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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/422", "html_url": "https://github.com/huggingface/datasets/pull/422", "diff_url": "https://github.com/huggingface/datasets/pull/422.diff", "patch_url": "https://github.com/huggingface/datasets/pull/422.patch" }

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[ "What about the other PR #419 ?", "Oh this is the PR where I ran make quality and make style and some previous files from master were changed", "Oh right ! Let me fix the style myself if you don't mind" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/420", "html_url": "https://github.com/huggingface/datasets/pull/420", "diff_url": "https://github.com/huggingface/datasets/pull/420.diff", "patch_url": "https://github.com/huggingface/datasets/pull/420.patch" }

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/419", "html_url": "https://github.com/huggingface/datasets/pull/419", "diff_url": "https://github.com/huggingface/datasets/pull/419.diff", "patch_url": "https://github.com/huggingface/datasets/pull/419.patch" }

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[ "I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ", "Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`? it should work with google drive links.\r\n", "Yes it worked, thank you!" ]

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{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/417", "html_url": "https://github.com/huggingface/datasets/pull/417", "diff_url": "https://github.com/huggingface/datasets/pull/417.diff", "patch_url": "https://github.com/huggingface/datasets/pull/417.patch" }

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[ "great, I think I did not download the data the way you do, but yours is more reasonable." ]

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[ "`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https://github.com/huggingface/nlp.git\r\n```", "> `from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\n> Right now if you want to use `from_dict` you have to install the package from the master branch\r\n> \r\n> ```\r\n> pip install git+https://github.com/huggingface/nlp.git\r\n> ```\r\nOK, thank you.\r\n" ]

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[ "Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.", "Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially also others. \r\nFor us, it will in this case make the difference of using the library or keeping the old downloads of the raw dev datasets. \r\nHowever, I don't know if that fits into your plans with the library and can also understand if you don't want to support this.", "I don't think we could force this behavior generally since the dataset script authors are free to organize the file download as they want (sometimes the mapping between split and files can be very much nontrivial) but we can add an additional configuration for Natural Question indeed as @lhoestq indicate." ]

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[ "Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`", "I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !", "Thanks for the rapid fix @lhoestq!" ]

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[ "It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?", "Thanks @lhoestq, works fine now!" ]

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[ "Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ", "Works for me! Thanks.", "I found the same issue with almost any language other than English. (For English, it works). Will someone need to update the file on S3 again?", "This is because only some languages are already preprocessed (en, de, fr, it) and stored on our google storage.\r\nWe plan to have a systematic way to preprocess more wikipedia languages in the future.\r\n\r\nFor the other languages you have to process them on your side using apache beam. That's why the lib asks for a Beam runner." ]

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[ "I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?", "> @lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?\r\n\r\nI just tried with `writer.write_table` with tables of 1000 elements and it's slower that the solution in #405 \r\n\r\nOn my side (select 10 000 examples):\r\n- Original implementation: 12s\r\n- Batched solution: 100ms\r\n- solution using arrow tables: 350ms\r\n\r\nI'll try with arrays and record batches to see if we can make it work.", "I tried using `.take` from pyarrow recordbatches but it doesn't improve the speed that much:\r\n```python\r\nimport nlp\r\nimport numpy as np\r\n\r\ndset = nlp.Dataset.from_file(\"dummy_test_select.arrow\") # dummy dataset with 100000 examples like {\"a\": \"h\"*512}\r\nindices = np.random.randint(0, 100_000, 1000_000)\r\n```\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\",\r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n table = pa.concat_tables(dset._data.slice(int(i), 1) for i in indices[i : min(len(indices), i + batch_size)])\r\n batch = table.to_pydict()\r\n writer.write_batch(batch)\r\nwriter.finalize()\r\n# 9.12s\r\n```\r\n\r\n\r\n```python\r\n%%time\r\nbatch_size = 10_000\r\nwriter = ArrowWriter(schema=dset.schema, path=\"dummy_path\", \r\n writer_batch_size=1000, disable_nullable=False)\r\nfor i in tqdm(range(0, len(indices), batch_size)):\r\n batch_indices = indices[i : min(len(indices), i + batch_size)]\r\n # First, extract only the indices that we need with a mask\r\n mask = [False] * len(dset)\r\n for k in batch_indices:\r\n mask[k] = True\r\n t_batch = dset._data.filter(pa.array(mask))\r\n # Second, build the list of indices for the filtered table, and taking care of duplicates\r\n rev_positions = {}\r\n duplicates = 0\r\n for i, j in enumerate(sorted(batch_indices)):\r\n if j in rev_positions:\r\n duplicates += 1\r\n else:\r\n rev_positions[j] = i - duplicates\r\n rev_map = [rev_positions[j] for j in batch_indices]\r\n # Third, use `.take` from the combined recordbatch\r\n t_combined = t_batch.combine_chunks() # load in memory\r\n recordbatch = t_combined.to_batches()[0]\r\n table = pa.Table.from_arrays(\r\n [recordbatch[c].take(pa.array(rev_map)) for c in range(len(dset._data.column_names))],\r\n schema=writer.schema\r\n )\r\n writer.write_table(table)\r\nwriter.finalize()\r\n# 3.2s\r\n```\r\n", "Shuffling is now significantly faster thanks to #513 \r\nFeel free to play with it now :)\r\n\r\nClosing this one, but feel free to re-open if you have other questions" ]

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[ "What does the `nlp-cli dummy_data` command returns ?", "`test.json` -> `test` \r\nand \r\n`train.json` -> `train`\r\n\r\nas shown by the `nlp-cli dummy_data` command ;-)", "LGTM for merge @lhoestq - I let you merge if you want to." ]

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[ "Thanks!" ]

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[ "Do you mind adding a link to the much more extended pages on adding and sharing a dataset in the new documentation?", "Sure, I will do that too" ]

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[ "Looks cool :)\r\n\r\nI feel like \r\n```python\r\nconcatenated_dataset = dataset1.concatenate(dataset2)\r\n```\r\ncould be more natural. What do you think ?\r\n\r\nAlso could you also concatenate the `nlp.Dataset._data_files` ?\r\n```python\r\nreturn cls(table, info=info, split=split, data_files=self._data_files + other_dataset._data_files)\r\n```", "I feel like \"WikiBooks\" would be a multi task dataset that could fit in the #217 discussion.\r\nNot sure concatenate should be the solution for a multi task dataset.", "Thanks for the suggestion! `dset1.concatenate(dset2)` does feel more natural. Although this seems to be a different \"class\" of transformation function than map() or filter(), acting on two datasets rather than on one. I would prefer the function signature treat both datasets symmetrically.\r\n\r\nPython lists have `list1 + list2` or `list1.extend(list2)`.\r\nNumPy has `np.concatenate((arr1, arr2))`.\r\nPandas has `pd.join((df1, df2))`.\r\nPyTorch has `ConcatDataset((dset1, dset2))`.\r\n\r\nGiven the symmetrical treatment and clear communication that this creates a new object, rather than a simple chaining on the first, my preference is now for `nlp.concatenate((dset1, dset2))`. This would place the function in the same API class as `nlp.load_dataset`. Does that work?", "The multi-task discussion is interesting, thanks for pointing me to that! I'll be focusing on T5 in a few weeks, so I'm sure I'll have many opinions then :). For now, I think a simple concatenate feature is important and orthogonal to that discussion. For example, a user may want to create a custom dataset that joins Wikipedia with their own custom text.", "> Given the symmetrical treatment and clear communication that this creates a new object, rather than a simple chaining on the first, my preference is now for `nlp.concatenate((dset1, dset2))`. This would place the function in the same API class as `nlp.load_dataset`. Does that work?\r\n\r\nYep I like this idea. Maybe `nlp.concatenate_datasets()` ?\r\n\r\n> For now, I think a simple concatenate feature is important and orthogonal to that discussion. For example, a user may want to create a custom dataset that joins Wikipedia with their own custom text.\r\n\r\nI agree :)", "Great, just updated!" ]

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[ "By the way, the reason this is an issue for me is because I want to be able to \"save\" changes made to a dataset by writing something to disk. In this case, I would like to pre-process my dataset once, and then train multiple models on the dataset later without having to re-process the data. \r\n\r\nIs pickling/unpickling the Dataset object the \"sanctioned\" way of doing this? Or is there a better way that I'm missing?", "I've had success with saving datasets to disk via:\r\n\r\n```python\r\ncache_file = \"/my/dset.cache\"\r\ndset = dset.map(whatever, cache_file_name=cache_file)\r\n# then, later\r\ndset = nlp.Dataset.from_file(cache_file)\r\n```\r\n\r\nThis restores the dataset with all the attributes I need.", "Thanks @jarednielsen, that makes sense. I'm a little wary of messing with the cache files, since I still don't really understand what's going on under the hood with Apache Arrow. \r\n\r\nRelated question: I'd like to do parallel pre-processing of the dataset. I know how to break the dataset up via sharding, but is there any way to combine the shards back together again once the processing is done? Right now I'm probably just going to iterate over each shard, write the contexts to a txt file, and then cat the txt files, but it feels like there ought to be a nicer way to concatenate datasets.", "Haha, opened a PR for that functionality about an hour ago: https://github.com/huggingface/nlp/pull/390. Glad we're on the same page :)", "Datasets are not supposed to be pickled as pickle tries to put all the dataset in memory if I'm not wrong (and write all the data on disk).\r\nThe concatenate method however is a very cool feature, looking forward to having it merged :)", "Ah, yes, you are correct. The pickle file contains the whole dataset, not just the cache names, which is not quite what I expected.\r\n\r\nI tried adding a warning when pickling a Dataset, to prevent others like me from trying it. Interestingly, however, the warning is raised whenever any function on the dataset is called (select, shard, etc.). \r\n\r\n```\r\nimport nlp\r\nwiki = nlp.load_dataset('wikipedia', split='train')\r\nwiki = wiki.shard(16, 0) # Triggers pickling of dataset\r\n```\r\n\r\nI believe this is because [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626), which gets the function signature, is actually pickling the whole dataset (and thereby serializing all the data to text). I checked by printing that string, and sure enough it was full of Wikipedia articles.\r\n\r\nI don't think the whole pickling thing is worth the effort, so I'll close the PR. But I did want to mention this serialization behavior in case it's not intended.", "Thanks for reporting. Indeed this line shouldn't serialize the data but only the function itself.\r\n", "Keeping this open because I would like to keep brainstorming a bit on this.\r\n\r\nOne note on this is that we should have a clean serialization workflow, probably one that could serialize to a few formats (arrow, parquet and tfrecords come to mind).", "This PR could be useful. My specific use case is `multiprocessing.Pool` for parallel preprocessing (because of the Python tokenization bottleneck at https://github.com/huggingface/transformers/issues/5729). I shard a large dataset, run map on each shard within a multiprocessing pool, and then concatenate them back together. This is only possible if a dataset can be pickled, otherwise the logic is much more complex. There's no reason to make it un-picklable, even if it's not the recommended usage.\r\n\r\n```python\r\nimport nlp\r\nimport multiprocessing\r\n\r\ndef func(ex):\r\n return {\"text\": \"Prefix: \" + ex[\"text\"]}\r\n\r\ndef map_helper(dset):\r\n return dset.map(func)\r\n\r\nn_shards = 16\r\ndset = nlp.load_dataset(\"wikitext-2-raw-v1\", split=\"train\")\r\nwith multiprocessing.Pool(processes=n_shards) as pool:\r\n shards = pool.map(map_helper, [dset.shard(n_shards, i, contiguous=True) for i in range(n_shards)])\r\ndset = nlp.concatenate_datasets(shards)\r\n```\r\n", "Yes I agree.\r\n#423 just got merged and should allow serialization of `SplitDict`. Could you try it and see if it'ok on your side now ?", "Closing this, assuming it was fixed in #423." ]

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[ "similar slow download speed here for nlp.load_dataset('wmt14', 'fr-en')\r\n`\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 658M/658M [1:00:42<00:00, 181kB/s]\r\nDownloading: 100%|██████████████████████████████████████████████████████████| 918M/918M [1:39:38<00:00, 154kB/s]\r\nDownloading: 2%|▉ | 40.9M/2.37G [04:48<5:03:06, 128kB/s]\r\n`\r\nCould we just download a specific subdataset in 'wmt14', such as 'newstest14'? ", "> The code runs but the download speed is extremely slow, the same behaviour is not observed on wmt16 and wmt18\r\n\r\nThe original source for the files may provide slow download speeds.\r\nWe can probably host these files ourselves.\r\n\r\n> When trying to download wmt17 zh-en, I got the following error:\r\n> ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-zh.tar.gz\r\n\r\nLooks like the file`UNv1.0.en-zh.tar.gz` is missing, or the url changed. We need to fix that\r\n\r\n> Could we just download a specific subdataset in 'wmt14', such as 'newstest14'?\r\n\r\nRight now I don't think it's possible. Maybe @patrickvonplaten knows more about it\r\n", "Yeah, the download speed is sadly always extremely slow :-/. \r\nI will try to check out the `wmt17 zh-en` bug :-) ", "Maybe this can be used - https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 " ]

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[ "To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe we can have to_pydict/to_pylist as the default and use to_numpy or to_pandas when the format (set by `set_format`) is 'numpy' or 'pandas'" ]

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[ "I just copied the files that are on google storage to follow the new `_relative_data_dir ` format. It should be good to merge now :)\r\n\r\nWell actually it seems there are some merge conflicts to fix first" ]

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[ "We can probably scan the dataset scripts with a regexpr to try to identify this pattern cc @patrickvonplaten maybe", "@mariamabarham This script should work. I tested it for a couple of datasets. There might be exceptions where the script breaks - did not test everything.\r\n\r\n```python\r\n#!/usr/bin/env python3\r\n\r\nfrom nlp import prepare_module, DownloadConfig, import_main_class, hf_api\r\nimport tempfile\r\n\r\n\r\ndef scan_for_nested_unnecessary_dict(dataset_name):\r\n\r\n def load_builder_class(dataset_name):\r\n module_path = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))\r\n return import_main_class(module_path)\r\n\r\n def load_configs(dataset_name):\r\n builder_cls = load_builder_class(dataset_name)\r\n if len(builder_cls.BUILDER_CONFIGS) == 0:\r\n return [None]\r\n return builder_cls.BUILDER_CONFIGS\r\n\r\n def scan_features_for_nested_dict(features):\r\n is_sequence = False\r\n if hasattr(features, \"_type\"):\r\n if features._type != 'Sequence':\r\n return False\r\n else:\r\n is_sequence = True\r\n features = features.feature\r\n\r\n if isinstance(features, list):\r\n for value in features:\r\n if scan_features_for_nested_dict(value):\r\n return True\r\n return False\r\n\r\n elif isinstance(features, dict):\r\n for key, value in features.items():\r\n if is_sequence and len(features.keys()) == 1 and hasattr(features[key], \"_type\") and features[key]._type != \"Sequence\":\r\n return True\r\n if scan_features_for_nested_dict(value):\r\n return True\r\n return False\r\n elif hasattr(features, \"_type\"):\r\n return False\r\n else:\r\n raise ValueError(f\"{features} should be either a list, a dict or a feature\")\r\n\r\n configs = load_configs(dataset_name)\r\n\r\n for config in configs:\r\n with tempfile.TemporaryDirectory() as processed_temp_dir:\r\n # create config and dataset\r\n dataset_builder_cls = load_builder_class(dataset_name)\r\n name = config.name if config is not None else None\r\n dataset_builder = dataset_builder_cls(name=name, cache_dir=processed_temp_dir)\r\n\r\n is_nested_dict_in_dataset = scan_features_for_nested_dict(dataset_builder._info().features)\r\n if is_nested_dict_in_dataset:\r\n print(f\"{dataset_name} with {name} needs refactoring\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n scan_for_nested_unnecessary_dict(\"race\") # prints True\r\n scan_for_nested_unnecessary_dict(\"mlqa\") # prints True\r\n scan_for_nested_unnecessary_dict(\"squad\") # prints Nothing\r\n\r\n # ran the following lines for 1min and seems to work -> didn't check for all datasets though\r\n# api = hf_api.HfApi()\r\n# all_datasets = [x.id for x in api.dataset_list(with_community_datasets=False)]\r\n# for dataset in all_datasets:\r\n# scan_for_nested_unnecessary_dict(dataset)\r\n```", "> @mariamabarham This script should work. I tested it for a couple of datasets. There might be exceptions where the script breaks - did not test everything.\r\n> \r\n> ```python\r\n> #!/usr/bin/env python3\r\n> \r\n> from nlp import prepare_module, DownloadConfig, import_main_class, hf_api\r\n> import tempfile\r\n> \r\n> \r\n> def scan_for_nested_unnecessary_dict(dataset_name):\r\n> \r\n> def load_builder_class(dataset_name):\r\n> module_path = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))\r\n> return import_main_class(module_path)\r\n> \r\n> def load_configs(dataset_name):\r\n> builder_cls = load_builder_class(dataset_name)\r\n> if len(builder_cls.BUILDER_CONFIGS) == 0:\r\n> return [None]\r\n> return builder_cls.BUILDER_CONFIGS\r\n> \r\n> def scan_features_for_nested_dict(features):\r\n> is_sequence = False\r\n> if hasattr(features, \"_type\"):\r\n> if features._type != 'Sequence':\r\n> return False\r\n> else:\r\n> is_sequence = True\r\n> features = features.feature\r\n> \r\n> if isinstance(features, list):\r\n> for value in features:\r\n> if scan_features_for_nested_dict(value):\r\n> return True\r\n> return False\r\n> \r\n> elif isinstance(features, dict):\r\n> for key, value in features.items():\r\n> if is_sequence and len(features.keys()) == 1 and hasattr(features[key], \"_type\") and features[key]._type != \"Sequence\":\r\n> return True\r\n> if scan_features_for_nested_dict(value):\r\n> return True\r\n> return False\r\n> else:\r\n> raise ValueError(f\"{features} should be either a list of a dict\")\r\n> \r\n> configs = load_configs(dataset_name)\r\n> \r\n> for config in configs:\r\n> with tempfile.TemporaryDirectory() as processed_temp_dir:\r\n> # create config and dataset\r\n> dataset_builder_cls = load_builder_class(dataset_name)\r\n> name = config.name if config is not None else None\r\n> dataset_builder = dataset_builder_cls(name=name, cache_dir=processed_temp_dir)\r\n> \r\n> is_nested_dict_in_dataset = scan_features_for_nested_dict(dataset_builder._info().features)\r\n> if is_nested_dict_in_dataset:\r\n> print(f\"{dataset_name} with {name} needs refactoring\")\r\n> \r\n> \r\n> if __name__ == \"__main__\":\r\n> scan_for_nested_unnecessary_dict(\"race\") # prints True\r\n> scan_for_nested_unnecessary_dict(\"mlqa\") # prints True\r\n> scan_for_nested_unnecessary_dict(\"squad\") # prints Nothing\r\n> \r\n> # ran the following lines for 1min and seems to work -> didn't check for all datasets though\r\n> # api = hf_api.HfApi()\r\n> # all_datasets = [x.id for x in api.dataset_list(with_community_datasets=False)]\r\n> # for dataset in all_datasets:\r\n> # scan_for_nested_unnecessary_dict(dataset)\r\n> ```\r\n\r\nGreat, I will try it", "I'm not sure the work on this PR was finished @lhoestq cc @mariamabarham @patrickvonplaten ", "Sorry for that, apparently there are other datasets that could have unnecessary nested dicts.\r\nWe can have another PR to scan and fix the other datasets.\r\n" ]

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[ "I am checking the details of the CI log for the failed test, but I don't see how the error relates to the code I added; the error is coming from a config builder different than the `LinceConfig`, and it crashes when `self.config.data_files` because is self.config is None. I would appreciate if someone could help me find out where I could have messed things up :)\r\n\r\nAlso, the real and dummy data tests passed before committing and pushing my changes.\r\n\r\nThanks a lot in advance!\r\n\r\n```\r\n=================================== FAILURES ===================================\r\n____________________ AWSDatasetTest.test_load_dataset_text _____________________\r\n\r\nself = <tests.test_dataset_common.AWSDatasetTest testMethod=test_load_dataset_text>\r\ndataset_name = 'text'\r\n\r\n def test_load_dataset(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name)[:1]\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs)\r\n\r\ntests/test_dataset_common.py:243: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_dataset_common.py:137: in check_load_dataset\r\n try_from_hf_gcs=False,\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:432: in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:466: in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = <nlp.datasets.text.bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b.text.Text object at 0x7efa744ffb70>\r\ndl_manager = <nlp.utils.mock_download_manager.MockDownloadManager object at 0x7efb304c52b0>\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" The `datafiles` kwarg in load_dataset() can be a str, List[str], Dict[str,str], or Dict[str,List[str]].\r\n \r\n If str or List[str], then the dataset returns only the 'train' split.\r\n If dict, then keys should be from the `nlp.Split` enum.\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n # Handle case with only one split\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={\"files\": files})]\r\n else:\r\n # Handle case with several splits and a dict mapping\r\n splits = []\r\n for split_name in [nlp.Split.TRAIN, nlp.Split.VALIDATION, nlp.Split.TEST]:\r\n> if split_name in self.config.data_files:\r\nE TypeError: argument of type 'NoneType' is not iterable\r\n\r\n../.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.py:24: TypeError\r\n=============================== warnings summary ===============================\r\n... \r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_text\r\n====== 1 failed, 963 passed, 532 skipped, 5 warnings in 166.33s (0:02:46) ======\r\n\r\nExited with code exit status 1\r\n```", "@lhoestq Hi Quentin, I was wondering if you could give some feedback on this error from the `run_dataset_script_tests` script. It seems that's coming from a different config builder than the one I added, so I am not sure why this error would occur. Thanks in advance!", "Awesome! Thank you for all your comments! 👌 I will update the PR in a bit with all the required changes 🙂 \r\n\r\nLet me just provide a bit of context for my changes:\r\n\r\nI was referring to the GLUE, XTREME and WNUT_17 dataset scripts to build mine (not sure if the new documentation was available last week). This is where I took the naming convention for the citation and description variables. Also, these scripts didn't have the `BUILDER_CONFIG_CLASS = LinceConfig` line so I commented this out thinking I didn't need that; I tried this line in my attempts to make the real and dummy data tests pass but it was not helping. \r\n\r\nThe problem I was facing was that the tests were passing a default `BuilderConfig` (i.e., `self.config.name` property was set to `'default'` and my custom properties were not available). This means, for example, that within the `def _info(...)` method, I was not able to access the specific fields of my `LinceConfig` class (which is why I have now a global variable `_LINCE_CITATIONS`, to detach the individual citations from the corresponding LinceConfig objects, as well as I am constructing manually the feature infos). This default `BuilderConfig` is why I added the `if not isinstance(self.config, LinceConfig): return []` statement. Otherwise, accessing custom properties like `self.config.colnames` was failing the test because such properties did not exist in the default config (i.e., it was not a `LinceConfig`).\r\n\r\nI will update the PR and see if these problems happen in the CI tests.\r\n\r\nThanks again for the follow-up! @lhoestq ", "Ok I see !\r\n\r\nTo give you more details: the line `BUILDER_CONFIG_CLASS = LinceConfig` tells the tests how to instantiate a config for this dataset. Therefore if you have this line you should have all the fields of your config available.\r\n\r\nTo fix the errors you get you'll have to, first, have the `BUILDER_CONFIG_CLASS = LinceConfig` line, and second, add default values for the parameters of your config (or the tests functions will be unable to instantiate it by calling `LinceConfig()`.\r\n\r\nAn example of dataset with a custom config with additional filed like this one is [biomrc](https://github.com/huggingface/nlp/blob/master/datasets/biomrc/biomrc.py).\r\nFeel free to give a look at it if you want.", "Thanks for the reference!\r\n\r\nI just updated the PR with the suggested changes. It seems the CI failed on the same test you said we could ignore, so I guess it's okay :) \r\n\r\nPlease let me know if there is something else I may need to change." ]

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[ "Same for the RACE dataset: https://github.com/huggingface/nlp/blob/master/datasets/race/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?", "You're right, I think we don't need to use the nested dictionary. \r\n" ]

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[ "**Edit**: other topic previously in this message moved to a new issue: https://github.com/huggingface/nlp/issues/387", "Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for example" ]

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[ "I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n 371 return sorted(list(set(l)), key=lambda x: len(x.split(\" \")))\r\n 372 \r\n--> 373 sentences = dedup_and_sort(refs + hyps)\r\n 374 embs = []\r\n 375 iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat's because it gets numpy arrays as input and not lists. See #387 ", "The other issue was fixed by #403 \r\n\r\nDo you still get this issue @willywsm1013 ?\r\n" ]

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[ "I changed the `wiki_dpr` script to ignore the last 24 examples for now. Hopefully we'll have the full version soon.\r\nThe datasets_infos.json and the data on GCS are updated.\r\n\r\nAnd I also added a check to make sure we don't have post processing resources in sub-directories.", "I added a dummy config that can be loaded with:\r\n```python\r\nwiki = load_dataset(\"wiki_dpr\", \"dummy_psgs_w100_no_embeddings\", with_index=True, split=\"train\")\r\n```\r\nIt's only 6MB of arrow files and 30MB of index" ]

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[ "I've seen this sort of thing before -- it might help to delete the directory -- I've also noticed that there is an error with the json Dataloader for any data I've tried to load. I've replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n\r\n```", "Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `/home/XXX/.cache/lib/python3.7/site-packages/nlp/datasets/json/(...)/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory.", "I see, diving in the JSON file for SQuAD it's a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset('path/to/your/new/script', data_files={nlp.Split.TRAIN: \"./datasets/train-v2.0.json\"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.", "This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. ", "Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n", "Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don't have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset(\"json\", data_files=\"train-v2.0.json\", field=\"data\", split=\"train\")\r\ns[0]\r\n# {'title': 'Normans', 'paragraphs': [{'qas': [{'question': 'In what country is Normandy located?', 'id':...\r\n```", "Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps://huggingface.co/nlp/add_dataset.html", "@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset(\"json\", data_files=\"datasets/train-v2.0.json\", field=\"data\", split=\"train\")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 558, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py\", line 498, in _getitem\r\n outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict(\"list\"))\r\n File \"pyarrow/array.pxi\", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n File \"pyarrow/table.pxi\", line 1367, in pyarrow.lib.Table._to_pandas\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 766, in table_to_blockmanager\r\n blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n File \"/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py\", line 1101, in _table_to_blocks\r\n list(extension_columns.keys()))\r\n File \"pyarrow/table.pxi\", line 881, in pyarrow.lib.table_to_blocks\r\n File \"pyarrow/error.pxi\", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n", "Very happy to see some extended documentation! ", "#376 seems to be reporting the same issue as mentioned above. ", "This issue helped me a lot, thanks.\r\nHope this issue will be fixed soon." ]

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[ "Thanks for the fast fix!" ]

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[ "Looks like a flaky CI, failed download from S3." ]

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[ "I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/", "I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?" ]

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[ "I found that, in the same process (or the same interactive session), if I do\r\n\r\nimport nlp\r\n\r\nm1 = nlp.load_metric('glue', 'mrpc')\r\nm2 = nlp.load_metric('glue', 'sst2')\r\n\r\nI will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`." ]

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[ "Tests seem to be failing because of pandas", "Kaggle needs authentification to download datasets. We don't have a way to handle that in the lib for now" ]

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[ "Using batched map is probably the easiest way at the moment.\r\nWhat kind of augmentation would you like to do ?", "Some samples in the dataset are too long, I want to divide them in several samples.", "Using batched map is the way to go then.\r\nWe'll make it clearer in the docs that map could be used for augmentation.\r\n\r\nLet me know if you think there should be another way to do it. Or feel free to close the issue otherwise.", "It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.\r\n\r\nBut to be honest I have no idea of a good API...", "Or for non-batched samples, how about returning a tuple ?\r\n\r\n```python\r\ndef aug(sample):\r\n # Simply copy the existing data to have x2 amount of data\r\n return sample, sample\r\n\r\ndataset = dataset.map(aug)\r\n```\r\n\r\nIt feels really natural and easy, but :\r\n\r\n* it means the behavior with batched data is different\r\n* I don't know how doable it is backend-wise\r\n\r\n@lhoestq ", "As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.\r\nIf we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.\r\n\r\nIt's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches." ]

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[ "The dummy data for v2.1 is missing as far as I can see. I think running the dummy data command should work correctly here. ", "Also, it might be that the structure of the dummy data is wrong - looking at `generate_examples` the structure does not look too easy.", "The fact that the dummy data for v2.1 is missing shouldn't make the test fails I think. But as you mention the dummy data structure of v1.1 is wrong. I tried to rename files but it does not solve the issue.", "Is MS mARCO added to nlp library?I am not able to view it?", "> Is MS mARCO added to nlp library?I am not able to view it?\r\n\r\nHi @parthplc ,the PR is not merged yet. The dummy data structure is still failing. Maybe @patrickvonplaten can help with it.", "Dataset is fixed and should be ready for use. @mariamabarham @lhoestq feel free to merge whenever!", "> Dataset is fixed and should be ready for use. @mariamabarham @lhoestq feel free to merge whenever!\r\n\r\nthanks" ]

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[ "Thank you! I just marked this as a draft PR. It probably would be better to create specific Array2D and Array3D classes as needed instead of a generic MultiArray for now, it should simplify the code a lot too so, I'll update it as such. Also i was meaning to reply earlier, but I wanted to thank you for the testing script you sent me earlier since it ended up being tremendously helpful. ", "Okay, I just converted the MultiArray class to Array2D, and got rid of all those \"globals()\"! \r\n\r\nThe main issues I had were that when including a \"pa.ExtensionType\" as a column, the ordinary methods to batch the data would not work and it would throw me some mysterious error, so I first cleaned up my code to order the row to match the schema (because when including extension types the row is disordered ) and then made each row a pa.Table and then concatenated all the tables. Also each n-dimensional vector class we implement will be size invariant which is some good news. ", "Okay awesome! I just added your suggestions and changed up my recursive functions. \r\n\r\nHere is the traceback for the when I use the original code in the write_on_file method:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 33, in <module>\r\n File \"/home/eltoto/nlp/src/nlp/arrow_writer.py\", line 214, in finalize\r\n self.write_on_file()\r\n File \"/home/eltoto/nlp/src/nlp/arrow_writer.py\", line 134, in write_on_file\r\n pa_array = pa.array(self.current_rows, type=self._type)\r\n File \"pyarrow/array.pxi\", line 269, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 38, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 106, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>\r\n\r\nshell returned 1\r\n```\r\n\r\nI think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn't get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround. \r\n\r\nIn the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(***batch_size***) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.", "> I think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn't get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround.\r\n\r\nIndeed that's weird.\r\n\r\n> In the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(batch_size) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.\r\n\r\nThe argument of `pa.Table.to_batches` is not `batch_size` but `max_chunksize`, which means that right now it would have no effects (each chunk is of length 1).\r\n\r\nWe can fix that just by doing `entries.combine_chunks().to_batches(batch_size)`. In that case it would write by chunk of 1000 which is what we want. I don't think it will slow down the writing by much, but we may have to do a benchmark just to make sure. If speed is ok we could even replace the original code to always write chunks this way.\r\n\r\nDo you still have errors that need to be fixed ?", "@lhoestq Nope all should be good! \r\n\r\nWould you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?", "> @lhoestq Nope all should be good!\r\n\r\nAwesome :)\r\n\r\nI think it would be good to start to add some tests then.\r\nYou already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n\r\n> Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n\r\nThat would be interesting. We don't want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n- write speed + read speed a dataset with `nlp.Array2D` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value(\"float32\")))` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Value(\"float32\"))` features (same data but flatten)\r\nIt will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n\r\nWhat do you think ?", "Well actually it looks like we're still having the `print(dataset[0])` error no ?", "I just tested your code to try to understand better.\r\n\r\n\r\n- First thing you must know is that we've switched from `dataset._data.to_pandas` to `dataset._data.to_pydict` by default when we call `dataset[0]` in #423 . Right now it raises an error but it can be fixed by adding this method to `ExtensionArray2D`:\r\n\r\n```python\r\n def to_pylist(self):\r\n return self.to_numpy().tolist()\r\n```\r\n\r\n- Second, I noticed that `ExtensionArray2D.to_numpy()` always return a (5, 5) shape in your example. I thought `ExtensionArray` was for possibly multiple examples and so I was expecting a shape like (1, 5, 5) for example. Did I miss something ?\r\nTherefore when I apply the fix I mentioned (adding to_pylist), it returns one example per row in each image (in your example of 2 images of shape 5x5, I get `len(dataset._data.to_pydict()[\"image\"]) == 10 # True`)\r\n\r\n[EDIT] I changed the reshape step in `ExtensionArray2D.to_numpy()` by\r\n```python\r\nnumpy_arr = numpy_arr.reshape(len(self), *ExtensionArray2D._construct_shape(self.storage))\r\n```\r\nand it did the job: `len(dataset._data.to_pydict()[\"image\"]) == 2 # True`\r\n\r\n- Finally, I was able to make `to_pandas` work though, by implementing custom array dtype in pandas with arrow conversion (I got inspiration from [here](https://gist.github.com/Eastsun/a59fb0438f65e8643cd61d8c98ec4c08) and [here](https://pandas.pydata.org/pandas-docs/version/1.0.0/development/extending.html#compatibility-with-apache-arrow))\r\n\r\nMaybe you could add me in your repo so I can open a PR to add these changes to your branch ?", "`combine_chunks` doesn't seem to work btw:\r\n`ArrowNotImplementedError: concatenation of extension<arrow.py_extension_type>`", "> > @lhoestq Nope all should be good!\r\n> \r\n> Awesome :)\r\n> \r\n> I think it would be good to start to add some tests then.\r\n> You already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n> \r\n> > Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n> \r\n> That would be interesting. We don't want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n> \r\n> * write speed + read speed a dataset with `nlp.Array2D` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value(\"float32\")))` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Value(\"float32\"))` features (same data but flatten)\r\n> It will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n> \r\n> What do you think ?\r\n\r\nYa! that should be no problem at all, Ill use the timeit module and get back to you with the results sometime over the weekend.", "Thank you for all your help getting the pandas and row indexing for the dataset to work! For `print(dataset[0])`, I considered the workaround of doing `print(dataset[\"col_name\"][0])` a temporary solution, but ya, I was never able to figure out how to previously get it to work. I'll add you to my repo right now, let me know if you do not see the invite. Also lastly, it is strange how the to_batches method is not working, so I can check that out while I add some speed tests + add the multi dim test under the unit tests this weekend. ", "I created the PR :)\r\nI also tested `to_batches` and it works on my side", "Sorry for the bit of delay! I just added the tests, the PR into my fork, and some speed tests. It should be fairly easy to add more tests if we need. Do you think there is anything else to checkout?", "Cool thanks for adding the tests :) \r\n\r\nNext step is merge master into this branch.\r\nNot sure I understand what you did in your last commit, but it looks like you discarded all the changes from master ^^'\r\n\r\nWe've done some changes in the features logic on master, so let me know if you need help merging it.\r\n\r\nAs soon as we've merged from master, we'll have to make sure that we have extensive tests and we'll be good to do !\r\nAbout the lxmert dataset, we can probably keep it for another PR as soon as we have working 2d features. What do you think ?", "We might want to merge this after tomorrow's release though to avoid potential side effects @lhoestq ", "Yep I'm sure we can have it not for tomorrow's release but for the next one ;)", "haha, when I tried to rebase, I ran into some conflicts. In that last commit, I restored the features.py from the previous commit on the branch in my fork because upon updating to master, the pandasdtypemanger and pandas extension types disappeared. If you actually could help me with merging in what is needed, that would actually help a lot. \r\n\r\nOther than that, ya let me go ahead and move the dataloader code out of this PR. Perhaps we could discuss in the slack channelk soon about what to do with that because we can either just support the pretraining corpus for lxmert or try to implement the full COCO and visual genome datasets (+VQA +GQA) which im sure people would be pretty happy about. \r\n\r\nAlso we can talk more tests soon too when you are free. \r\n\r\nGoodluck on the release tomorrow guys!", "Not sure why github thinks there are conflicts here, as I just rebased from the current master branch.\r\nMerging into master locally works on my side without conflicts\r\n```\r\ngit checkout master\r\ngit reset --hard origin/master\r\ngit merge --no-ff eltoto1219/support_multi_dim_tensors_for_images\r\nMerge made by the 'recursive' strategy.\r\n datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py | 89 +++++++++++++++++++++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/test_multi_array.py | 45 +++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/to_arrow_data.py | 371 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n src/nlp/arrow_dataset.py | 24 +++++-----\r\n src/nlp/arrow_writer.py | 22 ++++++++--\r\n src/nlp/features.py | 229 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++---\r\n tests/test_array_2d.py | 210 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n 7 files changed, 969 insertions(+), 21 deletions(-)\r\n create mode 100644 datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/test_multi_array.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/to_arrow_data.py\r\n create mode 100644 tests/test_array_2d.py\r\n```", "I put everything inside one commit from the master branch but the merge conflicts on github'side were still there for some reason.\r\nClosing and re-opening the PR fixed the conflict check on github's side.", "Almost done ! It still needs a pass on the docs/comments and maybe a few more tests.\r\n\r\nI had to do several changes for type inference in the ArrowWriter to make it support custom types.", "Ok this is now ready for review ! Thanks for your awesome work in this @eltoto1219 \r\n\r\nSummary of the changes:\r\n- added new feature type `Array2D`, that can be instantiated like `Array2D(\"float32\")` for example\r\n- added pyarrow extension type `Array2DExtensionType` and array `Array2DExtensionArray` that take care of converting from and to arrow. `Array2DExtensionType`'s storage is a list of list of any pyarrow array.\r\n- added pandas extension type `PandasArrayExtensionType` and array `PandasArrayExtensionArray` for conversion from and to arrow/python objects\r\n- refactor of the `ArrowWriter` write and write_batch functions to support extension types while preserving the type inference behavior.\r\n- added a utility object `TypedSequence` that is helpful to combine extension arrays and type inference inside the writer's methods.\r\n- added speed test for sequences writing (printed as warnings in pytest)\r\n- breaking: set disable_nullable to False by default as pyarrow's type inference returns nullable fields\r\n\r\nAnd there are plenty of new tests, mainly in `test_array2d.py` and `test_arrow_writer.py`.\r\n\r\nNote that there are some collisions in `arrow_dataset.py` with #513 so let's be careful when we'll merge this one.\r\n\r\nI know this is a big PR so feel free to ask questions", "I'll add Array3D, 4D.. tomorrow but it should take only a few lines. The rest won't change", "I took your comments into account and I added Array[3-5]D.\r\nI changed the storage type to fixed lengths lists. I had to update the `to_numpy` function because of that. Indeed slicing a FixedLengthListArray returns a view a of the original array, while in the previous case slicing a ListArray copies the storage.\r\n" ]

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[ "You're right, thanks for pointing it out. We will update it " ]

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[ "Hi, can you give a full self-contained example to reproduce this behavior?", "> Hi, can you give a full self-contained example to reproduce this behavior?\r\n\r\nThere is a notebook in the post ;)", "> If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.\r\n> \r\n> Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n> \r\n> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n> \r\n> > ['0.3350', '0.1470', '0.2329']\r\n> > ['0.3358', '0.1451', '0.2332']\r\n> \r\n> Why ROUGE is not deterministic ?\r\n\r\nThis is because of rouge's `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).\r\nYou can get deterministic scores per sentence pair by using\r\n```python\r\nscore = rouge.compute(rouge_types=[\"rouge1\", \"rouge2\", \"rougeL\"], use_agregator=False)\r\n```\r\nOr you can set numpy's random seed if you still want to use the aggregator.", "Maybe we can set all the random seeds of numpy/torch etc. while running `metric.compute` ?", "We should probably indeed!", "Now if you re-run the notebook, the two printed results are the same @colanim\r\n```\r\n['0.3356', '0.1466', '0.2318']\r\n['0.3356', '0.1466', '0.2318']\r\n```\r\nHowever across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:\r\n```python\r\nrouge = nlp.load_metric('rouge', seed=42)\r\n```" ]

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[ "Actually `map(batched=True)` can already change the size of the dataset.\r\nIt can accept examples of length `N` and returns a batch of length `M` (can be null or greater than `N`).\r\n\r\nI'll make that explicit in the doc that I'm currently writing.", "You're two steps ahead of me :) In my testing, it also works if `M` < `N`.\r\n\r\nA batched map of different length seems to work if you directly overwrite all of the original keys, but fails if any of the original keys are preserved.\r\n\r\nFor example,\r\n```python\r\n# Create a dummy dataset\r\ndset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")[\"test\"]\r\ndset = dset.map(lambda ex: {\"length\": len(ex[\"text\"]), \"foo\": 1})\r\n\r\n# Do an allreduce on each batch, overwriting both keys\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])], \"foo\": [1]})\r\n# Dataset(schema: {'length': 'int64', 'foo': 'int64'}, num_rows: 5)\r\n\r\n# Now attempt an allreduce without touching the `foo` key\r\ndset.map(lambda batch: {\"length\": [sum(batch[\"length\"])]})\r\n# This fails with the error message below\r\n```\r\n\r\n```bash\r\n File \"/path/to/nlp/src/nlp/arrow_dataset.py\", line 728, in map\r\n arrow_schema = pa.Table.from_pydict(test_output).schema\r\n File \"pyarrow/io.pxi\", line 1532, in pyarrow.lib.Codec.detect\r\n File \"pyarrow/table.pxi\", line 1503, in pyarrow.lib.Table.from_arrays\r\n File \"pyarrow/public-api.pxi\", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n File \"pyarrow/error.pxi\", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named foo expected length 1 but got length 2\r\n```\r\n\r\nAdding the `remove_columns=[\"length\", \"foo\"]` argument to `map()` solves the issue. Leaving the above error for future visitors. Perfect, thank you!" ]

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[ "Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset(\"json\", data_files=rel_datafiles)\r\n```", "The behavior I'm seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n \"\"\" We handle string, list and dicts in datafiles\r\n \"\"\"\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={\"files\": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={\"files\": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = \"{}-{}.arrow\".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=\" tables\", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n # this is where the error is coming from\r\n # def parse_schema(schema, schema_dict):\r\n # for field in schema:\r\n # if pa.types.is_struct(field.type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type, schema_dict[field.name])\r\n # elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type.value_type, schema_dict[field.name])\r\n # else:\r\n # schema_dict[field.name] = Value(str(field.type))\r\n # \r\n # parse_schema(writer.schema, features)\r\n # self.info.features = Features(features)\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n```\r\n\r\nSo I basically just don't populate the `self.info.features` though this doesn't seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I'd prefer to use that.\r\n\r\n", "Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with `@abstract_methods`.", "Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon." ]

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[ "Ok this is starting to be really big so it's probably good to merge this first version of the doc and continue in another PR :)\r\n\r\nThis first version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html" ]

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[ "Looks you to me :)\r\n\r\nCould you also update the json file that goes with the dataset script by doing \r\n```\r\nnlp-cli test ./datasets/cnn_dailymail --save_infos --all_configs\r\n```\r\nIt will update the features metadata and the size of the dataset with your changes.", "@lhoestq I ran that command.\r\n\r\nThanks for the helpful repository!" ]

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[ "I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)\r\n\r\nWe are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed.", "Closing this one. Feel free to re-open if you have other questions :)", "Thank you!" ]

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[ "> Ok, so we're getting rid of the `FaissGpuOptions`?\r\n\r\nWe support `device=...` because it's simple, but faiss GPU options can be used in so many ways (you can set different gpu options for the different parts of your index for example) that it's probably better to let the user create and configure its index and then use `custom_index=...`" ]

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[ "Pinging @mariamabarham as well", "- `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification dataset", "Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n", "This is the homepage for the Amazon dataset: https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!", "Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq", "Pretty sure the quora dataset is the same one I implemented here: https://github.com/huggingface/nlp/pull/366", "Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https://arxiv.org/abs/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos/Neg/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https://arxiv.org/abs/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer ", "On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps://jmcauley.ucsd.edu/data/amazon/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer " ]

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[ "I think this is good but can you detail a bit the behavior before and after your fix?", "examples:\r\n\r\ninput: `['B', 'I', 'I', 'O', 'B', 'I']`\r\nbefore: `[('B', 0, 0), ('I', 1, 2), ('B', 4, 4), ('I', 5, 5)]`\r\nafter: `[('_', 0, 2), ('_', 4, 5)]`\r\n\r\ninput: `['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'O', 'B-ARGM-TIME', 'I-ARGM-TIME']`\r\nbefore: `[('LOC', 0, 2), ('TIME', 4, 5)]`\r\nafter: `[('ARGM-LOC', 0, 2), ('ARGM-TIME', 4, 5)]`\r\n\r\nThis is my test code:\r\n\r\n```python\r\nfrom metrics.seqeval.seqeval import end_of_chunk, start_of_chunk\r\n\r\n\r\ndef before_get_entities(seq, suffix=False):\r\n \"\"\"Gets entities from sequence.\r\n Args:\r\n seq (list): sequence of labels.\r\n Returns:\r\n list: list of (chunk_type, chunk_start, chunk_end).\r\n \"\"\"\r\n if any(isinstance(s, list) for s in seq):\r\n seq = [item for sublist in seq for item in sublist + ['O']]\r\n\r\n prev_tag = 'O'\r\n prev_type = ''\r\n begin_offset = 0\r\n chunks = []\r\n for i, chunk in enumerate(seq + ['O']):\r\n if suffix:\r\n tag = chunk[-1]\r\n type_ = chunk.split('-')[0]\r\n else:\r\n tag = chunk[0]\r\n type_ = chunk.split('-')[-1]\r\n\r\n if end_of_chunk(prev_tag, tag, prev_type, type_):\r\n chunks.append((prev_type, begin_offset, i - 1))\r\n if start_of_chunk(prev_tag, tag, prev_type, type_):\r\n begin_offset = i\r\n prev_tag = tag\r\n prev_type = type_\r\n\r\n return chunks\r\n\r\n\r\ndef after_get_entities(seq, suffix=False):\r\n \"\"\"Gets entities from sequence.\r\n Args:\r\n seq (list): sequence of labels.\r\n Returns:\r\n list: list of (chunk_type, chunk_start, chunk_end).\r\n \"\"\"\r\n if any(isinstance(s, list) for s in seq):\r\n seq = [item for sublist in seq for item in sublist + ['O']]\r\n\r\n prev_tag = 'O'\r\n prev_type = ''\r\n begin_offset = 0\r\n chunks = []\r\n for i, chunk in enumerate(seq + ['O']):\r\n if suffix:\r\n tag = chunk[-1]\r\n type_ = chunk[:-1].rsplit('-', maxsplit=1)[0] or '_'\r\n else:\r\n tag = chunk[0]\r\n type_ = chunk[1:].split('-', maxsplit=1)[-1] or '_'\r\n\r\n if end_of_chunk(prev_tag, tag, prev_type, type_):\r\n chunks.append((prev_type, begin_offset, i - 1))\r\n if start_of_chunk(prev_tag, tag, prev_type, type_):\r\n begin_offset = i\r\n prev_tag = tag\r\n prev_type = type_\r\n\r\n return chunks\r\n\r\n\r\ndef main():\r\n examples_1 = ['B', 'I', 'I', 'O', 'B', 'I']\r\n print(before_get_entities(examples_1))\r\n print(after_get_entities(examples_1))\r\n examples_2 = ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'O', 'B-ARGM-TIME', 'I-ARGM-TIME']\r\n print(before_get_entities(examples_2))\r\n print(after_get_entities(examples_2))\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n```", "And we can get more examples not correct, such as:\r\n\r\ninput: `['B', 'I', 'I-I']`\r\nbefore: `[('B', 0, 0), ('I', 1, 2)]`\r\nafter: `[('_', 0, 1), ('I', 2, 2)]`\r\n\r\ninput: `['B-ARGM-TIME', 'I-ARGM-TIME', 'I-TIME']`\r\nbefore: `[('TIME', 0, 2)]`\r\nafter: `[('ARGM-TIME', 0, 1), ('TIME', 2, 2)]`", "I think i didn't break any thing. Maybe the checks should be restart?", "Could you please rebase from master @AlongWY ? This should fix the CI stuff", "ok, i will do it", "Indeed the official repo is quite stale. Let's merge it here, thanks @AlongWY " ]

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[ "Thank you so much for working on this! This is awesome!\r\n\r\nHow much would it help you if we would remove the manual request?\r\n\r\nWe are naturally interested in getting some broad idea of how many people and who are using our dataset. But if you consider hosting the dataset yourself, I would rather remove this small barrier on our side (so that we then still get the download count from your library).", "This is an interesting aspect indeed!\r\nDo you want to send me an email (see my homepage) and I'll invite you on our slack channel to talk about that?\r\n@ghomasHudson wanna reach out to me as well? I tried to find your email to invite you without success." ]

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[ "@pjox I think the tests don't pass because you haven't provided any dummy data (`dummy_data.zip`).\r\n\r\n ", "> @pjox I think the tests don't pass because you haven't provided any dummy data (`dummy_data.zip`).\r\n\r\nBut can I do the dummy data without running `python nlp-cli test datasets/<your-dataset-folder> --save_infos --all_configs` first? 🤔 ", "You make a good point! Do you know how big is it uncompressed?", "Between 7T and 9T I think.", "Hi ! I've been busy but I plan to compute the missing metadata soon !\r\nLooking forward to be able to load a memory mapped version of OSCAR :) ", "> Hi ! I've been busy but I plan to compute the missing metadata soon !\r\n> Looking forward to be able to load a memory mapped version of OSCAR :)\r\n\r\nAmazing! Thanks! 😄 ", "Hi there, are there any plans to complete this issue soon? I'm planning to use this dataset on a project. Let me know if there's anything I can do to help to finish this 🤗 ", "Yes it will be added soon :) \r\nRecently the OSCAR data files were moved to another host. We just need to update the script and compute the dataset_infos.json (it will probably take a few days).", "@lhoestq I've seen in oscar.py that it isn't a dataset script with manual download way. Is that correct? \r\nSome time ago, @pjox had some troubles with his servers providing that dataset 'cause it's really huge. Providing it on an automatic download way seems to be a little bit dangerous for me 😄 ", "Now thanks to @pjox 's help OSCAR is hosted on HF's S3, which is probably more robust that the previous servers :)\r\n\r\nAlso small update on my side:\r\nI launched the computation of the dataset_infos.json file, it will take a few days.", "Now it seems to be a good plan for me 🤗 ", "But is there a plan to provide the OSCAR's unshuffled version too?", "The one we have on S3 is currently the unshuffled version", "I've thought that you won't provide the unshuffled version 'cause this comment on oscar.py:\r\n\r\n`# TODO(oscar): Implement unshuffled OSCAR`\r\n\r\n", "That TODO is normal, I haven't touched the python script in months (I haven't had the time, sorry), but I guess @lhoestq fixed the paths if he's already working on the metadata. In any case from now on, only the unshuffled versions of OSCAR will be distributed through the hf/datasets library as in any case it is the version most people use to train language models.\r\n\r\nIf for any reason, you need the shuffled version it will always be available on the [OSCAR website](https://oscar-corpus.com).\r\n\r\nAlso future versions of OSCAR will be unshuffled only.", "Should we close this PR now that the other one was merged?", "Sure.\r\nClosing since #1694 is merged", "@lhoestq just a little detail, is the Oscar version that HF offers the same one that was available on INRIA? By that I mean, have you done any further filtering or removing of data inside it? Thanks a lot! ", "Hello @jchwenger, this is exactly the same (unshuffled) version that's available at Inria. Sadly no further filtering is provided, but after the latest OSCAR audit (https://arxiv.org/abs/2103.12028) we're already working on future versions of OSCAR that will be \"filtered\" and that will be available on the OSCAR website and hopefully here as well.", "@pjox brilliant, in my case I was hoping it would be unfiltered, good news!" ]

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[ "This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\nTry to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\nSee issues #242 and #307 ", "It should be in `xtreme.py:L755`:\r\n```python\r\n if self.config.name == \"tydiqa\" or self.config.name.startswith(\"MLQA\") or self.config.name == \"SQuAD\":\r\n with open(filepath) as f:\r\n data = json.load(f)\r\n```\r\n\r\nCould you try to add the encoding parameter:\r\n```python\r\nopen(filepath, encoding='utf-8')\r\n```", "Hello @jerryIsHere :) Did it work ?\r\nIf so we may change the dataset script to force the utf-8 encoding", "@lhoestq sorry for being that late, I found 4 copy of xtreme.py. I did the changes as what has been told to all of them.\r\nThe problem is not solved", "Could you provide a better error message so that we can make sure it comes from the opening of the `tydiqa`'s json files ?\r\n", "@lhoestq \r\nThe error message is same as before:\r\nException has occurred: UnicodeDecodeError\r\n'cp950' codec can't decode byte 0xe2 in position 111: illegal multibyte sequence\r\n File \"D:\\python\\test\\test.py\", line 3, in <module>\r\n dataset = load_dataset('xtreme', 'tydiqa')\r\n\r\n\r\n\r\nI said that I found 4 copy of xtreme.py and add the 「, encoding='utf-8'」 parameter to the open() function\r\nthese python script was found under this directory\r\nC:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python37\\Lib\\site-packages\\nlp\\datasets\\xtreme\r\n", "Hi there !\r\nI encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\nI added ```encoding='UTF-8'``` to both lines that have ```open``` in ```imdb.py``` (108 and 114) and it worked for me.\r\nThank you !", "> Hi there !\r\n> I encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\n> I added `encoding='UTF-8'` to both lines that have `open` in `imdb.py` (108 and 114) and it worked for me.\r\n> Thank you !\r\n\r\nHello !\r\nGlad you managed to fix this issue on your side.\r\nDo you mind opening a PR for IMDB ?", "> This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\n> Try to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\n> See issues #242 and #307\r\n\r\nSorry for not responding for about a month.\r\nI have just found that it is necessary to change / add the environment variable as what was told in #242.\r\nEverything works after I add the new environment variable and restart my PC.\r\n\r\nI think the encoding issue for windows isn't limited to the open() function call specific to few dataset, but actually in the entire library, depends on the machine / os you use.", "Since #481 we shouldn't have other issues with encodings as they need to be set to \"utf-8\" be default.\r\n\r\nClosing this one, but feel free to re-open if you gave other questions" ]

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[ "I've tried it and am getting the same error as you.\r\n\r\nYou could use the text files rather than the pickle:\r\n```\r\nhttps://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt\r\nhttps://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt\r\nhttps://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt\r\n```\r\n\r\nThen you would get all 3 splits rather than just the train split.", "Thanks a lot @ghomasHudson - silly me for not spotting that! \r\n\r\nI'll keep the PR open for now since I'm quite close to wrapping it up.", "Hi @ghomasHudson your suggestion worked like a charm - the PR is now ready for review 😎 ", "Hello, I probably have a silly question but the labels of the emotion dataset are in the form of numbers and not string, so I can not use the function classification_report because it mixes numbers and string (prediction). How can I access the label in the form of a string and not a number?\r\nThank you in advance.", "Hi @juliette-sch! Yes, I believe that having the labels as integers is now the default for many classification datasets. You can access the string label via the `ClassLabel.int2str` function ([docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=int2str#datasets.ClassLabel.int2str)), so you could add a new column to the dataset as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset \r\n\r\nemotions = load_dataset(\"emotion\")\r\n\r\ndef label_int2str(row):\r\n return {\"label_name\": emotions[\"train\"].features[\"label\"].int2str(row[\"label\"])}\r\n\r\n# adds a new column called `label_name`\r\nemotions = emotions.map(label_int2str)\r\n```", "Great, thank you very much @lewtun !" ]

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[ "Hi @saverymax ! For licensing reasons, the original team was unable to release pre-processed CommonCrawl documents. Instead, they provided a script to re-create them from a CommonCrawl dump, but it unfortunately requires access to a medium-large size cluster:\r\nhttps://github.com/facebookresearch/ELI5#downloading-support-documents-from-the-commoncrawl\r\n\r\nIn order to make the task accessible to people who may not have access to this kind of infrastructure, we suggest to use Wikipedia as a knowledge source rather than the full CommonCrawl. The following blog post shows how you can create Wikipedia support documents and get a performance that is on par with a system that uses CommonCrawl pages.\r\nhttps://yjernite.github.io/lfqa.html#task_description\r\n\r\nHope that helps, using ElasticSearch to index Wiki40b and create the documents should take about 4 hours. Let us know if you have any trouble with the blog post though!", "Hi, thanks for the quick response. The blog post is quite an interesting working example, thanks for sharing it.\r\nTwo follow-up points/questions about my original question:\r\n\r\n1. Yes, I read that the facebook team could not share the CommonCrawl b/c of licensing reasons. They state \"No, we are not allowed to host processed Reddit or CommonCrawl data,\" which indicates they could also not share the Reddit data for licensing reasons. But it seems that HuggingFace is able to share the Reddit data, so why not a subset of CommonCrawl?\r\n\r\n2. Thanks for the suggestion about ElasticSearch and Wiki40b. This is good to know about performance. I definitely could do the indexing and querying myself. What I like about the ELI5 dataset though, at least what is suggested by the paper, is that to create the dataset they had already selected the top 100 web sources and made a single support document from those. Though it doesn't appear to be too sophisticated an approach, having a single support document pre-computed (without having to run the facebook code or a replacement with another dataset) is super useful for my work, especially since I'm not working on developing the latest and greatest retrieval model. Of course, I don't expect HF NLP datasets to be perfectly tailored to my use-case. I know there is overhead to any project, I'm just illustrating a use-case of ELI5 which is not possible with the data provided as-is. If it's for licensing reasons, that is perfectly acceptable a reason, and I appreciate your response." ]

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[ "Could you rebase from master just to make sure we won't break anything for `fever` pls @mariamabarham ?" ]

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[ "`dataset.column_names` are being updated but `dataset.features` aren't indeed..." ]

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[ "Thanks @brainshawn for this update" ]

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[ "Really cool @jarednielsen !\r\nDo you think we can make it work with dataset with nested features like `squad` ?\r\n\r\nI just did a PR to fix `.set_format` for datasets with nested features, but as soon as it's merged we could try to make the conversion work on a dataset like `squad`.", "For datasets with nested features we have two aspects to take into account:\r\n1) There can be nested dict of features. What is done in tensorflow_datasets to make things work is to flatten the dictionaries to end up with one single dictionary. A dict like `{\"column1\": {\"subfeature\": ...}}` is converted to `{\"column1/subfeature\":...}`\r\n2) There can be ragged tensors, i.e. lists of objects with non-fixed shapes. For example in squad there are often multiple possible answers per question. What is done in tensorflow_datasets to make things work is to concatenate everything and add ragged attributes (cf serialization code [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/example_serializer.py))", "Note that we have `flatten` method in `ArrowDataset`", "I added support for nested dictionaries. A few more design decisions popped up:\r\n\r\n_Should we serialize from NumPy arrays or from tf.Tensors?_\r\n- The [tfds example serializer](url) works from NumPy arrays.\r\n- Calling `dset.set_format(\"tensorflow\")` makes `__getitem__` return a tf.Tensor. So serializing from NumPy arrays would mean calling `dset.export()` before setting the format, which is confusing.\r\n- NumPy arrays can be serialized as their underlying datatype (int, float), while tf.Tensors must be converted to strings before serialization. This adds another step when serializing and deserializing, and removes the static-typing advantages of the TFRecord format.\r\n\r\nI think we should export directly from the underlying NumPy arrays into TFRecords, rather than using an intermediate step of tf.Tensor.\r\n\r\n_Should we serialize lists of dictionaries?_\r\n- The test_format_nested() test creates a list of dictionaries: https://github.com/huggingface/nlp/blob/911d5596f9b500e39af8642fe3d1b891758999c7/tests/test_arrow_dataset.py#L278-L288\r\n- This is difficult to serialize effectively, and I'm not aware of any dataset that has this format. SQuAD has a dictionary of lists, such as the `answers` key. Is this necessary?", "Thanks @thomwolf, used dset.flatten() to simplify. That handles the case of nested dictionaries, and then lists can be read into a tf.io.RaggedFeature in the case of something like squad answers.", "@jarednielsen I just checked and indeed we don't have lists of dicts, we can just focus on the squad format as a reference then :) I'll change the test to remove this format that's not supposed to happen", "Actually I realised that `flatten` also handles nested things like pyarrow's list<struct> so it's fine :D \r\nThis is so cool !\r\n\r\nCould you also add a test with a squad-like dataset ? As soon as we have that I think we'll be good to merge @jarednielsen :)\r\nGood job !", "Great, done! I think this could be a great canonical way to generate a dataset.", "I tried to match the format of Dataset.sort() and Dataset.shuffle() with the docstring. What difference are you referring to specifically?", "Oh my bad they're fine actually (I was thinking of the backticks that we don't use in the docstrings of the transformers repo for argument names)", "One final thing: now that we have a brand new documentation, could you just add `export` to the list of documented methods in [docs/source/package_reference/main_classes.rst](https://github.com/huggingface/nlp/blob/master/docs/source/package_reference/main_classes.rst) (so that it will appear in the docs [here](https://huggingface.co/nlp/package_reference/main_classes.html)) ?\r\n", "Done", "Cool thanks :)", "Since #403 (it just got merged), we return python objects and not numpy arrays anymore (unless format=\"numpy\" is specified).\r\nDo you think it can break the export method ? Could you try to rebase from master to run the CI to make sure it's fine ?", "Good catch. I fixed it up so it works with the new format. By the way, when dset.format == \"numpy\", it now returns single items (like `0`) as a 0-dimensional NumPy array. Not sure if that is desired.", "I played a little bit with the code and it works quite well :)\r\n\r\nI found two cases for which it doesn't work though:\r\n- if the features dict depth is > 2 (ex: wikisql), because `flatten` only flattens the first level of nesting (it can be fixed by calling `flatten` several times in a row, see [here](https://issues.apache.org/jira/browse/ARROW-4090))\r\n- Or if there are 2d features (ex: wikisql, `table.rows` is a sequence of sequences of strings), because tf.train.Features only support 1-d lists. That's why tensorflow-datasets flattens these 2-d features to 1-d and adds ragged features that are the shapes of the arrays, so that they can be reconstructed.\r\n\r\nI think we can ignore the 2d stuff right now (some work is being done in #363 ), but I'd like to see the `flatten` issue fixed soon\r\n", "That seems like a bug in `pyarrow`, or at least in `flatten()`. Looks like it should be a separate PR.", "I made `.flatten` work on our side (it calls pyarrow's flatten several times until it's really flat).\r\n\r\nThe only datasets that won't work are those with lists of lists of features, which is a rare case. Hopefully we can make this work with the multi-dimensional arrays changes we're also doing.\r\n\r\nI think we can merge now :) cc @thomwolf " ]

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