Datasets:

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[ "I'm not sure if the documentation explains why the dataset identifiers might have a namespace or not (the user/org): 'glue' vs 'severo/glue'. Do you think we should explain it, and relate it to the GitHub/Hub distinction?" ]

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[ "Hi @jvanz, thank you for your proposal.\r\n\r\nIn fact, we are aware that it is very common the problem you mention. Because of that, we are currently working in implementing a new version of wikipedia on the Hub, with all data preprocessed (no need to use Apache Beam), from where you will be able to use `data_files` to load only a specific subset of the data files.\r\n\r\nSee:\r\n- #3401 " ]

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[ "Thanks for reporting, @anna-kay. We are fixing it." ]

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[ "Thanks for reporting, @kolk.\r\n\r\nWe are fixing it. " ]

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[ "Oh, this URL shouldn't be updated to the tagging app as it's actually used for creating the README - closing this." ]

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[ "Hi @jvanz, thanks for reporting.\r\n\r\nPlease note that Wikimedia website does not longer host Wikipedia dumps for so old dates.\r\n\r\nFor a list of accessible dump dates of `pt` Wikipedia, please see: https://dumps.wikimedia.org/ptwiki/\r\n\r\nYou can load for example `20220220` `pt` Wikipedia:\r\n```python\r\ndataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n```", "> ```python\r\n> dataset = load_dataset(\"wikipedia\", language=\"pt\", date=\"20220220\", beam_runner=\"DirectRunner\")\r\n> ```\r\n\r\nThank you! I did not know that I can do this. I was following the example in the error message when I do not define which language dataset I'm trying to download.\r\n\r\nI've tried something similar changing the date in the `load_dataset` call that I've shared in the bug description. Obviously, it did not work. I need to read the docs more carefully next time. My bad!\r\n\r\nThanks again and sorry for the noise.\r\n\r\n" ]

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[ "Hi @Dref360, thanks a lot for your proposal.\r\n\r\nIt totally makes sense to have more flexibility when class encoding, I agree.\r\n\r\nYou could even further customize the class encoding by passing an instance of `ClassLabel` itself (instead of replicating `ClassLabel` instantiation arguments as `Dataset.class_encode_column` arguments).\r\n\r\nAnd the latter made me think of `Dataset.cast_column`...\r\n\r\nMaybe better to have some others' opinions @lhoestq @mariosasko ", "Hi @Dref360! You can use [`Dataset.align_labels_with_mapping`](https://huggingface.co/docs/datasets/master/package_reference/main_classes.html#datasets.Dataset.align_labels_with_mapping) after `Dataset.class_encode_column` to assign a different mapping of labels to ids.\r\n\r\n@albertvillanova I'd like to avoid adding more complexity to the API where it's not (absolutely) needed, so I don't think introducing a new param in `Dataset.class_encode_column` is a good idea.\r\n\r\n", "I wasn't aware that it existed thank you for the link.\n\nClosing then! " ]

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[ "Hi @Muhtasham you should take a look at https://huggingface.co/blog/data-measurements-tool and accompanying demo app at https://huggingface.co/spaces/huggingface/data-measurements-tool\r\n\r\nWe would be interested in your feedback. cc @meg-huggingface @sashavor @yjernite " ]

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[ "Hi @kingabzpro.\r\n\r\nI think you need to create a loading script that creates the dataset from the CSV file and the image paths.\r\n\r\nAs example, you could have a look at the Food-101 dataset: https://huggingface.co/datasets/food101\r\n- Loading script: https://huggingface.co/datasets/food101/blob/main/food101.py\r\n\r\nOnce the loading script is created, the viewer will show a previsualization of your dataset. ", "@albertvillanova I don't think this is the issue. I have created another dataset with similar files and format and it works. https://huggingface.co/datasets/kingabzpro/savtadepth-flags-V2", "Yes, you are right, that was not the issue.\r\n\r\nJust take into account that sometimes the viewer can take some time until it shows the preview of the dataset.\r\nAfter some time, yours is finally properly shown: https://huggingface.co/datasets/kingabzpro/savtadepth-flags", "The problem was resolved by deleted the dataset and creating new one with similar name and then clicking on flag button.", "I think if you make manual changes to dataset the whole system breaks. " ]

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[ "Thanks! Used here: https://github.com/huggingface/datasets-preview-backend/blob/main/src/datasets_preview_backend/models/dataset.py#L26 :) " ]

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[ "We usually find issues with files hosted at Google Drive...\r\n\r\nIn this case we download the Google Drive Virus scan warning instead of the data file." ]

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[ "Awesome thank you ! The implementation of the parent `Metric` class was assuming that all metrics were supposed to have references/predictions pairs - I just changed that so you don't have to override `compute()`. I took the liberty of doing the changes directly inside this PR to make sure it works as expected with perplexity.\r\n\r\nOther than that it looks in pretty good shape :) I just did minor changes like remove a remaining `print` as well as fixing the `Features` defined in `_info()`. I also renamed `input_text` to `input_texts` since it makes it more obvious that it's a list of strings - let me know if it sounds good to you.\r\n\r\nLet me know if you'd like to make other changes or if it's all good for you !" ]

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[ "Hi! If I'm not mistaken, this behavior is intentional, but I agree it could be more intuitive.\r\n\r\n@albertvillanova Do you remember why you decided not to decode columns in the `Audio` feature PR when `input_columns` is not `None`? IMO we should decode those columns, and we don't even have to use lazy structures here because the user explicitly requires them in the map transform. \r\n\r\ncc @lhoestq for visibility", "I think I excluded to decorate the function when `input_columns` were passed as a quick fix for some non-passing tests: \r\n- https://github.com/huggingface/datasets/pull/2324/commits/9d7c3e8fa53e23ec636859b4407eeec904b1b3f9\r\n\r\nThat PR was quite complex and I decided to focus on the main feature requests, leaving refinements for subsequent PRs.\r\n\r\nNote that when `input_columns` are passed, the signature of the function is effectively changed, while the decorated function expects an item (whether an example or a batch) as first arg (which is not the case when passing `input_columns`.\r\n\r\nI agree we should consider supporting the case when `input_columns` are passed." ]

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[ "Thanks for reporting. The dataset viewer depends on some backend treatments, and for now, they might take some hours to get processed. We're working on improving it.", "It has finally been processed. Thanks for the patience.", "Thanks for the info @severo !" ]

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[ "Fixed on master (see https://github.com/huggingface/datasets/pull/3719).", "Awesome, I did not find that one! Thanks." ]

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[ "Related to: https://github.com/huggingface/datasets/issues/3444", "Cool ! `filter` will be very useful. There can be a filter that you can apply on a streaming dataset:\r\n```python\r\nload_dataset(..., streaming=True).filter(lambda x: x[\"lang\"] == \"sw\")\r\n```\r\n\r\nOtherwise if you want to apply a filter on the source files that are going to be used for streaming, the logic has to be impIemented directly in the dataset script, or if there's no dataset script this can be done with pattern matching\r\n```python\r\nload_dataset(..., lang=\"sw\") # if the dataset script supports this parameter\r\nload_dataset(..., data_files=\"data/lang=sw/*\") # if there's no dataset script, but only data files\r\n```\r\n\r\n--------------\r\n\r\nHere are also some additional ideas of API to convert from iterable to map-style dataset:\r\n```python\r\non_disk_dataset = streaming_dataset.to_disk()\r\non_disk_dataset = streaming_dataset.to_disk(path=\"path/to/my/dataset/dir\")\r\n\r\nin_memory_dataset = streaming_dataset.take(100).to_memory() # to experiment without having to write files\r\n```\r\n--------------\r\n\r\nFinally regarding `push_to_hub`, we can replace `batch_size` by `shard_size` (same API as for on-disk datasets). The default is 500MB per file\r\n\r\nLet me know what you think !" ]

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[ "Thnaks for reporting @jaketae. We are fixing it. " ]

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[ "@albertvillanova just FYI, since you were so helpful with the previous pubmed issue :) ", "IIRC streaming from FTP is not fully tested yet, so I'm fine with switching to HTTPS for now, as long as the download speed/availability is great", "@albertvillanova Thanks for pointing me to the `ET` module replacement. It should look a lot cleaner now.\r\n\r\nUnfortunately I tried keeping the `ftp://` protocol but was seeing timeout errors? in streaming mode (below). I think the `https://` performance is not an issue, when I was profiling the `open(..) -> f.read() -> etree.fromstring(xml_str)` codepath, most of the time was spent in the XML parsing rather than the data download.\r\n\r\n\r\nError when using `ftp://`:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/venv/lib/python3.8/site-packages/fsspec/implementations/ftp.py\", line 301, in _fetch_range\r\n self.fs.ftp.retrbinary(\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/ftplib.py\", line 430, in retrbinary\r\n callback(data)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/venv/lib/python3.8/site-packages/fsspec/implementations/ftp.py\", line 293, in callback\r\n raise TransferDone\r\nfsspec.implementations.ftp.TransferDone\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"test_pubmed_streaming.py\", line 9, in <module>\r\n print (next(iter(pubmed_train_streaming)))\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/iterable_dataset.py\", line 365, in __iter__\r\n for key, example in self._iter():\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/iterable_dataset.py\", line 362, in _iter\r\n yield from ex_iterable\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/iterable_dataset.py\", line 79, in __iter__\r\n yield from self.generate_examples_fn(**self.kwargs)\r\n File \"/Users/abhinav/.cache/huggingface/modules/datasets_modules/datasets/pubmed/af552ed918e2841e8427203530e3cfed3a8bc3213041d7853bea1ca67eec683d/pubmed.py\", line 362, in _generate_examples\r\n tree = ET.parse(filename)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/streaming.py\", line 65, in wrapper\r\n return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/utils/streaming_download_manager.py\", line 636, in xet_parse\r\n return ET.parse(f, parser=parser)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py\", line 1202, in parse\r\n tree.parse(source, parser)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/xml/etree/ElementTree.py\", line 595, in parse\r\n self._root = parser._parse_whole(source)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/abhi-datasets/src/datasets/utils/streaming_download_manager.py\", line 293, in read_with_retries\r\n out = read(*args, **kwargs)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/gzip.py\", line 292, in read\r\n return self._buffer.read(size)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/_compression.py\", line 68, in readinto\r\n data = self.read(len(byte_view))\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/gzip.py\", line 479, in read\r\n if not self._read_gzip_header():\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/gzip.py\", line 422, in _read_gzip_header\r\n magic = self._fp.read(2)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/gzip.py\", line 96, in read\r\n self.file.read(size-self._length+read)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/venv/lib/python3.8/site-packages/fsspec/spec.py\", line 1485, in read\r\n out = self.cache._fetch(self.loc, self.loc + length)\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/venv/lib/python3.8/site-packages/fsspec/caching.py\", line 153, in _fetch\r\n self.cache = self.fetcher(start, end) # new block replaces old\r\n File \"/Users/abhinav/Documents/mosaicml/hf_datasets/venv/lib/python3.8/site-packages/fsspec/implementations/ftp.py\", line 311, in _fetch_range\r\n self.fs.ftp.getmultiline()\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/ftplib.py\", line 224, in getmultiline\r\n line = self.getline()\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/ftplib.py\", line 206, in getline\r\n line = self.file.readline(self.maxline + 1)\r\n File \"/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/socket.py\", line 669, in readinto\r\n return self._sock.recv_into(b)\r\nsocket.timeout: timed out\r\n```" ]

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[ "Thanks for reporting, @abhi-mosaic (related to #3655).\r\n\r\nPlease note that `xml.etree.ElementTree.parse` already supports streaming:\r\n- #3476\r\n\r\nNo need to refactor to use `open`/`xopen`. Is is enough with importing the package `as ET` (instead of `as etree`)." ]

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[ "Note that we might change the heuristic and create a different config per file, at least in that case.", "Hi @severo, thanks for reporting.\r\n\r\nYes, this happens because when non-streaming, a cast of all data is done in order to \"concatenate\" it all into a single dataset (thus the error), while this casting is not done while yielding item by item in streaming mode.\r\n\r\nMaybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?", "Why do we want to concatenate the files? Is it the expected behavior for most datasets that lack a script and dataset info?", "These files are two different dataset configurations since they don't share the same schema.\r\n\r\nIMO the streaming mode should fail in this case, as @albertvillanova said.\r\n\r\nThere is one challenge though: inferring the schema from the first example is not robust enough in the general case - especially if some fields are nullable. I guess we can at least make sure that no new columns are added", "OK. So, if we make the streaming also fail, the dataset https://huggingface.co/datasets/huggingface/transformers-metadata will never be [viewable](https://github.com/huggingface/datasets-preview-backend/issues/144) (be it using streaming or fallback to downloading the files), right?\r\n", "Yes, until we have a way for the user to specify explicitly that those two files are different configurations.\r\n\r\nWe can maybe have some rule to detect this automatically, maybe checking the first line of each file ? That would mean that for dataset of 10,000+ files we would have to verify every single one of them just to know if there is one ore more configurations, so I'm not sure if this is a good idea", "i think requiring the user to specify that those two files are different configurations is in that case perfectly reasonable.\r\n\r\n(Maybe at some point we could however detect this type of case and prompt them to define a config mapping etc)", "OK, so, before closing the issue, what do you think should be done?\r\n\r\n> Maybe in streaming mode we should keep the schema (inferred from the first item) and throw an exception if a subsequent item does not conform to the inferred schema?\r\n\r\nor nothing?", "We should at least raise an error if a new sample has column names that are missing, or if it has extra columns. No need to check for the type for now.\r\n\r\nI'm in favor of having an error especially because we want to avoid silent issues as much as possible - i.e. when something goes wrong (when schemas don't match or some data are missing) and no errors/warnings are raised.\r\n\r\nConsistency between streaming and non-streaming is also important." ]

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[ "I just changed to `dl_manager.is_streaming` rather than an additional parameter `streaming` that has to be handled by the DatasetBuilder class - this way the streaming logic doesn't interfere with the base builder's code.\r\n\r\nI think it's better this way, but let me know if you preferred the previous way and I can revert\r\n\r\n> But on the other hand, IMHO, I think this specific solution adds complexity to handling streaming/non-streaming, and moves this complexity to the loading script and thus to the contributors/users who want to create the loading script for their canonical/community datasets (instead of keeping it hidden form the end users).\r\n\r\nI'm down to discuss this more in the future !", "@lhoestq good idea: much cleaner this way! That way each class has its own responsibilities without mixing around..." ]

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[ "> using command line git-lfs - [...] 300MB/s!\r\n\r\nwhich server location did you upload from?", "From GCP region `us-central1-a`.", "The most surprising part to me is the saving time. Wondering if it could be due to compression (`ParquetWriter` uses SNAPPY compression by default; it can be turned off with `to_parquet(..., compression=None)`). " ]

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[ "would this allow to include the size in the dataset info without downloading the files? related to https://github.com/huggingface/datasets/pull/3670", "@severo I don't think so. We could use this to get `info.download_checksums[\"num_bytes\"]`, but we must process the files to get the rest of the size info. " ]

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