktech/tree/raw-data/rdfn/data/raw\r\n4. https://github.com/INF800/marktech/tree/raw-data/irbt/data/raw\r\n5. https://github.com/INF800/marktech/tree/raw-data/hll/data/raw\r\n6. https://github.com/INF800/marktech/tree/raw-data/infy/data/raw\r\n7. https://github.com/INF800/marktech/tree/raw-data/reli/data/raw\r\n8. https://github.com/INF800/marktech/tree/raw-data/hdbk/data/raw\r\n\r\n> Scraping is automated using GitHub Actions. So, everyday we will see a new file added in the above links.\r\n\r\nI can rewrite the cleaning scripts to make sure it fits HF dataset standards. (P.S I am very much new to HF dataset)\r\n\r\nThe data set above can be converted into univariate regression / multivariate regression / sequence to sequence generation dataset etc. So, do we have some kind of transformation modules that will read the dataset as some type of dataset (`GenericTimeData`) and convert it to other possible dataset relating to a specific ML task. **By having this kind of transformation module, I only have to add data once** and use transformation module whenever necessary\r\n\r\nAdditionally, having some kind of versioning for the dataset will be really helpful because it will keep on updating - especially time series datasets ",
"thanks @INF800 I'll have a look. I believe it should be possible to incorporate this into the time-series format."
] | 1,649,224,018,000 | 1,649,235,564,000 | null | NONE | null | null | null | ## Adding a Time Series Dataset
- **Name:** 2min ticker data for stock market
- **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image
- **Data:** Collected by myself from investing.com
- **Motivation:** Test applicability of transformer based model on stock market / time series problem
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4102). All of your documentation changes will be reflected on that endpoint."
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"Hi! Can you please specify the full name of the dataset? IIRC `full_numbers` is one of the configs of the `svhn` dataset, and its generation is slow due to data being stored in binary Matlab files. Even if you specify a specific split, `datasets` downloads all of them, but we plan to fix that soon and only download the requested split.\r\n\r\nIf you are in a hurry, download the `svhn` script [here](`https://huggingface.co/datasets/svhn/blob/main/svhn.py`), remove [this code](https://huggingface.co/datasets/svhn/blob/main/svhn.py#L155-L162), and run:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"path/to/your/local/script.py\", \"full_numbers\")\r\n```\r\n\r\nAnd to make loading easier in Colab, you can create a dataset repo on the Hub and upload the script there. Or push the script to Google Drive and mount the drive in Colab."
] | 1,649,174,415,000 | 1,649,250,541,000 | null | NONE | null | null | null | How can I download only the train and test split for full numbers using load_dataset()?
I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4100). All of your documentation changes will be reflected on that endpoint."
] | 1,649,174,234,000 | 1,649,174,847,000 | null | CONTRIBUTOR | null | false | {
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} | This PR modifies the RedCaps card to:
* fix the formatting of the Point of Contact fields on the Hub
* speed up the image fetching logic (aligns it with the [img2dataset](https://github.com/rom1504/img2dataset) tool) and make it more robust (return None if **any** exception is thrown) | {
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https://api.github.com/repos/huggingface/datasets/issues/4099 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4099/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4099/comments | https://api.github.com/repos/huggingface/datasets/issues/4099/events | https://github.com/huggingface/datasets/issues/4099 | 1,193,253,768 | I_kwDODunzps5HH5uI | 4,099 | UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) | {
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"Hi @andreybond, thanks for reporting.\r\n\r\nUnfortunately, I'm not able to able to reproduce your issue:\r\n```python\r\nIn [4]: from datasets import load_dataset\r\n ...: datasets = load_dataset(\"nielsr/XFUN\", \"xfun.ja\")\r\n\r\nIn [5]: datasets\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 194\r\n })\r\n validation: Dataset({\r\n features: ['id', 'input_ids', 'bbox', 'labels', 'image', 'entities', 'relations'],\r\n num_rows: 71\r\n })\r\n})\r\n```\r\n\r\nThe only reason I can imagine this issue may arise is if your default encoding is not \"UTF-8\" (and it is ASCII instead). This is usually the case on Windows machines; but you say your environment is a Linux machine. Maybe you change your machine default encoding?\r\n\r\nCould you please check this?\r\n```python\r\nIn [6]: import sys\r\n\r\nIn [7]: sys.getdefaultencoding()\r\nOut[7]: 'utf-8'\r\n```",
"I opened a PR in the original dataset loading script:\r\n- microsoft/unilm#677\r\n\r\nand fixed the corresponding dataset script on the Hub:\r\n- https://huggingface.co/datasets/nielsr/XFUN/commit/73ba5e026621e05fb756ae0f267eb49971f70ebd",
"import sys\r\nsys.getdefaultencoding()\r\n\r\nreturned: 'utf-8'\r\n\r\n---------------------\r\n\r\nI've just cloned master branch - your fix works! Thank you!"
] | 1,649,169,758,000 | 1,649,227,064,000 | 1,649,226,954,000 | NONE | null | null | null | ## Describe the bug
Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset("nielsr/XFUN", "xfun.ja")
```
## Expected results
Dataset should be downloaded without exceptions
## Actual results
Stack trace (for the second-time execution):
Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477...
Downloading data files: 100%
2/2 [00:00<00:00, 88.48it/s]
Extracting data files: 100%
2/2 [00:00<00:00, 79.60it/s]
UnicodeDecodeErrorTraceback (most recent call last)
<ipython-input-31-79c26bd1109c> in <module>
1 from datasets import load_dataset
2
----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja")
/usr/local/lib/python3.6/dist-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, revision, use_auth_token, task, streaming, **config_kwargs)
/usr/local/lib/python3.6/dist-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)
604 )
605
--> 606 # By default, return all splits
607 if split is None:
608 split = {s: s for s in self.info.splits}
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
692 Args:
693 split: `datasets.Split` which subset of the data to read.
--> 694
695 Returns:
696 `Dataset`
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys)
/usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self)
252 if not self.disable:
253 self.display(check_delay=False)
--> 254
255 def __iter__(self):
256 try:
/usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self)
1183 for obj in iterable:
1184 yield obj
-> 1185 return
1186
1187 mininterval = self.mininterval
~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths)
140 logger.info("Generating examples from = %s", filepath)
141 with open(filepath[0], "r") as f:
--> 142 data = json.load(f)
143
144 for doc in data["documents"]:
/usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
294
295 """
--> 296 return loads(fp.read(),
297 cls=cls, object_hook=object_hook,
298 parse_float=parse_float, parse_int=parse_int,
/usr/lib/python3.6/encodings/ascii.py in decode(self, input, final)
24 class IncrementalDecoder(codecs.IncrementalDecoder):
25 def decode(self, input, final=False):
---> 26 return codecs.ascii_decode(input, self.errors)[0]
27
28 class StreamWriter(Codec,codecs.StreamWriter):
UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 (but reproduced with many previous versions)
- Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu
- Python version: 3.6.9
- PyArrow version: 6.0.1
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https://api.github.com/repos/huggingface/datasets/issues/4098 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4098/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4098/comments | https://api.github.com/repos/huggingface/datasets/issues/4098/events | https://github.com/huggingface/datasets/pull/4098 | 1,193,245,522 | PR_kwDODunzps41qXjo | 4,098 | Proposing WikiSplit metric card | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"A quick Github tip ;) To avoid running N times the CI, you can push all the changes at once: go to Files Changed tab, and on each suggestion there's a \"add to commit batch\" and then you can do one commit for all the suggestions you want to approve ;)"
] | 1,649,169,394,000 | 1,649,173,717,000 | 1,649,173,348,000 | CONTRIBUTOR | null | false | {
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} | Pinging @lhoestq to ensure that my distinction between the dataset and the metric are clear :sweat_smile: | {
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https://api.github.com/repos/huggingface/datasets/issues/4097 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4097/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4097/comments | https://api.github.com/repos/huggingface/datasets/issues/4097/events | https://github.com/huggingface/datasets/pull/4097 | 1,193,205,751 | PR_kwDODunzps41qPEu | 4,097 | Updating FrugalScore metric card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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] | open | false | null | [] | null | [] | 1,649,165,912,000 | 1,649,165,920,000 | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily.
**Describe the solution you'd like**
A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec.
**Describe alternatives you've considered**
Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users.
Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4095). All of your documentation changes will be reflected on that endpoint."
] | 1,649,130,956,000 | 1,649,148,886,000 | 1,649,148,353,000 | CONTRIBUTOR | null | false | {
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] | closed | false | null | [] | null | [] | 1,649,126,577,000 | 1,649,143,002,000 | 1,649,143,002,000 | NONE | null | null | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"It's a bug! Thanks for reporting, I'm looking at it.",
"By the way, the error on our part is due to the huge size of every row (~90MB). The dataset viewer does not support such big dataset rows for the moment.\r\nAnyway, we're working to give a hint about this in the dataset viewer."
] | 1,649,125,519,000 | 1,649,149,814,000 | null | NONE | null | null | null | elena-soare/crawled-ecommerce
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
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https://api.github.com/repos/huggingface/datasets/issues/4092 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4092/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4092/comments | https://api.github.com/repos/huggingface/datasets/issues/4092/events | https://github.com/huggingface/datasets/pull/4092 | 1,192,499,903 | PR_kwDODunzps41n40R | 4,092 | Fix dataset `amazon_us_reviews` metadata - 4/4/2022 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4092). All of your documentation changes will be reflected on that endpoint."
] | 1,649,122,785,000 | 1,649,149,026,000 | null | NONE | null | false | {
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"Hi! Yes, the problem with `add_item` is that it keeps examples in memory, so you are left with these options:\r\n* writing a dataset loading script in which you iterate over `custom_example_dict_streamer` and yield the examples (in `_generate examples`)\r\n* storing the data in a JSON/CSV/Parquet/TXT file and using `Dataset.from_{format}`\r\n* using `add_item` + `save_to_disk` on smaller chunks: \r\n ```python\r\n from datasets import Dataset, concatenate_datasets\r\n MAX_SAMPLES_IN_MEMORY = 1000\r\n samples_in_dset = 0\r\n dset = Dataset.from_dict({\"col1\": [], \"col2\": []}) # empty dataset\r\n path_to_save_dir = \"path/to/save/dir\"\r\n num_chunks = 0\r\n for example_dict in custom_example_dict_streamer(\"/path/to/raw/data\"):\r\n dset = dset.add_item(example_dict)\r\n samples_in_dset += 1\r\n if samples_in_dset == MAX_SAMPLES_IN_MEMORY:\r\n samples_in_dset = 0\r\n dset.save_to_disk(f\"{path_to_save_dir}{num_chunks}\")\r\n num_chunks =+ 1\r\n dset = Dataset.from_dict({\"col1\": [], \"col2\": []}) # empty dataset\r\n if samples_in_dset > 0:\r\n dset.save_to_disk(f\"{path_to_save_dir}{num_chunks}\")\r\n num_chunks =+ 1\r\n loaded_dsets = [] # memory-mapped\r\n for chunk_num in range(num_chunks):\r\n dset = Dataset.load_from_disk(f\"{path_to_save_dir}{chunk_num}\") \r\n loaded_dsets.append(dset)\r\n final_dset = concatenate_datasets(dset)\r\n ```\r\n If you still have issues with this approach, you can try to delete unused datasets with `gc.collect()` to free some memory. ",
"This is really elegant, thank you @mariosasko! I will try this."
] | 1,649,089,164,000 | 1,649,186,552,000 | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
I have a very large dataset stored on disk in a custom format. I have some custom code that reads one data example at a time and yields it in the form of a dictionary. I want to construct a `Dataset` with all examples, and then save it to disk. I later want to load the saved `Dataset` and use it like any other HuggingFace dataset, get splits, wrap it in a PyTorch `DataLoader`, etc. **Crucially, I do not ever want to materialize all the data in memory while building the dataset.**
**Describe the solution you'd like**
I would like to be able to do something like the following. Notice how each example is read and then immediately added to the dataset. We do not store all the data in memory when constructing the `Dataset`. If it helps, I will know the schema of my dataset before hand.
```
# Initialize an empty Dataset, possibly from a known schema.
dataset = Dataset()
# Read in examples one by one using a custom data streamer.
for example_dict in custom_example_dict_streamer("/path/to/raw/data"):
# Add this example to the dict but do not store it in memory.
dataset.add_item(example_dict)
# Save the final dataset to disk as an Arrow-backed dataset.
dataset.save_to_disk("/path/to/dataset")
...
# I'd like to be able to later `load_from_disk` and use the loaded Dataset
# just like any other memory-mapped pyarrow-backed HuggingFace dataset...
loaded_dataset = Dataset.load_from_disk("/path/to/dataset")
loaded_dataset.set_format(type="torch", columnns=["foo", "bar", "baz"])
dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=16)
...
```
**Describe alternatives you've considered**
I initially tried to read all the data into memory, construct a Pandas DataFrame and then call `Dataset.from_pandas`. This would not work as it requires storing all the data in memory. It seems that there is an `add_item` method already -- I tried to implement something like the desired API written above, but I've not been able to initialize an empty `Dataset` (this seems to require several layers of constructing `datasets.table.Table` which requires constructing a `pyarrow.lib.Table`, etc). I also considered writing my data to multiple sharded CSV files or JSON files and then using `from_csv` or `from_json`. I'd prefer not to do this because (1) I'd prefer to avoid the intermediate step of creating these temp CSV/JSON files and (2) I'm not sure if `from_csv` and `from_json` use memory-mapping.
Do you have any suggestions on how I'd be able to achieve this use case? Does something already exist to support this? Thank you very much in advance! | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4090). All of your documentation changes will be reflected on that endpoint."
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https://api.github.com/repos/huggingface/datasets/issues/4089 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4089/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4089/comments | https://api.github.com/repos/huggingface/datasets/issues/4089/events | https://github.com/huggingface/datasets/pull/4089 | 1,191,915,196 | PR_kwDODunzps41l7yd | 4,089 | Create metric card for Frugal Score | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,649,084,029,000 | 1,649,168,086,000 | 1,649,167,610,000 | CONTRIBUTOR | null | false | {
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} | Proposing metric card for Frugal Score.
@albertvillanova or @lhoestq -- there are certain aspects that I'm not 100% sure on (such as how exactly the distillation between BertScore and FrugalScore is done) -- so if you find that something isn't clear, please let me know! | {
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https://api.github.com/repos/huggingface/datasets/issues/4088 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4088/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4088/comments | https://api.github.com/repos/huggingface/datasets/issues/4088/events | https://github.com/huggingface/datasets/pull/4088 | 1,191,901,172 | PR_kwDODunzps41l4yE | 4,088 | Remove unused legacy Beam utils | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,649,083,431,000 | 1,649,172,207,000 | 1,649,171,861,000 | MEMBER | null | false | {
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} | This PR removes unused legacy custom `WriteToParquet`, once official Apache Beam includes the patch since version 2.22.0:
- Patch PR: https://github.com/apache/beam/pull/11699
- Issue: https://issues.apache.org/jira/browse/BEAM-10022
In relation with:
- #204 | {
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This PR:
- writes Parquet file preserving original schema and without serialization, thus avoiding serialization overhead and resulting in a smaller output file size.
- fixes `parquet_to_arrow` function | {
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https://api.github.com/repos/huggingface/datasets/issues/4086 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4086/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4086/comments | https://api.github.com/repos/huggingface/datasets/issues/4086/events | https://github.com/huggingface/datasets/issues/4086 | 1,191,373,374 | I_kwDODunzps5HAuo- | 4,086 | Dataset viewer issue for McGill-NLP/feedbackQA | {
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"Hi @cslizc, thanks for reporting.\r\n\r\nI have just forced the refresh of the corresponding cache and the preview is working now.",
"thank you so much"
] | 1,649,057,240,000 | 1,649,111,393,000 | 1,649,059,305,000 | NONE | null | null | null | ## Dataset viewer issue for '*McGill-NLP/feedbackQA*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)*
*short description of the issue*
The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message:
```
Status code: 400
Exception: Status400Error
Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist.
```
Am I the one who added this dataset ? Yes
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https://api.github.com/repos/huggingface/datasets/issues/4085 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4085/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4085/comments | https://api.github.com/repos/huggingface/datasets/issues/4085/events | https://github.com/huggingface/datasets/issues/4085 | 1,190,621,345 | I_kwDODunzps5G93Ch | 4,085 | datasets.set_progress_bar_enabled(False) not working in datasets v2 | {
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"Now, I can't find any reference to set_progress_bar_enabled in the code.\r\n\r\nI think it have been deleted",
"Hi @virilo,\r\n\r\nPlease note that since `datasets` version 2.0.0, we have aligned with `transformers` the management of the progress bar (among other things):\r\n- #3897\r\n\r\nNow, you should update your code to use `datasets.logging.disable_progress_bar`.\r\n\r\nYou have more info in our docs: [Logging methods](https://huggingface.co/docs/datasets/package_reference/logging_methods)"
] | 1,648,903,210,000 | 1,649,056,499,000 | 1,649,054,674,000 | NONE | null | null | null | ## Describe the bug
datasets.set_progress_bar_enabled(False) not working in datasets v2
## Steps to reproduce the bug
```python
datasets.set_progress_bar_enabled(False)
```
## Expected results
datasets not using any progress bar
## Actual results
AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled
## Environment info
datasets version 2
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"Hi @blackhat-coder, thanks for reporting.\r\n\r\nPlease note that the `transformers` library updated their data collators API last year (version 4.10.0):\r\n- huggingface/transformers#13105\r\n\r\nnow requiring to pass `return_tensors` argument at Data Collator instantiation.\r\n\r\nAnd therefore, we also updated in the `datasets` library documentation all the examples using `transformers` data collators.\r\n\r\nIf you would like to follow our examples, please update your installed `transformers` version:\r\n```\r\npip install -U transformers\r\n```"
] | 1,648,832,567,000 | 1,649,057,077,000 | 1,649,056,891,000 | NONE | null | null | null | ## Describe the bug
Hi
### Error 1
Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors'
### Error 2
`DataCollatorWithPadding` isn't imported
## Steps to reproduce the bug
```python
import tensorflow as tf
from datasets import load_dataset
from transformers import AutoTokenizer
dataset = load_dataset('glue', 'mrpc', split='train')
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
```
This is the same code on Huggingface.co
## Actual results
TypeError: __init__() got an unexpected keyword argument 'return_tensors'
## Environment info
- `datasets` version: 2.0.0
- Platform: Windows-10-10.0.19044-SP0
- Python version: 3.9.7
- PyArrow version: 6.0.0
- Pandas version: 1.4.1
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https://api.github.com/repos/huggingface/datasets/issues/4081 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4081/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4081/comments | https://api.github.com/repos/huggingface/datasets/issues/4081/events | https://github.com/huggingface/datasets/pull/4081 | 1,189,916,472 | PR_kwDODunzps41fsxW | 4,081 | Close parquet writer properly in `push_to_hub` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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} | We don’t call writer.close(), which causes https://github.com/huggingface/datasets/issues/4077. It can happen that we upload the file before the writer is garbage collected and writes the footer.
I fixed this by explicitly closing the parquet writer.
Close https://github.com/huggingface/datasets/issues/4077. | {
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https://api.github.com/repos/huggingface/datasets/issues/4080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4080/comments | https://api.github.com/repos/huggingface/datasets/issues/4080/events | https://github.com/huggingface/datasets/issues/4080 | 1,189,667,296 | I_kwDODunzps5G6OHg | 4,080 | NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset | {
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"Hi @richarddwang,\r\n\r\n\r\nIndeed, we have recently updated the loading script of that dataset (and fixed that bug as well):\r\n- #4002\r\n\r\nThat fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by:\r\n- installing `datasets` from our GitHub repo:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\n- forcing the data files to be redownloaded\r\n```python\r\nds = load_dataset('conll2012_ontonotesv5', 'english_v4', split=\"test\", download_mode=\"force_redownload\")\r\n```\r\n\r\nFeel free to re-open this issue if the problem persists. \r\n\r\nDuplicate of:\r\n- #4031"
] | 1,648,812,868,000 | 1,648,821,550,000 | 1,648,821,550,000 | CONTRIBUTOR | null | null | null | ## Steps to reproduce the bug
```python
datasets.load_dataset("conll2012_ontonotesv5", "english_v12")
```
## Actual results
```
Downloading builder script: 32.2kB [00:00, 9.72MB/s]
Downloading metadata: 20.0kB [00:00, 10.4MB/s]
Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size
, total: 379.12 MiB) to ...
Traceback (most recent call last): [315/390]
File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module>
train()
File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train
trainer.fit(model, datamodule=dm)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit
self._call_and_handle_interrupt(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte
rrupt
return trainer_fn(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run
self._data_connector.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre
pare_data
self.trainer.datamodule.prepare_data()
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn
fn(*args, **kwargs)
File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data
raw_dsets = datasets.load_dataset(**load_dataset_kwargs)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset
builder_instance.download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare
self._download_and_prepare(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare
verify_checksums(
File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4079 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4079/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4079/comments | https://api.github.com/repos/huggingface/datasets/issues/4079/events | https://github.com/huggingface/datasets/pull/4079 | 1,189,521,576 | PR_kwDODunzps41eYRC | 4,079 | Increase max retries for GitHub datasets | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,648,805,643,000 | 1,648,827,160,000 | 1,648,826,831,000 | MEMBER | null | false | {
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} | As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub datasets, as previously done for GitHub metrics:
- #4063
Note that this is a temporary solution, while we decide when and how to load GitHub datasets from the Hub:
- #4059
Fix #2048
Related to:
- #4051
- #3210
- #2787
- #2075
- #2036
CC: @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/4078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4078/comments | https://api.github.com/repos/huggingface/datasets/issues/4078/events | https://github.com/huggingface/datasets/pull/4078 | 1,189,513,572 | PR_kwDODunzps41eWnl | 4,078 | Fix GithubMetricModuleFactory instantiation with None download_config | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 1,648,805,218,000 | 1,648,824,291,000 | 1,648,823,967,000 | MEMBER | null | false | {
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} | Recent PR:
- #4063
introduced a potential bug if `GithubMetricModuleFactory` is instantiated with None `download_config`.
This PR add instantiation tests and fix that potential issue.
CC: @lhoestq | {
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] | null | [] | 1,648,802,953,000 | 1,648,829,779,000 | 1,648,829,779,000 | CONTRIBUTOR | null | null | null | ## Describe the bug
When uploading a relatively large image dataset of > 1GB, reloading doesn't work for me, even though pushing to the hub went just fine.
Basically, I do:
```
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_files="path_to_my_files")
dataset.push_to_hub("dataset_name") # works fine, no errors
reloaded_dataset = load_dataset("dataset_name")
```
and it returns:
```
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
```
I created a Colab notebook to reproduce my error: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing
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https://api.github.com/repos/huggingface/datasets/issues/4076 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4076/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4076/comments | https://api.github.com/repos/huggingface/datasets/issues/4076/events | https://github.com/huggingface/datasets/pull/4076 | 1,188,478,867 | PR_kwDODunzps41a1n2 | 4,076 | Add ROUGE Metric Card | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4076). All of your documentation changes will be reflected on that endpoint."
] | 1,648,751,674,000 | 1,648,824,172,000 | null | CONTRIBUTOR | null | false | {
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I've left the 'Values from popular papers' section empty for the time being because I don't know the summarization literature very well and am therefore not sure which paper(s) to pull from (note that the original rouge paper does not seem to present specific values, just correlations with human judgements). Any suggestions on which paper(s) to pull from would be helpful! :) | {
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https://api.github.com/repos/huggingface/datasets/issues/4075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4075/comments | https://api.github.com/repos/huggingface/datasets/issues/4075/events | https://github.com/huggingface/datasets/issues/4075 | 1,188,462,162 | I_kwDODunzps5G1n5S | 4,075 | Add CCAgT dataset | {
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"Awesome ! Let us know if you have questions or if we can help ;) I'm assigning you\r\n\r\nPS: if possible, please try to not use Google Drive links in your dataset script, since Google Drive has download quotas and is not always reliable."
] | 1,648,750,828,000 | 1,649,070,205,000 | null | NONE | null | null | null | ## Adding a Dataset
- **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique
- **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR).
- **Paper:** https://doi.org/10.1109/cbms49503.2020.00110
- **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0
- **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible | {
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"Hi @wranai, thanks for reporting.\r\n\r\nPlease note that the information about language families and groups is taken form the original paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752).\r\n\r\nIf that information is wrong, feel free to contact the paper's authors to suggest that correction.\r\n\r\nJust note that Hungarian language (contrary to their geographically surrounding neighbor languages) belongs to the Uralic (languages) family, together with (among others) Finnish, Estonian, some other languages in northern regions of Scandinavia..."
] | 1,648,750,065,000 | 1,648,800,776,000 | 1,648,800,776,000 | NONE | null | null | null | **Link:** https://huggingface.co/datasets/google/xtreme_s
Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).
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"_The documentation is not available anymore as the PR was closed or merged._"
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"Hi @Jun-jie-Huang,\r\n\r\nAs the error message says, \".pkl\" data files are not supported.\r\n\r\nIf you would like to share your dataset on the Hub, you would need:\r\n- either to create a Python loading script, that loads the data in any format\r\n- or to transform your data files to one of the supported formats (listed in the error message above: CSV, JSON, Parquet, TXT,...)\r\n\r\nYou can find the details in our docs: \r\n- How to share a dataset: https://huggingface.co/docs/datasets/share\r\n- How to create a dataset loading script: https://huggingface.co/docs/datasets/dataset_script\r\n\r\nFeel free to re-open this issue and ping us if you need further assistance."
] | 1,648,708,589,000 | 1,648,714,621,000 | 1,648,714,576,000 | NONE | null | null | null | ## Dataset viewer issue for '*xuyeliu/notebookCDG*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)*
*Couldn't load the xuyeliu/notebookCDG with provided scripts: *
```
from datasets import load_dataset
dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl")
```
I get an error message as follows:
FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
Am I the one who added this dataset ? No
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4069). All of your documentation changes will be reflected on that endpoint.",
"Love it !\r\n\r\n+1 to using JSON Lines rather than CSV. I've also seen image datasets for which JSON Lines was used.\r\n\r\nA `file_name` column sounds good as well, and it means we could reuse the same name for audio. And ok to check the metadata file by default :)\r\n\r\nYou suggested to name the file infos.json - since we already have a datasets_infos.json file, maybe it would be nice to have a name for the metadata/annotations that doesn't contain \"info\" ? (e.g. metadata.json, annotations.json, labels.json)",
"@lhoestq I've addressed your comments and my TODOs. Additionally, I've updated `encode_nested_example`/`decode_nested_example` to support null values in place of a dictionary (if it's not top-level) since JSON Lines also supports this. "
] | 1,648,662,471,000 | 1,649,157,894,000 | null | CONTRIBUTOR | null | false | {
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} | This PR adds support for metadata files to `imagefolder` to add an ability to specify image fields other than `image` and `label`, which are inferred from the directory structure in the loaded dataset.
To be parsed as an image metadata file, a file should be named `"info.csv"` and should have the following structure:
```
image_id,some_col1_name,some_col2_name
rel/path/to/image1.jpg,image1_col1_value,image1_col2_value
rel/path/to/image2.jpg,image2_col1_value,image2_col2_value
...
```
This is how the resolution works:
```
- path/to/imagefolder/directory
- info.csv
- 10.jpg # referenced as 10.jpg in "info.csv"
- Cat
- 0.jpg # referenced as Cat/0.jpg in "info.csv"
- 1.jpg # referenced as Cat/1.jpg in "info.csv"
- Dog
- 0.jpg # referenced as Dog/0.jpg in "info.csv"
- 1.jpg # referenced as Dog/1.jpg in "info.csv"
```
Open questions:
1. IMO it makes more sense to store image metadata as JSON Lines than CSV. CSV is sufficient for textual metadata but not the best for representing bounding boxes, for instance. Also, JSON Lines is more strict, which is good in this case (CSV supports various delimiters, the header line is optional, etc., so it's easier to enforce rules on JSON Lines that it's on CSV)
2. A better name for the `image_id` column, which contains image identifiers? Maybe `image_file` or `image_filename`?
3. WDYT about making `with_metadata=True` the default behavior if the loaded repo/directory contains an `info.csv` file?
An example repository: https://huggingface.co/datasets/mariosasko/PetImages. Can be loaded by installing `datasets` from the PR branch and running `load_dataset("mariosasko/PetImages", with_metadata=True)`.
cc: @abhishekkrthakur (this PR should address https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF)
TODOs:
- [x] Test
- [x] Metadata file nesting
```
- path/to/imagefolder/directory
- info.csv
- 10.jpg
- Cat
- info.csv # should have higher precedence in this directory than the top-level info.csv, but we choose the first "eligible" metadata file currently
- 0.jpg
- 1.jpg
``` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
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} | In 1.18.4 with https://github.com/huggingface/datasets/pull/3719 we introduced an error message for users using `select` with out of bounds indices. The message ended up being confusing for some users because it mentioned negative indices, which is not the main use case.
I replaced it with a message that is very similar to the one you get with you try to access a list with an out-of-range index. | {
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https://api.github.com/repos/huggingface/datasets/issues/4067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4067/comments | https://api.github.com/repos/huggingface/datasets/issues/4067/events | https://github.com/huggingface/datasets/pull/4067 | 1,186,731,905 | PR_kwDODunzps41U7qc | 4,067 | Update datasets task tags to align tags with models | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4067). All of your documentation changes will be reflected on that endpoint."
] | 1,648,658,972,000 | 1,648,715,716,000 | null | MEMBER | null | true | {
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} | **Requires https://github.com/huggingface/datasets/pull/4066 to be merged first**
Following https://github.com/huggingface/datasets/pull/4066 we need to update many dataset tags to use the new ones. This PR takes case of this and is quite big - feel free to review only certain tags if you don't want to spend too much time on it.
Note that the CI will never be green for this PR, because many dataset cards have missing tags or sections, and fixing them is out of scope of this PR (the CI on master will be green anyway) | {
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https://api.github.com/repos/huggingface/datasets/issues/4066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4066/comments | https://api.github.com/repos/huggingface/datasets/issues/4066/events | https://github.com/huggingface/datasets/pull/4066 | 1,186,728,104 | PR_kwDODunzps41U63x | 4,066 | Tasks alignment with models | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4066). All of your documentation changes will be reflected on that endpoint.",
"Yay! This is exciting! Note that we would probably be able to generate this JSON directly from `huggingface/hub-docs`' `Types.ts` file (cc @osanseviero)",
"The following issue should make this much easier :smile: https://github.com/huggingface/hub-docs/issues/83",
"So far I think I've addressed all the comments that I got on slack, but feel free to do a review @osanseviero and let me know if it sounds good to you",
"It just occurred to me that we should probably restart the `datasets-tagging` space once this is merged to update all the task categories there: https://huggingface.co/spaces/huggingface/datasets-tagging",
"Yes, let me update it now",
"Updated: https://huggingface.co/spaces/huggingface/datasets-tagging"
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} | I updated our `tasks.json` file with the new task taxonomy that is aligned with models.
The rule that defines a task is the following:
**Two tasks are different if and only if the steps of their pipelines** are different, i.e. if they can’t reasonably be implemented using the same coherent code (level of granularity/complexity of the code to be defined - ideally I’d like to say “HF user’s level”) - this is the same definition in `transformers`
I will update the tags of all the datasets in this repository [in another PR](https://github.com/huggingface/datasets/pull/4067) for readability.
Main changes:
- conditional-text-generation is split between summarization, translation, text-generation and text2text-generation
- speech-processing is split into automatic-speech-recognition, audio-classification, etc.
- structure-prediction is renamed token-classification
- abstractive-qa now belongs to text2text-generation
Here is just a simplified YAML dump of `tasks.json`:
```yaml
audio-classification:
- keyword-spotting
- speaker-identification
- speaker-intent-classification
- emotion-recognition
- speaker-language-identification
audio-to-audio: []
automatic-speech-recognition: []
conversational:
- dialogue-generation
feature-extraction: []
fill-mask:
- slot-filling
- masked-language-modeling
image-classification:
- multi-label-image-classification
- multi-class-image-classification
image-segmentation:
- instance-segmentation
- semantic-segmentation
- panoptic-segmentation
image-to-text:
- image-captioning
multiple-choice:
- multiple-choice-qa
- multiple-choice-coreference-resolution
object-detection:
- face-detection
- vehicle-detection
question-answering:
- extractive-qa
- open-domain-qa
- closed-domain-qa
sentence-similarity: []
tabular-classification: []
tabular-to-text:
- rdf-to-text
summarization:
- news-articles-summarization
- news-articles-headline-generation
table-to-text: []
table-question-answering: []
text-classification:
- acceptability-classification
- entity-linking-classification
- fact-checking
- intent-classification
- multi-class-classification
- multi-label-classification
- natural-language-inference
- semantic-similarity-classification
- sentiment-classification
- topic-classification
- semantic-similarity-scoring
- sentiment-scoring
- sentiment-analysis
- hate-speech-detection
- text-scoring
text-generation:
- dialogue-modeling
- language-modeling
text-retrieval:
- document-retrieval
- utterance-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
text-to-image: []
text-to-tabular:
- relation-extraction
- semantic-role-labeling
text-to-speech: []
text2text-generation:
- text-simplification
- explanation-generation
- abstractive-qa
- open-domain-abstractive-qa
- closed-domain-qa
- open-book-qa
- closed-book-qa
time-series-forecasting:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
token-classification:
- named-entity-recognition
- part-of-speech-tagging
- parsing
- lemmatization
- word-sense-disambiguation
- coreference-resolution
translation: []
visual-question-answering: []
voice-activity-detection: []
zero-shot-classification: []
zero-shot-image-classification: []
reinforcement-learning: []
other: []
```
Feel free to comment and give suggestions, especially if you think we can also align this list with other projects
cc @julien-c @osanseviero @severo @lewtun @yjernite @albertvillanova @mariosasko @polinaeterna | {
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