Datasets:

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[ "@lhoestq note that the segfault also occurs on Linux.", "Created the ticket at\r\nhttps://issues.apache.org/jira/browse/ARROW-12568", "@lhoestq the ticket you mentioned is now in state resolved. Pyarrow supports AArch64 after version 4.0.0. Because of this restriction `datasets` is not installing in AArch64 systems." ]

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[ "Thanks for reporting ! We're looking into it", "I'm not able to reproduce this, do you think you can provide a code that creates a DatasetDict that has this issue when saving and reloading ?", "Hi, I just ran into a similar error. Here is the minimal code to reproduce:\r\n```python\r\nfrom datasets import load_dataset, DatasetDict\r\nds = load_dataset('super_glue', 'multirc')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\n```bash\r\nReusing dataset super_glue (/home/idahl/.cache/huggingface/datasets/super_glue/multirc/1.0.2/2fb163bca9085c1deb906aff20f00c242227ff704a4e8c9cfdfe820be3abfc83)\r\nTraceback (most recent call last):\r\n File \"/home/idahl/eval-util-expl/multirc/tmp.py\", line 7, in <module>\r\n ds = DatasetDict.load_from_disk('tempds')\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/dataset_dict.py\", line 710, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 687, in load_from_disk\r\n return Dataset(\r\n File \"/home/idahl/miniconda3/envs/eval-util-expl/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 274, in __init__\r\n raise ValueError(\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'answer': Value(dtype='string', id=None), 'idx': {'answer': Value(dtype='int32', id=None), 'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None)}, 'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<answer: int32, paragraph: int32, question: int32>, label: int64, paragraph: string, question: string>\r\n\r\nbut expected something like\r\n{'answer': Value(dtype='string', id=None), 'idx': {'paragraph': Value(dtype='int32', id=None), 'question': Value(dtype='int32', id=None), 'answer': Value(dtype='int32', id=None)}, 'label': Value(dtype='int64', id=None), 'paragraph': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None)}\r\nwith type\r\nstruct<answer: string, idx: struct<paragraph: int32, question: int32, answer: int32>, label: int64, paragraph: string, question: string>\r\n\r\n```\r\n\r\nThe non-matching part seems to be\r\n`'label': ClassLabel(num_classes=2, names=['False', 'True'], names_file=None, id=None),`\r\nvs \r\n`'label': Value(dtype='int64', id=None),`\r\n\r\nAnd the order in the `<struct...` being different, which might cause the [features.type != inferred_features.type](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_dataset.py#L274) condition to become true and raise this ValueError.\r\n\r\n\r\nI am using datasets version 1.6.2.\r\n\r\nEdit: can confirm, this works without error in version 1.5.0", "My current workaround is to remove the idx feature:\r\n\r\n```\r\n\r\nfrom datasets import load_dataset, DatasetDict, Value\r\nds = load_dataset('super_glue', 'multirc')\r\nds = ds.remove_columns('idx')\r\n\r\nds.save_to_disk('tempds')\r\n\r\nds = DatasetDict.load_from_disk('tempds')\r\n\r\n```\r\n\r\nworks.", "It looks like this issue comes from the order of the fields in the 'idx' struct that is different for some reason.\r\nI'm looking into it. Note that as a workaround you can also flatten the nested features with `ds = ds.flatten()`", "I just pushed a fix on `master`. We'll do a new release soon !\r\n\r\nThanks for reporting" ]

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[ "LOL, I was also working on something similar 😅. I'm gonna have a look!!!", "Sorry I didn't know you were also working on it ^^'\r\nAnd yes I 100% agree with you on the points you mentioned. We should definitely improve the coverage. It would be nice to have a clearer separation to know which tests in the suite are unit tests and which ones are integration tests\r\n", "Never mind: we both noticed tests can be improved. More PRs to come... 😉 \r\n\r\nAccording to the literature, unit tests are those that test a behavior unit, isolated from the other components and must be very fast: for me, this last requirement implies that they must be performed completely _in memory_.\r\n\r\nAs opposed, integration tests are those which also test interactions with _external_ components, like web services, databases, file system, etc.\r\n\r\nThe problem I see is that our code is still too coupled and it is difficult to isolate components for testing. Therefore, I would suggest acting iteratively, by refactoring to decouple components and then implement unit tests for each component in isolation." ]

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[ "The code quality check is going to be fixed by #2265 ", "The memory issue didn't come from `self.__dict__.copy()` but from the fact that this dict contains `_batches` which has all the batches of the table in it.\r\nTherefore for a MemoryMappedTable all the data in `_batches` were copied in memory when pickling and this is the issue.", "I'm still investigating why we didn't catch this issue in the tests.\r\nThis test should have caught it but didn't:\r\n\r\nhttps://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/tests/test_table.py#L350-L353", "I'll focus on the patch release and fix the test in another PR after the release", "Yes, I think it is better that way..." ]

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[ "Thanks @bhavitvyamalik for the fix !\r\nThe fix will be available in the next release.\r\nIt's already available on the `master` branch. For now you can either install `datasets` from source or use `script_version=\"master\"` in `load_dataset` to use the fixed version of this dataset." ]

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[ "Ready for the final review" ]

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[ "Thanks for adding this one !\r\nThe download manager does support downloading files on git lfs via their github url. No need for a manual download option ;)" ]

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[ "Honestly, I think we should fix some other issues in Split API before this change. E. g. currently the following will not work, even though it should:\r\n```python\r\nimport datasets\r\ndatasets.load_dataset(\"sst\", split=datasets.Split.TRAIN+datasets.Split.TEST) # AssertionError\r\n```\r\n\r\nEDIT:\r\nActually, think it's OK to merge this PR because the fix will not touch this PR's code." ]

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[ "@lhoestq Maybe a test that runs the functions that call `update_metadata_with_features` and checks if metadata was updated would be nice to prevent this from happening in the future." ]

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[ "> For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here\r\n\r\n@bhavitvyamalik I guess the mentioned metrics are enough but it would be better if exact match is also added since the standard SQUAD dataset also has it.", "I would like to quote it from the website that I am following to learn\nthese things.\nExact Match:\nThis metric is as simple as it sounds. For each question+answer pair, if\nthe characters of the model's prediction exactly match the characters of\n*(one\nof) the True Answer(s)*, EM = 1, otherwise EM = 0. This is a strict\nall-or-nothing metric; being off by a single character results in a score\nof 0. When assessing against a negative example, if the model predicts any\ntext at all, it automatically receives a 0 for that example.\n\nSo, I guess you need to ensure at least 1 predicted answer matches for EM\nto be 1.\nSource:\nhttps://qa.fastforwardlabs.com/no%20answer/null%20threshold/bert/distilbert/exact%20match/f1/robust%20predictions/2020/06/09/Evaluating_BERT_on_SQuAD.html\n\nYou can go to their homepage and read the other links. They have detailed\nexplanations on evaluation metrics. You can also have a look at the\nsquad_v2 metric file for further clarification.\n\nRegards,\nMohammed Rakib\n\nOn Sun, 25 Apr 2021 at 15:20, Bhavitvya Malik ***@***.***>\nwrote:\n\n> I'm a little confused when it comes to 2 ground truths which can be a\n> possible answer. Like here for eg.\n>\n> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User:\n> Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id':\n> 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply\n> Agreement__Parties'}]\n>\n> references = [{'answers': {'answer_start': [143, 49], 'text': ['The\n> seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co.,\n> Ltd.']}, 'id':\n> 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply\n> Agreement__Parties'}]\n>\n> Should I ensure at least 1 predicted answer matches or both predicted\n> answers should match (like in this case) for EM to be 1?\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/2257#issuecomment-826289753>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AHMYZAZSAEZNFWEMVAPK6M3TKPNHLANCNFSM43QFZVPQ>\n> .\n>\n", "Updated the same @MohammedRakib! Even if a single answer matches I'm returning 1 in that case for EM (not traversing all predictions once we have one `exact_match` from prediction)" ]

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[ "Thanks for reporting ! We are working on this and we'll do a patch release very soon.", "We did a patch release to fix this issue.\r\nIt should be fixed in the new version 1.6.1\r\n\r\nThanks again for reporting and for the details :)" ]

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[ "cc @abhi1thakur ", "Looks really nice so far, thanks !\r\nMaybe if a dataset doesn't have a template for a specific task we could try the default template of this task ?", "hey @SBrandeis @lhoestq,\r\n\r\ni now have a better idea about what you guys are trying to achieve with the task templates and have a few follow-up questions:\r\n\r\n1. how did you envision using `DatasetInfo` for running evaluation? my understanding is that all `dataset_infos.json` files are stored in the `datasets` repo (unlike `transformers` where each model's weights etc are stored in a dedicated repo). \r\nthis suggests the following workflow:\r\n\r\n```\r\n- git clone datasets\r\n- load target dataset to evaluate\r\n- load `dataset_infos.json` for target dataset\r\n- run eval for each task template in `task_templates`\r\n- store metrics as evaluation cards (similar to what is done in `autonlp`)\r\n```\r\n2. assuming the above workflow, i see that the current `TaskTemplate` attributes of `task`, `input_schema`, and `label_schema` still require some wrangling from `dataset_infos.json` to reproduce additional mappings like `label2id` that we'd need for e.g. text classification. an alternative would be to instantiate the task template class directly from the JSON with something like\r\n```python\r\nfrom datasets.tasks import TextClassification\r\nfrom transformers import AutoModelForSequenceClassification, AutoConfig\r\n\r\ntc = TextClassification.from_json(\"path/to/dataset_infos.json\")\r\n# load a model with the desired config\r\nmodel_ckpt = ...\r\nconfig = AutoConfig.from_pretrained(model_ckpt, label2id=tc.label2id, id2label=tc.id2label)\r\nmodel = AutoModelForSequenceClassification.from_pretrained(model_ckpt, config=config)\r\n# run eval ...\r\n```\r\nperhaps this is what @SBrandeis had in mind with the `TaskTemplate.from_dict` method?\r\n\r\n3. i personally prefer using `task_templates` over `supervised_keys` because it encourages the contributor to think in terms of 1 or more tasks. my question here is do we currently use `supervised_keys` for anything important in the `datasets` library?", "1. How do you envision using DatasetInfo for running evaluation?\r\n\r\nThe initial idea was to be able to do something like this:\r\n```python\r\nfrom datasets import load_dataset\r\ndset = load_dataset(\"name\", task=\"binary_classification\")\r\n# OR\r\ndset = load_dataset(\"name\")\r\ndset = dset.prepare_for_task(\"binary_classification\")\r\n```\r\n\r\n2. I don't think that's needed if we proceed as mentioned above\r\n\r\n3. `supervised_keys` are mostly a legacy compatibility thing with TF datasets, not sure it's used for anything right now. I'll let @lhoestq give more details on that\r\n\r\n[Edit 1] Typo", "> The initial idea was to be able to do something like this:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> dset = load_dataset(\"name\", task=\"binary_classification\")\r\n> # OR\r\n> dset = load_dataset(\"name\")\r\n> dset = dset.prepare_for_task(\"binary_classification\")\r\n> ```\r\n\r\nah that's very elegant! just so i've completely understood, the result would be that the relevant column names of `dset` would be mapped to e.g. `text` and `label` and thus we'd have a uniform schema for the evaluation of all `binary_classification` tasks?", "That's correct! Also, the features need to be appropriately casted\r\nFor a classification task for example, we would need to cast the datasets features to something like this:\r\n```python\r\ndatasets.Features({\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.ClassLabel(names=[...]),\r\n})\r\n```\r\n", "3. We can ignore `supervised_keys` (it came from TFDS and we're not using it) and use `task_templates`", "great, thanks a lot for your answers! now it's much clearer what i need to do next 😃 ", "hey @lhoestq @SBrandeis, \r\n\r\ni've made some small tweaks to @SBrandeis's code so that `Dataset.prepare_for_task` is called in `DatasetBuilder`. using the `emotion` dataset as a test case, the following now works:\r\n\r\n ```python\r\n# DatasetDict with default columns\r\nds = load_dataset(\"./datasets/emotion/\")\r\n# DatasetDict({\r\n# train: Dataset({\r\n# features: ['tweet', 'emotion'],\r\n# num_rows: 16000\r\n# })\r\n# validation: Dataset({\r\n# features: ['tweet', 'emotion'],\r\n# num_rows: 2000\r\n# })\r\n# test: Dataset({\r\n# features: ['tweet', 'emotion'],\r\n# num_rows: 2000\r\n# })\r\n# })\r\n\r\n# DatasetDict with remapped columns\r\nds = load_dataset(\"./datasets/emotion/\", task=\"text_classification\")\r\nDatasetDict({\r\n# train: Dataset({\r\n# features: ['text', 'label'],\r\n# num_rows: 16000\r\n# })\r\n# validation: Dataset({\r\n# features: ['text', 'label'],\r\n# num_rows: 2000\r\n# })\r\n# test: Dataset({\r\n# features: ['text', 'label'],\r\n# num_rows: 2000\r\n# })\r\n# })\r\n\r\n# Dataset with default columns\r\nds = load_dataset(\"./datasets/emotion/\", split=\"train\")\r\n# Map/cast features\r\nds = ds.prepare_for_task(\"text_classification\")\r\n# Dataset({\r\n# features: ['text', 'label'],\r\n# num_rows: 16000\r\n# })\r\n```\r\n\r\ni have a few follow-up questions / remarks:\r\n\r\n1. i'm working under the assumption that contributors / users only provide a unique set of task types. in particular, the current implementation does not support something like:\r\n```python\r\ntask_templates=[TextClassification(labels=class_names, text_column=\"tweet\", label_column=\"emotion\"), TextClassification(labels=class_names, text_column=\"some_other_column\", label_column=\"some_other_column\")]\r\n```\r\nsince we use `TaskTemplate.task` and the filter for compatible templates in `Dataset.prepare_for_task`. should we support these scenarios? my hunch is that this is rare in practice, but please correct me if i'm wrong.\r\n\r\n2. when we eventually run evaluation for `transformers` models, i expect we'll be using the `Trainer` for which we can pass the standard label names to `TrainingArguments.label_names`. if that's the case, it might be prudent to heed the warning from the [docs](https://huggingface.co/transformers/main_classes/trainer.html?highlight=trainer#trainer) and use `labels` instead of `label` in the schema:\r\n> your model can accept multiple label arguments (use the label_names in your TrainingArguments to indicate their name to the Trainer) but none of them should be named \"label\".\r\n\r\n3. i plan to forge ahead on the rest of the pipeline taxonomy. please let me know if you'd prefer smaller, self-contained pull requests (e.g. one per task)", "hey @lhoestq @SBrandeis, i think this is ready for another review 😃 \r\n\r\nin addition to a few comments / questions i've left in the pr, here's a few remarks:\r\n\r\n1. after some experimentation, i decided against allowing the user to specify nested column names for question-answering. i couldn't find a simple solution with the current api and suspect that i'd have to touch many areas of `datasets` to \"unflatten\" columns in a generic fashion.\r\n2. in the current implementation, the user can specify the outer column name for question-answering, but is expected to follow the inner schema for e.g. `answers.text` and `answers.answer_start`. we can decide later how much flexibility we want to give users\r\n3. i added a few unit tests\r\n4. as discussed, let's keep this pr focused on text classification / question answering and i'll add the other tasks in separate prs\r\n5. i renamed the tasks e.g. `text_classification` -> `text-classification` for consistency with the `Trainer` model cards [here](https://github.com/huggingface/transformers/pull/11599#pullrequestreview-656371007).", "i'm not sure why the benchmarks are getting cancelled - is this expected?", "> i'm not sure why the benchmarks are getting cancelled - is this expected?\r\n\r\nHmm I don't know. It's certainly unrelated to this PR though. Maybe github has some issues", "Something is happening with actions: https://www.githubstatus.com/", "hey @lhoestq and @SBrandeis, i've: \r\n\r\n* extended the `prepare_for_task` API along the lines that @lhoestq suggested. i wasn't entirely sure what the `datasets` convention is for docstrings with mixed types, so please see if my proposal makes sense\r\n* added a few new tests to check that we trigger the value errors on incorrect input\r\n\r\ni think this is ready for another review :)", "> Looks all good thank you :)\r\n> \r\n> Can you also add `prepare_for_task` in the `main_classes.rst` file of the documentation ?\r\n\r\nDone! I also remembered that I needed to do the same for `DatasetDict`, so included this as well :)" ]

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[ "I renamed the variable, added a test for dataset._indices and fixed an issue with class_encode_column" ]

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[ "@lhoestq is there a problem in the master branch? I got a segmentation fault...\r\n```\r\ntests/test_table.py::test_concatenation_table_cast[in_memory] Fatal Python error: Segmentation fault\r\n```", "Oh wow. Let me re-run the CI just to make sure", "Hmm interesting, the segfault is still there. I'm investigating this issue on my windows machine", "Feel free to merge master into this branch to fix the CI :)" ]

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[ "Hi ! Sorry to hear that. This may come from another issue then.\r\n\r\nFirst can we check if this latency comes from the dataset itself ?\r\nYou can try to load your dataset and benchmark the speed of querying random examples inside it ?\r\n```python\r\nimport time\r\nimport numpy as np\r\n\r\nfrom datasets import load_from_disk\r\n\r\ndataset = load_from_disk(...) # or from load_dataset...\r\n\r\n_start = time.time()\r\nn = 100\r\nfor i in np.random.default_rng(42).integers(0, len(dataset), size=n):\r\n _ = dataset[i]\r\nprint(time.time() - _start)\r\n```\r\n\r\nIf we see a significant speed difference between your two datasets then it would mean that there's an issue somewhere", "Hi @lhoestq, here is the result. I additionally measured time to `load_from_disk`:\r\n* 60GB\r\n```\r\nloading took: 22.618776321411133\r\nramdom indexing 100 times took: 0.10214924812316895\r\n```\r\n\r\n* 600GB\r\n```\r\nloading took: 1176.1764674186707\r\nramdom indexing 100 times took: 2.853600025177002\r\n```\r\n\r\nHmm.. I double checked that it's version 1.6.0. The difference seems quite big, could it be related to the running environment? \r\n", "I'm surprised by the speed change. Can you give more details about your dataset ?\r\nThe speed depends on the number of batches in the arrow tables and the distribution of the lengths of the batches.\r\nYou can access the batches by doing `dataset.data.to_batches()` (use only for debugging) (it doesn't bring data in memory).\r\n\r\nAlso can you explain what parameters you used if you used `map` calls ?\r\nAlso if you have some code that reproduces the issue I'd be happy to investigate it.", "Also if you could give us more info about your env like your OS, version of pyarrow and if you're using an HDD or a SSD", "Here are some details of my 600GB dataset. This is a dataset AFTER the `map` function and once I load this dataset, I do not use `map` anymore in the training. Regarding the distribution of the lengths, it is almost uniform (90% is 512 tokens, and 10% is randomly shorter than that -- typical setting for language modeling).\r\n```\r\nlen(batches):\r\n492763\r\n\r\nbatches[0]: \r\npyarrow.RecordBatch\r\nattention_mask: list<item: uint8>\r\n child 0, item: uint8\r\ninput_ids: list<item: int16>\r\n child 0, item: int16\r\nspecial_tokens_mask: list<item: uint8>\r\n child 0, item: uint8\r\ntoken_type_ids: list<item: uint8>\r\n child 0, item: uint8\r\n```\r\n\r\nHere the some parameters to `map` function just in case it is relevant:\r\n```\r\nnum_proc=1 # as multi processing is slower in my case\r\nload_from_cache_file=False\r\n```\r\n", "Regarding the environment, I am running the code on a cloud server. Here are some info:\r\n```\r\nUbuntu 18.04.5 LTS # cat /etc/issue\r\npyarrow 3.0.0 # pip list | grep pyarrow\r\n```\r\nThe data is stored in SSD and it is mounted to the machine via Network File System.\r\n\r\nIf you could point me to some of the commands to check the details of the environment, I would be happy to provide relevant information @lhoestq !", "I am not sure how I could provide you with the reproducible code, since the problem only arises when the data is big. For the moment, I would share the part that I think is relevant. Feel free to ask me for more info.\r\n\r\n```python\r\nclass MyModel(pytorch_lightning.LightningModule)\r\n def setup(self, stage):\r\n self.dataset = datasets.load_from_disk(path)\r\n self.dataset.set_format(\"torch\")\r\n\r\n def train_dataloader(self):\r\n collate_fn = transformers.DataCollatorForLanguageModeling(\r\n tokenizer=transformers.ElectraTokenizerFast.from_pretrained(tok_path)\r\n )\r\n dataloader = torch.utils.DataLoader(\r\n self.dataset,\r\n batch_size=32,\r\n collate_fn=collate_fn,\r\n num_workers=8,\r\n pin_memory=True,\r\n )\r\n```", "Hi ! Sorry for the delay I haven't had a chance to take a look at this yet. Are you still experiencing this issue ?\r\nI'm asking because the latest patch release 1.6.2 fixed a few memory issues that could have lead to slow downs", "Hi! I just ran the same code with different datasets (one is 60 GB and another 600 GB), and the latter runs much slower. ETA differs by 10x.", "@lhoestq and @hwijeen\r\n\r\nDespite upgrading to datasets 1.6.2, still experiencing extremely slow (2h00) loading for a 300Gb local dataset shard size 1.1Gb on local HDD (40Mb/s read speed). This corresponds almost exactly to total data divided by reading speed implying that it reads the entire dataset at each load.\r\n\r\nStack details:\r\n=========\r\n\r\n> GCC version: Could not collect\r\n> Clang version: Could not collect\r\n> CMake version: Could not collect\r\n> \r\n> Python version: 3.7 (64-bit runtime)\r\n> Is CUDA available: True\r\n> CUDA runtime version: 10.2.89\r\n> GPU models and configuration: GPU 0: GeForce GTX 1050\r\n> Nvidia driver version: 457.63\r\n> cuDNN version: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\cudnn64_7.dll\r\n> HIP runtime version: N/A\r\n> MIOpen runtime version: N/A\r\n> \r\n> Versions of relevant libraries:\r\n> [pip3] datasets==1.6.2\r\n> [pip3] transformers==4.5.1\r\n> [pip3] numpy==1.19.1\r\n> [pip3] numpydoc==1.1.0\r\n> [pip3] pytorch-metric-learning==0.9.98\r\n> [pip3] torch==1.8.1\r\n> [pip3] torchaudio==0.8.1\r\n> [pip3] torchvision==0.2.2\r\n> [conda] blas 2.16 mkl conda-forge\r\n> [conda] cudatoolkit 10.2.89 hb195166_8 conda-forge\r\n> [conda] libblas 3.8.0 16_mkl conda-forge\r\n> [conda] libcblas 3.8.0 16_mkl conda-forge\r\n> [conda] liblapack 3.8.0 16_mkl conda-forge\r\n> [conda] liblapacke 3.8.0 16_mkl conda-forge\r\n> [conda] mkl 2020.1 216\r\n> [conda] numpy 1.19.1 py37hae9e721_0 conda-forge\r\n> [conda] numpydoc 1.1.0 py_1 conda-forge\r\n> [conda] pytorch 1.8.1 py3.7_cuda10.2_cudnn7_0 pytorch\r\n> [conda] pytorch-metric-learning 0.9.98 pyh39e3cac_0 metric-learning\r\n> [conda] torchaudio 0.8.1 py37 pytorch\r\n> [conda] torchvision 0.2.2 py_3 pytorch", "Hi @BenoitDalFerro how do your load your dataset ?", "Hi @lhoestq thanks for the quick turn-around, actually the plain vanilla way, without an particular knack or fashion, I tried to look into the documentation for some alternative but couldn't find any\r\n\r\n> dataset = load_from_disk(dataset_path=os.path.join(datasets_dir,dataset_dir))", "I’m facing the same issue when loading a 900GB dataset (stored via `save_to_disk`): `load_from_disk(path_to_dir)` takes 1.5 hours and htop consistently shows high IO rates > 120 M/s.", "@tsproisl same here, smells like ~~teen spirit~~ intended generator inadvertently ending up iterator\r\n\r\n@lhoestq perhaps solution to detect bug location in code is to track its signature via HD read usage monitoring, option is to add tracking decorator on top each function and sequentially close all hatches from top to bottom, suggest PySmart https://pypi.org/project/pySMART/ a Smartmontools implementation", "I wasn't able to reproduce this on a toy dataset of around 300GB:\r\n\r\n```python\r\nimport datasets as ds\r\n\r\ns = ds.load_dataset(\"squad\", split=\"train\")\r\ns4000 = ds.concatenate_datasets([s] * 4000)\r\nprint(ds.utils.size_str(s4000.data.nbytes)) # '295.48 GiB'\r\n\r\ns4000.save_to_disk(\"tmp/squad_4000\")\r\n```\r\n\r\n```python\r\nimport psutil\r\nimport time\r\nfrom datasets import load_from_disk\r\n\r\ndisk = \"disk0\" # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\n\r\ns4000_reloaded = load_from_disk(\"tmp/squad_4000\")\r\n\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\n\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\") # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\") # Elapsed time: 14.60s\r\n```\r\n\r\nCould you run this on your side and tell me if how much time it takes ? Please run this when your machine is idle so that other processes don't interfere.\r\n\r\nI got these results on my macbook pro on datasets 1.6.2", "@lhoestq thanks, test running as we speak, bear with me", "Just tried on google colab and got ~1min for a 15GB dataset (only 200 times SQuAD), while it should be instantaneous. The time is spent reading the Apache Arrow table from the memory mapped file. This might come a virtual disk management issue. I'm trying to see if I can still speed it up on colab.", "@lhoestq what is Google Colab's HD read speed, is it possible to introspect incl. make like SSD or HDD ?", "@lhoestq Thank you! The issue is getting more interesting. The second script is still running, but it's definitely taking much longer than 15 seconds.", "Okay, here’s the ouput:\r\nBlocks read 158396\r\nElapsed time: 529.10s\r\n\r\nAlso using datasets 1.6.2. Do you have any ideas, how to pinpoint the problem?", "@lhoestq, @tsproisl mmmh still writing on my side about 1h to go, thinking on it are your large datasets all monoblock unsharded ? mine is 335 times 1.18Gb shards.", "The 529.10s was a bit too optimistic. I cancelled the reading process once before running it completely, therefore the harddrive cache probably did its work.\r\n\r\nHere are three consecutive runs\r\nFirst run (freshly written to disk):\r\nBlocks read 309702\r\nElapsed time: 1267.74s\r\nSecond run (immediately after):\r\nBlocks read 113944\r\nElapsed time: 417.55s\r\nThird run (immediately after):\r\nBlocks read 42518\r\nElapsed time: 199.19s\r\n", "@lhoestq \r\nFirst test\r\n> elapsed time: 11219.05s\r\n\r\nSecond test running bear with me, for Windows users slight trick to modify original \"disk0\" string:\r\n\r\nFirst find physical unit relevant key in dictionnary\r\n```\r\nimport psutil\r\npsutil.disk_io_counters(perdisk=True)\r\n```\r\n\r\n> {'PhysicalDrive0': sdiskio(read_count=18453286, write_count=4075333, read_bytes=479546467840, write_bytes=161590275072, read_time=20659, write_time=2464),\r\n> 'PhysicalDrive1': sdiskio(read_count=1495778, write_count=388781, read_bytes=548628622336, write_bytes=318234849280, read_time=426066, write_time=19085)}\r\n\r\nIn my case it's _PhysicalDrive1_\r\n\r\nThen insert relevant key's string as _disk_ variable\r\n\r\n```\r\npsutil.disk_io_counters()\r\ndisk = 'PhysicalDrive1' # You may have to change your disk here\r\niocnt1 = psutil.disk_io_counters(perdisk=True)[disk]\r\ntime1 = time.time()\r\ns4000_reloaded = load_from_disk(\"your path here\")\r\ntime2 = time.time()\r\niocnt2 = psutil.disk_io_counters(perdisk=True)[disk]\r\nprint(f\"Blocks read {iocnt2.read_count - iocnt1.read_count}\") # Blocks read 18\r\nprint(f\"Elapsed time: {time2 - time1:.02f}s\") # Elapsed time: 14.60s\r\n```", "@lhoestq\r\nSecond test\r\n\r\n> Blocks read 1265609\r\n> Elapsed time: 11216.55s", "@lhoestq any luck ?", "Unfortunately no. Thanks for running the benchmark though, it shows that you machine does a lot of read operations. This is not expected: in other machines it does almost no read operations which enables a very fast loading.\r\n\r\nI did some tests on google colab and have the same issue. The first time the dataset arrow file is memory mapped takes always a lot of time (time seems linear with respect to the dataset size). Reloading the dataset is then instantaneous since the arrow file has already been memory mapped.\r\n\r\nI also tried using the Arrow IPC file format (see #1933) instead of the current streaming format that we use but it didn't help.\r\n\r\nMemory mapping is handled by the OS and depends on the disk you're using, so I'm not sure we can do much about it. I'll continue to investigate anyway, because I still don't know why in some cases it would go through the entire file (high `Blocks read ` as in your tests) and in other cases it would do almost no reading.", "@lhoestq thanks for the effort, let's stay in touch", "Just want to say that I am seeing the same issue. Dataset size if 268GB and it takes **3 hours** to load `load_from_disk`, using dataset version `1.9.0`. Filesystem underneath is `Lustre` ", "Hi @lhoestq, confirmed Windows issue, exact same code running on Linux OS total loading time about 3 minutes.", "Hmm that's different from what I got. I was on Ubuntu when reporting the initial issue." ]

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[ "Hi,\r\n\r\n1. try\r\n ```python\r\n dataset = load_dataset(\"text\", data_files={\"train\": [\"a1.txt\", \"b1.txt\"], \"test\": [\"c1.txt\"]})\r\n ```\r\n instead.\r\n\r\n Sadly, I can't reproduce the error on my machine. If the above code doesn't resolve the issue, try to update the library to the \r\n newest version (`pip install datasets --upgrade`).\r\n\r\n2. https://github.com/huggingface/transformers/blob/3ed5e97ba04ce9b24b4a7161ea74572598a4c480/examples/pytorch/language-modeling/run_mlm.py#L258-L259\r\nThis is the original code. You'll have to modify the example source to work with multiple train files. To make it easier, let's say \"|\" will act as a delimiter between files:\r\n ```python\r\n if data_args.train_file is not None:\r\n data_files[\"train\"] = data_args.train_file.split(\"|\") # + .split(\"|\")\r\n ```\r\n Then call the script as follows (**dataset_name must be None**):\r\n ```bash\r\n python run_mlm.py [... other args] --train_file a1.txt|b1.txt\r\n ```", "i meet the same error with datasets 1.11.0, is there any insight about this?" ]

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[ "> If you pass a dictionary like this:\r\n> \r\n> ```\r\n> {\"main_metadata\": url_to_main_data,\r\n> \"secondary_metadata\": url_to_sec_data,\r\n> \"train\": url_train_data,\r\n> \"test\": url_test_data}\r\n> ```\r\n> \r\n> then only the train or test keys will be kept, which I feel not intuitive.\r\n> \r\n> For example if the users asks to load the \"train\" split, then the main and secondary metadata won't be downloaded.\r\n> You can fix that by keeping all the keys except the splits to ignore\r\n\r\nHi @lhoestq, I have been thinking about this and I think it is worth that we discuss about it.\r\n\r\nWhen I created this PR, my first idea was to create a \"hack\" inside the download manager that will be able to filter some split(s) without touching any dataset script. Of course, the download manager does not know about splits logic, and thus this trick would only work for some very specific datasets: only the ones containing that pass a dict to the download manager containing only the keys \"train\", \"validation\", \"test\" (or the one passed by the user for advanced users knowing they can do it), e.g. the `natural_questions` dataset (which was one of the targets).\r\n\r\nThe big inconvenient of this approach is that it is not applicable to many datasets (or worse, it should be constantly tweaked to cope with exceptional cases). One exceptional case is the one you pointed out. But I see others:\r\n- the split keys can be different: train, test, dev, val, validation, eval,...\r\n- in `hope_edi` dataset, the split keys are: TRAIN_DOWNLOAD_URL, VALIDATION_DOWNLOAD_URL\r\n- in `few_rel` dataset, the split keys are: train_wiki, val_nyt, val_pubmed,..., pid2name\r\n- in `curiosity_dialogs`, the split keys are: train, val, test, test_zero; this means that for every split we pass, we will also get test_zero\r\n- in `deal_or_no_dialog`, each of the splits URL is passed separately to the download manager, so all splits would be always downloaded\r\n- etc.\r\n\r\nThen after discussing, another idea emerged: pass a `split` parameter to `_split_generators`, which know about the splits logic, so that it can handle which splits are passed to the download manager. This approach is more accurate and can be tweaked so that it works with all the datasets we want. The only inconvenient is that then for every target dataset, we must modify its corresponding `_split_generators` script method.\r\n\r\nMy point is that I don't think it is a good idea to implement both approaches. They could even interfere with each other! \r\n\r\nIf you agree, I would implement ONLY the second one, which is simpler, more consistent and stable and will avoid future problems.", "Hi @albertvillanova !\r\nYup I agree with you, implementing the 2nd approach seems to be the right solution" ]

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[ "Hi @albertvillanova , I'll implement the parquet builder as an ArrowBasedBuilder if you don't mind", "closing in favor of #2537 that is already merged" ]

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[ "@lhoestq Just fixed the code style issues— I think it should be good to merge now :)" ]

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[ "@lhoestq The tests for key type and duplicate keys have been added and verified successfully.\r\nAfter generating with an intentionally faulty `mnist` script, when there is an incompatible key type, it shows:\r\n\r\n```\r\nDownloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 60.62 MiB, post-processed: Unknown size, total: 71.67 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\mnist\\mnist\\1.0.0\\5064c25e57a1678f700d2dc798ffe8a6d519405cca7d33670fffda477857a994...\r\n0 examples [00:00, ? examples/s]2021-04-26 02:50:03.703836: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\n\r\nFAILURE TO GENERATE DATASET: Invalid key type detected\r\nFound Key [0, 0] of type <class 'list'>\r\nKeys should be either str, int or bytes type\r\n```\r\n\r\nIn the case of duplicate keys, it now gives:\r\n\r\n```\r\nDownloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 60.62 MiB, post-processed: Unknown size, total: 71.67 MiB) to C:\\Users\\nikhil\\.cache\\huggingface\\datasets\\mnist\\mnist\\1.0.0\\5064c25e57a1678f700d2dc798ffe8a6d519405cca7d33670fffda477857a994...\r\n0 examples [00:00, ? examples/s]2021-04-26 02:53:13.498579: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\load.py\", line 746, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 587, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\builder.py\", line 1002, in _prepare_split\r\n writer.write(example, key)\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\arrow_writer.py\", line 321, in write\r\n self.check_duplicates()\r\n File \"f:\\datasets\\datasets-1\\src\\datasets\\arrow_writer.py\", line 331, in check_duplicates\r\n raise DuplicatedKeysError(key)\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 234467\r\nKeys should be unique and deterministic in nature\r\n```\r\nPlease let me know if this is what we wanted to implement. Thanks!", "This looks pretty cool !\r\nWe can make focus on the GeneratorBasedBuilder for now yes.\r\n\r\nDo you think we could make the ArrowWriter not look for duplicates by default ?\r\nThis way we can just enable duplicate detections when instantiating the writer in the GeneratorBasedBuilder for now.", "Thank you @lhoestq\r\n\r\n\r\n\r\n> Do you think we could make the ArrowWriter not look for duplicates by default ?\r\n\r\nWe can definitely do that by including a `check_duplicates` argument while instantiating `ArrowWriter()`. \r\n\r\nHowever, since only `GeneratorBasedBuilder` uses the `write()` function (which includes the detection code) and the others like `ArrowBasedBuilder` use `write_table()` which remains as it was (without duplicate detection). I don't think it would be necessary.\r\n\r\nNonetheless, doing this would require just some small changes. Please let me know your thoughts on this. Thanks!", "I like the idea of having the duplicate detection optional for other uses of the ArrowWriter.\r\nThis class is the main tool to write python data in arrow format so I'd expect it to be flexible.\r\nThat's why I think by default it shouldn't require users to provide keys or do any duplicates detection.\r\n\r\nAn alternative would be to subclass the writer to include duplicates detection in another class.\r\n\r\nBoth options are fine for me, let me know what you think !", "> This class is the main tool to write python data in arrow format so I'd expect it to be flexible.\r\n> That's why I think by default it shouldn't require users to provide keys or do any duplicates detection.\r\n\r\nWell, that makes sense as the writer can indeed be used for other purposes as well.\r\n\r\n> We can definitely do that by including a `check_duplicates` argument while instantiating `ArrowWriter()`.\r\n\r\nI think that this would be the simplest and the more efficient option for achieving this as subclassing the writer only for this would lead to unnecessary complexity and code duplication (in case of `writer()`). \r\n\r\nI will be adding the changes soon. Thanks for the feedback @lhoestq!", "@lhoestq I have pushed the final changes just now. \r\nNow, the keys and duplicate checking will be necessary only when the `ArrowWriter` is initialized with `check_duplicates=True` specifically (in this case, for `GeneratorBasedBuilders`)\r\n\r\nLet me know if this is what was required. Thanks!", "@lhoestq Thanks for the feedback! I will be adding the tests for the same very soon. \r\n\r\nHowever, I'm not sure as to what exactly is causing the `segmentation fault` in the failing CI tests. It seems to be something from `test_concatenation_table_cast` from `test_table.py`, but I'm not sure as to what exactly. Would be great if you could help. Thanks!", "You can merge master into your branch to fix this issue.\r\nBasically pyarrow 4.0.0 has a segfault issue (which has now been resolved on the master branch of pyarrow).\r\nSo until 4.0.1 comes out we changed to using `pyarrow<4.0.0` recently.", "@lhoestq Thanks for the help with the CI failures. Apologies for the multiple merge commits. My local repo got messy while merging which led to this.\r\nWill be pushing the commit for the tests soon!", "Hey @lhoestq, I've just added the required tests for checking key duplicates and invalid key data types.\r\nI think we have caught a nice little issue as 27 datasets are currently using non-unique keys (hence, the failing tests: All these datasets are giving `DuplicateKeysError` during testing). \r\nThese datasets were not detected earlier as there was no key checking when `num_examples < writer_batch_size` due to which they passed the dummy data generation test. This bug was fixed by adding the test to `writer.finalize()` method as well for checking any leftover examples from batches. \r\n\r\nI'd like to make changes to the faulty datasets' scripts. However, I was wondering if I should do that in this PR itself or open a new PR as this might get messy in the same PR. Let me know your thoughts on this. Thanks!", "Hi ! Once https://github.com/huggingface/datasets/pull/2333 is merged, feel free to merge master into your branch to fix the CI :)", "Thanks a lot for the help @lhoestq. Besides merging the new changes, I guess this PR is completed for now :)", "I just merged the PR, feel free to merge `master` into your branch. It should fix most most of the CI issues. If there are some left we can fix them in this PR :)", "@lhoestq Looks like the PR is completed now. Thanks for helping me out so much in this :)", "Hey @lhoestq, I've added the test and corrected the Cl errors as well. Do let me know if this requires any change. Thanks!", "Merging. I'll update the comment on the master branch (for some reason I can edit files on this branch)", "@lhoestq Thank you for the help and feedback. Feels great to contribute!" ]

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[ "@lhoestq, I think this reaches some memory limit on Linux instances... (?)", "It looks like the `comet` metric test fails because it tries to load a model in memory.\r\nIn the tests I think we have `patch_comet` that mocks the model download + inference. Not sure why it didn't work though.\r\nI can take a look tomorrow (this afternoon is the pytorch ecosystem day)", "@lhoestq thanks for the hint: I'm going to have a look at that mock... ;)", "@lhoestq finally I did not find out why the mock is not used... If you can give me some other hint tomorrow..." ]

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[ "Hi @villmow, thanks for reporting.\r\n\r\nCould you please try with the Datasets version 1.6? We released it yesterday and it fixes some issues about the processing speed. You can see the fix implemented by @lhoestq here: #2122.\r\n\r\nOnce you update Datasets, please confirm if the problem persists.", "Hi @albertvillanova, thanks for the reply. I just tried the new version and the problem still persists. \r\n\r\nDo I need to rebuild the saved dataset (which I load from disk) with the 1.6.0 version of datasets? My script loads this dataset and creates new datasets from it. I tried it without rebuilding.\r\n\r\nSee this short video of what happens. It does not create all processes at the same time:\r\n\r\nhttps://user-images.githubusercontent.com/2743060/115720139-0da3a500-a37d-11eb-833a-9bbacc70868d.mp4\r\n\r\n", "There can be a bit of delay between the creations of the processes but this delay should be the same for both your `map` calls. We should look into this.\r\nAlso if you hav some code that reproduces this issue on google colab that'd be really useful !\r\n\r\nRegarding the speed differences:\r\nThis looks like a similar issue as https://github.com/huggingface/datasets/issues/1992 who is experiencing the same speed differences between processes.\r\nThis is a known bug that we are investigating. As of now I've never managed to reproduce it on my machine so it's pretty hard for me to find where this issue comes from.\r\n", "Upgrade to 1.6.1 solved my problem somehow. I did not change any of my code, but now it starts all processes around the same time.", "Nice ! I'm glad this works now.\r\nClosing for now, but feel free to re-open if you experience this issue again." ]

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[ "This should be fixed now!\r\n\r\ncc @srush " ]

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[ "> And yet another one ! Thanks a lot :)\r\n\r\nI just hope you don’t get fed up with openslr PR 😊 there are still few other datasets created by google in openslr that is not in hf dataset yet\r\n" ]

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[ "Hi @odellus, thanks for reporting.\r\n\r\nThe `wikihow` dataset has 2 versions:\r\n- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.\r\n- `sep`: Consisting of each paragraph and its summary.\r\n\r\nTherefore, in order to load it, you have to specify which version you would like, for example:\r\n```python\r\ndataset = load_dataset('wikihow', 'all')\r\n```\r\n\r\nPlease, tell me if this solves your problem.", "Good call out. I did try that and that's when it told me to download the\ndataset. Don't believe I have tried it with local files. Will try first\nthing in the morning and get back to you.\n\nOn Mon, Apr 19, 2021, 11:17 PM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hi @odellus <https://github.com/odellus>, thanks for reporting.\n>\n> The wikihow dataset has 2 versions:\n>\n> - all: Consisting of the concatenation of all paragraphs as the\n> articles and the bold lines as the reference summaries.\n> - sep: Consisting of each paragraph and its summary.\n>\n> Therefore, in order to load it, you have to specify which version you\n> would like, for example:\n>\n> dataset = load_dataset('wikihow', 'all')\n>\n> Please, tell me if this solves your problem.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2239#issuecomment-823004146>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABDYI3HVRTBI2QT3BOG262DTJUL57ANCNFSM43GV5BZQ>\n> .\n>\n", "Hi @odellus, yes you are right.\r\n\r\nDue to the server where the `wikihow` dataset is hosted, the dataset can't be downloaded automatically by `huggingface` and you have to download it manually as you did.\r\n\r\nNevertheless, you have to specify which dataset version you would like to load anyway:\r\n```python\r\ndataset = load_dataset('wikihow', 'all', data_dir='./wikihow')\r\n```\r\nor\r\n```python\r\ndataset = load_dataset('wikihow', 'sep', data_dir='./wikihow')\r\n```\r\nI find that the instructions given by `huggingface` are not clear enough: I am going to fix this.\r\nPlease tell me if this eventually works for you.", "That was it. Thank you Albert!" ]

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[ "@albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks!" ]

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[ "I replaced all the \"we\" and applied your suggestion", "Merging this for now, we can continue improving this card in other PRs :)" ]

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[ "Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?", "Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how the `ArrowWriter` functions, I think we can implement this as follows:\r\n\r\n1. First, we would have to update the `ArrowWriter.write()` function here:\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L296\r\nso that it accepts an additional argument `key` which would be appended along with the example here after hashing.\r\n\r\n2. Then, we would need to create a `Hasher` class which will take the key as its input and return a hash for it (We might need to use some hash salt which can be passed to the ArrowWriter.writer() with value equal to the `split_name` for differentiating between same keys of different splits)\r\n\r\n We can use the `hashlib.md5` function for hashing which will conert each key to its byte code before hashing (depending on the data type of the key) **Thus, the `key` type will be verified here**.\r\n\r\n3. Now, we would have to edit this\r\nhttps://github.com/huggingface/datasets/blob/fcd3c3c8e3b1d9a2f3686a496082e21f06591380/src/datasets/arrow_writer.py#L257\r\n so that it iterates over each `(hash, example)` pair (sorted according to hash). We can then simply **check whether each hash is different from the previous hash** (since they will be sorted)\r\n\r\nHowever, since I'm not very familiar with how the data is being written on disk in the form of a table, I might need some guidance for Step 3. \r\nPlease let me know your thought on this. Thanks!", "Interesting !\r\nWe keep the dataset sorted in the order examples are generated by the builder (we expect the dataset builders to generate examples in deterministic order). Therefore I don't think we should shuffle the examples with the hashing. Let me know what you think.\r\nOther that that, I really like the idea of checking for keys duplicates in `write_examples_on_file` :)\r\n\r\nThis looks like a great plan ! Feel free to open a PR and ping me if you have questions or if I can help\r\n", "@lhoestq I'm glad you liked the idea!\r\nI think that since the keys will be unique and deterministic in the nature themselves, so even if we shuffle the examples according to the hash, a deterministic order would still be maintained (as the keys will always have the same hash, whenever the dataset is generated). \r\nAnd since, we are not dealing with time series data (which would require the data to be in original order), I don't think the order of examples would matter much, as long as the order is deterministic and constant for all users.\r\n\r\nI think that this is also what was originally envisioned as mentioned in the documentation here:\r\nhttps://github.com/huggingface/datasets/blob/6775661b19d2ec339784f3d84553a3996a1d86c3/src/datasets/builder.py#L973\r\n\r\nAlso, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\nLet me know your thoughts in it! I would be opening a PR soon :)", "When users load their own data, they expect the order to stay the same. I think that shuffling the data can make things inconvenient.\r\n\r\n> I think that this is also what was originally envisioned as mentioned in the documentation here:\r\n\r\nThis part was originally developed by tensorflow datasets, and tensorflow datasets indeed does the shuffling. However in this library this is probably not what we want in the general case. But if @albertvillanova and @thomwolf you have opinions on this please let us know.\r\n\r\n> Also, if we avoid this, we would need to keep track of all the hashed keys in some place and compare each individual key with all others. This can cause some major overhead as each dataset consists of tens of thousands of examples.\r\n\r\nMaybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch, but there might still be duplicates across batches. For 10 000 examples the hashes can just be stored as a python `set`.\r\n\r\nOtherwise if we want full deduplication, we need an extra tool that allows to temporarily save and query hashes that may need to use disk space rather than memory.", "Yes I think we want to keep the original order by default and only shuffle when the user ask for it (for instance by calling `dataset.shuffle()`). That’s how I had it in mind originally.", "Hey @lhoestq, I just had a more in-depth look at the original TFDS code about why the keys and hash were used in the first place.\r\n\r\nIn my opinion, the only use that the `hash(key)` serves is that it allows us to shuffle the examples in a deterministic order (as each example will always yield the same key and thus, the same hash on every system) so that the same dataset is generated for each user, irrespective of the order the examples are yielded by the dataset builder on different user systems.\r\n\r\nOtherwise, if we are not shuffling, then while yielding and writing the data, after getting the key and hashing it for an example, I can't quite see the use of the hash or the key. The hash will simply be generated for each example but not actually used anywhere?\r\n\r\n@lhoestq @thomwolf It would be great if you could explain a bit more about the usage of keys. Thanks!\r\n", "In `datasets` the keys are currently ignored.\r\nFor shuffling we don't use the keys. Instead we shuffle an array of indices. Since both the original order of the dataset and the indices shuffling are deterministic, then `dataset.shuffle` is deterministic as well.\r\nWe can use it to:\r\n1. detect duplicates\r\n2. verify that the generation order is indeed deterministic\r\n3. maybe more ?", "Thanks a lot @lhoestq. I think I understand what we need to do now. The keys can indeed be used for detecting duplicates in generated examples as well as ensuring the order.\r\n\r\n> Maybe we cam simply keep track of the hashes of of each batch being written ? The size of the batch when the data are save in arrow is 10 000 examples. This would only ensure that we don't have duplicates in each batch,\r\n\r\nI think that checking for duplicates in every batch independently would be sufficient as the probability of collisions using something like `MD5` is very low. I would be opening a draft PR soon. It would be great to have your guidance. Thanks!" ]

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[ "Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str/int, you can also fix them !\r\nthanks :)", "@lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!" ]

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[ "Awesome thanks ! To fix the CI you just need to merge master into your branch.\r\nThe error is unrelated to your PR" ]

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[ "I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nsst = load_dataset(\"sst\")\r\nsst.set_format(\"torch\", columns=[\"label\"], output_all_columns=True)\r\nds = sst[\"train\"]\r\n\r\n# crashes\r\nds.map(\r\n lambda x: {\"a\": list(range(20))},\r\n remove_columns=ds.column_names,\r\n load_from_cache_file=False,\r\n num_proc=1,\r\n batched=True,\r\n)\r\n```", "Thanks for reporting and for providing this code to reproduce the issue, this is really helpful !", "I merged a fix, it should work on `master` now :)\r\nWe'll do a new release soon !" ]

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[ "Thanks ! good catch\r\n\r\nCould you also update the metadata of this dataset ?\r\nYou can do so by running\r\n```\r\ndatasets-cli test ./datasets/newsgroup --all_configs --save_infos --ignore_verifications\r\n```\r\nThis should update the dataset_infos.json file that contains the size of all the splits for example.", "Hi,\r\n`dataset_infos.json` should be updated now.\r\n" ]

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[ "> why a cache dir per test function does not work?\r\n\r\nProbably because we end up with multiple `datasets_module` in the python path. This breaks the import of all the datasets/metrics modules.\r\nIf you want to use one modules cache per test, you may need remove the `datasets_module` that was added to the python path during the test.\r\nIndeed if the module cache hasn't been initialized, then it's added to the python path by calling `init_dynamic_modules`:\r\n\r\nhttps://github.com/huggingface/datasets/blob/ba76012a19193a35053b9e20243ff40c2b4204ab/src/datasets/load.py#L291-L291", "@lhoestq, for the moment, this PR avoids populating the `~/.cache` dir during training, which is already an improvement, isn't it?", "Yes we can merge it this way if you're fine with it !\r\nThis is a good improvement", "I will eventually try to implement a `cache_dir` per test function in another PR, but I think I should first fix some side effects in tests: each test function should be atomic and able to have its own `cache_dir` without being affected by the `cache_dir` set in other test functions.", "Yes this would be ideal !" ]

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[ "Windows users should disable the max path length limit. It's a nightmare to handle it.\r\nAlso the lock path must not be changed in a random way. Otherwise from another process the lock path might not be the same and the locking mechanism won't work.", "Do you agree with handling the case where MAX_PATH is not disabled? If not, we can close this PR.\r\n\r\nIf so, would it work a deterministic lock path instead of random?", "I'd rather not handle this at all, since there will be other places in the code where the limit will break things" ]

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[ "How is it possible to get an infinite loop ? Can you add more details ?", "Yes, in Windows, if the filename is too long, a `FileNotFoundError` is raised. The exception should be raised in this case. Otherwise, we get into an infinite loop.\r\n\r\nIf other process has the file locked, then `PermissionError` is raised. In this case, `pass` is OK.", "Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n\r\nUnless we have proper tests for this, I wouldn't recommend to change it", "I'm pretty sure many things from the library could break for windows users that haven't disabled the max path length limit.\r\nMaybe it would be simpler to simply raise an error on startup. For exampe, for windows users the error could ask them to disable the limit if it's not been disabled yet ?" ]

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[ "1) Changed the language in a few places apart from those you mentioned in README\r\n2) Reduced the size of dummy data folder by removing all other entries except the first\r\n3) Updated YAML tags by using to the past version of `datasets-tagging` app. Will update the quick fix on that repository too in a while", "@bhavitvyamalik Thanks for adding the dataset on huggingface! Can you please add a metric also for the dataset using the squad_v2 metric file? ", "@MohammedRakib you can check [#2257](https://github.com/huggingface/datasets/pull/2257)" ]

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[ "Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', split='train')\r\n>>> dataset = Dataset.from_pandas(dataset.to_pandas().drop_duplicates(subset=...)) # specify a subset of the columns to consider in a list or use all of the columns if None\r\n```\r\n\r\nNote that the same can be achieved with the `Dataset.filter` method but this would requrie some extra work (filter function, speed?).", "Oh, seems like my question wasn't specified well. I'm _not_ asking how to remove duplicates, but whether duplicates should be removed if I want to do the evaluation on the LAMA dataset as it was proposed in the original paper/repository? In other words, will I get the same result if evaluate on the de-duplicated dataset loaded from HF's `datasets` as the results I'd get if I use the original data format and data processing script in https://github.com/facebookresearch/LAMA? ", "So it looks like the person who added LAMA to the library chose to have one item per piece of evidence rather than one per relation - and in this case, there are duplicate pieces of evidence for the target relation\r\n\r\nIf I understand correctly, to reproduce reported results, you would have to aggregate predictions for the several pieces of evidence provided for each relation (each unique `uuid`), but the original authors will know better \r\n\r\ncc @fabiopetroni " ]

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[ "Hi @lhoestq,\r\nCould you please help me, I got this error message in all \"ci/circleci: run_dataset_script_tests_pyarrow*\" tests:\r\n```\r\n...\r\n \"\"\"Wrapper classes for various types of tokenization.\"\"\"\r\n \r\n from bleurt.lib import bert_tokenization\r\n import tensorflow.compat.v1 as tf\r\n> import sentencepiece as spm\r\nE ModuleNotFoundError: No module named 'sentencepiece'\r\n...\r\n```\r\nI am not sure why I do get it. Thanks.\r\n", "Hi ! This issue appeared on master since the last update of `BLEURT`.\r\nI'm working on a fix. You can ignore this issue for this PR", "> Hi ! This issue appeared on master since the last update of `BLEURT`.\r\n> I'm working on a fix. You can ignore this issue for this PR\r\n\r\nThanks for the info", "Merging since the CI is fixed on master" ]

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[ "Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```", "There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are downloaded from `master` instead of the `1.2.1` repo.\r\n\r\nYou can try setting the env var `HF_SCRIPTS_VERSION=\"1.2.1\"` as a workaround. Let me know if that helps.", "I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip installs in the same environments but this will have to do for now, until 1.5.0 is made available through conda.", "Yep, seems to have fixed things! The conda package could really do with an update. Thanks!" ]

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[ "Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available", "I saw this on their website when we request to download the dataset:\r\n\r\n\r\nCan we still request them link for the dataset and make a PR? @lhoestq @yjernite ", "I've contacted Martin (first author of the fquad paper) regarding a possible new url. Hopefully we can get one soon !", "They now made a website to force people who want to use the dataset for commercial purposes to seek a commercial license from them ..." ]

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[ "Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n", "Ah sorry, I tried searching but couldn't find any related PR. \r\n\r\nThank you! " ]

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[ "Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` branch.", "Hi, thank you for your answer. I did not realize that my issue stems from the same problem. " ]

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[ "merging since the CI is fixed on master" ]

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[ "Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features['label'].int2str(i)`.\r\n" ]

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[ "Hi,\r\n\r\nthe output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assumed range.\r\nCan you please provide a minimal reproducible example for more help?", "Hi @yana-xuyan, thanks for reporting.\r\n\r\nAs clearly @mariosasko explained, `datasets` performs some optimizations in order to reduce the size of the dataset cache files. And one of them is storing the field `special_tokens_mask` as `int8`, which means that this field can only contain integers between `-128` to `127`. As your message error states, one of the values of this field is `50259`, and therefore it cannot be stored as an `int8`.\r\n\r\nMaybe we could implement a way to disable this optimization and allow using any integer value; although the size of the cache files would be much larger.", "I'm facing same issue @mariosasko @albertvillanova \r\n\r\n```\r\nArrowInvalid: Integer value 50260 not in range: -128 to 127\r\n```\r\n\r\nTo reproduce:\r\n```python\r\nSPECIAL_TOKENS = ['<bos>','<eos>','<speaker1>','<speaker2>','<pad>']\r\nATTR_TO_SPECIAL_TOKEN = {\r\n 'bos_token': '<bos>', \r\n 'eos_token': '<eos>', \r\n 'pad_token': '<pad>',\r\n 'additional_special_tokens': ['<speaker1>', '<speaker2>']\r\n }\r\n\r\ntokenizer = AutoTokenizer.from_pretrained(\"gpt2\", use_fast=False)\r\nnum_added_tokens =tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN)\r\nvocab_size = len(self.tokenizer.encoder) + num_added_tokens\r\nvocab =tokenizer.get_vocab()\r\n\r\npad_index = tokenizer.pad_token_id\r\neos_index = tokenizer.eos_token_id\r\nbos_index = tokenizer.bos_token_id\r\nspeaker1_index = vocab[\"<speaker1>\"]\r\nspeaker2_index = vocab[\"<speaker2>\"]\r\n```\r\n\r\n```python\r\ntokenizer.decode(['50260'])\r\n'<speaker1>'\r\n```", "@mariosasko \r\nI am hitting this bug in the Bert tokenizer too. I see that @albertvillanova labeled this as a bug back in April. Has there been a fix released yet?\r\nWhat I did for now is to just disable the optimization in the HF library. @yana-xuyan and @thomas-happify, is that what you did and did that work for you?\r\n\r\n", "Hi @gregg-ADP, \r\n\r\nThis is still a bug.\r\n\r\nAs @albertvillanova has suggested, maybe it's indeed worth adding a variable to `config.py` to have a way to disable this behavior.\r\n\r\nIn the meantime, this forced optimization can be disabled by specifying `features` (of the returned examples) in the `map` call:\r\n```python\r\nfrom datasets import *\r\n... # dataset init\r\nds.map(process_example, features=Features({\"special_tokens_mask\": Sequence(Value(\"int32\")), ... rest of the features}) \r\n```\r\n\r\ncc @lhoestq so he is also aware of this issue", "Thanks for the quick reply @mariosasko. What I did was to changed the optimizer to use int32 instead of int8. \r\nWhat you're suggesting specifies the type for each feature explicitly without changing the HF code. This is definitely a better option. However, we are hitting a new error later:\r\n```\r\n File \"/Users/ccccc/PycharmProjects/aaaa-ml/venv-source/lib/python3.8/site-packages/torch/nn/modules/module.py\", line 1051, in _call_impl\r\n return forward_call(*input, **kwargs)\r\nTypeError: forward() got an unexpected keyword argument 'pos'\r\n\r\n```\r\nWhere 'pos' is the name of a new feature we added. Do you agree that your way of fixing the optimizer issue will not fix our new issue? If not, I will continue with this optimizer fix until we resolve our other issue.\r\n", "Hi @gwc4github,\r\n\r\nthe fix was merged a few minutes ago, and it doesn't require any changes on the user side (e.g. no need for specifying `features`). If you find time, feel free to install `datasets` from master with:\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```\r\nand let us know if it works for your use case! " ]

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[ "Hi ! Can you add a description regarding this PR ? Why do you think we need to update the dummy data used to test the `banking77` dataset loading script ?", "Closing for inactivity. Feel free to re-open if you want to push this change" ]

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[ "Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201", "> Hi ! This might be related to #2153\r\n> \r\n> You're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\n> I'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n> \r\n> EDIT: opened #2201\r\n\r\nGlad to hear that! Thank you for your fix, I'm new to huggingface, it's a fantastic project 😁" ]

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[ "Hi @lhoestq, could you please check if this makes sense? Thanks.", "What about using `_indices_data_files` field in save_to_disk instead of `_indices_files` ?\r\nThis way future datasets can also be reloaded from older versions of the lib\r\n\r\n`_indices_files` was introduced in a recent PR and was not released", "Yes, I have seen it is not released yet...\r\n\r\nYou are right! It was your awesome PR on Tables which renamed this. If there is no particular reason for this renaming, yes, we could switch it back to the previous `_indices_data_files`. ;)" ]

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[ "From offline discussions: we want to make the permissions handling consistent with `transformers`. However from discussion in https://github.com/huggingface/transformers/pull/11119 it looks like it might not be a good solution to provide this argument. Users should use umask for now, and we'll see how things evolve.\r\n\r\n@bhavitvyamalik I'm closing the PR for now if you don't mind" ]

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[ "Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map/filter/cast/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid having to load the dataset in RAM, even after many transforms", "Thanks @lhoestq! Hmm.. that's strange because I specifically turned off auto caching, and saved mapped result, using `save_to_disk`, to another location. At this location, the following file is created:`355G\tcache-ed205e500a7dc44c.arrow`\r\n\r\nTo my observation, both `load_dataset` and `map` creates `cache-*` files, and I wonder what the `cache-*` file from `load_dataset` is for (as I believe the same information is stored in `json-train.arrow`.", "This is a wrong report -- `cache-*` files are created only my `map`, not by `load_dataset`. " ]

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