Datasets:

runningsnake

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github-issues

like 0

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[ "Hi! `datasets` caches every modification/loading, so you can either rerun the pipeline up to the `map` call or use `Dataset.from_file(modified_dataset)` to load the dataset directly from the cache file.", "Awesome, hvala Mario! This works. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Added back the `[:]` and a comment to explain why this is needed. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "These test errors are related to issue #4428 \r\n", "_The documentation is not available anymore as the PR was closed or merged._", "I only made a trivial modification in my commit https://github.com/huggingface/datasets/pull/4431/commits/402c893d35224d7828176717233909ac5f1e7b3e\r\n\r\nI have submitted a PR #4434 for the about issue.", "> Awesome thanks for adding this dataset :)\r\n> \r\n> I just have one comment about the licensing.\r\n> \r\n> Also it seems that you already have the dataset in https://huggingface.co./datasets/silver/personal_dialog, so it's unnecessary to add it here\r\n\r\nThank you very much for your comment.\r\n\r\nSo, should I close this PR?", "Thanks for fixing the licensing section :)\r\n\r\n> So, should I close this PR?\r\n\r\nYes you can close this PR, it's better if your dataset is under your namespace at https://huggingface.co./datasets/silver/personal_dialog :)\r\n\r\nDon't forget to update the licensing section on https://huggingface.co./datasets/silver/personal_dialog as well" ]

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[ "Hi! Our versioning is based on Git revisions (the `revision` param in `load_dataset`), so you can just replace the old URL with the new one and open a PR :). I can also give you some pointers if needed.", "@mariosasko Awesome thanks! I will do that. Looks like this new version of the data is not available as a zip but as three files (train/dev/test). How is this usually handled in HF Datasets, should `_URL` be a dict with keys `train`, `val`, `test` perhaps?", "Yes! Let me help you with more detailed instructions.\r\n\r\nIn the first step, we need to update the URLs. One of the possible dictionary structures is as follows:\r\n```python\r\n_URLs = {\r\n \"train\": {\"src\": \"https://drive.google.com/uc?export=download&id=1wHAWDOwOoQWSj7HYpyJ3Aeud8WhhaJ7P\", \"tgt\": \"https://drive.google.com/uc?export=download&id=1QVgswwhVTkd3VLCzajK6eVkcrSWEK6kq\"}\r\n \"val\": ...\r\n \"test\": ...\r\n}\r\n```\r\n\r\n(You can use this page to generate direct download links: https://sites.google.com/site/gdocs2direct/)\r\n\r\nThen we move to the `split_generators` method:\r\n```python\r\ndef _split_generators(self, dl_manager):\r\n \"\"\"Returns SplitGenerators.\"\"\"\r\n files = dl_manager.download(_URLs)\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={\"src_file\": files[\"train\"][\"src\"], \"tgt_file\": files[\"train\"][\"tgt\"]},\r\n ),\r\n ... # same for val and test\r\n ]\r\n```\r\nFinally, we adjust the signature of `_generate_examples`:\r\n```python\r\ndef _generate_examples(self, src_file, tgt_file):\r\n \"\"\"Yields examples.\"\"\"\r\n with open(src_file, encoding=\"utf-8\") as src_f, open(\r\n tgt_file, encoding=\"utf-8\"\r\n ) as tgt_f:\r\n ... # the rest is the same\r\n```\r\n\r\nAnd that's it!\r\n\r\nPS: Let me know if you need help updating the dummy data and regenerating the metadata file.", "Awesome! Thanks for the detailed help, that was straightforward with your instruction. However, I think I am being blocked by this issue: https://github.com/huggingface/datasets/issues/4428", "Feel free to open a PR, and I can fix this manually.", "Awsome, done in #4451!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mishig25 is investigating why deprecated/added do not affect the enclosed text format when used in args docstring: no special formatting appears: \r\n- https://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder", "@albertvillanova please check now 👍 \r\nhttps://moon-ci-docs.huggingface.co/docs/datasets/pr_4429/en/package_reference/builder_classes#datasets.DatasetBuilder\r\n\r\n<img width=\"500\" alt=\"Screenshot 2022-06-06 at 10 20 34\" src=\"https://user-images.githubusercontent.com/11827707/172123471-fab97138-c903-4a71-ab7f-c90e5e43c58f.png\">\r\n", "Thanks @mishig25.\r\n\r\nJust one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?", "> Just one question: is it expected to have the deprecated box right edge not filling all the page width (contrary to the added box)?\r\n\r\nYes, that is expected 😊 because the depreacted box is being bounded by its parent box (the box for `name` argument in the screenshot above)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks ! As for https://github.com/huggingface/datasets/pull/4431 please also update the licensing section in https://huggingface.co./datasets/silver/mmchat ;)\r\n\r\nThen if it's fine for you feel free to close this PR" ]

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[ "Thanks for reporting, @bhaddow.\r\n\r\nI'm fixing it.", "Thanks for the quick fix!", "@bhaddow we have also made a fix so that you don't have to convert to uppercase the file extensions of the LDC data.\r\n\r\nWould you mind checking if it works OK now for you and reporting if there are any issues? Thanks. ", "Hi @albertvillanova -It loads fine on a copy of the data from deepai - although I have to remove the copies of the .WAV files (with extension .WAV,wav). On a copy of the data that was obtained from the LDC, the glob still fails to find the files. The LDC copy looks like it was copied from CD, in 2004, so the structure may be different to a current download.", "Ah, if I change the train/ and test/ directories to TRAIN/ and TEST/ then it works!", "Thanks for your investigation and report, @bhaddow. I'm adding another fix for the TRAIN/train and TEST/test directory names." ]

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[ "If you call `metric.compute` in a distributed setup like yours, then `metric.compute` is called in each process. `metric.compute` first calls `metric.add_batch`, and it looks like your error appears at that stage.\r\n\r\nTo make sure that all the processes have started writing their predictions/references at the same time, each process waits for process 0 to lock `slurm-{world_size}-0.arrow.lock`. Process 0 locks this file when `metric.add_batch` is called, so here when `metric.compute` is called.\r\n\r\nTherefore your error can happen when process 0 takes too much time to call `metric.compute` compared to process 3 (>100 seconds by default). I haven't tried running your code but could it be the case ?\r\n\r\nI guess it could also happen if you run multiple times the same distributed job at the same time with the same `experiment_id` because they would collide.\r\n", "We've finally been able to isolate the problem, it wasn't a timing problem, but rather a file locking one. \r\nThe locks produced by calling `flock` where not visible between nodes (so the master node couldn't check other node's locks nor the other way around). \r\n\r\nWe are now having issues with the pre-processing in our runner script, but are not related with the rendezvous process during the evaluation phase. We will let you know about it once we address it. \r\n\r\nOur solution to the rendezvous is as follows:\r\n- We solved the problem by calling `lockf` instead of `flock`.\r\n- We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one). \r\n\r\nWe use a shared file system between nodes using GPFS in our cluster setup. Maybe the difference between the behavior we see with respect to your usage in multi-node executions comes from that fact. Which file system scheme do you use for the multi-node executions? \r\n\r\n`lockf` seems to work in more settings than `flock`, so maybe we could write a PR so you could test it in your environment. ", "Cool, I'm glad you managed to make evaluation work :)\r\n\r\nI'm not completely aware of the differences between lockf and flock, but I've read somewhere that flock is preferable over lockf in multithreading and multiprocessing situations. Here we definitely are in such a situation so unless it is super important I don't think we will switch to lockf", "> * We had to change slightly the `_check_all_processes_locks` method so that the main process (i.e. process 0) didn't check it's own lock (because `lockf` permits recursive locks and thus checking it only replaced the current lock with a new one).\r\n\r\nHi @panserbjorn , Can you share your `_check_all_processes_locks` function? thanks!", "```\r\ndef _check_all_processes_locks(self):\r\n expected_lock_file_names = [\r\n os.path.join(self.data_dir, f\"{self.experiment_id}-{self.num_process}-{process_id}.arrow.lock\")\r\n for process_id in range(self.num_process)\r\n ]\r\n #for expected_lock_file_name in expected_lock_file_names: # OUR CHANGE process 0 shouldn't check its own lock\r\n for expected_lock_file_name in expected_lock_file_names[1:]:\r\n nofilelock = FileFreeLock(expected_lock_file_name)\r\n try:\r\n nofilelock.acquire(timeout=self.timeout)\r\n except Timeout:\r\n raise ValueError(\r\n f\"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist.\"\r\n )\r\n else:\r\n nofilelock.release()\r\n```\r\n\r\n### Changed files:\r\n- metric.py file in the datasets library \r\n- filelock.py file in the datasets/utils library. \r\n\r\n\r\nChanges we made:\r\n\r\n1. We changed the flock for lockf \r\n flock and lockf both perform a lock over a file (like the lock for writing). \r\n The difference is that flock only works in local file systems, but if you have a shared file system (like what we have in the clusters) the flock fails to “see” the lock of another node. The only disadvantage we had was that a single process couldn’t detect it’s own lock so we did the second change.\r\n2. We prevented the process 0 (which is the one that coordinates the rendezvous) from checking its own lock on its arrow because it didn't work with lockf (as stated in the previous change). \r\n3. We made a second rendezvous so that all the process had the results of the metrics (other than the loss) and not only the process 0.\r\n What happened was that only process 0 computed the metric and that didn’t present any problem if you are using the loss. However, if you are using another metric, the only process which had the information to choose the best checkpoint at evaluation time was the process 0. But since the evaluation was performed over all processes, every process except the process 0 chose a bad check point (bad meaning it wasn’t the best one) because they didn’t have the information of the metric of the best checkpoint. \r\n The consequence was that the evaluation was different from what would result if using only the best checkpoint, because each process chose a different checkpoint to run the evaluation and thus the numbers were often worse than the numbers that would be obtained if all processes choose the best checkpoint (correct one) to perform the evaluation of their samples. \r\n We performed a second rendezvous so that all processes had the same best_metric and best_model as process 0 after the evaluation cycle. \r\n", "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]

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[ "Hi! If the only goal is to improve readability, it's better to use `assertTupleEqual` than `assertSequenceEqual` for Python tuples. Also, note that this function is called internally by `assertEqual`, but I guess we can accept a PR to be more verbose.", "Hi @mariosasko, right! I'll update the issue title/desc with `assertTupleEqual` even though as you said it seems to be internally using `assertEqual` so I'm not sure whether it's worth it or not...\r\n\r\nhttps://docs.python.org/3/library/unittest.html#unittest.TestCase.assertTupleEqual", "I thought we were supposed to move gradually from `unittest` to `pytest`..." ]

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[ "@albertvillanova @lhoestq , could you please help me on this issue. ", "Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co./docs/datasets/dataset_script) which can contain your generator. But we can also explore adding something like `ds = Dataset.from_iterable(seqio_dataset)`", "@lhoestq , hey i did as you instructed, but sadly i cannot get pass through the download_manager, as i dont have anything to download. i was skipping the ` def _split_generators(self, dl_manager):` function. but i cannot get around it. I get a `NotImplementedError: `\r\n\r\nthe following is my code for the same: \r\n\r\n\r\n\r\n```\r\nimport datasets \r\nimport functools\r\nimport glob \r\nfrom datasets import load_from_disk\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_dataset\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\n\r\n_CITATION = \"Not ready yet\"\r\n_DESCRIPTION = \"a custom seqio based mixed samples on a given temperature value, that again returns a dataset in HF dataset format well samples on the Mixture temperature\"\r\n_HOMEPAGE = \"ldcil.org\"\r\n\r\nclass CustomSeqio(datasets.GeneratorBasedBuilder):\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 }\r\n ),\r\n homepage=\"https://ldcil.org\",\r\n citation=_CITATION,)\r\n\r\ndef generate_examples(self):\r\n seqio_train_list = []\r\n for lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n \r\n lang_name_list = []\r\n for lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\n seqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7)\r\n \r\n seqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\n for id, ex in enumerate(seqio_mixture_dataset):\r\n yield id, {\"text\": ex[\"targets\"].numpy().decode()}\r\n```\r\n\r\nand i load it by:\r\n\r\n`seqio_mixture = load_dataset(\"seqio_loader\")`", "@lhoestq , just to make things clear ... \r\n\r\nthe following is my original code, thats not in the HF dataset loading script: \r\n\r\n```\r\nimport functools\r\nimport seqio\r\nimport tensorflow as tf\r\nimport t5.data\r\nfrom datasets import load_from_disk\r\nfrom t5.data import postprocessors\r\nfrom t5.data import preprocessors\r\nfrom t5.evaluation import metrics\r\nfrom seqio import FunctionDataSource, utils\r\nimport glob \r\n\r\nTaskRegistry = seqio.TaskRegistry\r\n\r\n\r\n\r\ndef gen_dataset(split, shuffle=False, seed=None, column=\"text\", dataset_path=None):\r\n dataset = load_from_disk(dataset_path)\r\n if shuffle:\r\n if seed:\r\n dataset = dataset.shuffle(seed=seed)\r\n else:\r\n dataset = dataset.shuffle()\r\n while True:\r\n for item in dataset[str(split)]:\r\n yield item[column]\r\n\r\n\r\ndef dataset_fn(split, shuffle_files, seed=None, dataset_path=None):\r\n return tf.data.Dataset.from_generator(\r\n functools.partial(gen_dataset, split, shuffle_files, seed, dataset_path=dataset_path),\r\n output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_path)\r\n )\r\n\r\n\r\[email protected]_over_dataset\r\ndef target_to_key(x, key_map, target_key):\r\n \"\"\"Assign the value from the dataset to target_key in key_map\"\"\"\r\n return {**key_map, target_key: x}\r\n\r\ndata_path = glob.glob(\"/home/stephen/Desktop/MEGA_CORPUS/COMBINED_CORPUS/*\", recursive=False)\r\n\r\nseqio_train_list = []\r\n\r\nfor lang in data_path:\r\n dataset_name = lang.split(\"/\")[-1]\r\n dataset_shapes = None \r\n\r\n TaskRegistry.add(\r\n str(dataset_name),\r\n source=seqio.FunctionDataSource(\r\n dataset_fn=functools.partial(dataset_fn, dataset_path=lang),\r\n splits=(\"train\", \"test\"),\r\n caching_permitted=False,\r\n num_input_examples=dataset_shapes,\r\n ),\r\n preprocessors=[\r\n functools.partial(\r\n target_to_key, key_map={\r\n \"targets\": None,\r\n }, target_key=\"targets\")],\r\n output_features={\"targets\": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},\r\n metric_fns=[]\r\n )\r\n\r\n seqio_train_dataset = seqio.get_mixture_or_task(dataset_name).get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n seqio_train_list.append(seqio_train_dataset)\r\n\r\nlang_name_list = []\r\nfor lang in data_path:\r\n lang_name = lang.split(\"/\")[-1]\r\n lang_name_list.append(lang_name)\r\n\r\nseqio_mixture = seqio.MixtureRegistry.add(\r\n \"seqio_mixture\",\r\n lang_name_list,\r\n default_rate=0.7\r\n)\r\n\r\nseqio_mixture_dataset = seqio.get_mixture_or_task(\"seqio_mixture\").get_dataset(\r\n sequence_length=None,\r\n split=\"train\",\r\n shuffle=True,\r\n num_epochs=1,\r\n shard_info=seqio.ShardInfo(index=0, num_shards=10),\r\n use_cached=False,\r\n seed=42)\r\n\r\nfor _, ex in zip(range(15), seqio_mixture_dataset):\r\n print(ex[\"targets\"].numpy().decode())\r\n```\r\n\r\nwhere the seqio_mixture_dataset is the generator that i wanted to be wrapped in HF dataset. \r\n\r\nalso additionally, could you please tell me how do i set the `default_rate=0.7` args where `seqio_mixture` is defined to be made as a custom option in the HF load_dataset() method,\r\n\r\nmaybe like this: \r\n`seqio_mixture_dataset = datasets.load_dataset(\"seqio_loader\",temperature=0.5)`", "I like the idea of having `Dataset.from_iterable(iterable)` in the API. The only problem is that we also want to make this part cachable, which is tricky if `iterable` is a generator. \r\n\r\nSome resources on this issue:\r\n* https://github.com/uqfoundation/dill/issues/311\r\n* https://stackoverflow.com/questions/7180212/why-cant-generators-be-pickled\r\n* https://github.com/tonyroberts/generator_tools - python package for pickling generators; pickles bytecode, so it creates version-specific dumps", "For the caching maybe we can have `Dataset.from_generator` as TF and pickle+hash the generator function (not the generator object itself) ?\r\n\r\nAnd then keep `Dataset.from_iterable` fo pickable objects like lists", "@lhoestq, @mariosasko do you too have any examples where the dataset is a generator and needs to be wrapped into hf dataset ? ", "@lhoestq, following to my previous question ... what possibly could be done in this [link1](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627404) [link2](https://github.com/huggingface/datasets/issues/4417#issuecomment-1146627593) case? do you have any ideas? ", "@lhoestq +1 for the `Dataset.from_generator` idea.\r\n\r\nHaving thought about it, let's avoid adding `Dataset.from_iterable` to the API since dictionaries are technically iteralbles (\"iterable\" is a broad term in Python), and we already provide `Dataset.from_dict`. And for lists maybe we can add `Dataset.from_list` similar to `pa.Table.from_pylist`. WDYT?\r\n", "Hi @StephennFernandes!\r\n\r\nTo fix the issues in the copied code, rename `generate_examples` to` _generate_examples` and add one level of indentation as this is a method of `GeneratorBasedBuilder` and define `_split_generators` as follows (again as a method of `GeneratorBasedBuilder):\r\n```python\r\n def _split_generators(self, dl_manager):\r\n return [\r\n datasets.SplitGenerator(\r\n name=datasets.Split.TRAIN,\r\n gen_kwargs={},\r\n ),\r\n ]\r\n```\r\n\r\nAnd if you are feeling extra adventurous, you can try to use ArrowWriter to directly create a cache file:\r\n```python\r\nfrom datasets import Dataset\r\nfrom datasets.arrow_writer import ArrowWriter\r\n\r\nwriter = ArrowWriter(path=\"path/to/cache_file.arrow\", writer_batch_size=1000)\r\n\r\nwith writer:\r\n for ex in generator:\r\n writer.write(ex) \r\n writer.finalize()\r\n\r\ndset = Dataset.from_file(\"path/to/cache_file.arrow\")\r\n```\r\n\r\n", "I have a problem which I think is very similar: I would like to \"stream\" data to a HF Array (memory-mapped) Dataset, where the final size of the dataset is unknown, but could be much larger than what fits into memory.\r\nWhat I want to end up with is an Array Dataset which I can open using `Dataset.load_from_disk(dataset_path=\"somename\")` and use e.g. as the training set. \r\n\r\nFor this I would have thought there should be an API which allows me to open/create the dataset (and define the features etc), then write examples to the dataset, but I could not find a way to do this. \r\n\r\nI tried doing this and it looks like it works, but it feels very hacky and I am not sure if this might fail to update some of the fields in the json files which may turn out to be important:\r\n```\r\nfrom datasets import Dataset, Features, ClassLabel, Sequence, Value\r\nfrom datasets.arrow_writer import ArrowWriter \r\n# 1) define the features\r\nfeatures = Features(dict(\r\n id=Value(dtype=\"string\"),\r\n tokens=Sequence(feature=Value(dtype=\"string\")),\r\n ner_tags=Sequence(feature=ClassLabel(names=['O', 'B-corporation', 'I-corporation', 'B-creative-work', 'I-creative-work', 'B-group', 'I-group', 'B-location', 'I-location', 'B-person', 'I-person', 'B-product', 'I-product'])),\r\n))\r\n# 2) create empty dataset for examples with these features and store to disk\r\nempty = dict(\r\n id = [],\r\n tokens = [],\r\n ner_tags = [],\r\n)\r\nds = Dataset.from_dict(empty, features=features)\r\nds.save_to_disk(dataset_path=\"debug_ds1\")\r\n\r\n# 3) directly write all the examples to the arrow dataset \r\nwith ArrowWriter(path=\"debug_ds1/dataset.arrow\") as writer: \r\n writer.write(dict(id=0, tokens=[\"a\", \"b\"], ner_tags=[0, 0])) \r\n writer.write(dict(id=1, tokens=[\"x\", \"y\"], ner_tags=[1, 0])) \r\n writer.finalize() \r\n \r\nds2 = Dataset.load_from_disk(dataset_path=\"debug_ds1\")\r\nlen(ds2)\r\n```\r\nIs there a cleaner/proper way to do this?\r\n\r\nI like the sound of `Dataset.from_iterable` or `Dataset.from_generator` (should not from iterable be able to handle from generator too as all generators are iterables?) but how would I define the features for me examples there? ", "Hi @johann-petrak! You can pass the features directly to ArrowWriter's initializer like so `ArrowWriter(..., features=features)`.\r\n\r\nAnd the reason why I prefer `Dataset.from_generator` over `Dataset.from_iterable` is mentioned in one of my previous comments.", "@mariosasko so at the moment we still have to create a fake `Dataset` first and then use `ArrowWriter` to write an actual dataset? I'm using the latest version of `datasets` on pypi but my final file is always empty. Is there anything wrong with the code below?\r\n\r\n```python\r\n total = 0\r\n with ArrowWriter(path=str(final_data_path), features=features) as writer:\r\n for batch in loader:\r\n for traj in batch:\r\n for generator in question_generators:\r\n for xi in generator(traj):\r\n # print(f\"Question: {xi.question}, answer: {xi.answer}\")\r\n total += 1\r\n writer.write(\r\n {\r\n \"id\": f\"qa_{total}\",\r\n \"question\": xi.question,\r\n \"answer\": xi.answer,\r\n }\r\n )\r\n writer.finalize()\r\n print(f\"Total #questions = {total}\") # this prints 402\r\n```", "This works for me if I then (actually I also close the writer: `writer.close()`) open the Arrow file as a dataset using `ds=Dataset.from_file(final_data_path)` then `ds.save_to_disk(somedir)`. The Dataset created that way contains the expected examples.", "Oh thanks. That did the trick I believe. Shouldn't ArrowWriter have a context manager that does these operations?", "You can just use `Dataset.from_file` to get your dataset, no need to do an extra `save_to_disk` somewhere else ;)", "I was thinking that `save_to_disk` is necessary when one wants to re-use that dataset as a proper HF dataset later, no?\r\nAt least what I wanted to achieve is create a dataset that can be opened like any other local or remote dataset. ", "`save_to_disk`/`load_from_disk` is indeed more general, e.g. it supports datasets that consist in several files, and saves some extra info in a dataset_info.json file (description, citation, split sizes, etc.)\r\n\r\nIf you have one single file it's fine to simply do `.from_file()`" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thank you very much for your help @albertvillanova .\r\n\r\nI think I have fixed all the comments.\r\n\r\nPlease let me know if this PR need further modification ;)", "@albertvillanova Thank you very much for your kind help.\r\nThese suggestions make the code looks more pythonic.\r\n\r\nI have commited these changes", "Hi ! The dataset seems to be a duplicate of https://huggingface.co./datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)", "> Hi ! The dataset seems to be a duplicate of https://huggingface.co./datasets/silver/lccc - next time no need to add it on github if it's already available on huggingface.co ;)\r\n\r\nOK, sorry for the inconvenience. I have closed another two PRs since these datasets are already available on huggingface.co", "It's fine, thanks @silverriver for adding these datasets !" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting @dgcnz.\r\n\r\nI have checked that the dataset works fine in streaming mode.\r\n\r\nAdditionally, other datasets containing timestamps are properly rendered by the viewer: https://huggingface.co./datasets/blbooks\r\n\r\nI have tried to force the refresh of the preview, but the endpoint is not responsive: Connection timed out\r\n\r\nCC: @severo ", "I've just resent the refresh of the preview to the new endpoint, without success.\r\n\r\nCC: @severo ", "Fixed!\r\n\r\nhttps://huggingface.co./datasets/ett/viewer/h1/test\r\n\r\n<img width=\"982\" alt=\"Capture d’écran 2022-06-15 à 09 30 22\" src=\"https://user-images.githubusercontent.com/1676121/173769035-a075d753-ecfc-4a43-b54b-973105d464d3.png\">\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This PR (via new release) broke many transformers tests.\r\n\r\nI will try to post a summary shortly.\r\n\r\ncc: @ydshieh ", "So now it can't handle a local path via: `--train_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/train.json` even though it's there. it works just fine if I change the path to not have `..`\r\n\r\nYou can reproduce the original problem with:\r\n\r\n```\r\n$ cd transformers \r\n$ python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --train_file tests/fixtures/tests_samples/wmt_en_ro/train.json --validation_file tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json --output_dir /tmp/tmp5o5to4k0 --overwrite_output_dir --max_source_length 32 --max_target_length 32 --val_max_target_length 32 --warmup_steps 8 --predict_with_generate --save_steps 0 --eval_steps 1 --group_by_length --label_smoothing_factor 0.1 --source_lang en --target_lang ro --report_to none --source_prefix \"translate English to Romanian: \" --fp16 --do_train --num_train_epochs 1 --max_train_samples 16 --per_device_train_batch_size 2 --learning_rate 3e-3\r\n[...]\r\nTraceback (most recent call last):\r\n File \"examples/pytorch/translation/run_translation.py\", line 656, in <module>\r\n main()\r\n File \"examples/pytorch/translation/run_translation.py\", line 346, in main\r\n raw_datasets = load_dataset(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1656, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1439, in load_dataset_builder\r\n dataset_module = dataset_module_factory(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 1097, in dataset_module_factory\r\n return PackagedDatasetModuleFactory(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/load.py\", line 743, in get_module\r\n data_files = DataFilesDict.from_local_or_remote(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 588, in from_local_or_remote\r\n DataFilesList.from_local_or_remote(\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 556, in from_local_or_remote\r\n data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 194, in resolve_patterns_locally_or_by_urls\r\n for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):\r\n File \"/home/stas/anaconda3/envs/py38-pt111/lib/python3.8/site-packages/datasets/data_files.py\", line 144, in _resolve_single_pattern_locally\r\n raise FileNotFoundError(error_msg)\r\nFileNotFoundError: Unable to find '/mnt/nvme0/code/huggingface/transformers-master/tests/deepspeed/../fixtures/tests_samples/wmt_en_ro/val.json' at /mnt/nvme0/code/huggingface/transformers-master\r\n```", "will apply a workaround to `transformers` tests here https://github.com/huggingface/transformers/pull/17721\r\n", "This has been fixed with https://github.com/huggingface/datasets/pull/4505, will do a patch release tomorrow for `datasets` ;)", "Thank you for the quick fix, @lhoestq " ]

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[ "🤗 This PR closes https://github.com/huggingface/datasets/issues/4398", "_The documentation is not available anymore as the PR was closed or merged._", "Hi! Thanks for reporting and providing a fix. I made a small change to make the fix easier to understand.", "Hi, @mariosasko thanks! It makes sense, sorry I'm not that familiar with `datasets` code 😩 ", "Sure @albertvillanova I'll do that later today and ping you once done, thanks! :hugs:", "Hi again @albertvillanova! Let me know if those tests are fine 🤗 ", "Hi @alvarobartt,\r\n\r\nI think your tests are failing. I don't know why previously, after your last commit, the CI tests were not triggered. \r\n\r\nIn order to force the re-running of the CI tests, I had to edit your file using the GitHub UI.\r\n\r\nFirst I tried to do it using my terminal, but I don't have push right to your PR branch: I would ask you next time you open a PR, please mark the checkbox \"Allow edits from maintainers\": https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork#enabling-repository-maintainer-permissions-on-existing-pull-requests", "Hi @albertvillanova, let me check those again! And regarding that checkbox I thought it was already checked so my bad there 😩 ", "@albertvillanova again it seems that the tests were not automatically triggered, but I tested those locally and now they work, as previously those were failing as I created an assertion as `self.assertEqual` over an empty list that was compared as `None` while the value was `[]` so I updated it to be `self.assertListEqual` and changed the comparison value to `[]`.", "@lhoestq any idea why the CI is not triggered?", "@alvarobartt I have tested locally and the tests continue failing.\r\n\r\nI think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n", "You're right @albertvillanova I was indeed running the tests with `datasets==2.2.0` rather than with the branch version, I'll check it again! Sorry for the inconvenience...", "> @alvarobartt I have tested locally and the tests continue failing.\r\n> \r\n> I think there is a basis error: `new_dset._format_columns` is always `None` in those cases.\r\n\r\nIn order to have some regressions tests for the fixed scenario, I've manually updated the value of `_format_columns` in the `ArrowDataset` so as to check whether it's updated or not right after calling `remove_columns`, and it does behave as expected, so with the latest version of this branch the reported issue doesn't occur anymore.", "Hi again @albertvillanova sorry I was on leave! I'll do that ASAP :hugs:", "@albertvillanova, does it make sense to add regression tests for `DatasetDict`? As `DatasetDict` doesn't have the attribute `_format_columns`, when we call `remove_columns` over a `DatasetDict` it removes the columns and updates the attributes of each split which is an `ArrowDataset`.\r\n\r\nSo on, we can either:\r\n- Update first the `_format_columns` attribute of each split and then remove the columns over the `DatasetDict`\r\n- Loop over the splits of `DatasetDict` and remove the columns right after updating `_format_columns` of each `ArrowDataset`.\r\n\r\nI assume that the best regression test is the one implemented (mentioned first above), let me know if there's a better way to do that 👍🏻 ", "I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?", "> I think there's already a decorator to support transmitting the right `_format_columns`: `@transmit_format`, have you tried adding this decorator to `remove_columns` ?\r\n\r\nHi @lhoestq I can check now!", "It worked indeed @lhoestq, thanks for the proposal and the review! 🤗 ", "Oops, I forgot about `@transmit_format`'s existence. From what I see, we should also use this decorator in `flatten`, `rename_column` and `rename_columns`. \r\n\r\n@alvarobartt Let me know if you'd like to work on this (in a subsequent PR).", "Sure @mariosasko I can prepare another PR to add those too, thanks! " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "@lhoestq Maybe I'm missing something, but what's the reason to read and encode PCM files to WAV in `Audio.encode_example`. Isn't the whole purpose of the decodable types to operate on raw files whenever possible? IMO this PR should only modify `Audio.decode_example` to support PCM files/bytes decoding.", "Because the PCM file is not enough, we also need the `sampling_rate` associated to it. Therefore the two alternatives are either:\r\n- convert to WAV\r\n- add a `sampling_rate` field to the Audio arrow storage (not sure how it would behave for backward compatibility though)", "But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file's sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one.", "How does it get the sampling rate of a PCM file then ? According to [SO](https://stackoverflow.com/a/57027667/17517845) it's not possible to infer it from the file alone", "> Awesome thanks ! Could you also add tests in `tests/features/test_audio.py` ?\r\n> \r\n> Maybe add a small pcm file in `tests/features/data` and check that everything works as expected in tests cases like `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` for example.\r\n\r\n@lhoestq how can i test test_audio.py? where is \"__main__\" func?\r\ndo you have some example or guideline?", "> But [`scipy.io.wavfile.read`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html), which is used for reading such files, returns a file's sampling rate. The only tricky part is [resampling](https://stackoverflow.com/questions/33682490/how-to-read-a-wav-file-using-scipy-at-a-different-sampling-rate) to a different sampling rate than the default one.\r\n\r\n@mariosasko @lhoestq \r\nthanks for comment!\r\n\r\nFirst of all, \"PCM file\" can not read alone to any audio library.\r\n\"PCM file\" has not any audio META information header. (it just purely audio byte data. therefore, we don't have to encoding and decoding)\r\nbut, \"PCM file\" is audio extension, so we can use `datasets.Audio`\r\n\r\nif you want to read \"PCM file\" to audio file likely, it have to needs additional parameter. (channel, sampling_rate, else....)\r\nbut, in many situation, we only know sampling_rate for PCM\r\n\r\nand, if we want to use `datasets.Audio` for \"PCM file\", we must process encode_example.\r\ntherefore, i have to use sampling_rate for encoding for making wav-style byte. (we only know sampling_rate)\r\n\r\nIn my source code, I don't compare sampling rate(`datasets.Audio's self.sampling_rate` and `read pcm sampling_rate(value[\"sampling_rate\"])`) and checking mono\r\n@mariosasko ! do you want to process resampling and making mono? then i can modify my source\r\n", "There is no \"main\" function in test scripts :) To run a test script you must use the `pytest` command:\r\n```\r\npytest tests/features/test_audio.py\r\n```\r\n\r\nto run only one function you can also do\r\n```\r\npytest tests/features/test_audio.py::test_audio_feature_type_to_arrow\r\n```\r\nfor example", "@lhoestq\r\nmaybe, if i write test code, i have to commit test_audio.py and send pr?\r\nbecause, we need to keep `test_audio_encode_example_pcm` and `test_audio_decode_example_pcm` method after my pr merged?", "You can add your tests in this PR with the other changes you did", "@lhoestq \r\ntest complete & commit my test_audio.py\r\n\r\nAND, some change in my code.\r\n\r\naudio.py\r\ni think \"sampling_rate\" is already Audio object initial variable. so, we don`t have to use input parameter.\r\n\r\ntest_audio.py\r\nwe can check \"PCM\" file to path (exactly, extenstion)\r\nso, test case has to know `path`. if only have `bytes`, we don`t know that is \"PCM\" or not", "@lhoestq\r\nand, why circleci raised exception?\r\nmaybe, [repo](https://huggingface.co./api/datasets/lhoestq/_dummy?full=true) url is not found!\r\nPLZ, CHK!", "@lhoestq\r\nhello????", "@lhoestq \r\ntest_audio.py\r\nif we don`t use path in pcm, test-case need to be changed\r\nso, we check path just None", "i'm merge branch already and `multiprocess` in `setup.py` but circleci error only win version\r\n\r\nhow can i fixed it?", "@lhoestq thx for comment!\r\ntest_audio.py test complete. it runs sucessfully\r\nand, self.get(\"sampling_rate\") -> value.get(\"sampling_rate\") changed\r\n\r\nand, some comment is not agreed to me, plz check my sub comment!", "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting, @jiangwy99.\r\n\r\nI guess this could be addressed only once we fix our issue with irresponsive backend endpoint.\r\n\r\nCC: @severo ", "I've just sent the forcing of the refresh of the preview to the new endpoint.", "Fixed, thanks for the patience. The issue was the amount of RAM allowed to extract the first rows of the dataset was not sufficient." ]

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[ "And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[\"\"]]` can perform normally, I still wonder if there is a more elegant way?" ]

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[ "Hi! You can already use `dset.builder_name` and `dset.config_name` for that purpose. And when it comes to versioning, it's better to use `dset._fingerprint` than the `version` attribute as the former represents a deterministic hash that encodes all the mutable ops executed on a dataset, and the latter stays the same unless it's manually updated after each op.", "@mariosasko Can we make ._fingerprint not private? seems a critical component for tracking how a model was generated to ensure reproducibility." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Thanks for reporting, @OmarAlaaeldein.\r\n\r\nDatasets hosted at Google Drive give problems quite often due to a change in their service:\r\n- #3786 \r\n\r\nRelated to:\r\n- #3906\r\n\r\nI'm having a look.", "We have made a Pull Request to replace the Google Drive URL. This fix will be accessible in our next `datasets` library release.\r\n\r\nIn the meantime, once the PR merged into master, you can get this fix by installing our library from the GitHub master branch:\r\n```shell\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```\r\nThen, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file):\r\n```shell\r\nload_dataset(\"...\", download_mode=\"force_redownload\")\r\n```" ]

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[ "I tried in this way.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(path=\"wikitext\", name=\"wikitext-103-v1\", split=\"train\")\r\n```" ]

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[ "Ok, so\r\n```\r\nos.path.basename(\"/home/user/\")\r\n```\r\ngives `''` while \r\n```\r\nos.path.basename(\"/home/user\")\r\n```\r\ngives `user`. \r\nThe code should check if the last char is a slash.\r\n", "The fix is:\r\n```\r\n\"name\": os.path.basename(self.path[:-1] if self.path[-1] == \"/\" else self.path)\r\n```", "I came through the same issue , just removing the last slash in the dataset path fixed it for me, may be this repo moderators could accept this as an accepted answer atleast if this could not be integrated\r\n\r\n> The fix is:\r\n> \r\n> ```\r\n> \"name\": os.path.basename(self.path[:-1] if self.path[-1] == \"/\" else self.path)\r\n> ```\r\n\r\n@apohllo consider making a pull request on this \r\n\r\nThanks for the amazing contributions from huggingface people !!\r\n", "@apohllo Would you be interested in submitting a PR with the fix?", "@mariosasko here we go:\r\n\r\nhttps://github.com/huggingface/datasets/pull/4967\r\n\r\nTBH I haven't tested it yet, but should work, since this is a basic change." ]

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[ "It works if we either remove the `ds = ds.cast_column(\"id\", Value(\"int32\"))` line from the code above, or if instead calling `ds.remove_columns()` we remove the columns inside each mapping as `ds.map(..., remove_columns=[...])` instead of right after the mapping.\r\n\r\nBoth of those solutions seem to fix the issue, so the root cause of it may be around that. Sorry I cannot provide you more insights, in case I get to fix it I'll submit a PR, in the meanwhile the code that I'm using as a workaround is the following:\r\n\r\n```python\r\nfrom transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\nimport torch\r\n\r\ntorch.set_grad_enabled(False)\r\nctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\nctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n\r\nfrom datasets import load_dataset, Value\r\n\r\nds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\nds = ds.cast_column(\"id\", Value(\"int32\"))\r\nds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n\r\ndef generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n\r\nds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\nds.add_faiss_index(column=\"embeddings\")\r\n```", "FYI the main reason I want to use `dataset.remove_columns` rather than the function inside `dataset.map` is because according to the 🤗 Datasets documentation, it's faster.\r\n\r\n\"🤗 Datasets also has a [Dataset.remove_columns()](https://huggingface.co./docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Dataset.remove_columns) method that is functionally identical, but faster, because it doesn’t copy the data of the remaining columns.\"\r\n\r\nMore information at https://huggingface.co./docs/datasets/process#map", "Here I'm presenting all the scenarios so that you can further investigate the issue:\r\n\r\n- ✅ `cast_column` -> `map` with `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ❌ `cast_column` -> `map` with `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])}, remove_columns=[\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ✅ `cast_column` -> `map` -> `remove_columns` -> `map` with `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.cast_column(\"id\", Value(\"int32\"))\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings, remove_columns=[\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```\r\n\r\n- ✅ `map` -> `remove_columns` -> `map` -> `remove_columns` -> `add_faiss_index`\r\n\r\n\r\n ```python\r\n from transformers import DPRContextEncoder, DPRContextEncoderTokenizer\r\n import torch\r\n \r\n torch.set_grad_enabled(False)\r\n ctx_encoder = DPRContextEncoder.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained(\"facebook/dpr-ctx_encoder-single-nq-base\")\r\n \r\n from datasets import load_dataset, Value\r\n \r\n ds = load_dataset(\"csv\", data_files=[\"sample.csv\"], split=\"train\")\r\n ds = ds.map(lambda x: {\"inputs\": f\"{ctx_tokenizer.sep_token}\".join([\"title\", \"summary\"])})\r\n ds = ds.remove_columns([\"title\", \"summary\"])\r\n \r\n def generate_embeddings(x):\r\n return {\"embeddings\": ctx_encoder(**ctx_tokenizer(x[\"inputs\"], return_tensors=\"pt\"))[0][0].numpy()}\r\n \r\n ds = ds.map(generate_embeddings)\r\n ds = ds.remove_columns([\"inputs\"])\r\n ds.add_faiss_index(column=\"embeddings\")\r\n ```", "So on, I've created #4411 so as to fix the bug with `remove_columns` under certain conditions before `add_faiss_index`, which means that the scenarios not working above are already working fine." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Some CI fails are unrelated to your PR and fixed on master, feel free to merge master into your branch :)", "Thanks @nateraw for the addition of this dataset.\r\n\r\nI would suggest to transfer it to the Hugging Face Hub, under a \"pascal\" organization namespace: \"pascal/voc\".\r\n\r\nWhat do you think?", "FYI I think this dataset is also available at (internal) https://huggingface.co./datasets/HuggingFaceM4/pascal_voc", "@lhoestq @albertvillanova what do you think best path forward is? No idea when I'll get to looking at this again, but would be nice to know plan so when I find time I can just get it done in one sitting. ", "My (not strong) opinion on this:\r\n- as we are removing dataset scripts from GitHub, this dataset should be created directly on the Hub\r\n- I proposed doing it under some kind of \"official\" org namespace, like pascal or pascal2; other suggestions are welcome\r\n- the link given by @lhoestq might serve as inspiration for your implementation (I think yours misses data about action classification): their implementation comprises tasks: classification/detection, segmentation, action classification, person layout; it misses other tasks though\r\n\r\nWhat do you think?" ]

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[ "I tried to make the dataset much more smaller (100000 rows) , then the speed became `33.88it/s` from`8.62s/it`. It's nearly 200 times... Do you have any idea? Thank you!", "Similar issue: https://github.com/huggingface/transformers/issues/8818\r\n\r\nI changed `RandomSampler` to `SequentialSampler` in the `trainer.py`, but the speed didn't become faster.", "I changed\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\"\r\n )\r\n```\r\nto\r\n```\r\ntokenized_datasets = load_from_disk(\r\n \"/pathto/dataset\", keep_in_memory=True\r\n )\r\n```\r\nand obtained normal speed. It's seems that the problem is on the os's speed limit.", "Hi ! Currently `save_to_disk` saves one big Arrow file, which causes some slow downs. This has been discussed in #3735 and we'll implement sharding pretty soon to solve this\r\n\r\nFor now you can try splitting and saving your dataset in several Arrow files. Then you can load them one by one and use `concatenate_datasets` to have your big dataset again and hopefully with a better speed" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Thanks.\r\n> \r\n> I have no visibility about this, but if you say it is more useful for AutoTrain this way...\r\n\r\nThanks for the review @albertvillanova ! Yes, I need some way to reconstruct the original column names with a period because that's how they appear after we flatten the nested columns. In any case, we can adjust this later if needed :)", "Does that mean that we need to change the metadata?", "> Does that mean that we need to change the metadata?\r\n\r\nYes, but this PR takes care of it :)", "Oh good! thanks for the heads up!" ]

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[ "Thanks for reporting, @StevenTang1998.\r\n\r\nI'm looking into it. ", "Hi @StevenTang1998,\r\n\r\nWe have fixed the issue:\r\n- #4389\r\n\r\nThe fix will be available in our next `datasets` library release. In the meantime, you can incorporate that fix by installing `datasets` from our GitHub repo:\r\n```\r\npip install git+https://github.com/huggingface/datasets#egg=datasets\r\n```", "Thanks for your reply!!\r\nAnd the totto dataset has the same problem. The url should be change to [https://storage.googleapis.com/totto-public/totto_data.zip](https://storage.googleapis.com/totto-public/totto_data.zip).", "Hi again @StevenTang1998,\r\n\r\nI don't see any problem when loading `totto` dataset:\r\n```python\r\nIn [4]: import datasets\r\n ...: ds = datasets.load_dataset(\"totto\")\r\nDownloading builder script: 5.58kB [00:00, 5.33MB/s] \r\nDownloading metadata: 2.78kB [00:00, 2.96MB/s] \r\nUsing custom data configuration default\r\nDownloading and preparing dataset totto/default (download: 179.03 MiB, generated: 706.59 MiB, post-processed: Unknown size, total: 885.62 MiB) to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2...\r\nDownloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 188M/188M [00:32<00:00, 5.77MB/s]\r\nDataset totto downloaded and prepared to .../.cache/huggingface/datasets/totto/default/1.0.0/263c85871e5451bc892c65ca0306c0629eb7beb161e0eb998f56231562335dd2. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 147.95it/s]\r\n\r\nIn [5]: ds\r\nOut[5]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 120761\r\n })\r\n validation: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 7700\r\n })\r\n test: Dataset({\r\n features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],\r\n num_rows: 7700\r\n })\r\n})\r\n```", "Sorry, I didn't express it clearly. It's the totto dataset from gem.\r\ndatasets.load_dataset('gem', 'totto')\r\n", "@StevenTang1998 fixed in:\r\n- #4396", "Thanks!!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I should point out that the hash will be the same if computed twice with the same code on the same version of dill (after adding huggingface's code that removes line numbers and file names, and sorts globals.) My changes in dill 0.3.5 and ones that I will make in 0.3.6 will result in different pickles than the ones dill 0.3.4 was making. This should still be fine for caching.", "Just some comments @lhoestq:\r\n\r\nThe best practice for testing is to have a `test_<filename>.py` for each `<filename>.py`. Therefore in order to have the filenames aligned, I would propose:\r\n- either renaming `fingerprint.py` to `caching.py`\r\n- or renaming `test_caching.py` to `test_fingerprint.py`\r\n\r\nOn the other hand, my idea when implementing this test was not to test all the functionalities of the `Hasher`, but just to have a regression test that fails if dill version is > 0.3.4 and the pin in our `setup.py` is not present. Just recall that we had no failing test in our CI when the issue with dill was found on `transformers`.\r\n\r\nThe objective of this PR is just to have a regression test for that case: I tested and I got `AttributeError: module 'dill._dill' has no attribute 'stack'`\r\n\r\nFor this regression test, I took into account this comment by @gugarosa: https://github.com/huggingface/datasets/issues/4379#issuecomment-1133131825\r\n\r\nThere is no equivalent test in `test_caching.py` because our CI did not fail before pinning dill.", "Ok I see, renaming it to `test_fingerprint.py` sounds like a good idea :)" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This looks like a breaking change no ?\r\nAlso could you explain why it would be better this way ?", "The might be only there to help type checkers, but I am not too familiar with the code base to know for sure. I think this might be useful:\n\nhttps://docs.python.org/3/library/typing.html#typing.TYPE_CHECKING", "> This looks like a breaking change no ?\r\n> Also could you explain why it would be better this way ?\r\n\r\nSorry, @lhoestq, I naively thought it was obvious. I have tried to give some arguments in the motivation of this PR (see above). I can give additional arguments if needed. " ]

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[ "Hi @NouamaneTazi, thanks for reporting.\r\n\r\nPlease note that both datasets are cached in the same directory because their loading builder classes have the same name: `class MTOP(datasets.GeneratorBasedBuilder)`.\r\n\r\nYou should name their builder classes differently, e.g.:\r\n- `MtopDomain`\r\n- `MtopIntent`", "Hi @NouamaneTazi, please note that after our fix:\r\n- #4388\r\n\r\nwe do not consider the class name anymore, but the name of the file where the loading builder class is implemented. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "Fixed by:\r\n- #4380 ", "Just an additional insight, the latest dill (either 0.3.5 or 0.3.5.1) also broke the hashing/fingerprinting of any mapping function.\r\n\r\nFor example:\r\n```\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"rotten_tomatoes\")\r\nd.map(lambda x: x)\r\n```\r\n\r\nReturns the standard non-dillable error:\r\n```\r\nParameter 'function'=<function <lambda> at 0x7fe7d18c9560> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly....\r\n```", "@albertvillanova ExamplesTests.test_run_speech_recognition_seq2seq is in which file?", "Thanks a lot @gugarosa for the insight: we will incorporate it in our CI as regression testing for future dill releases.", "Hi @anivegesana, that test is in `transformers` library:\r\n- https://github.com/huggingface/transformers/blob/main/examples/pytorch/test_pytorch_examples.py#L449\r\n- https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py ", "@albertvillanova\n\nI did a deep dive into @gugarosa's problem and found the issue and it might be related to the one @sgugger discovered. In dill 0.3.5(.1), I created a new `save_function` that fixes a bug in dill that prevented the pickling of recursive inner functions. It was a more complete solution to the problem that `dill._dill.stack` tried to solve in the internal API of dill. Since `dill._dill.stack` was no longer needed, I removed it. Since datasets copies the `save_function` directly from the dill API, it stops working with the new dill version since `dill._dill.stack` is no longer present and the `save_function` has been updated with new code.\r\n\r\nhttps://github.com/huggingface/datasets/blob/95193ae61e92aa537d0c65d37a1fd9d2393aae89/src/datasets/utils/py_utils.py#L607-L678\r\n\r\n~If the dill version is below 0.3.5, you should keep this function. If it is after, you would need to update your copy of `save_function` to use the code I introduced, or manually add a `stack` variable to `dill._dill` if it doesn't exist. Fortunately, in any version of Python 3.7+, dictionaries are always in insertion order and dill no longer supports Python 3.6 or older. So, any globals dictionary saved by dill 0.3.5+ will be deterministic given that the version of dill is held constant and this save_function is unnecessary for newer versions of dill.~\r\n\r\nAh. I see what is happening. I guess a different copy of the function code is needed that sorts the global variables by name.\r\n\r\n```py\r\nif dill.__version__.split('.') < ['0', '3', '5']:\r\n # current save_function code inside here\r\nelse:\r\n # new save_function code inside here with the following line inserted after creating the globals\r\n globs = {k: globs[k] for k in sorted(globs.keys())} \r\n```\r\n\r\nWill look into the test case @sgugger pointed out after that and verify if this is causing the problem.\r\n\r\nI am actually looking into rewriting the global variables code in uqfoundation/dill#466 and will keep this in mind and will try to create an easy way to modify the global variables in dill 0.3.6 (for example, sort them by key like datasets does).", "Thanks a lot for your investigation @anivegesana.\r\n\r\nYes, we copied-pasted the old `save_function` function from `dill`, just adding a line to make deterministic the order of global variables `globs`. \r\n\r\nHowever, this function has changed a lot from version 0.3.5, after your PR (thank you for the fix in recursiveness, indeed):\r\n- uqfoundation/dill#443\r\n\r\nWe have to address this change.\r\n\r\nIf finally your PR to sort global variables is merged into dill 0.3.6, that will make our life easier, as the tweak will no longer be necessary. ;)\r\n\r\nI have included a regression test so that we are sure future releases of dill do not break `datasets`:\r\n- #4385 ", "I should note that because Python 3.6 and older are now deprecated and Python 3.7 has insertion order dictionaries, the globals in dill will have a deterministic order, just not sorted. I would still keep it sorted like you have it to help with stability (for example, if someone reorders variables in a file, then sorting the globals would not invalidate the cache.)\n\nIt seems that the order is not quite deterministic in IPython. Huggingface datasets seems to do well in Jupyter regardless, so it is not a good idea to remove the sorting. uqfoundation/dill#19" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "& thank you!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "DUPLICATED comment from https://github.com/huggingface/datasets/issues/3807:\r\n\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\nhowever, it produces the same error\r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\nI attempted to use the `ignore_verifications' as such:\r\n\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks for reporting, @labouz. I'm addressing it. ", "The issue with checksum and empty dataset has been fixed by:\r\n- #4377\r\n\r\nTo load the dataset, you should force the re-generation of the dataset from the downloaded file by passing `download_mode=\"reuse_cache_if_exists\"` to `load_dataset`.\r\n\r\nIn relation with the issue with the dataset viewer, first the dataset should be refactored to support streaming.", "parfait!\r\nit works now, thank you 🙏 ", "Hi there, \r\nI see this issue is closed, but I am wondering if there is any chance the source files have been moved since this fix? I am stumbling into the same NonMatchingChecksumError noted by lebouz's second post once 118MB of data has been downloaded, and have tried the solutions noted in the various fix checksum posts linked here and in other posts regarding passing in \"reuse_cache_if_exists\" to download_mode. Any suggestions? Thank you!\r\n\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Alright this is finally ready for review ! It's quite long I'm sorry, but it's not easy to disentangle everything ^^'\r\n\r\nThe main additions are in\r\n- src/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py\r\n- src/datasets/iterable_dataset.py\r\n- src/datasets/utils/patching.py", "Added some comments and an error when lists have different lengths for sharding :)", "Let's resolve the merge conflict and the CI error (if it's related to the changes), and I can review the PR again.", "Feel free to review again :) The CI fail is unrelated to this PR and will be fixed by https://github.com/huggingface/datasets/pull/4472 (the hub now returns 401 instead of 404 for unauthenticated requests to non-existing repos)", "CI failures are unrelated to this PR - merging :)\r\n\r\n(CI fails are a mix of pip install fails and Hub fails)", "@lhoestq you're our hero :)" ]

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[ "Hi !\r\n\r\nMy guess is that some examples in your dataset are bigger than your RAM, and therefore loading them in RAM to pass them to `remove_non_indic_sentences` takes forever because it might use SWAP memory.\r\n\r\nMaybe several examples in your dataset are grouped together, can you check `len(lang_dataset[\"train\"])` and `lang_dataset[\"train\"].data.nbytes` of both datasets please ? It can also be helpful to check the distribution of lengths of each examples in your dataset.", "Closing due to inactivity" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Once we have added `long_answer_candidates` maybe it would be worth to also add the missing `candidate_index` (inside `long_answer`). What do you think, @seirasto ?", "Also note the \"Data Fields\" section in the README is missing the `long_answer` field.\r\n\r\nMoreover, there is no instance example in \"Data Instances\" section.", "We could either make these fixes in this PR or in a subsequent PR.", "@albertvillanova I've added the missing fields and updated the README to include a data instance and some other things. ", "Great! I've made the updates to align the README. Please let me know if I missed anything.", "As there were many minor little fixes, I thought it would be easier to fix them directly.", "I think the loading script is OK now. If it is also validated by another datasets maintainer, I could run the generation of the pre-processed data and then merge this PR into master (once all the tests are green).\r\n\r\nCC: @lhoestq ", "It looks good to me, thanks @seirasto !", "I have merged the master branch, so that we include all the fixes on Apache Beam + Google Dataflow.", "Pre-processing is running!\r\n\r\nAlready finished for \"dev\" config:\r\n```python\r\nIn [2]: ds = load_dataset(\"datasets/natural_questions\", \"dev\")\r\n\r\nIn [3]: ds\r\nOut[3]: \r\nDatasetDict({\r\n validation: Dataset({\r\n features: ['id', 'document', 'question', 'long_answer_candidates', 'annotations'],\r\n num_rows: 7830\r\n })\r\n})\r\n```", "There is an issue while running the preprocessing for the \"default\" (train+dev) config. Train data files are larger than than dev ones and workers run out of memory.\r\n\r\nI'm opening a separate issue to handle this problem: #4525", "@seirasto is proposing uploading their preprocessed data files to our Datasets bucket.\r\n\r\nI think @lhoestq can give a more informed answer about authentication requirements.", "Now that the data fiels are uploaded, can you merge the `main` branch into yours to re-trigger the CI @seirasto please ? :) Then I think we can merge if it's good for you @albertvillanova ", "Merge is done! I think someone needs to approve the CI to run :) ", "Can you run `make style` to fix the code formatting required by the CI please ?", "Thanks @albertvillanova! I've committed all your suggestions.", "The CI is green. I'm merging this PR." ]

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[ "I included the change from https://github.com/huggingface/datasets/pull/4302 directly in this PR, this way the datasets will be updated right away in the CI (the CI is only triggered when a dataset card is changed)", "_The documentation is not available anymore as the PR was closed or merged._", "Alright it's ready now :)\r\n\r\nHere is an example for the `ade_corpus_v2` dataset card. Notice the new `configs` key:\r\n\r\nhttps://github.com/huggingface/datasets/blob/76d9a141740a03f6836feb251f6059894b8d8046/datasets/ade_corpus_v2/README.md#L1-L78\r\n\r\nCI failures are only related to dataset cards missing some content." ]

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[ "Duplicate of:\r\n- #3956\r\n\r\nI think you should report that issue to `elasticsearch` library: https://github.com/elastic/elasticsearch-py" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Closing in favor of https://github.com/huggingface/datasets/pull/4367" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

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