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[ "Thanks! See https://public.paws.wmcloud.org/User:Isaac_(WMF)/HuggingFace%20Wikipedia%20Processing.ipynb for more implementation details / some data around the overhead induced by adding the extra preprocessing steps (stripping link prefixes and magic words)" ]

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[ "This issue was fixed by me calling `all_datasets.push_to_hub(\"hackernews_hiring_posts\")`.\r\n\r\nThe previous problems were from calling `all_datasets.save_to_disk` and then pushing with `my_repo.git_add` and `my_repo.push_to_hub`.\r\n" ]

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[ "duplicate of #2655" ]

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[ "Got solved it with push_to_hub, closing" ]

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[ "cc @juliensimon " ]

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[ "CI failed because of a server error - merging" ]

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[ "Locally the `make quality` passes with the same dependencies. I would suggest upgrading flake8. (I can take care of it in another PR)\r\ncc @lhoestq " ]

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[ "Hi ! Feel free to access `batch[\"audio\"][\"array\"]` and `batch[\"audio\"][\"sampling_rate\"]` instead\r\n\r\n`datasets` 1.16 introduced some changes in `common_voice` and now the `path` field is no longer a path to a local file (but rather the path to the file in the archive it's extracted from)", "Thanks for the information. It works.", "Cool ! Closing this issue then" ]

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[ "Hello @lhoestq, thank you for your answer.\r\n\r\nI don't use pytest a lot so I think I might need some help on it :) but I tried some tests for `streaming_download_manager.py` only. I don't know how to test `download_manager.py` since we need to use local files.\r\n\r\n# Comments : \r\n* In **download_manager.py** I removed some unnecessary imports after the simplification of `_get_extraction_protocol_local`.\r\n* In **streaming_download_manager** I moved the raised Error as suggested.\r\n \r\n### I also started some tests on `StreamingDownloadManager()` :\r\n* Used an existing zipfile url and added a new one that has a folder and many files : \r\n```python\r\nTEST_GG_DRIVE_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh\"\r\nTEST_GG_DRIVE2_ZIPPED_URL = \"https://drive.google.com/uc?export=download&id=1X4jyUBBbShyCRfD-vCO1ZvfqFXP3NEeU\"\r\n``` \r\n* **For now is being tested :**\r\n * Return type of the function : should be tuple\r\n * Files names\r\n * Files content\r\n * Added an `xfail` test for the gzip file, because I get a `zipfile.BadZipFile exception`.\r\n\r\n\r\n * And lastly, changed the test for `_get_extraction_protocol_throws` since it was moved to `_extract` : \r\n ```diff\r\n@pytest.mark.xfail(raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n- _get_extraction_protocol(urlpath)\r\n\r\n@pytest.mark.xfail(raises=NotImplementedError)\r\ndef test_streaming_dl_manager_get_extraction_protocol_throws(urlpath):\r\n+ StreamingDownloadManager()._extract(urlpath)\r\n```\r\n\r\n\r\n" ]

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[ "@mariosasko I fixed couple of bugs", "TO-DO: \r\n- [x] Add unlabeled 2017 splits, train and validation splits of 2015\r\n- [ ] Add Class Labels as list instead" ]

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[ "The CI error is due to missing tags in the CLUE dataset card - merging !" ]

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[ "I just tested and I think this only opens one file ? If there are several files in the ZIP, only the first one is opened. To open several files from a ZIP, one has to call `open` several times.\r\n\r\nWhat about updating the CSV loader to make it `download_and_extract` zip files, and open each extracted file ?", "I have implemented the glob of ZIP files in the packaged modules:\r\n- csv\r\n- json\r\n- text" ]

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[ "Seems like the issue still exists,:\r\n`Downloading and preparing dataset clue/chid (download: 127.15 MiB, generated: 259.71 MiB, post-processed: Unknown size, total: 386.86 MiB) to /mnt/cache/tanhaochen/.cache/huggingface/datasets/clue/chid/1.0.0/e55b490cb7809dcd8db31b9a87119f2e2ec87cdc060da8a9ac070b070ca3e379...\r\nTraceback (most recent call last):\r\n File \"/mnt/cache/tanhaochen/PromptCLUE/test_datasets.py\", line 3, in <module>\r\n cluewsc2020 = datasets.load_dataset(\"clue\",\"chid\")\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/load.py\", line 1667, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 593, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/builder.py\", line 663, in _download_and_prepare\r\n verify_checksums(\r\n File \"/mnt/cache/tanhaochen/dependencies/datasets/src/datasets/utils/info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://storage.googleapis.com/cluebenchmark/tasks/chid_public.zip']\r\n`", "Hi,\r\n\r\nthe fix hasn't been merged yet (it should be merged early next week)." ]

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[ "Closing this PR in favor of #3266." ]

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[ "This PR can be merged after #3163 is merged (this PR is pretty big because I was working on the forked branch).\r\n\r\n@lhoestq @albertvillanova Could you please take a look at the changes in `src/datasets/utils/streaming_download_manager.py`? These changes were required to support streaming of the `cats_vs_dogs` and the `beans` datasets.", "The CI failures are due to the missing fields in the README files." ]

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[ "@albertvillanova Is there a specific reason you’re adding the Pile under “the” instead of under “pile”? That does not appear to be consistent with other datasets.", "Hi @StellaAthena,\r\n\r\nI asked myself the same question, but at the end I decided to be consistent with previously added Pile subsets:\r\n- #2817\r\n\r\nI guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\"). Other datasets are not usually preceded by the definite article, like \"the SQuAD\" or \"the GLUE\" or \"the Common Voice\"...\r\n\r\nCC: @lhoestq ", "> I guess the reason is to stress that the definite article is always used before the name of the dataset (your site says: \"The Pile. An 800GB Dataset of Diverse Text for Language Modeling\").\r\n\r\nYes that's because of this that it starts with \"the\"" ]

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[ "Hi, \r\n\r\ncould you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests? ", "> Hi,\r\n> \r\n> could you please post this question on our [Forum](https://discuss.huggingface.co/) as we keep issues for bugs and feature requests?\r\n\r\nok." ]

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[ "Also, please don't merge yet - I need to make sure all the code samples and notebooks have a collate_fn specified, since we're removing the ability for this method to work without one!", "Hi @lhoestq @mariosasko, the other PRs this was depending on in Transformers and huggingface/notebooks are now merged, so this is ready to go. Do you want to take one more look at it, or are you happy at this point?", "The documentation for the method is fine, it doesn't need to be changed, but the tutorial notebook definitely looks a little out of date. Let me see what I can do!", "@lhoestq I rewrote the last bit of the notebook - let me know what you think!" ]

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[ "Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.\r\n\r\nHowever I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.\r\n\r\nHowever you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.\r\n\r\nNote that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called", "Hi, I have set **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.\r\n```\r\ndef main():\r\n argp = HfArgumentParser(TrainingArguments)\r\n # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).\r\n # In particular, TrainingArguments has several keys that you'll need/want to specify (when you call run.py from the command line):\r\n # --do_train\r\n # When included, this argument tells the script to train a model.\r\n # See docstrings for \"--task\" and \"--dataset\" for how the training dataset is selected.\r\n # --do_eval\r\n # When included, this argument tells the script to evaluate the trained/loaded model on the validation split of the selected dataset.\r\n # --per_device_train_batch_size <int, default=8>\r\n # This is the training batch size.\r\n # If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.\r\n # For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.\r\n # --num_train_epochs <float, default=3.0>\r\n # How many passes to do through the training data.\r\n # --output_dir <path>\r\n # Where to put the trained model checkpoint(s) and any eval predictions.\r\n # *This argument is required*.\r\n\r\n argp.add_argument('--model', type=str,\r\n default='google/electra-small-discriminator',\r\n help=\"\"\"This argument specifies the base model to fine-tune.\r\n This should either be a HuggingFace model ID (see https://huggingface.co/models)\r\n or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).\"\"\")\r\n argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,\r\n help=\"\"\"This argument specifies which task to train/evaluate on.\r\n Pass \"nli\" for natural language inference or \"qa\" for question answering.\r\n By default, \"nli\" will use the SNLI dataset, and \"qa\" will use the SQuAD dataset.\"\"\")\r\n argp.add_argument('--dataset', type=str, default=None,\r\n help=\"\"\"This argument overrides the default dataset used for the specified task.\"\"\")\r\n argp.add_argument('--max_length', type=int, default=128,\r\n help=\"\"\"This argument limits the maximum sequence length used during training/evaluation.\r\n Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.\"\"\")\r\n argp.add_argument('--max_train_samples', type=int, default=None,\r\n help='Limit the number of examples to train on.')\r\n argp.add_argument('--max_eval_samples', type=int, default=None,\r\n help='Limit the number of examples to evaluate on.')\r\n\r\n argp.remove_unused_columns = False\r\n training_args, args = argp.parse_args_into_dataclasses()\r\n args.remove_unused_columns=False\r\n training_args.remove_unused_columns=False\r\n```\r\n\r\n\r\n```\r\n**************** train_dataset: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n})\r\n\r\n\r\n**************** train_dataset_featurized: Dataset({\r\n features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],\r\n num_rows: 87714\r\n})\r\n```", "Hi, I print the value, all are set to False, but don't work.\r\n```\r\n********************* training_args: TrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\nadam_epsilon=1e-08,\r\ndataloader_drop_last=False,\r\ndataloader_num_workers=0,\r\ndataloader_pin_memory=True,\r\nddp_find_unused_parameters=None,\r\ndebug=[],\r\ndeepspeed=None,\r\ndisable_tqdm=False,\r\ndo_eval=False,\r\ndo_predict=False,\r\ndo_train=True,\r\neval_accumulation_steps=None,\r\neval_steps=None,\r\nevaluation_strategy=IntervalStrategy.NO,\r\nfp16=False,\r\nfp16_backend=auto,\r\nfp16_full_eval=False,\r\nfp16_opt_level=O1,\r\ngradient_accumulation_steps=1,\r\ngreater_is_better=None,\r\ngroup_by_length=False,\r\nignore_data_skip=False,\r\nlabel_names=None,\r\nlabel_smoothing_factor=0.0,\r\nlearning_rate=5e-05,\r\nlength_column_name=length,\r\nload_best_model_at_end=False,\r\nlocal_rank=-1,\r\nlog_level=-1,\r\nlog_level_replica=-1,\r\nlog_on_each_node=True,\r\nlogging_dir=./re_trained_model/runs/Dec01_14-15-08_399b9290604c,\r\nlogging_first_step=False,\r\nlogging_steps=500,\r\nlogging_strategy=IntervalStrategy.STEPS,\r\nlr_scheduler_type=SchedulerType.LINEAR,\r\nmax_grad_norm=1.0,\r\nmax_steps=-1,\r\nmetric_for_best_model=None,\r\nmp_parameters=,\r\nno_cuda=False,\r\nnum_train_epochs=3.0,\r\noutput_dir=./re_trained_model,\r\noverwrite_output_dir=False,\r\npast_index=-1,\r\nper_device_eval_batch_size=8,\r\nper_device_train_batch_size=8,\r\nprediction_loss_only=False,\r\npush_to_hub=False,\r\npush_to_hub_model_id=re_trained_model,\r\npush_to_hub_organization=None,\r\npush_to_hub_token=None,\r\nremove_unused_columns=False,\r\nreport_to=['tensorboard'],\r\nresume_from_checkpoint=None,\r\nrun_name=./re_trained_model,\r\nsave_on_each_node=False,\r\nsave_steps=500,\r\nsave_strategy=IntervalStrategy.STEPS,\r\nsave_total_limit=None,\r\nseed=42,\r\nsharded_ddp=[],\r\nskip_memory_metrics=True,\r\ntpu_metrics_debug=False,\r\ntpu_num_cores=None,\r\nuse_legacy_prediction_loop=False,\r\nwarmup_ratio=0.0,\r\nwarmup_steps=0,\r\nweight_decay=0.0,\r\n)\r\n```\r\n```\r\n********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google/electra-small-discriminator', remove_unused_columns=False, task='qa')\r\n2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\nSome weights of the model checkpoint at google/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\r\n- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\nPreprocessing data... (this takes a little bit, should only happen once per dataset)\r\n```", "Hmmm, it might be because the default data collator removes all the fields with `string` type:\r\n\r\nhttps://github.com/huggingface/transformers/blob/4c0dd199c8305903564c2edeae23d294edd4b321/src/transformers/data/data_collator.py#L107-L112\r\n\r\nI guess you also need a custom data collator that doesn't remove them.", "can you give a tutorial about how to do this?", "I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.\r\n\r\n```\r\n def get_train_dataloader(self) -> DataLoader:\r\n \"\"\"\r\n Returns the training :class:`~torch.utils.data.DataLoader`.\r\n\r\n Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted\r\n to distributed training if necessary) otherwise.\r\n\r\n Subclass and override this method if you want to inject some custom behavior.\r\n \"\"\"\r\n if self.train_dataset is None:\r\n raise ValueError(\"Trainer: training requires a train_dataset.\")\r\n\r\n train_dataset = self.train_dataset\r\n # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\r\n # train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\r\n\r\n if isinstance(train_dataset, torch.utils.data.IterableDataset):\r\n if self.args.world_size > 1:\r\n train_dataset = IterableDatasetShard(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_processes=self.args.world_size,\r\n process_index=self.args.process_index,\r\n )\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n collate_fn=self.data_collator,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n\r\n train_sampler = self._get_train_sampler()\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n sampler=train_sampler,\r\n collate_fn=self.data_collator,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n```", "Hi, it works now, thank you.\r\n1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**\r\n2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**\r\n3. add new fields, and can be got in **inputs**. " ]

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[ "Hello @patrickvonplaten, I hope it's okay to ping you with a (dumb) question!\r\n\r\nI've been trying to get `dl_manager.download_and_extract(_DL_URL)` to work with no avail. I verified that this is a problem on two different machines (lab server, GCP), so I doubt it's an issue with network connectivity. Here is the full trace.\r\n\r\n```\r\n(venv) (base) jaketae@jake-gpu1:~/documents/datasets$ datasets-cli test datasets/vctk --save_infos --all_configs\r\nTesting builder 'main' (1/1)\r\nDownloading and preparing dataset vctk/main to /home/jaketae/.cache/huggingface/datasets/vctk/main/0.9.2/2bfa52a93469fa9d6d4b1831c6511db5442b9f4e48620aef2bc3890d7a5268a8...\r\nTraceback (most recent call last):\r\n File \"/home/jaketae/documents/datasets/venv/bin/datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/home/jaketae/documents/datasets/src/datasets/commands/datasets_cli.py\", line 33, in main\r\n service.run()\r\n File \"/home/jaketae/documents/datasets/src/datasets/commands/test.py\", line 146, in run\r\n builder.download_and_prepare(\r\n File \"/home/jaketae/documents/datasets/src/datasets/builder.py\", line 593, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/jaketae/documents/datasets/src/datasets/builder.py\", line 659, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/home/jaketae/.cache/huggingface/modules/datasets_modules/datasets/vctk/2bfa52a93469fa9d6d4b1831c6511db5442b9f4e48620aef2bc3890d7a5268a8/vctk.py\", line 76, in _split_generators\r\n root_path = dl_manager.download_and_extract(_DL_URL)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 283, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 195, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/py_utils.py\", line 234, in map_nested\r\n return function(data_struct)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/download_manager.py\", line 216, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/file_utils.py\", line 298, in cached_path\r\n output_path = get_from_cache(\r\n File \"/home/jaketae/documents/datasets/src/datasets/utils/file_utils.py\", line 608, in get_from_cache\r\n raise ConnectionError(f\"Couldn't reach {url}\")\r\nConnectionError: Couldn't reach https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip\r\n```\r\n\r\nOn my local, however, the URL correctly points to the download zip file. My admittedly naive guess is that the website is web-crawler or scraper proof (requiring specific headers, etc.), but I also think I might have just missed a very basic step in the process.\r\n\r\nApologies for the delayed PR, and TIA for the help!", "Hey @jaketae, \r\n\r\nHmm, yeah I don't know really either - the link also works correctly for me when doing:\r\n\r\n```\r\nwget https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip\r\n```\r\n\r\nI think however that I had a similar problem previously with Edinburgh's (`.ed.ac.uk`) websites which I solved with the following hack. Not sure if this could be the same problem here...\r\nhttps://github.com/huggingface/datasets/blob/e1104ad5d3e83f8b1571e0d6fef4fdabf0a1fde5/datasets/ami/ami.py#L364\r\n\r\n", "The AMI dataset is stored under a different website though it seems: `\"https://groups.inf.ed.ac.uk/ami/AMICorpusMirror//amicorpus/{}/audio/{}\"`\r\n\r\nso not 100p sure if this solves the problem", "Hi @patrickvonplaten,\r\n\r\nThanks for the feedback! Sadly, disabling multi-processing didn't cut it for me. \r\n\r\nI've been looking at VCTK code in [`torchaudio`](https://pytorch.org/audio/stable/_modules/torchaudio/datasets/vctk.html) and [`tfds`](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/vctk.py). I don't think they're using a hack to accomplish this, so I'll try to look into it to see if I can pinpoint the cause. I'll keep you in the loop here. Thank you!", "Hi @patrickvonplaten, \r\n\r\nAfter more investigation, I found that simply increasing `etag_timeout` in `get_from_cache` from 10 to 100 solved it. However, unless I'm missing something, an issue is that `etag_timeout` is basically hard-coded as a default parameter because `cached_path`, which calls `get_from_cache` has no way of modifying the default. \r\n\r\nhttps://github.com/huggingface/datasets/blob/b25ac1d62670e7b339ed552ecc37846d2abd30c7/src/datasets/utils/file_utils.py#L298-L310\r\n\r\nhttps://github.com/huggingface/datasets/blob/b25ac1d62670e7b339ed552ecc37846d2abd30c7/src/datasets/utils/file_utils.py#L497-L510\r\n\r\n\r\nI can think of two solutions.\r\n\r\n* Simply increase the default to 100\r\n* Allow `etag_timeout` to be modifiable on a per-dataset basis by integrating it to `download_config` (maybe this is already supported?)\r\n\r\nThank you!", "I think in this case we can increase the `etag_timeout` - what do you think @lhoestq @albertvillanova ?", "Yes let's increase it to 100 for the moment. Later we can see if it really needed to move it into `download_config` or not", "Thanks for the feedback @patrickvonplaten @lhoestq, I'll continue working on this in that direction!" ]

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[ "@mariosasko - Thanks for the review & suggestions. Updated as per the suggestions. ", "@mariosasko - Hello, Are there any additional changes required from my end??. Wondering if this PR can be merged or still pending on additional steps." ]

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[ "Thanks for the suggestion! I think the current checked-in `CHECKPOINT_URLS` is already not working. I believe anyone who tried using the new ckpts (`BLEURT-20-X`) can't unless this fix is in. The zip file from bleurt side unzips to directory name matching the filename (capitalized for new ones). For example without current changes I'd get the following error\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<ipython-input-5-f6832fe20f84> in <module>()\r\n 1 predictions = [\"hello there\", \"general kenobi\"]\r\n 2 references = [\"hello there\", \"general kenobi\"]\r\n----> 3 bleurt = datasets.load_metric(\"bleurt\", \"bleurt-20\")\r\n 4 results = bleurt.compute(predictions=predictions, references=references)\r\n\r\n4 frames\r\n/usr/local/lib/python3.7/dist-packages/bleurt/checkpoint.py in read_bleurt_config(path)\r\n 84 \"\"\"Reads and checks config file from a BLEURT checkpoint.\"\"\"\r\n 85 assert tf.io.gfile.exists(path), \\\r\n---> 86 \"Could not find BLEURT checkpoint {}\".format(path)\r\n 87 config_path = os.path.join(path, CONFIG_FILE)\r\n 88 assert tf.io.gfile.exists(config_path), \\\r\n\r\nAssertionError: Could not find BLEURT checkpoint /root/.cache/huggingface/metrics/bleurt/bleurt-20/downloads/extracted/e34c60f1a05394ecda54e253a10413ca7b5d59f9a23f3cc73258c6b78ffa2f50/bleurt-20\r\n```\r\ninspecting specified path I see that directory name is `BLEURT-20` instead of `bleurt-20`. \r\nOther solution similar to your suggestion is meddle with `dl_manager.download_and_extract` to unzip to paths with lowering all the paths but I imagine this will affect other parts of the library. ", "Indeed, good catch ! Your solution that fixes `CHECKPOINT_URLS ` is simple and works well, thanks :)\r\n\r\nFurthermore to avoid breaking changes though we could also keep the support for the lowercase one:\r\n```python\r\n if self.config_name.lower() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.lower()\r\n elif self.config_name.upper() in CHECKPOINT_URLS:\r\n checkpoint_name = self.config_name.upper()\r\n else:\r\n raise KeyError(\r\n f\"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}\"\r\n )\r\n```\r\nand then we can use `checkpoint_name` instead of `self.config_name` to download and instantiate the model:\r\n```python\r\n model_path = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])\r\n self.scorer = score.BleurtScorer(os.path.join(model_path, checkpoint_name))\r\n```\r\n\r\nPlease let me know if that sounds reasonable to you !", "Thanks for the suggestion! I believe your suggestion should work to make keys case insensitive. Changes are committed to the PR now. " ]

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[ "And also don't always try streaming with Google Drive - it can have issues because of how Google Drive works (with quotas, restrictions, etc.) and it can indeed cause `BlockSizeError`.\r\n\r\nFeel free to host your test data elsewhere, such as in a dataset repository on https://huggingface.co (see [here](https://huggingface.co/docs/datasets/upload_dataset.html#upload-your-files) for a tutorial on how to upload files)" ]

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[ "Scratch that, probably the old and incompatible usage of dataset builder from promptsource." ]

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[ "Hi,\r\n\r\nthe dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?", "> Hi,\r\n> \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI have tried that many times with both load_dataset() and a browser almost simultaneously. The browser always works for me while load_dataset() fails.", "@mariosasko \r\n> the dataset is downloaded normally on my machine. Maybe the URL was down at the time of your download. Could you try again?\r\n\r\nI've tried yet again just a moment ago. This time I realize that, the step `(... post-processed: Unknown size, total: 20.89 MiB) to /Users/mike/.cache/huggingface/datasets/species800/species_800/1.0.0/532167f0bb8fbc0d77d6d03c4fd642c8c55527b9c5f2b1da77f3d00b0e559976...` and the one after seem unstable. If I want to retry, I will have to delete it (and probably other cache lock files). It **_sometimes_** works.\r\n\r\nBut I didn't try `download_mode=\"force_redownload\"` yet.\r\n\r\nAnyway, I suppose this isn't really a pressing issue for the time being, so I'm going to close this. Thank you.\r\n\r\n" ]

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[ "> Hi @tianjianjiang, thanks for the fix.\r\n> The links should also be updated in the `dataset_infos.json` file.\r\n> The failing tests are due to the missing tag in the header of the `README.md` file:\r\n\r\nHi @albertvillanova, thank you for the info! My apologies for the messy PR.\r\n" ]

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[ "I created a GitHub project to keep track of what needs to be done:\r\nhttps://github.com/huggingface/datasets/projects/3\r\n\r\nI also store my code in a (private for now) repository at https://github.com/huggingface/mirror_canonical_datasets_on_hub" ]

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[ "This might be related to https://github.com/tensorflow/tensorflow/issues/38762 , what do you think @Rocketknight1 ?\r\n> Dataset.from_generator is expected to not work with TPUs as it uses py_function underneath which is incompatible with Cloud TPU 2VM setup. If you would like to read from large datasets, maybe try to materialize it on disk and use TFRecordDataest instead.", "Hi @lhoestq @nbroad1881, I think it's very similar, yes. Unfortunately `to_tf_dataset` uses `tf.numpy_function` which can't be compiled - this is a necessary evil to load from the underlying Arrow dataset. We need to update the notebooks/examples to clarify that this won't work, or to identify a workaround. You may be able to get it to work on an actual cloud TPU VM, but those are quite new and we haven't tested it yet. ", "Thank you for the explanation. I didn't realize the nuances of `tf.numpy_function`. In this scenario, would it be better to use `export(format='tfrecord')` ? It's not quite the same, but for very large datasets that don't fit in memory it looks like it is the only option. I haven't used `export` before, but I do recall reading that there are suggestions for how big and how many tfrecords there should be to not bottleneck the TPU. It might be nice if there were a way for the `export` method to split the files up into appropriate chunk sizes depending on the size of the dataset and the number of devices. And if that is too much, it would be nice to be able to specify the number of files that would be created when using `export`. Well... maybe the user should just do the chunking themselves and call `export` a bunch of times. Whatever the case, you have been helpful. Thanks Tensorflow boy ;-) ", "Yeah, this is something we really should have a proper guide on. I'll make a note to test some things and make a 'TF TPU best practices' notebook at some point, but in the meantime I think your solution of exporting TFRecords will probably work. ", "Also: I knew that tweet would haunt me" ]

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[ "I manage to remove the mentions of ellipsis in the code by launching the command as follows:\r\n\r\n```\r\npython -m doctest -v docs/source/load_hub.rst -o=ELLIPSIS\r\n```\r\n\r\nThe way you put your ellipsis will only work on mac, I've adapted it for linux as well with the following:\r\n\r\n```diff\r\n >>> from datasets import load_dataset_builder\r\n >>> dataset_builder = load_dataset_builder('imdb')\r\n- >>> print(dataset_builder.cache_dir) #doctest: +ELLIPSIS\r\n- /Users/.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n+ >>> print(dataset_builder.cache_dir)\r\n+ /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\n```\r\n\r\nThis passes on my machine:\r\n\r\n```\r\nTrying:\r\n print(dataset_builder.cache_dir)\r\nExpecting:\r\n /.../.cache/huggingface/datasets/imdb/plain_text/1.0.0/...\r\nok\r\n```\r\n\r\nI'm getting a last error:\r\n\r\n```py\r\nExpected:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['sentence1', 'sentence2', 'label', 'idx'],\r\n num_rows: 1725\r\n })\r\n })\r\nGot:\r\n DatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 3668\r\n })\r\n validation: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 408\r\n })\r\n test: Dataset({\r\n features: ['idx', 'label', 'sentence1', 'sentence2'],\r\n num_rows: 1725\r\n })\r\n })\r\n```\r\n\r\nBut this is due to `doctest` looking for an exact match and the list having an unordered print order. I wish `doctest` would be a bit more flexible with that." ]

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[ "Hi ! Thanks for the suggestion, I didn't know about this decorator.\r\n\r\nIf you are interesting in contributing, feel free to open a pull request to add the overload methods for each typing combination :) To assign you to this issue, you can comment `#self-assign` in this thread.\r\n\r\n`Dataset.__getitem__` is defined right here: https://github.com/huggingface/datasets/blob/e6f1352fe19679de897f3d962e616936a17094f5/src/datasets/arrow_dataset.py#L1840", "#self-assign" ]

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[ "@anton-l ping", "@lhoestq \r\nHi Quentin! Thank you for your feedback and suggestions! 🤗\r\n\r\nYes, that was actually what I wanted to do next - I mean the steaming stuff :)\r\nAlso, I need to make some changes to the readme (to account for the updated features set).\r\n\r\nHopefully, I will be done by tomorrow afternoon if that's ok. \r\n", "@lhoestq Hi Quentin!\r\n\r\nI've implemented (hopefully, correctly) the streaming compatibility but the problem with the current approach is that we first need to iterate over the full archive anyway to get the list of filenames for train and validation sets (see [this](https://github.com/huggingface/datasets/pull/3335/files#diff-aeea540d136025e30a842856779e9c6485a5dc6fc9eb7fd6d3be2acd2f49b8e3R186), the same approach is implemented in TFDS version). Only after that, we can generate examples, so we cannot stream the dataset before the first iteration ends and it takes some time. It's probably not the most effective way. \r\n\r\nIf the streaming mode is turned off, this approach (with two iterations) is actually slower than the previous implementation (with archive extraction). \r\n\r\nMy suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them [here](https://drive.google.com/drive/folders/1oMrZHzPgHAKprKJuvih91CM8KMSzh_pL?usp=sharing). I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n", "Hi ! Thanks for the changes :)\r\n\r\n> My suggestion is to host separate archives for each split prepared in advance. That way there would be no need for iterating over the common archive to collect train and validation filenames. @anton-l suggested to make AWS mirrors for them. I've prepared these archives, for now you can take a look at them here. I simplified their structure a bit so if we switch to using them, the code then should be changed (and simplified) a bit too.\r\n\r\nI agree, I just uploaded them on AWS\r\n\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_train.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.01/v0.01_validation.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_test.tar.gz\r\nhttps://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/v0.02/v0.02_validation.tar.gz\r\n\r\nNote that in the future we can move those files to actual repositories on the Hugging Face Hub, since we are migrating the datasets from this repository to the Hugging Face Hub (as mirrors), to make them more accessible to the community.", "@lhoestq Thank you! Gonna look at this tomorrow :)", "@lhoestq I've modified the code to fit new data format, now it works for v0.01 but doesn't work for v0.02 as the training archive is missing. Could you please create a mirror for that one too? You can find it [here](https://drive.google.com/file/d/1mPjnVMYb-VhPprGlOX8v9TBT1GT-rtcp/view?usp=sharing)\r\n\r\nAnd when it's done I'll need to regenerate all the meta / dummy stuff, and this version will be ready for a review :)" ]