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[ "@lhoestq Better?" ]

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[ "Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed" ]

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[ "As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table that is used to re-order/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n\r\nBefore saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n", "> As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n> \r\n> The indices mapping correspond to a mapping on top of the data table that is used to re-order/select a sample of the original data table. For example if you do `dataset.train_test_split`, then the resulting train and test datasets will have both an indices mapping to tell which examples are in train and which ones in test.\r\n> \r\n> Before saving your datasets on disk, you should call `dataset.flatten_indices()` to remove the indices mapping. It should fix your issue. Under the hood it will create a new data table using the indices mapping. The new data table is going to be a subset of the old one (for example taking only the test set examples), and since the indices mapping will be gone you'll be able to concatenate your datasets.\r\n\r\n`dataset.flatten_indices()` solved my problem, thanks so much!", "@lhoestq we can add a mention of `dataset.flatten_indices()` in the error message (no rush, just put it on your TODO list or I can do it when I come at it)", "Yup I agree ! And in the docs as well" ]

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[ "It looks like an issue with wandb/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?", "hi facing the same issue here - \r\n\r\n`AssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 996, in emit\r\n stream.write(msg)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"<ipython-input-8-a4d9a08d114e>\", line 20, in __getitem__\r\n return_token_type_ids=True\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2405, in encode_plus\r\n **kwargs,\r\n File \"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py\", line 2125, in _get_padding_truncation_strategies\r\n \"Truncation was not explicitly activated but `max_length` is provided a specific value, \"\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1320, in warning\r\n self._log(WARNING, msg, args, **kwargs)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1444, in _log\r\n self.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1454, in handle\r\n self.callHandlers(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1516, in callHandlers\r\n hdlr.handle(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 865, in handle\r\n self.emit(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 1000, in emit\r\n self.handleError(record)\r\n File \"/usr/lib/python3.6/logging/__init__.py\", line 917, in handleError\r\n sys.stderr.write('--- Logging error ---\\n')\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/usr/local/lib/python3.6/dist-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/usr/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process`\r\n", "It looks like this warning : \r\n\"Truncation was not explicitly activated but max_length is provided a specific value, \"\r\nis not handled well by wandb.\r\n\r\nThe error occurs when calling the tokenizer.\r\nMaybe you can try to specify `truncation=True` when calling the tokenizer to remove the warning ?\r\nOtherwise I don't know why wandb would fail on a warning. Maybe one of its logging handlers have some issues with the logging of tokenizers. Maybe @n1t0 knows more about this ?", "I'm having a similar issue but when I try to do multiprocessing with the `DataLoader`\r\n\r\nCode to reproduce:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nbook_corpus = load_dataset('bookcorpus', 'plain_text', cache_dir='/home/ad/Desktop/bookcorpus', split='train[:1%]')\r\nbook_corpus = book_corpus.map(encode, batched=True, num_proc=20, load_from_cache_file=True, batch_size=5000)\r\nbook_corpus.set_format(type='torch', columns=['text', \"input_ids\", \"attention_mask\", \"token_type_ids\"])\r\n\r\nfrom transformers import DataCollatorForWholeWordMask\r\nfrom transformers import Trainer, TrainingArguments\r\n\r\ndata_collator = DataCollatorForWholeWordMask(\r\n tokenizer=tokenizer, mlm=True, mlm_probability=0.15)\r\n\r\ntraining_args = TrainingArguments(\r\n output_dir=\"./mobile_linear_att_8L_128_128_03layerdrop_shared\",\r\n overwrite_output_dir=True,\r\n num_train_epochs=1,\r\n per_device_train_batch_size=64,\r\n save_steps=50,\r\n save_total_limit=2,\r\n logging_first_step=True,\r\n warmup_steps=100,\r\n logging_steps=50,\r\n gradient_accumulation_steps=1,\r\n fp16=True,\r\n **dataloader_num_workers=10**,\r\n)\r\n\r\ntrainer = Trainer(\r\n model=model,\r\n args=training_args,\r\n data_collator=data_collator,\r\n train_dataset=book_corpus,\r\n tokenizer=tokenizer)\r\n\r\ntrainer.train()\r\n```\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nAssertionError Traceback (most recent call last)\r\n<timed eval> in <module>\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/transformers/trainer.py in train(self, model_path, trial)\r\n 869 self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)\r\n 870 \r\n--> 871 for step, inputs in enumerate(epoch_iterator):\r\n 872 \r\n 873 # Skip past any already trained steps if resuming training\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)\r\n 433 if self._sampler_iter is None:\r\n 434 self._reset()\r\n--> 435 data = self._next_data()\r\n 436 self._num_yielded += 1\r\n 437 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _next_data(self)\r\n 1083 else:\r\n 1084 del self._task_info[idx]\r\n-> 1085 return self._process_data(data)\r\n 1086 \r\n 1087 def _try_put_index(self):\r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_data(self, data)\r\n 1109 self._try_put_index()\r\n 1110 if isinstance(data, ExceptionWrapper):\r\n-> 1111 data.reraise()\r\n 1112 return data\r\n 1113 \r\n\r\n~/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/_utils.py in reraise(self)\r\n 426 # have message field\r\n 427 raise self.exc_type(message=msg)\r\n--> 428 raise self.exc_type(msg)\r\n 429 \r\n 430 \r\n\r\nAssertionError: Caught AssertionError in DataLoader worker process 0.\r\nOriginal Traceback (most recent call last):\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py\", line 198, in _worker_loop\r\n data = fetcher.fetch(index)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in fetch\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py\", line 44, in <listcomp>\r\n data = [self.dataset[idx] for idx in possibly_batched_index]\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1087, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 1074, in _getitem\r\n format_kwargs=format_kwargs,\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 890, in _convert_outputs\r\n v = map_nested(command, v, **map_nested_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/utils/py_utils.py\", line 225, in map_nested\r\n return function(data_struct)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/datasets/arrow_dataset.py\", line 851, in command\r\n return torch.tensor(x, **format_kwargs)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 101, in _showwarnmsg\r\n _showwarnmsg_impl(msg)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/warnings.py\", line 30, in _showwarnmsg_impl\r\n file.write(text)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py\", line 100, in new_write\r\n cb(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/wandb_run.py\", line 723, in _console_callback\r\n self._backend.interface.publish_output(name, data)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 153, in publish_output\r\n self._publish_output(o)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 158, in _publish_output\r\n self._publish(rec)\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py\", line 456, in _publish\r\n if self._process and not self._process.is_alive():\r\n File \"/home/ad/anaconda3/envs/tfm/lib/python3.6/multiprocessing/process.py\", line 134, in is_alive\r\n assert self._parent_pid == os.getpid(), 'can only test a child process'\r\nAssertionError: can only test a child process\r\n```\r\n\r\nAs a workaround I have commented line 456 and 457 in `/home/ad/anaconda3/envs/tfm/lib/python3.6/site-packages/wandb/sdk/interface/interface.py`", "Isn't it more the pytorch warning on the use of non-writable memory for tensor that trigger this here @lhoestq? (since it seems to be a warning triggered in `torch.tensor()`", "Yep this time this is a warning from pytorch that causes wandb to not work properly.\r\nCould this by a wandb issue ?", "Hi @timothyjlaurent @gaceladri \r\nIf you're running `transformers` from `master` you can try setting the env var `WAND_DISABLE=true` (from https://github.com/huggingface/transformers/pull/9896) and try again ?\r\nThis issue might be related to https://github.com/huggingface/transformers/issues/9623 ", "I have commented the lines that cause my code break. I'm now seeing my reports on Wandb and my code does not break. I am training now, so I will check probably in 6 hours. I suppose that setting wandb disable will work as well." ]

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[ "Hi @yjernite I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?", "Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), then follow the steps here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md", "Closed by #1399 " ]

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[ "Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)", "This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ", "Thanks for the heads up @pvl and for the PR as well :)", "Hello everyone,\r\n\r\nI think the problem is not solved: \r\n\r\n```\r\nfrom datasets import load_metric\r\nmetric=load_metric('bertscore')\r\nmetric.compute(\r\n predictions=predictions,\r\n references=references,\r\n lang='fr',\r\n rescale_with_baseline=True\r\n)\r\nTypeError: get_hash() missing 2 required positional arguments: 'use_custom_baseline' and 'use_fast_tokenizer'\r\n```\r\nThis code is produced using `Python 3.6.9 datasets==1.1.2 and bert_score==0.3.10`", "Hi ! This has been fixed by https://github.com/huggingface/datasets/pull/2770, we'll do a new release soon to make the fix available :)\r\n\r\nIn the meantime please use an older version of `bert_score`" ]

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[ "Right now multiprocessing only runs on single node.\r\n\r\nHowever it's probably possible to extend it to support multi nodes. Indeed we're using the `multiprocess` library from the `pathos` project to do multiprocessing in `datasets`, and `pathos` is made to support parallelism on several nodes. More info about pathos [on the pathos repo](https://github.com/uqfoundation/pathos).\r\n\r\nIf you're familiar with pathos or if you want to give it a try, it could be a nice addition to the library :)" ]

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[ "It seems the process needs '/datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py'\r\nWhere and how to assign this ```wikipedia.py``` after I manually download it ?", "\r\ndownload the ```wikipedia.py``` at the working directory and go with ```dataset = load_dataset('wikipedia.py', '20200501.en')``` works." ]

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[ "With this change all the checks are passed.", "Good" ]

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[ "Which version of pyarrow do you have ? Could you try to update pyarrow and try again ?", "Thanks for the fast response. I have the latest version '2.0.0' (I tried to update)\r\nI am working with Python 3.8.5", "I think that the issue is similar to this one:https://issues.apache.org/jira/browse/ARROW-9612\r\nThe problem is in arrow when the column data contains long strings.\r\nAny ideas on how to bypass this?", "We should expose the [`block_size` argument](https://arrow.apache.org/docs/python/generated/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https://github.com/huggingface/datasets/blob/master/datasets/csv/csv.py).\r\n\r\n\r\nIn the meantime you can specify yourself the `ReadOptions` config like this:\r\n```python\r\nimport pyarrow.csv as pac # PyArrow is installed with `datasets`\r\n\r\nread_options = pac.ReadOptions(block_size=1e9) # try to find the right value for your use-case\r\ndataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n```\r\n", "This did help to load the data. But the problem now is that I get:\r\nArrowInvalid: CSV parse error: Expected 5 columns, got 187\r\n\r\nIt seems that this change the parsing so I changed the table to tab-separated and tried to load it directly from pyarrow\r\nBut I got a similar error, again it loaded fine in pandas so I am not sure what to do.\r\n\r\n\r\n\r\n", "Got almost the same error loading a ~5GB TSV file, first got the same error as OP, then tried giving it my own ReadOptions and also got the same CSV parse error.", "> We should expose the [`block_size` argument](https://arrow.apache.org/docs/python/generated/pyarrow.csv.ReadOptions.html#pyarrow.csv.ReadOptions) of Apache Arrow csv `ReadOptions` in the [script](https://github.com/huggingface/datasets/blob/master/datasets/csv/csv.py).\r\n> \r\n> In the meantime you can specify yourself the `ReadOptions` config like this:\r\n> \r\n> ```python\r\n> import pyarrow.csv as pac # PyArrow is installed with `datasets`\r\n> \r\n> read_options = pac.ReadOptions(block_size=1e9) # try to find the right value for your use-case\r\n> dataset = load_dataset('csv', data_files=files, read_options=read_options)\r\n> ```\r\n\r\nThis did not work for me, I got\r\n`TypeError: __init__() got an unexpected keyword argument 'read_options'`", "Hi ! Yes because of issues with PyArrow's CSV reader we switched to using the Pandas CSV reader. In particular the `read_options` argument is not supported anymore, but you can pass any parameter of Pandas' `read_csv` function (see the list here in [Pandas documentation](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html))" ]

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[ "Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https://github.com/earwig/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal tool", "Ok, thanks! I'll try the Wiki40b dataset.", "If anyone else is concerned about this, `wiki40b` does indeed seem very well cleaned." ]

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[ "Hey @yjernite. This is a very interesting dataset. Would love to work on adding it but I see that the link to the data is to a gdrive folder. Can I just confirm wether dlmanager can handle gdrive urls or would this have to be a manual dl?", "Hi @KMFODA ! A version of WikiLingua is actually already accessible in the [GEM dataset](https://huggingface.co/datasets/gem)\r\n\r\nYou can use it for example to load the French to English translation with:\r\n```python\r\nfrom datasets import load_dataset\r\nwikilingua = load_dataset(\"gem\", \"wiki_lingua_french_fr\")\r\n```\r\n\r\nClosed by https://github.com/huggingface/datasets/pull/1807" ]

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[ "closed via #1098 " ]

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[ "Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface." ]

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[ "No comments ?", "I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the data already. I'm going to try option 2 you mention for now though! Thanks ;)", "Requiring online connection is a deal breaker in some cases unfortunately so it'd be great if offline mode is added similar to how `transformers` loads models offline fine.\r\n\r\n@mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?", "here is my way to load a dataset offline, but it **requires** an online machine\r\n1. (online machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_dataset(...)\r\ndata.save_to_disk(/YOUR/DATASET/DIR)\r\n```\r\n2. copy the dir from online to the offline machine\r\n3. (offline machine)\r\n```\r\nimport datasets\r\ndata = datasets.load_from_disk(/SAVED/DATA/DIR)\r\n```\r\n\r\nHTH.", "> here is my way to load a dataset offline, but it **requires** an online machine\n> \n> 1. (online machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_dataset(...)\n> \n> data.save_to_disk(/YOUR/DATASET/DIR)\n> \n> ```\n> \n> 2. copy the dir from online to the offline machine\n> \n> 3. (offline machine)\n> \n> ```\n> \n> import datasets\n> \n> data = datasets.load_from_disk(/SAVED/DATA/DIR)\n> \n> ```\n> \n> \n> \n> HTH.\n\n", "I opened a PR that allows to reload modules that have already been loaded once even if there's no internet.\r\n\r\nLet me know if you know other ways that can make the offline mode experience better. I'd be happy to add them :) \r\n\r\nI already note the \"freeze\" modules option, to prevent local modules updates. It would be a cool feature.\r\n\r\n----------\r\n\r\n> @mandubian's second bullet point suggests that there's a workaround allowing you to use your offline (custom?) dataset with `datasets`. Could you please elaborate on how that should look like?\r\n\r\nIndeed `load_dataset` allows to load remote dataset script (squad, glue, etc.) but also you own local ones.\r\nFor example if you have a dataset script at `./my_dataset/my_dataset.py` then you can do\r\n```python\r\nload_dataset(\"./my_dataset\")\r\n```\r\nand the dataset script will generate your dataset once and for all.\r\n\r\n----------\r\n\r\nAbout I'm looking into having `csv`, `json`, `text`, `pandas` dataset builders already included in the `datasets` package, so that they are available offline by default, as opposed to the other datasets that require the script to be downloaded.\r\ncf #1724 ", "The local dataset builders (csv, text , json and pandas) are now part of the `datasets` package since #1726 :)\r\nYou can now use them offline\r\n```python\r\ndatasets = load_dataset('text', data_files=data_files)\r\n```\r\n\r\nWe'll do a new release soon" ]

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[ "Hi I don’t think this is a request for a dataset like you labeled it.\r\n\r\nI also think this would be better suited for the forum at https://discuss.huggingface.co. we try to keep the issue for the repo for bug reports and new features/dataset requests and have usage questions discussed on the forum. Thanks.", "Hi Thomas,\nwhat I do not get from documentation is that why when you set batched=True,\nthis is processed in batch, while data is not divided to batched\nbeforehand, basically this is a question on the documentation and I do not\nget the batched=True, but sure, if you think this is more appropriate in\nforum I will post it there.\nthanks\nBest\nRabeeh\n\nOn Tue, Nov 10, 2020 at 12:21 PM Thomas Wolf <notifications@github.com>\nwrote:\n\n> Hi I don’t think this is a request for a dataset like you labeled it.\n>\n> I also think this would be better suited for the forum at\n> https://discuss.huggingface.co. we try to keep the issue for the repo for\n> bug reports and new features/dataset requests and have usage questions\n> discussed on the forum. Thanks.\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/823#issuecomment-724639476>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHH4FIPFHVVUHANAE4F3SPEO2JANCNFSM4TQQVEXQ>\n> .\n>\n", "Yes the forum is perfect for that. You can post in the `datasets` section.\r\nThanks a lot!" ]

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[ "Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text columns" ]

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[ "Sorry, asking for help here, but the dill thread stop around 2013. Is it possible to use dill deterministically? I tried to monkeypatch the solution presented here into dill, but I suppose it requires forking their project.", "Hi ! What we did was to subclass `dill`'s Pickler to fix the non-deterministic behaviors, and it's been working fine. A fork should also do the job" ]

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[ "Done! cf #1117 and #1022" ]

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[ "To show the issue:\r\n```\r\npython -c \"from datasets.fingerprint import Hasher; a=[]; func = lambda : len(a); print(Hasher.hash(func))\"\r\n```\r\ndoesn't always return the same ouput since `globs` is a dictionary with \"a\" and \"len\" as keys but sometimes not in the same order" ]

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[ "Hello !\r\nCould you give more details ?\r\n\r\nIf you mean iter through one dataset then yes, `Dataset` object does implement the `__iter__` method so you can use \r\n```python\r\nfor example in dataset:\r\n # do something\r\n```\r\n\r\nIf you want to iter through several datasets you can first concatenate them\r\n```python\r\nfrom datasets import concatenate_datasets\r\n\r\nnew_dataset = concatenate_datasets([dataset1, dataset2])\r\n```\r\nLet me know if this helps !", "Hi Huggingface/Datasets team,\nI want to use the datasets inside Seq2SeqDataset here\nhttps://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py\nand there I need to return back each line from the datasets and I am not\nsure how to access each line and implement this?\nIt seems it also has get_item attribute? so I was not sure if this is\niterative dataset? or if this is non-iterable datasets?\nthanks.\n\n\n\nOn Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Hello !\n> Could you give more details ?\n>\n> If you mean iter through one dataset then yes, Dataset object does\n> implement the __iter__ method so you can use\n>\n> for example in dataset:\n> # do something\n>\n> If you want to iter through several datasets you can first concatenate them\n>\n> from datasets import concatenate_datasets\n> new_dataset = concatenate_datasets([dataset1, dataset2])\n>\n> Let me know if this helps !\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/815#issuecomment-723881199>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n> .\n>\n", "could you tell me please if datasets also has __getitem__ any idea on how\nto integrate it with Seq2SeqDataset is appreciated thanks\n\nOn Mon, Nov 9, 2020 at 10:22 AM Rabeeh Karimi Mahabadi <rabeeh@google.com>\nwrote:\n\n> Hi Huggingface/Datasets team,\n> I want to use the datasets inside Seq2SeqDataset here\n> https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py\n> and there I need to return back each line from the datasets and I am not\n> sure how to access each line and implement this?\n> It seems it also has get_item attribute? so I was not sure if this is\n> iterative dataset? or if this is non-iterable datasets?\n> thanks.\n>\n>\n>\n> On Mon, Nov 9, 2020 at 10:18 AM Quentin Lhoest <notifications@github.com>\n> wrote:\n>\n>> Hello !\n>> Could you give more details ?\n>>\n>> If you mean iter through one dataset then yes, Dataset object does\n>> implement the __iter__ method so you can use\n>>\n>> for example in dataset:\n>> # do something\n>>\n>> If you want to iter through several datasets you can first concatenate\n>> them\n>>\n>> from datasets import concatenate_datasets\n>> new_dataset = concatenate_datasets([dataset1, dataset2])\n>>\n>> Let me know if this helps !\n>>\n>> —\n>> You are receiving this because you authored the thread.\n>> Reply to this email directly, view it on GitHub\n>> <https://github.com/huggingface/datasets/issues/815#issuecomment-723881199>,\n>> or unsubscribe\n>> <https://github.com/notifications/unsubscribe-auth/ARPXHHYRLSSYW6NZN2HYDBTSO6XV5ANCNFSM4TPB7OWA>\n>> .\n>>\n>\n", "`datasets.Dataset` objects implement indeed `__getitem__`. It returns a dictionary with one field per column.\r\n\r\nWe've not added the integration of the datasets library for the seq2seq utilities yet. The current seq2seq utilities are based on text files.\r\n\r\nHowever as soon as you have a `datasets.Dataset` with columns \"tgt_texts\" (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement your own Seq2SeqDataset class that wraps your dataset object. Does that make sense to you ?", "Hi\nI am sorry for asking it multiple times but I am not getting the dataloader\ntype, could you confirm if the dataset library returns back an iterable\ntype dataloader or a mapping type one where one has access to __getitem__,\nin the former case, one can iterate with __iter__, and how I can configure\nit to return the data back as the iterative type? I am dealing with\nlarge-scale datasets and I do not want to bring all in memory\nthanks for your help\nBest regards\nRabeeh\n\nOn Mon, Nov 9, 2020 at 11:17 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> datasets.Dataset objects implement indeed __getitem__. It returns a\n> dictionary with one field per column.\n>\n> We've not added the integration of the datasets library for the seq2seq\n> utilities yet. The current seq2seq utilities are based on text files.\n>\n> However as soon as you have a datasets.Dataset with columns \"tgt_texts\"\n> (str), \"src_texts\" (str), and \"id\" (int) you should be able to implement\n> your own Seq2SeqDataset class that wraps your dataset object. Does that\n> make sense ?\n>\n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/815#issuecomment-723915556>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ARPXHHYOC22EM7F666BZSOTSO66R3ANCNFSM4TPB7OWA>\n> .\n>\n", "`datasets.Dataset` objects are both iterative and mapping types: it has both `__iter__` and `__getitem__`\r\nFor example you can do\r\n```python\r\nfor example in dataset:\r\n # do something\r\n```\r\nor\r\n```python\r\nfor i in range(len(dataset)):\r\n example = dataset[i]\r\n # do something\r\n```\r\nWhen you do that, one and only one example is loaded into memory at a time.", "Hi there, \r\nHere is what I am trying, this is not working for me in map-style datasets, could you please tell me how to use datasets with being able to access ___getitem__ ? could you assist me please correcting this example? I need map-style datasets which is formed from concatenation of two datasets from your library. thanks \r\n\r\n\r\n```\r\nimport datasets\r\ndataset1 = load_dataset(\"squad\", split=\"train[:10]\")\r\ndataset1 = dataset1.map(lambda example: {\"src_texts\": \"question: {0} context: {1} \".format(\r\n example[\"question\"], example[\"context\"]),\r\n \"tgt_texts\": example[\"answers\"][\"text\"][0]}, remove_columns=dataset1.column_names)\r\ndataset2 = load_dataset(\"imdb\", split=\"train[:10]\")\r\ndataset2 = dataset2.map(lambda example: {\"src_texts\": \"imdb: \" + example[\"text\"],\r\n \"tgt_texts\": str(example[\"label\"])}, remove_columns=dataset2.column_names)\r\ntrain_dataset = datasets.concatenate_datasets([dataset1, dataset2])\r\ntrain_dataset.set_format(type='torch', columns=['src_texts', 'tgt_texts'])\r\ndataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)\r\nfor id, batch in enumerate(dataloader):\r\n print(batch)\r\n\r\n```", "closed since I found this response on the issue https://github.com/huggingface/datasets/issues/469" ]

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[ "found a solution here https://discuss.pytorch.org/t/train-simultaneously-on-two-datasets/649/35, closed for now, thanks " ]

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[ "Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. ", "Hey @rabeehkarimimahabadi I'm actually looking for the same feature. Did you manage to get somewhere?", "@rabeehkarimimahabadi need the same feature" ]

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[ "Hi ! Thanks for reporting :) \r\nI agree these one should be hidden when the logging level is warning, we'll fix that", "+1, the amount of logging is excessive.\r\n\r\nMost of it indeed comes from `filelock.py`, though there are occasionally messages from other sources too. Below is an example (all of these messages were logged after I already called `datasets.logging.set_verbosity_error()`)\r\n\r\n```\r\nI1109 21:26:01.742688 139785006901056 filelock.py:318] Lock 139778216292192 released on /home/kitaev/.cache/huggingface/datasets/9ed4f2e133395826175a892c70611f68522c7bc61a35476e8b51a31afb76e4bf.e6f3e3f3e3875a07469d1cfd32e16e1d06b149616b11eef2d081c43d515b492d.py.lock\r\nI1109 21:26:01.747898 139785006901056 filelock.py:274] Lock 139778216290176 acquired on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748258 139785006901056 filelock.py:318] Lock 139778216290176 released on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748412 139785006901056 filelock.py:274] Lock 139778215853024 acquired on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:26:01.748497 139785006901056 filelock.py:318] Lock 139778215853024 released on /home/kitaev/.cache/huggingface/datasets/_home_kitaev_.cache_huggingface_datasets_glue_mnli_1.0.0_7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4.lock\r\nI1109 21:07:17.029001 140301730502464 filelock.py:274] Lock 140289479304360 acquired on /home/kitaev/.cache/huggingface/datasets/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.029341 140301730502464 filelock.py:318] Lock 140289479304360 released on /home/kitaev/.cache/huggingface/datasets/b16d3a04bf2cad1346896852bf120ba846ea1bebb1cd60255bb3a1a2bbcc3a67.ec871b06a00118091ec63eff0a641fddcb8d3c7cd52e855bbb2be28944df4b82.py.lock\r\nI1109 21:07:17.058964 140301730502464 filelock.py:274] Lock 140251889388120 acquired on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.060933 140301730502464 filelock.py:318] Lock 140251889388120 released on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.061067 140301730502464 filelock.py:274] Lock 140296072521488 acquired on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\nI1109 21:07:17.069736 140301730502464 metric.py:400] Removing /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow\r\nI1109 21:07:17.069949 140301730502464 filelock.py:318] Lock 140296072521488 released on /home/kitaev/.cache/huggingface/metrics/glue/mnli/default_experiment-1-0.arrow.lock\r\n```", "So how to solve this problem?", "In the latest version of the lib the logs about locks are at the DEBUG level so you won't see them by default.\r\nAlso `set_verbosity_warning` does take into account these logs now.\r\nCan you try to update the lib ?\r\n```\r\npip install --upgrade datasets\r\n```", "Thanks. For some reason I have to use the older version. Is that possible I can fix this by some surface-level trick?\r\n\r\nI'm still using 1.13 version datasets.", "On older versions you can use\r\n```python\r\nimport logging\r\n\r\nlogging.getLogger(\"filelock\").setLevel(logging.WARNING)\r\n```", "Whoa Thank you! It works!" ]

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[ "and also for 'blog_authorship_corpus'\r\nhttps://huggingface.co/nlp/viewer/?dataset=blog_authorship_corpus\r\n\r\n", "Is this the problem of my local computer or ??" ]

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[ "Hey @yjernite. Was going to start working on this but found taskmaster 1,2 & 3 in the datasets library already so think this can be closed now?", "You are absolutely right :) \r\n\r\nClosed by https://github.com/huggingface/datasets/pull/1193 https://github.com/huggingface/datasets/pull/1197 https://github.com/huggingface/datasets/pull/1213" ]

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[ "Hi @AmitMY, \r\n\r\nWere you able to figure this out?", "I did not.\r\nWith all the limitations this repo currently has, I had to create a repo of my own using tfds to mitigate them. \r\nhttps://github.com/sign-language-processing/datasets/tree/master/sign_language_datasets/datasets/dgs_corpus\r\n\r\nClosing as I don't know how to support this PR further" ]

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[ "Hi !\r\nThe url works on my side.\r\n\r\nIs the url working in your navigator ?\r\nAre you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?", "> Hi !\r\n> The url works on my side.\r\n> \r\n> Is the url working in your navigator ?\r\n> Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n\r\nI tried another server, it's working now. Thanks a lot.\r\n\r\nAnd I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?", "It seems my network frequently crashed so most time it cannot work.", "\r\n\r\n\r\n> > Hi !\r\n> > The url works on my side.\r\n> > Is the url working in your navigator ?\r\n> > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> \r\n> I tried another server, it's working now. Thanks a lot.\r\n> \r\n> And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n\r\nI download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`？ \r\n\r\nThanks :D", "hello, how did you solve this problems?\r\n\r\n> > > Hi !\r\n> > > The url works on my side.\r\n> > > Is the url working in your navigator ?\r\n> > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > \r\n> > \r\n> > I tried another server, it's working now. Thanks a lot.\r\n> > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> \r\n> I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`？\r\n> \r\n> Thanks :D\r\n\r\nhello, I tried this. but it still failed. how do you fix this error?", "> hello, how did you solve this problems?\r\n> \r\n> > > > Hi !\r\n> > > > The url works on my side.\r\n> > > > Is the url working in your navigator ?\r\n> > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > \r\n> > > \r\n> > > I tried another server, it's working now. Thanks a lot.\r\n> > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > \r\n> > \r\n> > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`？\r\n> > Thanks :D\r\n> \r\n> hello, I tried this. but it still failed. how do you fix this error?\r\n\r\n你把那个脚本下载到你本地安装目录下，然后 `load_dataset(csv_script_path, data_fiels)`\r\n\r\n", "> > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`？\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> 你把那个脚本下载到你本地安装目录下，然后 `load_dataset(csv_script_path, data_fiels)`\r\n\r\n好的好的！解决了，感谢感谢！！！", "> \r\n> \r\n> > hello, how did you solve this problems?\r\n> > > > > Hi !\r\n> > > > > The url works on my side.\r\n> > > > > Is the url working in your navigator ?\r\n> > > > > Are you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried another server, it's working now. Thanks a lot.\r\n> > > > And I'm curious about why download things from \"github\" when I load dataset from local files ? Dose datasets work if my network crashed?\r\n> > > \r\n> > > \r\n> > > I download the scripts `https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py` and move it to the package dir `*/datasets/` solved the problem. Could you please put the file `datasets/datasets/csv/csv.py` to `datasets/src/datasets/`？\r\n> > > Thanks :D\r\n> > \r\n> > \r\n> > hello, I tried this. but it still failed. how do you fix this error?\r\n> \r\n> 你把那个脚本下载到你本地安装目录下，然后 `load_dataset(csv_script_path, data_fiels)`\r\n\r\n我照着做了，然后报错。\r\nValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-5-fd2106a3f053> in <module>\r\n----> 1 dataset = load_dataset('C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets/csv.py', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)\r\n 588 # Download/copy dataset processing script\r\n 589 module_path, hash = prepare_module(\r\n--> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True\r\n 591 )\r\n 592 \r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)\r\n 296 local_dataset_infos_path = cached_path(\r\n 297 dataset_infos,\r\n--> 298 download_config=download_config,\r\n 299 )\r\n 300 except (FileNotFoundError, ConnectionError):\r\n\r\nC:\\Software\\Anaconda\\envs\\ptk_gpu2\\lib\\site-packages\\datasets\\utils\\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)\r\n 316 else:\r\n 317 # Something unknown\r\n--> 318 raise ValueError(\"unable to parse {} as a URL or as a local path\".format(url_or_filename))\r\n 319 \r\n 320 if download_config.extract_compressed_file and output_path is not None:\r\n\r\nValueError: unable to parse C:/Software/Anaconda/envs/ptk_gpu2/Lib/site-packages/datasets\\dataset_infos.json as a URL or as a local path\r\n\r\n`", "I also experienced this issue this morning. Looks like something specific to windows.\r\nI'm working on a fix", "I opened a PR @wn1652400018", "> \r\n> \r\n> I opened a PR @wn1652400018\r\n\r\nThanks you!, It works very well." ]

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[ "Hi ! Thanks for reporting.\r\nWe should fix the urls and use quail 1.3.\r\nIf you want to contribute feel free to fix the urls and open a PR :) ", "Done! PR [https://github.com/huggingface/datasets/pull/820](https://github.com/huggingface/datasets/pull/820)\r\n\r\nUpdated links and also regenerated the metadata and dummy data for v1.3 in order to pass verifications as described here: [https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset](https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset). ", "Closing since #820 is merged.\r\nThanks again for fixing the urls :)" ]

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[ "Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object.\r\nCould you update pyarrow and try again ?\r\n```\r\npip install --upgrade pyarrow\r\n```" ]

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[ "cc @yjernite is this expected ?", "Yes: TriviaQA has a private test set for the leaderboard [here](https://competitions.codalab.org/competitions/17208)\r\n\r\nFor the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here:\r\nhttps://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md", "Oh ok, I guess I read the paper too fast 😅, thank you for your answer!" ]

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[ "Really cool to add XGlue, this will be a nice addition !\r\n\r\nSplits shouldn't depend on the language. There must be configurations for each language, as we're doing for xnli, xtreme, etc.\r\nFor example for XGlue we'll have these configurations: NER.de, NER.en etc." ]

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[ "Hi this is also my question. thanks ", "Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset\r\n", "Closing this one. Feel free to re-open if you have other questions about this issue.\r\n\r\nAlso linking another discussion about joining datasets: #853 " ]

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[ "Hi ! Indeed there's an issue with those links.\r\nWe should probably use the target urls of the redirections instead", "Hi, the same issue here, could you tell me how to download it through datasets? thanks ", "Same issue. ", "Actually it's already fixed on the master branch since #740 \r\nI'll do the 1.1.3 release soon", "Hi\nthanks, but I did tried to install from the pip install git+... and it does\nnot work for me,. thanks for the help. I have the same issue with wmt16,\n\"ro-en\"\nthanks.\nBest\nRabeeh\n\nOn Mon, Nov 16, 2020 at 10:29 AM Quentin Lhoest <notifications@github.com>\nwrote:\n\n> Actually it's already fixed on the master branch since #740\n> <https://github.com/huggingface/datasets/pull/740>\n> I'll do the 1.1.3 release soon\n>\n> —\n> You are receiving this because you commented.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/798#issuecomment-727854736>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ABP4ZCEUBJKPOCLABXCKMPDSQDWH3ANCNFSM4TJBUKSA>\n> .\n>\n", "I just tested on google colab using\r\n```python\r\n!pip install git+https://github.com/huggingface/datasets.git\r\nfrom datasets import load_dataset\r\nload_dataset(\"trec\")\r\n```\r\nand it works.\r\nCan you detail how you got the issue even when using the latest version on master ?\r\n\r\nAlso about wmt we'll look into it, thanks for reporting !", "I think the new url with .edu is also broken:\r\n```\r\nConnectionError: Couldn't reach https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label\r\n```\r\nCant download the dataset anymore.", "Hi ! The URL seems to work fine on my side, can you try again ?", "Forgot to update, i wrote an email to the webmaster of seas.upenn.edu because i couldnt reach the url on any machine. This was the answer:\r\n```\r\nThank you for your report. The server was offline for maintenance and is now available again.\r\n```\r\nGuess all back to normal now 🙂 " ]

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[ "Indeed. Pinging @stefan-it here if he want to give an expert opinion :)", "Related is https://github.com/huggingface/datasets/pull/636", "Should definitely be a ClassLabel 👍 " ]

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[ "Hi ! Thanks for letting us know your experience :) \r\nWe should at least improve the error messages indeed", "So what is the right way to add a batch to compute BLEU?", "prediction = [['Hey', 'how', 'are', 'you', '?']] \r\nreference=[['Hey', 'how', 'are', 'you', '?']]\r\nbleu.compute(predictions=prediction,references=reference)\r\n\r\nalso tried this kind of things lol\r\nI definitely need help too", "Hi !\r\n\r\nAs described in the documentation for `bleu`:\r\n```\r\nArgs:\r\n predictions: list of translations to score.\r\n Each translation should be tokenized into a list of tokens.\r\n references: list of lists of references for each translation.\r\n Each reference should be tokenized into a list of tokens.\r\n```\r\n\r\nTherefore you can use this metric this way:\r\n```python\r\nfrom datasets import load_metric\r\n\r\npredictions = [\r\n [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\r\n [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\r\n]\r\nreferences = [\r\n [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\r\n [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\r\n]\r\n\r\nbleu = load_metric(\"bleu\")\r\nbleu.compute(predictions=predictions, references=references)\r\n# Or you can also add batches before calling compute()\r\n# bleu.add_batch(predictions=predictions, references=references)\r\n# bleu.compute()\r\n```\r\n\r\nHope this helps :)" ]

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[ "Yes indeed ! Thanks for reporting" ]

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[ "Hi ! Thanks for reporting that's a bug on master indeed.\r\nWe'll fix that soon" ]

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[ "Just found out about https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md\r\n(Not very clear in https://huggingface.co/datasets/kilt_tasks links to http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md which is dead, closing the issue though :))" ]

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[ "@patrickvonplaten Yeah this is adapted from tfds so a lot is just how they wrote the code. Addressed your comments and also simplified the weird `AmazonUSReviewsConfig` definition. Will merge once tests pass.", "Thanks for checking this one :) \r\nLooks good to me \r\n\r\nJust one question : is there a particular reason to use `names=[\"Y\", \"N\"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification", "> is there a particular reason to use `names=[\"Y\", \"N\"]` in this order ? Usually the positive label is at index 1 and the negative one at index 0 for binary classification\r\n\r\nHmm that's a good point. I'll submit a quick fix.\r\n\r\n" ]

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[ "I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now", "Closing this one.\r\nFeel free to re-open if you still have issues" ]

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[ "Hi @AmitMY, sorry for leaving you hanging for a minute :) \r\n\r\nWe've developed a new pipeline for adding datasets with a few extra steps, including adding a dataset card. You can find the full process [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)\r\n\r\nWould you be up for adding the tags and description in the README.md so we can merge this cool dataset?", "@lhoestq should be ready for another review :) ", "Awesome thank you !\r\n\r\nIt looks like the PR now includes changes from other PR that were previously merged. \r\nFeel free to create another branch and another PR so that we can have a clean diff.\r\n", "Closing for #958 " ]

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[ "Thank you @lhoestq for the time you take to review our pull request. We appreciate your help.\r\n\r\nWe've made the changes you described. Hope that it is ready for being merged. Please let me know if you have any additional requests for revisions. " ]

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[ "I agree that would be cool :)\r\nRight now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik" ]

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[ "Hi ! I'm not sure we should make this split decision arbitrarily on our side. Users can split it afterwards to whatever they want using `dataset.train_test_split` for example.\r\nMoreover it looks like there's already papers that use this dataset and propose their own splits ([here](http://xanthippi.ceid.upatras.gr/HealthSign/resources/Publications/sitis_paper_25_10.pdf) 80-20) \r\nWhat do you think ?", "I was not aware of the `train_test_split` method, thanks!\r\nSoe ven though it contributes to reproducibility, no need to do this split then." ]

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[ "Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https://dumps.wikimedia.org/jvwiki) here for `jv`) ?", "@lhoestq\r\n\r\nI've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya.\r\n\r\nAlso, using another date (e.g. `load_dataset('wikipedia', '20201120.zh', beam_runner='DirectRunner')`) will give the following error message.\r\n\r\n```\r\nValueError: BuilderConfig 20201120.zh not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']\r\n```\r\n\r\nI am pretty sure that `https://dumps.wikimedia.org/enwiki/20201120/dumpstatus.json` exists.", "Thanks for reporting I created a PR to make the custom config work (language=\"zh\", date=\"20201120\").", "@lhoestq Thanks!" ]

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[ "we're using quail 1.3 now thanks.\r\nclosing this one" ]

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[ "Very nice !\r\nWhat do the `sentence1` and `sentence2` correspond to exactly ?\r\nAlso maybe you could use the `ClassLabel` feature type for the `label` field (see [snli](https://github.com/huggingface/datasets/blob/master/datasets/snli/snli.py) for example)", "> What do the `sentence1` and `sentence2` correspond to exactly ?\r\n\r\n`sentence1` is a question, and `sentence2` is a candidate answer sentence. The labels are [1, 2, 3, 4] defining a relation between the answer sentence and the question. For example, label 4 means that the answer sentence is inside the _long_answer_ passage AND that the _short_answer_ is within the answer sentence. All the other labels are the negatives with different characteristics. (the short_answer, long_answer terminology is borrowed from Google's NQ dataset)\r\n\r\nShould I label them simply as `question` and `answer`? I was going more with what I saw in the examples/run_glue.py script, but I realize now there is no restriction around this.\r\n\r\n> Also maybe you could use the `ClassLabel` feature type for the `label` field (see [snli](https://github.com/huggingface/datasets/blob/master/datasets/snli/snli.py) for example)\r\n\r\nI am finding it difficult to assign names to each class, but perhaps it's possible. Here's the description of each class from the paper.\r\n\r\n1. Sentences from the document that are in the long answer but do not contain the annotated short answers. It is possible that these sentences might contain the short answer.\r\n2. Sentences from the document that are not in the long answer but contain the short answer string, that is, such occurrence is purely accidental.\r\n3. Sentences from the document that are neither in the long answer nor contain the short answer.\r\n4. Sentences from the document that are in the long answer and do contain the annotated short answers.\r\n\r\nAny ideas?\r\n\r\n", "Yes it's better to have explicit feature names. Maybe go with question/answer or question/sentence.\r\nI read in the paper that 1,2 and 3 are considered negative and 4 positive.\r\nWe could have a binary classification label `label` (either positive of negative) and then two boolean fields `short_answser_in_sentence` and `sentence_in_long_answer`. What do you think ?", "> Yes it's better to have explicit feature names. Maybe go with question/answer or question/sentence.\r\n> I read in the paper that 1,2 and 3 are considered negative and 4 positive.\r\n> We could have a binary classification label `label` (either positive of negative) and then two boolean fields `short_answser_in_sentence` and `sentence_in_long_answer`. What do you think ?\r\n\r\nOk, sounds good. I went with `sentence` to keep it consistent with `short_answer_in_sentence` and `sentence_in_long_answer`. \r\n\r\nI changed it to a ClassLabel with pos and neg classes and added the two above as features. Let me know if this is not what you had in mind.\r\n\r\n" ]

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[ "Hi ! This looks interesting, thanks for adding it :) \r\n\r\nFor metrics there should only be two features fields: references and predictions.\r\nBoth of them can be defined as you want using nested structures if you need to.\r\nAlso I'm not sure what goes into references and what goes into predictions, could you give more details please ?\r\nAll the other computations parameters (model etc.) are fine though. Maybe explain a bit more what they're used for", "> Hi ! This looks interesting, thanks for adding it :)\r\n> \r\n> For metrics there should only be two features fields: references and predictions.\r\n> Both of them can be defined as you want using nested structures if you need to.\r\n> Also I'm not sure what goes into references and what goes into predictions, could you give more details please ?\r\n> All the other computations parameters (model etc.) are fine though. Maybe explain a bit more what they're used for\r\n\r\nThe `predictions` are the predicted labels by a model for a particular input. Do you mean making `prob_y_hat` - the probability of the prediction being the predicted label, `prob_y_hat_alpha` - the probability of the prediction being the predicted label when the input is reduced subject to alpha and the `null_difference` is the difference between the probability of the prediction being the predicted label in full information minus the probability in zero information a part of references? Also, I have added the description for other parameters in kwargs_description. I can expand it if that makes sense?", "I think every value that is generated by the model (so label, prob_y_hat, prob_y_hat_alpha etc.) should be in `predictions`.\r\nFeel free to add more details in the kwargs_description, this is very useful for the end user.", "Hi @lhoestq , I have updated the code according to your feedback. Please, let me know if it looks good and can be merged now." ]

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[ "Hi ! Thanks for reporting.\r\nThe same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 .\r\nThe fix will be available in the next release :)", "Thanks for the prompt reply and terribly sorry for the spam! \r\nLooking forward to the new release! " ]