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[ "If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n```python\r\ndset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\ndset_tc = dset.prepare_for_task(\"text-classification\")\r\ndset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n```\r\nI see 2 options:\r\n1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid) ", "> If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n> \r\n> ```python\r\n> dset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\n> dset_tc = dset.prepare_for_task(\"text-classification\")\r\n> dset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n> ```\r\n> \r\n> I see 2 options:\r\n> \r\n> 1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n> 2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid)\r\n\r\nthanks for the great idea @mariosasko and for spotting the problem with sequential task preparation! i am in favour of your option (1) since it is simple and saves us from having to keep track of the column mappings across multiple steps. \r\n\r\ni've implemented the change and refactored the tests to account for the new approach (including a new test that the templates are flushed after we call `prepare_for_task`). perhaps the slightly inelegant aspect here is that if we want to allow the user to set `labels` in the `TextClassification` template, then we have two places (`DatasetInfo.__post_init__` and `TextClassification.__post_init__`) where we need to update `label_schema`. \r\n\r\non the other hand, dropping `labels` from the `TextClassification` signature would have the nice effect that users only have to think about column names when defining their tasks.\r\n\r\nin any case, i think it would be a good idea to merge #2376 soon as the current PR is touching a lot of the same places in the codebase 😄 \r\n", "cc @SBrandeis who might also be interested in this feature :)", "Tests are failing only because the `emotion` dataset card doesn't pass our dataset card validator (tags are missing), you can ignore this since it's unrelated to this PR.", "@lhoestq @SBrandeis i've fixed the tests and think this is now in a good state for another review :)", "Maybe @SBrandeis you can also take a look to make sure you're fine with it ?" ]

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[ "That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ", "I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: I’m not sure what happens when you map `lambda x: {'input': ...}` as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['original_answer']`) I implemented it with a regular function (not lambda), see below\r\n\r\n```py\r\ndef add_original_answer(x, trivia_qa, triviaqa_map):\r\n i = triviaqa_map[x['id']]\r\n x['output']['original_answer'] = trivia_qa['validation'][i]['answer']['value']\r\n return x\r\n```" ]

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[ "LGTM now, thank you =)" ]

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[ "Update: found a few datasets that slipped through the net. Adding them shortly!", "You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?", "> You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?\r\n\r\nhi @yjernite, these code insertions are auto-generated so could certainly be improved :) \r\n\r\njust so i understand, your idea is that instead of doing something like\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n task_templates=[\r\n TextClassification(\r\n labels=(\"Business\", \"Sci/Tech\", \"Sports\", \"World\"),\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ],\r\n )\r\n```\r\n\r\nwe could do the following:\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n info = datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n )\r\n\r\n info.task_templates = [\r\n TextClassification(\r\n labels=info.features.names,\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ]\r\n return info\r\n```\r\n\r\n", "Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?", "> Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?\r\n\r\nOh yes, that would be great! It does mean enforcing that people use the right feature type (sometimes people still use a `string` feature still because they don't want to enumerate the classes, but I guess you've been catching most of those in reviews @lhoestq )\r\n\r\nThere might be reasons where there should be a legitimate difference, but I can't really think of nay right now, and we can always duplicate the feature", "Let's ignore the CI fails since they are unrelated to your changes. They're about dataset cards issues" ]

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[ "Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them)\r\n\r\n", "Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on making the caching work for datasets in RAM.\r\n\r\nUntil then, I'd recommend passing `keep_in_memory=False` to the calls to `load_dataset` like here:\r\n\r\nhttps://github.com/huggingface/transformers/blob/223943872e8c9c3fc11db3c6e93da07f5177423f/examples/pytorch/language-modeling/run_clm.py#L233\r\n\r\nThis way you say explicitly that you want your dataset to stay on the disk, and it will be able to recover previously computed results from the cache.", "gotcha! thanks Quentin", "OK, It doesn't look like we can use the proposed workaround - see https://github.com/huggingface/transformers/issues/11801\r\n\r\nCould you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often and needs a quick startup time. Manual editing of standard scripts is not a practical option when one uses examples.\r\n\r\nThis could also be a problem for tests, which will be slower because of lack of cache, albeit usually we use tiny datasets there. I think we want caching for tests.\r\n\r\nThank you.", "Hi @stas00, \r\n\r\nYou are right: an env variable is needed to turn off this behavior. I am adding it.\r\n\r\nFor the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`\r\n\r\nYou can find this info in the docs:\r\n- in the docstring of the parameter `keep_in_memory` of the function [`load_datasets`](https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset):\r\n- in a Note in the docs about [Loading a Dataset](https://huggingface.co/docs/datasets/loading_datasets.html#from-the-huggingface-hub)\r\n\r\n> The default in 🤗Datasets is to memory-map the dataset on drive if its size is larger than datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES (default 250 MiB); otherwise, the dataset is copied in-memory. This behavior can be disabled by setting datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None, and in this case the dataset is not loaded in memory.", "Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.\r\n\r\nMay be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in_memory=False` or `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0`?", "@stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.\r\n\r\nTell me if this is logical/convenient, or I should change it.", "In my PR, to turn off current default bahavior, you should set env variable to one of: `{\"\", \"OFF\", \"NO\", \"FALSE\"}`.\r\n\r\nFor example:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=\r\n```", "IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.\r\n\r\nAlso \"SIZE_IN_BYTES\" that can take one of `{\"\", \"OFF\", \"NO\", \"FALSE\"}` is also quite odd.\r\n\r\nI think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` that can accept any numerical value to match the name of the variable, requires minimal logic and is very straightforward. \r\n\r\nSo if you could adjust this logic - then `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` is all that's needed to not do in-memory datasets.\r\n\r\nDoes it make sense?", "I understand your point @stas00, as I am not very convinced with current implementation.\r\n\r\nMy concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this case.", "That's a good question, and again the normal bytes can be used for that:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)\r\n```\r\nSince it's unlikely that anybody will have more than 1TB RAM.\r\n\r\nIt's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in this context.\r\n\r\nNot sure when it was added and if there are back-compat issues here, but perhaps it could be renamed `MAX_IN_MEMORY_DATASET_SIZE` and support 1M, 1G, 1T, etc. \r\n\r\nBut scientific notation is quite intuitive too, as each 000 zeros is the next M, G, T multiplier. Minus the discrepancy of 1024 vs 1000, which adds up. And it is easy to write down `1e12`, as compared to `1099511627776` (2**40). (`1.1e12` is more exact).\r\n", "Great! Thanks, @stas00.\r\n\r\nI am implementing your suggestion to turn off default value when set to `0`.\r\n\r\nFor the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.", "Awesome! Thank you, @albertvillanova!!!\r\n\r\n" ]

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[ "Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working." ]

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[ "Thanks for the suggestions! I've included them and made a few minor tweaks along the way", "Please merge master into this branch to fix the CI, I just fixed metadata validation tests." ]

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[ "Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.\r\nMore info at #1933 " ]

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[ "Looks good thanks, what do you think @lewtun ?", "thanks for including the lazy `ClassLabel` class @mariosasko ! from my side this LGTM!" ]

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[ "Once this is merged, let's update `transformers` examples to use this new code. As currently all those tqdm bars are who knows what they are....\r\n\r\nhttps://github.com/huggingface/transformers/issues/11797", "Sure @stas00! Once this is merged let's discuss what all changes can be done on `transformers` side", "@bhavitvyamalik, as it has been merged would you like to tackle https://github.com/huggingface/transformers/issues/11797?\r\n", "Definitely @stas00. From what I could gather, you guys want more meaningful `.map` calls for all examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch)?", "That's exactly right, @bhavitvyamalik \r\n\r\nPerhaps the best approach is to do one example, see that other maintainers agree on it. and then replicate to other." ]

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[ "Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n data_files='/data/dir/corpus.txt', \r\n cache_dir='.')\r\n```" ]

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[ "Thanks for your helpful comments and suggestions! :)\r\nI integrated the additional fields, and extended some of the README/dataset card.\r\nAnd I actually realized that we had the cc-by-4.0 for the dataset, so this was also changed.", "I added the answers to the test set actually :)", "Oh great ! Let me revert my change then" ]

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[ "@lhoestq Thank you for the review. I've made the suggested changes. There still might be some problems with dummy data though due to some csv loading issues (which I haven't found the cause to).", "I took a look at the dummy data and some csv lines were cropped. I fixed them :)" ]

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[ "Regarding the license issue, I think we should allow it since it starts with `other-`. Cc @gchhablani what do you think ?", "@lhoestq I agree, I'll look into it." ]

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[ "also, I test the function on some little data , get the same message:\r\n\r\n```\r\nPython 3.8.5 (default, Jan 27 2021, 15:41:15)\r\n[GCC 9.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric('accuracy')\r\n>>> metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\n2021-05-15 16:39:17.240991: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\n>>> metric.compute()\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py\", line 391, in compute\r\n self._finalize()\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py\", line 342, in _finalize\r\n self.writer.finalize()\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/arrow_writer.py\", line 370, in finalize\r\n self.stream.close()\r\n File \"pyarrow/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n File \"pyarrow/error.pxi\", line 112, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```", "Hi @hyusterr,\r\nIf you look at the example provided in `metrics/accuracy.py`, it only does `metric.compute()` to calculate the accuracy. Here's an example:\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.compute(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nprint(output['accuracy']) # 0.5\r\n```\r\n", "I thought I can use Metric to collect predictions and references, this follows the step from huggingface's sample colab.\r\nBTW, I fix the problem by setting other cache_dir in load_metric, but I'm still wondering about the mechanism.", "I tried this code on a colab notebook and it worked fine (with gpu enabled):\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nfinal_score = metric.compute()\r\nprint(final_score) # 0.5\r\n```\r\nAlso, in `load_metric`, I saw `cache_dir` is optional and it defaults to `~/.datasets/`", "Hi ! By default it caches the predictions and references used to compute the metric in `~/.cache/huggingface/datasets/metrics` (not `~/.datasets/`). Let me update the documentation @bhavitvyamalik .\r\n\r\nThe cache is used to store all the predictions and references passed to `add_batch` for example in order to compute the metric later when `compute` is called.\r\n\r\nI think the issue might come from the cache directory that is used by default. Can you check that you have the right permissions ? Otherwise feel free to set `cache_dir` to another location." ]

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[ "Hi ! `release_v2.1` and the others are dataset configuration names.\r\n\r\nThe configuration names are used to show the right code snippet in the UI to load the dataset.\r\nFor example if the parsing of the web_nlg tags worked correctly we would have:\r\n\r\n\r\nTherefore I don't think it's a good idea to rename the configurations from `release_v2.1` to `release_v2_1` as the code snippet would be wrong in this case.\r\n\r\nMoreover we can't really disallow dots in configuration names and rename the configurations since it would be a big breaking change. It's commonly used, especially with multilingual datasets. For example `load_dataset(\"indic_glue\", \"sna.bn\")`.\r\n\r\nIs this something that can be fixed on the moonlanding side instead ?", "> Is this something that can be fixed on the moonlanding side instead ?\r\n\r\nNot really unless we change database:)\r\n\r\nWe'll maybe try to find another workaround, but super low-prio given that it's the only dataset that has those dotted keys in the YAML metadata", "Ok, should we close this PR then ?" ]

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[ "Hi @lhoestq, \r\nIt turns out that pyarrow `ListArray` are not recognized as list-like when we get output from `numpy_to_pyarrow_listarray`. This might cause tests to fail. If possible can we convert that `ListArray` output to list inorder for tests to pass? Under the hood it'll maintain the dtype as that of numpy array passed during input only", "Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch` https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039 and `test_map_tf`https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056 \r\nthey're expecting `float64`. Shouldn't that be `float32` now?", "It's normal: pytorch and tensorflow use `float32` by default, unlike numpy which uses `float64`.\r\n\r\nI think that we should always keep the precision of the original tensor (torch/tf/numpy).\r\nIt means that as it is in this PR it's fine (the precision is conserved when doing the torch/tf -> numpy conversion).\r\n\r\nThis is a breaking change but in my opinion the fact that we had Value(\"float64\") for torch.float32 tensors was an issue already.\r\n\r\nLet me know what you think. Cc @albertvillanova if you have an opinion on this\r\n\r\nIf we agree on doing this breaking change, we can just change the test. ", "Hi @lhoestq, \r\nMerged master into this branch. Only changing the test is left for now (mentioned below) after which all tests should pass.\r\n\r\n> Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch`\r\n> \r\n> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039\r\n> \r\n> and `test_map_tf`\r\n> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056\r\n> \r\n> \r\n> they're expecting `float64`. Shouldn't that be `float32` now?\r\n\r\n", "> they're expecting float64. Shouldn't that be float32 now?\r\n\r\nYes feel free to update those tests :)\r\n\r\nIt would be nice to have the same test for JAX as well", "Added same test for for JAX too. Also, I saw that I missed changing `test_cast_to_python_objects_jax` like I did for TF and PyTorch. Finished that as well" ]

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[ "Hi ! Thanks for sharing :)\r\nI think the best place to share this is probably the `Languages at Hugging Face` section of the forum:\r\n/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fc%2Flanguages-at-hugging-face%2F15%5Cr%5Cn%5Cr%5CnSince this is not a dataset, I'm closing this PR if you don't mind", "Thank you I will look into the link that you have shared with me.\n\n\n\n\nOn Mon, May 17, 2021 at 7:05 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> Closed #2358 <https://github.com/huggingface/datasets/pull/2358>.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/2358#event-4754836267>, or\n> unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AN7SJYJVY4C5XQRDNET743DTOEPC7ANCNFSM443AZ3MA>\n> .\n>\n" ]

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[ "Oh one other thing. Mentioned in the PR was that I would need to regenerate the dataset_infos.json once the camel casing was done. However, I am unsure why this is the case since there is no reference to any object names in the dataset_infos.json file.\r\n\r\nIf it needs to be reran, I can try it do it on my own machine, but I've had a memory issues with a previous dataset due to my compute constraints so I'd prefer to hopefully avoid it all together if not necessary to regenerate.", "Was just reviewing the `builder_name`s of each dataset and it seems like it is already following this format:\r\n\r\n`CodeXGlueCcCloneDetectionBigCloneBenchMain -> code_x_glue_cc_clone_detection_big_clone_bench_main` Is there a location I am missing?", "> Was just reviewing the `builder_name`s of each dataset and it seems like it is already following this format:\r\n> \r\n> `CodeXGlueCcCloneDetectionBigCloneBenchMain -> code_x_glue_cc_clone_detection_big_clone_bench_main` Is there a location I am missing?\r\n\r\nIf it's already in this format then it's fine thanks ! It's all good then\r\n\r\nTo fix the CI you just need to add the `encoding=` parameters to the `open()` calls", "@lhoestq I think everything should be good to go besides the code styling, which seem to be due to missing or unsupported metadata tags for the READMEs, is this something I should worry about since all the other datasets seem to be failing as well?", "Awesome! Just committed your changes and I will begin on adding the TOCs and filling in the content for the new sections/subsections.\r\n\r\nAlso, I see that we are having to only use the `code` tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.", "> Also, I see that we are having to only use the code tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.\r\n\r\nYes I agree. We'll be able to reuse the tags per programming language from this PR when we allow this feature\r\n\r\ncc @yjernite what do you think about extending our languages taxonomy to programming languages ?", "Hey @lhoestq, just finalizing the READMEs and testing them against the automated test. For the non, WIN tests, it seems like there is some dependency issue that doesn't have to do with the new datasets. For the WIN tests, it looks like some of the headings are mislabeled such as \"Supported Tasks and Leaderboards\" -> \"Supported Tasks\" in the TOC you posted. Should I base my TOC on the one you posted or on the one that the test script is using? Also, it throws errors for some of the fields being empty, such as \"Source Data\" in the `code_x_glue_tt_text_to_text` dataset. However, I am not familiar with this dataset, so I put the `[More Information Needed]` stub, similar to the other sections I couldn't easily answer. For some of the sections like \"Source Data\", is this info required?", "Yes you're right, it is `Supported Tasks and Leaderboards` that we need to use, sorry about that\r\n\r\nI also noticed the same for the splits section: we have to use `Data Splits` (not Data Splits Sample Size)\r\n", "Some subsections are also missing: `Initial Data Collection and Normalization`, `Who are the source language producers?`.\r\nIf you are interested you can fill those sections as well, or leave them empty for now.\r\nThis will also fix the error regarding \"Source Data\"\r\n\r\nYou can see the template of the readme here:\r\nhttps://github.com/huggingface/datasets/blob/9d8bf36fdb861d9b2922d7c782fb58f9f542997c/templates/README.md", "> > Also, I see that we are having to only use the code tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.\r\n> \r\n> Yes I agree. We'll be able to reuse the tags per programming language from this PR when we allow this feature\r\n> \r\n> cc @yjernite what do you think about extending our languages taxonomy to programming languages ?\r\n\r\nSounds good, as long as they all share a prefix! maybe `code_cpp`, `code_java`, etc. ? \r\n\r\nI don't think we currently have `_` in language codes/names, but also don't see what it would break *a priori*", "We don't use `_` but there are some languages that use `-` though like `en-US`. Let's use `-` maybe, to match the same hierarchy pattern ?", "Hi guys, I just started working on https://github.com/huggingface/datasets/pull/997 this morning and I just realized that you were finishing it... You may want to get the dataset cards from https://github.com/madlag/datasets, and maybe some code too, as I did a few things like moving _CITATION and _DESCRIPTION to globals.\r\n\r\n", "I am renaming the main classes to match the dataset names, for example : CodeXGlueTcTextToCodeMain -> CodeXGlueTcTextToCode . And I am regenerating the dataset_infos.json accordingly.", "Thanks for renaming the classes and updating the dataset_infos.json ! This looks all clean now :)\r\n\r\nThis PR looks all good to me :) One just needs to merge master into this branch to make sure the CI is green with the latest changes. It should also fix the current CI issues that are not related to this PR", "Woot woot :rocket:! All green, looks like it is ready for showtime. Thank you both @lhoestq and especially @madlag, I think these datasets are going to be a great new addition to :hugs: datasets and I can't wait to use them in my research :nerd_face:.", "Thanks @ncoop57 for you contribution! It will be really cool to see those datasets used as soon as they are released !" ]

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[ "Hi ! sorry for the late response \r\n\r\nIt would be fantastic to have a guide for adding metrics as well ! Currently we only have this template here:\r\nhttps://github.com/huggingface/datasets/blob/master/templates/new_metric_script.py\r\n\r\nWe can also include test utilities for metrics in the guide.\r\n\r\nWe have a pytest suite with commands that you can use to make sure your metric works as expected.\r\nIt has two useful commands:\r\n\r\n1. This commands tests the code in the `Examples:` desction of the docstring of the metric:\r\n```\r\npytest tests/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\nThis will run this code for example:\r\n\r\nhttps://github.com/huggingface/datasets/blob/e0787aa2a781cc15a80f7597f56d1f12e23df4c9/metrics/accuracy/accuracy.py#L40-L45\r\n\r\nMoreover this test is meant to be fast so users are free to add patches to the metric to avoid intensive computations.\r\nAnd example of intensive call patch can be found here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/e0787aa2a781cc15a80f7597f56d1f12e23df4c9/tests/test_metric_common.py#L138-L151\r\n\r\n2. This test runs the same thing as 1. except that it doesn't use patches (the real metric is used):\r\n```\r\nRUN_SLOW=1 pytest tests/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\n\r\nFinally additional metric-specific tests can be added to `test_metric_common.py`.\r\n\r\nVoila :) Feel free to ping me if you have any question or if I can help\r\n" ]

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[ "Oh right! I'd be in favor of still having the same TOC across the board, we can either leave it as is or add a `[More Info Needed]` `Contributions` Section wherever it's currently missing, wdyt?", "(I thought those were programmatically updated based on git history :D )", "Merging for now to avoid conflict since there are so many changes but let's figure out the contributions section next ;) " ]

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[ "This is perfect, @albertvillanova - thank you! Tested it to work.\r\n\r\nMight it be a good idea to document the args to `to_json`?\r\n\r\nand also even a very basic progress bar? took 10min for 8M large records for `openwebtext` so perhaps some indication of it's being alive every min or so?", "@lhoestq I added tests for both `lines` and `orient`." ]

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[ "Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```" ]

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[ "@lhoestq\r\n\r\nShould I remove the scripts? or atleast remove running them from the CircleCI config?\r\n\r\nAlso, I hope it is okay that the combined method (metadata+content) is only a slow test, and for the Circle CI, I assume only non-slow tests are run? If yes, this would mean separate tests for content and metadata.", "Also feel free to remove the scripts from the CI and also remove the scripts files :)" ]

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[ "Hi ! With @bhavitvyamalik we discussed about having something like\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\ndataset_card = load_dataset_card(\"squad\")\r\nprint(dataset_card.metadata.pretty_name)\r\n# Stanford Question Answering Dataset (SQuAD)\r\nprint(dataset_card.metadata.languages)\r\n# [\"en\"]\r\n\r\n```\r\nWhat do you think ?\r\n\r\nI don't know if you already have a way to load the model tags in `transformers` but we can agree on the API to have something consistent.\r\n\r\nAlso note that the pretty name would only be used to show users something prettier than a dataset id, but in the end the source of truth will stay the dataset id (here `squad`).", "That works for me!", "maybe use the hub-backed dataset_info method? (so there's only one parser of README.md metadata)?", "What dataset_info method are you talking about @julien-c ? In `huggingface_hub` I can only see `model_info`.", "hmm the equivalent method in `datasets` (which could go into `huggingface_hub` at some point)" ]

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[ "I saw that the README [template](https://github.com/huggingface/datasets/blob/master/templates/README.md) changed while I was working on this 😅 Some TOC titles may be different but I filled it to the best of my knowledge & readme quality check passes now.\r\nready for review @lhoestq " ]

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[ "@lhoestq @LysandreJik", "<s>Hi :) Can you share with us the code you used ?</s>\r\n\r\nEDIT: from https://github.com/huggingface/transformers/issues/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n", "Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same", "> Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same\r\n\r\nI only changed the `preprocessing_num_workers` maybe it is the problem~ I will try again~" ]

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[ "Hi ! We don't have `join`/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n" ]

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[ "Thanks for approving it!" ]

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[ "I've raised a PR for this. Should work with `dataset = load_dataset(\"web_of_science\", \"WOS11967\", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! " ]

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[ "~~Seems like the CI failure is unrelated to this PR~~ (fixed with the merge). \r\n\r\n@lhoestq Can you please verify that everything is OK in terms of speed? Another solution is to change the offsets array dtype to np.int64 (but this doesn't scale in theory compared to Python integer which is unbound). I'm not sure why on my 64-bit machine the default numpy dtype is np.int32 tho.", "Hi ! Thanks for the fix.\r\nUnfortunately in terms of speed this is not acceptable :/\r\nThe `get_batch_of_1024_random_rows` metric or the `benchmark_getitem_100B ` benchmark is almost at 1sec instead of a few milliseconds.\r\n\r\nWould it be possible to avoid the overflow by simply passing `dtype=np.int64` to `np.cumsum` ?\r\nOn windows machines the default is int32 unfortunately so we have to force the dtype to be int64\r\n\r\n", "Yes, casting the array to np.int64 should work as well. Another option would be to cast the array elements (`arr[i], arr[j]`) in interpolation search to Python integers (bound only with memory) before multiplication (the error stems from this part: `(j - i) * (x - arr[i])`) when working with big values. But for now, the first option is OK for the sake of simplicity." ]

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[ "@sebastianGehrmann " ]

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[ "- @jplu " ]

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[ "Hi @lhoestq,\r\nShould we change `desc` in [pbar](https://github.com/huggingface/datasets/blob/81fcf88172ed5e3026ef68aed4c0ec6980372333/src/datasets/arrow_dataset.py#L1860) to something meaningful?", "I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, as suggested by @cccntu.\r\n\r\nWhen there's no multiprocessing, the `desc` of the progress bar could be the `desc` passed by the user.\r\nIn multiprocessing, we were already using a `desc` equal to `\"#\" + str(rank)`.\r\nWe can change it to be `(desc or \"\") + \"#\" + str(rank)` instead.\r\n\r\nIn the end, since both `desc` and `rank` could be None, we can have:\r\n```python\r\npbar_desc = (desc or \"\") + \"#\" + str(rank) if rank is not None else desc\r\n```\r\n\r\nFinally let's remember that if we add `desc` as a new parameter to `map`, we should add it to the `ignore_kwargs` list of the `@fingerprint_transform` decorator of `Dataset._map_single` since we don't want this parameter to affect the fingerprint of the resulting dataset." ]

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[ "Yes, having `cache_dir` as an attribute looks cleaner.\r\n\r\n\r\n\r\n", "Good job! Looking forward to this new feature! 🥂", "@lhoestq Sorry for the late reply. Yes, I'll start working on tests. Thanks for the detailed explanation of the current issues with caching (like the idea of adding the `use_caching` parameter to `load_dataset`) ", "@lhoestq Sure. I'm aware this is a high-priority issue to some extent, so feel free to take over.\r\n\r\nFew suggestions I have:\r\n* there is a slight difference between setting `use_caching` to `False` in `load_dataset` and disabling caching globally with `set_caching_enabled(False)` because the former will never execute the following code (`self._cache_dir` is always `False`): \r\nhttps://github.com/huggingface/datasets/blob/c231abdb174987419bbde3360b5b9d6a4672c736/src/datasets/arrow_dataset.py#L1807-L1824\r\n, so I'm just checking whether this is intended (if yes, maybe the docs should mention this) or not?\r\n* think we should add the `use_caching` parameter to every method that has the `keep_in_memory` (and `in_memory` 😃) parameter in its signature for better consistency, but I say let's address this in a separate PR. IMO we need one more PR that will deal exclusively with consistency in the caching logic.", "Hi @mariosasko \r\nWe discussed internally and we think that this feature might not be the direction we're doing to take for these reasons:\r\n\r\n- it goes against our simple definition of caching: on-disk == uses file cache, and in-memory == nothing is written to disk. I think it adds too much complexity just for a minimal flexibility addition\r\n- there are a few edge cases which are really confusing:\r\n - map on an in memory dataset with a cache_file_name specified by the user -> should the result be in memory or from disk ?\r\n - it would require a special cache directory just for in memory datasets, since they don’t have a preferred directory for caching\r\n- it would break a lot of stuff and would require to rewrite significant parts of the core code and the tests\r\n\r\n\r\nSo in the end we're probably going to close this PR.\r\nLet me know what you think, and thank you anyway for your help on this !", "Hi,\r\n\r\nI'm fine with that. I agree this adds too much complexity. Btw, I like the idea of reverting default in-memory for small datasets that led to this PR.", "Superseded by #2460 (to close issue #2458)." ]

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[ "cc @beurkinger but I think this has been fixed internally and will soon be updated right ?", "This issue has been fixed." ]

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[ "Is there anything else needed from my end?", "Thanks Bhavitvya and Quentin, this was very streamlined!" ]

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[ "For optimal storage, it would be better to:\r\n- store only the audio file path in the cache Arrow file\r\n- perform decoding of the audio file (into audio array and sample rate) on the fly, while loading the dataset from cache (or by adding a convenient `load_audio` function)", "Thanks a lot @lhoestq for your helpful insights! 🤗 ", "Just one step before having a first running example to benchmark.\r\n\r\nDecision to make: how to call the function `dataset.features.decode_example`:\r\n- The usual approach until now in speech applications: call it in a subsequent `.map` function\r\n - Pros: multiprocessing can be used out of the box\r\n - Cons: large disk storage required for caching decoded audio files, although having it cached will enhance speed for further usage\r\n- Approach suggested by @lhoestq (see above: https://github.com/huggingface/datasets/pull/2324#discussion_r660758683): doing it in formatting\r\n - Pros: no large disk storage required, as it will be done on the fly while iterating on the dataset\r\n - Cons: it is not cached; need to implement multiprocessing for this case\r\n- Other pros/cons for the previous options?\r\n- Other options?\r\n\r\ncc: @lhoestq @patrickvonplaten @anton-l ", "@albertvillanova I'm in two minds about this, to be honest. For example, if we consider CommonVoice, which is encoded in lossy `mp3`:\n\n- If we decompress `mp3` into raw `wav` arrays, loading a batch will speed up about 40x.\n- However, a 60gb English mp3 dataset will blow up to about 600gb raw (iirc), which is why loading on-the-fly (optionally?) could be very beneficial as well.", "Users can do the conversion from mp3 to wav by themselves if they want to using `map`.\r\n\r\nIMO it's better if we can keep the decoding part with the minimal features to be both easy to understand and flexible, i.e. just having the on-the-fly decoding of the audio data (with the sampling rate parameter)\r\n\r\nDecompressing from mp3 to wav sounds like an optimization that depends on the problem that the user wants to solve, the constrains from its environment (disk space, IO speed), and other parameters (optimal training speed for example). Therefore I would leave this to the user to decide whether it has to do it or not.\r\n\r\nLet me know what you think about this", "@albertvillanova, In my opinion the pros strongly outweigh the cons in the @lhoestq's suggestion which is why I think we should go forward with it. \r\n\r\nThe cons:\r\n- \"the operation won't be cached\" is not to important as the user will most likely access just a couple of audio array to see how it looks like and then for the \"full\" feature extraction she/he will make use of `.map(...)` anyways which means that the result will be cached. \r\n- Regarding the multi-processing - if I understand correctly it'll follow the same logic here -> the user will only access some audio arrays for testing playing around with the model but use `.map(...)` for larger operations where multi-processing would still work as before.\r\n\r\nThe advantages mostly solve the main poinpoints being:\r\n- exploding disk space\r\n- bad user experience since the audio is not loaded on the go\r\n\r\n=> So I'm very much in favor of the \"direct-access\" feature", "Update: I've retaken this issue.\r\n\r\nIf the decoding logic is implemented when \"examples are accessed\", then if afterwards we use the `.map`, it tries to apply the decoding twice (as maps iterates over the examples, thus \"accessing them\", before trying to apply the map function)...\r\n\r\nI'm thinking on some other approach...", "I have reimplemented the previous approach, so that we can discuss about it: examples are decoded when accessed.", "What about creating a new specific formatting, just for decoding? This would be only active within a context manager.", "Hi @lhoestq, as we discussed, I've followed your suggestion of implementing the decoding step within the formatting logic: extract-decode-format. Feel free to tell me what you think.\r\n\r\n@patrickvonplaten and @anton-l, could you have a look at the use case in the test (https://github.com/huggingface/datasets/pull/2324/files#diff-58e348f6e4deaa5f3119e420a5d48ebb82875a78c28628831748fb54f59b2c78R34-R50) and tell me if this is aligned with your needs? Thanks.", "Hi @lhoestq, if you validate this approach, we could merge the Audio feature this (or early next) week.", "Sure it looks nice this way :) Feel free to continue !", "As discussed, we should pay attention when applying `map` to a dataset with `Audio` feature, in order to avoid decoding the audio data twice.\r\n\r\nOne proposed solution is to pass `input_columns` to `map`. Just, note that the field containing the Audio feature should not be passed in `input_columns` (not possible, for example, to map the audio file path to a new directory).\r\n\r\nI suggest again (3rd time, sorry, lol) using a formatting context manager (as we already use for PyTorch/TensorFlow: https://huggingface.co/docs/datasets/torch_tensorflow.html).\r\n\r\nAbove (https://github.com/huggingface/datasets/pull/2324#issuecomment-915244003), I suggested to define a formatting just for decoding: the decoding of the audio data is only performed if this specific formatting is set (`ds.set_format(\"decoding\")`) or within a context manager (`with ds.formatted_as(\"decoding\"): ...`)\r\n\r\nNow, I would like also to suggest an alternative formatting for **non-decoding** (if decoding is the default behavior), for a use case like this:\r\n```python\r\ndef change_dir(example):\r\n example[\"audio\"] = \"dir/\" + example[\"audio\"]\r\n\r\n\r\nwith ds.formatted_as(\"no_decoding\"):\r\n print(ds[0]) # {\"audio\": \"path/to/file.wav\"}\r\n ds.map(change_dir)\r\n print(ds[0]) # {\"audio\": \"dir/path/to/file.wav\"}\r\n\r\nprint(ds[0]) # {\"audio\": {\"path\": \"dir/path/to/file.wav\", \"array\": np.array([1., 2., 3...]), \"sampling_rate\": 44100}}\r\n```\r\n\r\nPlease, just tell me what you think.\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "> As discussed, we should pay attention when applying `map` to a dataset with `Audio` feature, in order to avoid decoding the audio data twice.\r\n> \r\n> One proposed solution is to pass `input_columns` to `map`. Just, note that the field containing the Audio feature should not be passed in `input_columns` (not possible, for example, to map the audio file path to a new directory).\r\n> \r\n> I suggest again (3rd time, sorry, lol) using a formatting context manager (as we already use for PyTorch/TensorFlow: https://huggingface.co/docs/datasets/torch_tensorflow.html).\r\n> \r\n> Above ([#2324 (comment)](https://github.com/huggingface/datasets/pull/2324#issuecomment-915244003)), I suggested to define a formatting just for decoding: the decoding of the audio data is only performed if this specific formatting is set (`ds.set_format(\"decoding\")`) or within a context manager (`with ds.formatted_as(\"decoding\"): ...`)\r\n> \r\n> Now, I would like also to suggest an alternative formatting for **non-decoding** (if decoding is the default behavior), for a use case like this:\r\n> \r\n> ```python\r\n> def change_dir(example):\r\n> example[\"audio\"] = \"dir/\" + example[\"audio\"]\r\n> \r\n> \r\n> with ds.formatted_as(\"no_decoding\"):\r\n> print(ds[0]) # {\"audio\": \"path/to/file.wav\"}\r\n> ds.map(change_dir)\r\n> print(ds[0]) # {\"audio\": \"dir/path/to/file.wav\"}\r\n> \r\n> print(ds[0]) # {\"audio\": {\"path\": \"dir/path/to/file.wav\", \"array\": np.array([1., 2., 3...]), \"sampling_rate\": 44100}}\r\n> ```\r\n> \r\n> Please, just tell me what you think.\r\n> CC: @lhoestq @patrickvonplaten @anton-l\r\n\r\nI'm fine with a context manager! There is no way to **not** decode the audio if its key is not accessed no?\r\n\r\nE.g.\r\n\r\n```python\r\ndef load(batch):\r\n batch[\"speech_array\"] = torchaudio.load(batch[\"file\"])\r\n return batch\r\n\r\nds.map(load)\r\n```\r\n\r\ndoes *e.g.* not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no? \r\n\r\n=> I'm happy with both the context manager and using `input_colmuns`. Both of those solutions are equally good to me if a \"not-access-key-no-decoding\" solution is just not feasible. I let you guys decide :-)", "> \r\n> There is no way to **not** decode the audio if its key is not accessed no?\r\n> \r\n> E.g...\r\n> \r\n> does _e.g._ not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no?\r\n\r\n@patrickvonplaten I think therefore we should rethink the implementation of the Audio feature: its goal is to enrich/simplify the user experience when working with audio files. If on the other hand, you see that the current implementation may be problematic/unsatisfying/not-optimal, then we miss the point of creating this feature. This feature should be useful to users, not inconvenient.", "> > There is no way to **not** decode the audio if its key is not accessed no?\r\n> > E.g...\r\n> > does _e.g._ not access the \"audio\" key `batch[\"audio\"}` but there is no way to not decode it without major changes no?\r\n> \r\n> @patrickvonplaten I think therefore we should rethink the implementation of the Audio feature: its goal is to enrich/simplify the user experience when working with audio files. If on the other hand, you see that the current implementation may be problematic/unsatisfying/not-optimal, then we miss the point of creating this feature. This feature should be useful to users, not inconvenient.\r\n\r\nThanks a lot for the message! I'm discussing a bit with @anton-l at the moment - will share our results as soon as possible", "Current implementation: see use cases in file https://github.com/huggingface/datasets/blob/0f80e6eaa6f596ff6287eb33587e2d9c69af0e73/tests/features/test_audio.py\r\n\r\nAutomatic decoding:\r\n- when directly accessing an example or a batch\r\n ```python\r\n dset[0]\r\n dset[:2]\r\n ```\r\n- during map, only if audio field is accessed:\r\n ```python\r\n def process_audio_sampling_rate(example):\r\n example[\"double_sampling_rate\"] = 2 * example[\"audio\"][\"sampling_rate\"]\r\n return example\r\n\r\n decoded_dset = dset.map(process_audio_sampling_rate)\r\n ```\r\n\r\nNo automatic decoding:\r\n- during map if audio field is not accessed:\r\n ```python\r\n def process_text(example):\r\n example[\"text\"] = example[\"text\"] + \" World!\"\r\n return example\r\n\r\n decoded_dset = dset.map(process_text)\r\n ```\r\n\r\nThe types of example and batch are kept as usual, `dict[str, Any]` and `dict[str, list[Any]]` respectively.\r\n\r\nCC: @patrickvonplaten @anton-l @lhoestq ", "That's awesome! Thanks so much for your work on this @albertvillanova!", "Oh and maybe have a test to make sure that casting the Audio feature to change the sampling rate works as expected ?", "@lhoestq the test for the resampling is already in place in `test_audio_resampling`: \r\nhttps://github.com/huggingface/datasets/pull/2324/files#diff-58e348f6e4deaa5f3119e420a5d48ebb82875a78c28628831748fb54f59b2c78R48-R56", "Please note that we should agree in the API: see 53d6d73\r\n\r\nThis is just a proposal implementation:\r\n- Create a new method named `cast_column`, which performs a shallow kind of cast (without using `map()` or caching)\r\n\r\nWe should agree in the name, because as it is, it might be confused with `cast` (and users might think `cast_column` caches the result as `cast`)\r\n\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "IMO cast and cast_column should have the exact same behavior, to make the experience simple for the user (no distinction between shallow or deep cast).\r\n\r\nMaybe we should change `cast` to use `cast_column` on every column and make `cast_column` use `map` if and only if it's necessary. For Audio for example `map` is not needed.\r\n\r\nWe just need to do some tests to know which casts always need map and which ones don't. This implies either looking at the PyArrow source code (the documentation doesn't mention all these details) or playing with PyArrow to figure it out.\r\n\r\nI guess for now we can just have the simplest `cast_column` which always uses map unless it's an Audio feature type.\r\n\r\nLet me know what you think !", "@lhoestq I totally agree: `cast` and `cast_column` should be analog to each other.\r\n\r\nFor the implementation, let me try something simpler than the one suggested by you...", "@lhoestq what do you think of an approach like this 633ef09?\r\n\r\nIf it's OK, then we should implement passing parameters to `cast`.", "@lhoestq maybe for now we could make a simple implementation and finish this PR. Then we could make a follow-up PR to deal specifically with the optimal implementation of `cast_column` and `cast`, as this issue is not specific to the Audio feature.", "> @lhoestq what do you think of an approach like this 633ef09?\r\n\r\nYea that's good enough for the time being :)\r\n\r\nI think the last thing we need to do is make sure that `cast_column` changes the fingerprint of the dataset. Feel free to use the `fingerprint_transform` decorator, as for `remove_columns`.\r\n\r\n(note that cast currently doesn't use the decorator since it's based on `map` that already updates the fingerprint).", "> \r\n> I think the last thing we need to do is make sure that `cast_column` changes the fingerprint of the dataset. Feel free to use the `fingerprint_transform` decorator, as for `remove_columns`.\r\n> \r\n> (note that cast currently doesn't use the decorator since it's based on `map` that already updates the fingerprint).\r\n\r\n@lhoestq note that `cast_column` may call `cast` in some cases, and the decorator would not be necessary for these cases...\r\n- I did it by setting `inplace=False`, and updating fingerprint explicitly only when `cast` is not called.", "I think current state of this PR could be included in our next release, as experimental feature, for stress testing it and try to find all potential issues. What do you think?\r\n\r\nCC: @lhoestq @patrickvonplaten @anton-l ", "Looks great! Ready to try it out on the transformers examples after the release :)", "Think we are good to merge here no? :-)" ]

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[ "Upgrading datasets to version 1.6 fixes the issue", "This bug was fixed in #1995. Upgrading the `datasets` should work! ", "Thanks @ekeleshian for having reported.\r\n\r\nI am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists." ]

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[ "I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.\r\n\r\nDowngrading to `1.5.0` works and produces the following output for me:\r\n\r\n```bash\r\nDownloading: 9.20kB [00:00, 3.94MB/s] \r\nDownloading: 5.99kB [00:00, 3.29MB/s] \r\nNo config specified, defaulting to: sst/default\r\nDownloading and preparing dataset sst/default (download: 6.83 MiB, generated: 3.73 MiB, post-processed: Unknown size, total: 10.56 MiB) to /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b...\r\n Dataset sst downloaded and prepared to /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b. Subsequent calls will reuse this data.\r\nexecuted [0, 1]\r\n#0: 0%| | 0/5 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/5 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\nexecuted [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]\r\nexecuted [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]\r\nexecuted [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]\r\nexecuted [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]\r\nexecuted [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]\r\nexecuted [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]\r\n#0: 100%|██████████| 5/5 [00:00<00:00, 94.83ba/s]\r\nexecuted [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]\r\n#1: 100%|██████████| 5/5 [00:00<00:00, 92.75ba/s]\r\nexecuted [0, 1]\r\n#0: 0%| | 0/1 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/1 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]\r\n#0: 100%|██████████| 1/1 [00:00<00:00, 118.81ba/s]\r\n#1: 100%|██████████| 1/1 [00:00<00:00, 123.06ba/s]\r\nexecuted [0, 1]\r\n#0: 0%| | 0/2 [00:00<?, ?ba/s]\r\n#1: 0%| | 0/2 [00:00<?, ?ba/s]\r\nexecuted [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\r\nexecuted [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]\r\nexecuted [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]\r\n#0: 100%|██████████| 2/2 [00:00<00:00, 119.42ba/s]\r\nexecuted [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]\r\n#1: 100%|██████████| 2/2 [00:00<00:00, 123.33ba/s]\r\n\r\n\r\n\r\n ############################## \r\n\r\n\r\n\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-6079777aa097c8f8.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-2dc05c46f68eda6e.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-1ca347e7430b98f1.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-c0f1a73ce3ba40cd.arrow\r\nexecuted [0, 1]\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-832a1407bf1ac5b7.arrow\r\nLoading cached processed dataset at /home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/a16a45566b63b2c3179e6c1d0f8edadde56e45570ee8cf99394fbb738491d34b/cache-036316a259b773c4.arrow\r\n- Datasets: 1.5.0\r\n- Python: 3.8.3 (default, May 19 2020, 18:47:26) \r\n[GCC 7.3.0]\r\n- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10\r\n```", "Hi,\r\n\r\nset `keep_in_memory` to False when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them):\r\n\r\nhttps://github.com/huggingface/datasets/blob/241a0b4a3a868778ee91e767ad406f9da7610df2/src/datasets/arrow_dataset.py#L1718\r\n\r\n@albertvillanova It seems like this behavior was overlooked in #2182.\r\n\r\n", "Hi @villmow, thanks for reporting. \r\n\r\nAs @mariosasko has pointed out, we did not consider this case when introducing the feature of automatic in-memory for small datasets. This needs to be fixed.", "Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read/write cache files.\r\nOn the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the dataset is located to read/write cache files.\r\n\r\nBecause of that, currently in-memory datasets simply don't use caching.\r\n\r\nMaybe a Dataset object could have a `cache_dir` that is set to the directory where the arrow files are created during `load_dataset` ?", "Fixed once reverted the default in-memory feature:\r\nClosed by #2460 (to close issue #2458).", "Please @villmow, feel free to update to `Datasets` latest version (1.8)." ]

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[ "Thanks for reporting, @sgraaf.\r\n\r\nI am going to have a look at it. \r\n\r\nI guess the expected codec is \"UTF-8\". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp1252`, which causes the problem.", "Awesome, thank you. 😃 ", "@sgraaf, I have just merged the fix in the master branch.\r\n\r\nYou can either:\r\n- install `datasets` from source code\r\n- wait until we make the next release of `datasets`\r\n- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https://www.python.org/dev/peps/pep-0540) either by passing the command-line option `-X utf8` or by setting the environment variable `PYTHONUTF8=1`." ]

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