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Update metadata for wikihow dataset
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Update metadata for wikihow dataset: - Remove leading new line character in description and citation - Update metadata JSON - Remove no longer necessary `urls_checksums/checksums.txt` file Related to #2748.
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Second concatenation of datasets produces errors
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[ "@albertvillanova ", "Hi @Aktsvigun, thanks for reporting.\r\n\r\nI'm investigating this.", "Hi @albertvillanova ,\r\nany update on this? Can I probably help in some way?", "Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. πŸ˜… \r\n\r\nIn the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md)" ]
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Hi, I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`. ``` from datasets import load_dataset, concatenate_datasets data = load_dataset('trec')['train'] concatenated = concatenate_datasets([data, data]) concatenated_2 = concatenate_datasets([concatenated, concatenated]) print('True features of features:', concatenated.features) print('\nProduced features of features:', concatenated_2.features) ``` outputs ``` True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)} Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)} ``` I am using `datasets` v.1.11.0
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958,968,748
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Raise a proper exception when trying to stream a dataset that requires to manually download files
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[ "Hi @severo, thanks for reporting.\r\n\r\nAs discussed, datasets requiring manual download should be:\r\n- programmatically identifiable\r\n- properly handled with more clear error message when trying to load them with streaming\r\n\r\nIn relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:\r\n```python\r\n# Dataset requiring manual download:\r\nbuilder.manual_download_instructions is not None\r\n```", "Thanks @albertvillanova " ]
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## Describe the bug At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("reclor", streaming=True) ``` ## Expected results Ideally: raise a specific exception, something like `ManualDownloadError`. Or at least give the reason in the message, as when we load in normal mode: ```python from datasets import load_dataset dataset = load_dataset("reclor") ``` ``` AssertionError: The dataset reclor with config default requires manual data. Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name') . Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>') ``` ## Actual results ``` TypeError: expected str, bytes or os.PathLike object, not NoneType ``` ## Environment info - `datasets` version: 1.11.0 - Platform: macOS-11.5-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1
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Generate metadata JSON for wikihow dataset
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Related to #2743.
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add multi-proc in `to_json`
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[ "Thank you for working on this, @bhavitvyamalik \r\n\r\n10% is not solving the issue, we want 5-10x faster on a machine that has lots of resources, but limited processing time.\r\n\r\nSo let's benchmark it on an instance with many more cores, I can test with 12 on my dev box and 40 on JZ. \r\n\r\nCould you please share the test I could run with both versions?\r\n\r\nShould we also test the sharded version I shared in https://github.com/huggingface/datasets/issues/2663#issue-946552273 so optionally 3 versions to test.", "Since I was facing `OSError: [Errno 12] Cannot allocate memory` in CircleCI tests, I've added `num_proc` option instead of always using full `cpu_count`. You can test both v1 and v2 through this branch (some redundancy needs to be removed). \r\n\r\nUpdate: I was able to convert into json which took 50% less time as compared to v1 on `ascent_kb` dataset. Will post the benchmarking script with results here.", "Here are the benchmarks with the current branch for both v1 and v2 (dataset: `ascent_kb`, 8.9M samples):\r\n| batch_size | time (in sec) | time (in sec) |\r\n|------------|---------------|---------------|\r\n| | num_proc = 1 | num_proc = 4 |\r\n| 10k | 185.56 | 170.11 |\r\n| 50k | 175.79 | 86.84 |\r\n| **100k** | 191.09 | **78.35** |\r\n| 125k | 198.28 | 90.89 |\r\n\r\nIncreasing the batch size on my machine helped in making v2 around 50% faster as compared to v1. Timings may vary depending on the machine. I'm including the benchmarking script as well. CircleCI errors are unrelated (something related to `bertscore`)\r\n```\r\nimport time\r\nfrom datasets import load_dataset\r\nimport pathlib\r\nimport os\r\nfrom pathlib import Path\r\nimport shutil\r\nimport gc\r\n\r\nbatch_sizes = [10_000, 50_000, 100_000, 125_000]\r\nnum_procs = [1, 4] # change this according to your machine\r\n\r\nSAVE_LOC = \"./new_dataset.json\"\r\n\r\nfor batch in batch_sizes:\r\n for num in num_procs:\r\n dataset = load_dataset(\"ascent_kb\")\r\n\r\n local_start = time.time()\r\n ans = dataset['train'].to_json(SAVE_LOC, batch_size=batch, num_proc=num)\r\n local_end = time.time() - local_start\r\n\r\n print(f\"Time taken on {num} num_proc and {batch} batch_size: \", local_end)\r\n\r\n # remove that dataset and its contents from cache and newly generated json\r\n new_json = pathlib.Path(SAVE_LOC)\r\n new_json.unlink()\r\n\r\n try:\r\n shutil.rmtree(os.path.join(str(Path.home()), \".cache\", \"huggingface\"))\r\n except OSError as e:\r\n print(\"Error: %s - %s.\" % (e.filename, e.strerror))\r\n\r\n gc.collect()\r\n```\r\nThis will download the dataset in every iteration and run `to_json`. I didn't do multiple iterations here for `to_json` (for a specific batch_size and num_proc) and took average time as I found that v1 got faster after 1st iteration (maybe it's caching somewhere). Since you'll be doing this operation only once, I thought it'll be better to report how both v1 and v2 performed in single iteration only. \r\n\r\nImportant: Benchmarking script will delete the newly generated json and `~/.cache/huggingface/` after every iteration so that it doesn't end up using any cached data (just to be on a safe side)", "Thank you for sharing the benchmark, @bhavitvyamalik. Your results look promising.\r\n\r\nBut if I remember correctly the sharded version at https://github.com/huggingface/datasets/issues/2663#issue-946552273 was much faster. So we probably should compare to it as well? And if it's faster than at least document that manual sharding version?\r\n\r\n-------\r\n\r\nThat's a dangerous benchmark as it'd wipe out many other HF things. Why not wipe out:\r\n```\r\n~/.cache/huggingface/datasets/ascent_kb/\r\n```\r\n\r\nRunning the benchmark now.", "Weird, I tried to adapt your benchmark to using shards and the program no longer works. It instead quickly uses up all available RAM and hangs. Has something changed recently in `datasets`? You can try:\r\n\r\n```\r\nimport time\r\nfrom datasets import load_dataset\r\nimport pathlib\r\nimport os\r\nfrom pathlib import Path\r\nimport shutil\r\nimport gc\r\nfrom multiprocessing import cpu_count, Process, Queue\r\n\r\nbatch_sizes = [10_000, 50_000, 100_000, 125_000]\r\nnum_procs = [1, 8] # change this according to your machine\r\n\r\nDATASET_NAME = (\"ascent_kb\")\r\nnum_shards = [1, 8]\r\nfor batch in batch_sizes:\r\n for shards in num_shards:\r\n dataset = load_dataset(DATASET_NAME)[\"train\"]\r\n #print(dataset)\r\n\r\n def process_shard(idx):\r\n print(f\"Sharding {idx}\")\r\n ds_shard = dataset.shard(shards, idx, contiguous=True)\r\n # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling\r\n print(f\"Saving {DATASET_NAME}-{idx}.jsonl\")\r\n ds_shard.to_json(f\"{DATASET_NAME}-{idx}.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\n local_start = time.time()\r\n queue = Queue()\r\n processes = [Process(target=process_shard, args=(idx,)) for idx in range(shards)]\r\n for p in processes:\r\n p.start()\r\n\r\n for p in processes:\r\n p.join()\r\n local_end = time.time() - local_start\r\n\r\n print(f\"Time taken on {shards} shards and {batch} batch_size: \", local_end)\r\n```\r\n\r\nJust careful, so that it won't crash your compute environment. As it almost crashed mine.", "So this part seems to no longer work:\r\n```\r\n dataset = load_dataset(\"ascent_kb\")[\"train\"]\r\n ds_shard = dataset.shard(1, 0, contiguous=True)\r\n ds_shard.to_json(\"ascent_kb-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n```", "If you are using `to_json` without any `num_proc`or `num_proc=1` then essentially it'll fall back to v1 only and I've kept it as it is (the tests were passing as well)\r\n\r\n> That's a dangerous benchmark as it'd wipe out many other HF things. Why not wipe out:\r\n\r\nThat's because some dataset related files were still left inside `~/.cache/huggingface/datasets` folder. You can wipe off datasets folder inside your cache maybe\r\n\r\n> dataset = load_dataset(\"ascent_kb\")[\"train\"]\r\n> ds_shard = dataset.shard(1, 0, contiguous=True)\r\n> ds_shard.to_json(\"ascent_kb-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\nI tried this `lama` dataset (1.3M) and it worked fine. Trying it with `ascent_kb` currently, will update it here.", "I don't think the issue has anything to do with your work, @bhavitvyamalik. I forgot to mention I tested to see the same problem with the latest datasets release.\r\n\r\nInteresting, I tried your suggestion. This:\r\n```\r\npython -c 'import datasets; ds=\"lama\"; dataset = datasets.load_dataset(ds)[\"train\"]; \\\r\ndataset.shard(1, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nworks fine and takes just a few GBs to complete.\r\n\r\nthis on the other hand blows up memory-wise:\r\n```\r\npython -c 'import datasets; ds=\"ascent_kb\"; dataset = datasets.load_dataset(ds)[\"train\"]; \\\r\ndataset.shard(1, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nand I have to kill it before it uses up all RAM. (I have 128GB of it, so it should be more than enough)", "> That's because some dataset related files were still left inside ~/.cache/huggingface/datasets folder. You can wipe off datasets folder inside your cache maybe\r\n\r\nI think recent datasets added a method that will print out the path for all the different components for a given dataset, I can't recall the name though. It was when we were discussing a janitor program to clear up space selectively.", "> and I have to kill it before it uses up all RAM. (I have 128GB of it, so it should be more than enough)\r\n\r\nSame thing just happened on my machine too. Memory leak somewhere maybe? Even if you were to load this dataset in your memory it shouldn't take more than 4GB. You were earlier doing this for `oscar` dataset. Is it working fine for that?", "Hmm, looks like `datasets` has changed and won't accept my currently cached oscar-en (crashes), so I'd rather not download 0.5TB again. \r\n\r\nWere you able to reproduce the memory blow up with `ascent_kb`? It's should be a much quicker task to verify.\r\n\r\nBut yes, oscar worked just fine with `.shard()` which is what I used to process it fast.", "What I tried is:\r\n```\r\nHF_DATASETS_OFFLINE=1 HF_DATASETS_CACHE=cache python -c 'import datasets; ds=\"oscar\"; \\\r\ndataset = datasets.load_dataset(ds, \"unshuffled_deduplicated_en\")[\"train\"]; \\\r\ndataset.shard(1000000, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nand got:\r\n```\r\nUsing the latest cached version of the module from /gpfswork/rech/six/commun/modules/datasets_modules/datasets/oscar/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d (last modified on Fri Aug 6 01:52:35 2021) since it couldn't be found locally at oscar/oscar.py or remotely (OfflineModeIsEnabled).\r\nReusing dataset oscar (cache/oscar/unshuffled_deduplicated_en/1.0.0/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d)\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/load.py\", line 755, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 737, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 764, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 834, in _as_dataset\r\n dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 217, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 238, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 173, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 308, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 327, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/table.py\", line 450, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/table.py\", line 43, in _memory_mapped_arrow_table_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```", "> Were you able to reproduce the memory blow up with ascent_kb? It's should be a much quicker task to verify.\r\n\r\nYes, this blows up memory-wise on my machine too. \r\n\r\nI found that a [similar error](https://discuss.huggingface.co/t/saving-memory-with-run-mlm-py-with-wikipedia-datasets/4160) was posted on the forum on 5th March. Since you already knew how much time [#2663 comment](https://github.com/huggingface/datasets/issues/2663#issue-946552273) took, can you try benchmarking v1 and v2 for now maybe until we have a fix for this memory blow up?", "OK, so I benchmarked using \"lama\" though it's too small for this kind of test, since the sharding is much slower than one thread here.\r\n\r\nResults: https://gist.github.com/stas00/dc1597a1e245c5915cfeefa0eee6902c\r\n\r\nSo sharding does really bad there, and your json over procs is doing great!\r\n\r\nAny suggestions to a somewhat bigger dataset, but not too big? say 10 times of lama?", "Looks great! I had a few questions/suggestions related to `benchmark-datasets-to_json.py`:\r\n \r\n1. You have used only 10_000 and 100_000 batch size. Including more batch sizes may help you find the perfect batch size for your machine and even give you some extra speed-up. \r\nFor eg, I found `load_dataset(\"cc100\", lang=\"eu\")` with batch size 125_000 took less time as compared to batch size 100_000 (71.16 sec v/s 67.26 sec) since this dataset has 2 fields only `['id', 'text']`, so that's why we can go for higher batch size here. \r\n \r\n2. Why have you used `num_procs` 1 and 4 only? \r\n\r\nYou can use:\r\n1. `dataset = load_dataset(\"cc100\", lang=\"af\")`. Even though it has only 2 fields but there are around 9.9 mil samples. (lama had around 1.3 mil samples)\r\n2. `dataset = load_dataset(\"cc100\", lang=\"eu\")` -> 16 mil samples. (if you want something more than 9.9 mil)\r\n3. `dataset = load_dataset(\"neural_code_search\", 'search_corpus')` -> 4.7 mil samples", "Thank you, @bhavitvyamalik \r\n\r\nMy apologies, at the moment I have not found time to do more benchmark with the proposed other datasets. I will try to do it later, but I don't want it to hold your PR, it's definitely a great improvement based on the benchmarks I did run! And the comparison to sharded is really just of interest to me to see if it's on par or slower.\r\n\r\nSo if other reviewers are happy, this definitely looks like a great improvement to me and addresses the request I made in the first place.\r\n\r\n> Why have you used num_procs 1 and 4 only?\r\n\r\nOh, no particular reason, I was just comparing to 4 shards on my desktop. Typically it's sufficient to go from 1 to 2-4 to see whether the distributed approach is faster or not. Once hit larger numbers you often run into bottlenecks like IO, and then numbers can be less representative. I hope it makes sense.", "Tested it with a larger dataset (`srwac`) and memory utilisation remained constant with no swap memory used. @lhoestq should I also add test for the same? Last time I tried this, I got `OSError: [Errno 12] Cannot allocate memory` in CircleCI tests" ]
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Closes #2663. I've tried adding multiprocessing in `to_json`. Here's some benchmarking I did to compare the timings of current version (say v1) and multi-proc version (say v2). I did this with `cpu_count` 4 (2015 Macbook Air) 1. Dataset name: `ascent_kb` - 8.9M samples (all samples were used, reporting this for a single run) v1- ~225 seconds for converting whole dataset to json v2- ~200 seconds for converting whole dataset to json 2. Dataset name: `lama` - 1.3M samples (all samples were used, reporting this for 2 runs) v1- ~26 seconds for converting whole dataset to json v2- ~23.6 seconds for converting whole dataset to json I think it's safe to say that v2 is 10% faster as compared to v1. Timings may improve further with better configuration. The only bottleneck I feel is writing to file from the output list. If we can improve that aspect then timings may improve further. Let me know if any changes/improvements can be done in this @stas00, @lhoestq, @albertvillanova. @lhoestq even suggested to extend this work with other export methods as well like `csv` or `parquet`.
https://api.github.com/repos/huggingface/datasets/issues/2747/timeline
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Cannot load `few-nerd` dataset
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[ "Hi @Mehrad0711,\r\n\r\nI'm afraid there is no \"canonical\" Hugging Face dataset named \"few-nerd\".\r\n\r\nThere are 2 kinds of datasets hosted at the Hugging Face Hub:\r\n- canonical datasets (their identifier contains no slash \"/\"): we, the Hugging Face team, supervise their implementation and we make sure they work correctly by means of our test suite\r\n- community datasets (their identifier contains a slash \"/\", where before the slash it is the username or the organization name): those datasets are uploaded to the Hub by the community, and we, the Hugging Face team, do not supervise them; it is the responsibility of the user/organization implementing them properly if they want them to be used by other users.\r\n\r\nIn this specific case, there is no \"canonical\" dataset named \"few-nerd\". On the other hand, there are two \"community\" datasets named \"few-nerd\":\r\n- [\"nbroad/few-nerd\"](https://huggingface.co/datasets/nbroad/few-nerd)\r\n- [\"dfki-nlp/few-nerd\"](https://huggingface.co/datasets/dfki-nlp/few-nerd)\r\n\r\nIf they were properly implemented, you should be able to load them this way:\r\n```python\r\n# \"nbroad/few-nerd\" community dataset\r\nds = load_dataset(\"nbroad/few-nerd\", \"supervised\")\r\n\r\n# \"dfki-nlp/few-nerd\" community dataset\r\nds = load_dataset(\"dfki-nlp/few-nerd\", \"supervised\")\r\n```\r\n\r\nHowever, they are not correctly implemented and both of them give errors:\r\n- \"nbroad/few-nerd\":\r\n ```\r\n TypeError: expected str, bytes or os.PathLike object, not dict\r\n ```\r\n- \"dfki-nlp/few-nerd\":\r\n ```\r\n ConnectionError: Couldn't reach https://cloud.tsinghua.edu.cn/f/09265750ae6340429827/?dl=1\r\n ```\r\n\r\nYou could try to contact their users/organizations to inform them about their bugs and ask them if they are planning to fix them. Alternatively you could try to implement your own script for this dataset.", "Thanks @albertvillanova for your detailed explanation! I will resort to my own scripts for now. " ]
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## Describe the bug Cannot load `few-nerd` dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset load_dataset('few-nerd', 'supervised') ``` ## Actual results Executing above code will give the following error: ``` Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError). Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53... Traceback (most recent call last): File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split disable=bool(logging.get_verbosity() == logging.NOTSET), File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__ for obj in iterable: File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples with open(filepath, encoding="utf-8") as f: FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json' ``` The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Python version: 3.8 - PyArrow version: 1.0.1
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added semeval18_emotion_classification dataset
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[ "For training the multilabel classifier, I would combine the labels into a list, for example for the English dataset:\r\n\r\n```\r\ndfpre=pd.read_csv(path+\"2018-E-c-En-train.txt\",sep=\"\\t\")\r\ndfpre['list'] = dfpre[dfpre.columns[2:]].values.tolist()\r\ndf = dfpre[['Tweet', 'list']].copy()\r\ndf.rename(columns={'list': 'labels'}, inplace=True)\r\n```", "Hi @maxpel , have you had a chance to take my comments into account ?\r\n\r\nLet me know if you have questions or if I can help :)", "Hi @lhoestq ! I did take your comments into account, changed the naming and tried to add dummy data (manually). I am not sure if the dummy data is correct, maybe you can take a look at that.\r\nThe model card is still missing as I am currently very busy.", "Thanks ! The dummy data looks all good, good job :)\r\n\r\nThe CI error can be fixed by merging `master` into your branch\r\n```bash\r\ngit fetch upstream\r\ngit merge upstream/master\r\n```", "Hi! I just added the model card and I did the merge you showed above. Should I then add and commit again? The CI error is still there right now." ]
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I added the data set of SemEval 2018 Task 1 (Subtask 5) for emotion detection in three languages. ``` datasets-cli test datasets/semeval18_emotion_classification/ --save_infos --all_configs RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_semeval18_emotion_classification ``` Both commands ran successfully. I couldn't create the dummy data (the files are tsvs but have .txt ending, maybe that's the problem?) and therefore the test on the dummy data fails, maybe someone can help here. I also formatted the code: ``` black --line-length 119 --target-version py36 datasets/semeval18_emotion_classification/ isort datasets/semeval18_emotion_classification/ flake8 datasets/semeval18_emotion_classification/ ``` That's the publication for reference: Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001
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Fix key by recreating metadata JSON for journalists_questions dataset
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Close #2743.
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Dataset JSON is incorrect
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[ "As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:\r\n> Indeed there is some problem/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...\r\nIn the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :\r\n```\r\nIn [25]: builder = datasets.load_dataset_builder(\"journalists_questions\")\r\nIn [26]: builder.info.splits\r\nOut[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}\r\n```\r\n\r\nAfter regenerating the metadata JSON file for this dataset, I get the right key:\r\n```\r\n{\"plain_text\": {\"description\": \"The journalists_questions corpus (\r\n```", "Thanks!" ]
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## Describe the bug The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json. The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead. ```json { "journalists_questions": { "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n", ... ``` ## Steps to reproduce the bug Look at the files. ## Expected results The first key should be `plain_text`: ```json { "plain_text": { "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n", ... ``` ## Actual results ```json { "journalists_questions": { "description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n", ... ```
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Improve detection of streamable file types
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[ "maybe we should rather attempt to download a `Range` from the server and see if it works?" ]
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**Is your feature request related to a problem? Please describe.** ```python from datasets import load_dataset_builder from datasets.utils.streaming_download_manager import StreamingDownloadManager builder = load_dataset_builder("journalists_questions", name="plain_text") builder._split_generators(StreamingDownloadManager(base_path=builder.base_path)) ``` raises ``` NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet ``` But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed: ```bash curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U 506938088174940160 yes 1 302221719412830209 yes 1 289761704907268096 yes 1 513820885032378369 yes % ``` Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats. **Describe the solution you'd like** In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`. **Describe alternatives you've considered** Add a variable in the dataset script to set the data format by hand.
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Add Hypersim dataset
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## Adding a Dataset - **Name:** Hypersim - **Description:** photorealistic synthetic dataset for holistic indoor scene understanding - **Paper:** *link to the dataset paper if available* - **Data:** https://github.com/apple/ml-hypersim Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Update release instructions
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Update release instructions.
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Pass tokenize to sacrebleu only if explicitly passed by user
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Next `sacrebleu` release (v2.0.0) will remove `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15 This PR passes `tokenize` to `sacrebleu` only if explicitly passed by the user, otherwise it will not pass it (and `sacrebleu` will use its default, no matter where it is and how it is called). Close: #2737.
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Sunbird AI Ugandan low resource language dataset
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[ "Hi @ak3ra , have you had a chance to take my comments into account ?\r\n\r\nLet me know if you have questions or if I can help :)" ]
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Multi-way parallel text corpus of 5 key Ugandan languages for the task of machine translation.
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SacreBLEU update
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[ "Hi @devrimcavusoglu, \r\nI tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:\r\n```\r\nsacrebleu = datasets.load_metric('sacrebleu')\r\npredictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\r\nreferences = [[\"It is a guide to action that ensures that the military will forever heed Party commands\"]] # double brackets here should do the work\r\nresults = sacrebleu.compute(predictions=predictions, references=references)\r\nprint(results)\r\noutput: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}\r\n```", "@bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ?", "You can check that here, I've reproduced your code in [Google colab](https://colab.research.google.com/drive/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https://github.com/pytorch/fairseq/issues/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work.", "It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing\r\n\r\nI'm reopening this Issue and making a Pull Request to fix it." ]
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With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error. AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER' this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0` ## Steps to reproduce the bug ```python sacrebleu= datasets.load_metric('sacrebleu') predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"] references = ["It is a guide to action that ensures that the military will forever heed Party commands"] results = sacrebleu.compute(predictions=predictions, references=references) print(results) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.11.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: Python 3.8.0 - PyArrow version: 5.0.0
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Add Microsoft Building Footprints dataset
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[ "Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!" ]
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## Adding a Dataset - **Name:** Microsoft Building Footprints - **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge. - **Paper:** *link to the dataset paper if available* - **Data:** https://www.microsoft.com/en-us/maps/building-footprints - **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Reported by: @sashavor
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Add Open Buildings dataset
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## Adding a Dataset - **Name:** Open Buildings - **Description:** A dataset of building footprints to support social good applications. Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa. See: "Mapping Africa's Buildings with Satellite Imagery" https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html - **Paper:** https://arxiv.org/abs/2107.12283 - **Data:** https://sites.research.google/open-buildings/ - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Reported by: @osanseviero
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Update BibTeX entry
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Update BibTeX entry.
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Add missing parquet known extension
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This code was failing because the parquet extension wasn't recognized: ```python from datasets import load_dataset base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/" data_files = {"train": base_url + "wikipedia-train.parquet"} wiki = load_dataset("parquet", data_files=data_files, split="train", streaming=True) ``` It raises ```python NotImplementedError: Extraction protocol for file at https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/wikipedia-train.parquet is not implemented yet ``` I added `parquet` to the list of known extensions EDIT: added pickle, conllu, xml extensions as well
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Updated TTC4900 Dataset
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[ "@lhoestq, lütfen bu PR'ı gâzden geçirebilir misiniz?", "> Thanks ! This looks all good now :)\r\n\r\nThanks" ]
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- The source address of the TTC4900 dataset of [@savasy](https://github.com/savasy) has been updated for direct download. - Updated readme.
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Adding to_tf_dataset method
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[ "This seems to be working reasonably well in testing, and performance is way better. `tf.py_function` has been dropped for an input generator, but I moved as much of the code as possible outside the generator to allow TF to compile it correctly. I also avoid `tf.RaggedTensor` at all costs, and do the shuffle in the dataset followed by accessing sequential chunks, instead of shuffling an index tensor. The combination of all of these gives us a more flexible data loader as well as a ~20X boost in performance compared to the first solution.", "I made a change to the `TFFormatter` in this PR that will need some changes to the tests, so I wanted to ping @lhoestq and anyone else before I made those changes.\r\n\r\nThe key problem is that up until now the `TFFormatter` always returns `RaggedTensor`, created using the very slow `tf.ragged.constant` function. This is a big performance penalty, but it's also (imo) surprising for users - `RaggedTensor` handles tensors where one dimension has variable length. This is a good choice for tokenized datasets with variable sequence length, but it's an odd choice when the non-batch dimensions are constant, such as in image datasets, or in datasets where all samples are padded to the same length (e.g. for TPU training).\r\n\r\nThe change I made was to try to return standard `Tensor` objects instead of `RaggedTensor` when all the samples in the batch had the same shape, and if that was not the case to fall back to fast `RaggedTensor` creation with `tf.ragged.stack`, and only falling back to the very slow `tf.ragged.constant` function as a last resort. I think this will match user expectations in most cases and greatly improve performance, but it's a (very slightly) breaking change, so any feedback is welcome!", "Also I really can't emphasize enough how slow `tf.ragged.constant` is, it's bad enough to create a data pipeline bottleneck in more or less any training setup:\r\n![image](https://user-images.githubusercontent.com/12866554/131121785-4fbe942a-1ca4-4af6-a9da-cd6d5ea67b30.png)\r\n", "Hi @lhoestq, the tests have been modified and everything is passing. The Windows tests look to be failing for an unrelated reason, but other than that I'm ready to merge if you are!", "Hi @Rocketknight1 ! Feel free to merge `master` into this branch to fix and run the full CI :)", "@lhoestq rebased onto master and it looks good! I'm doing some testing with new notebook examples, but are you happy to merge if that looks good?", "@lhoestq No, I'm happy to merge it as-is and add documentation afterwards!" ]
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Oh my **god** do not merge this yet, it's just a draft. I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work. A number of issues need to be resolved before it's ready to merge, though: 1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too? 2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon. 3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer? 4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is.
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Update CommonVoice with new release
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[ "cc @patrickvonplaten?", "Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj \r\n", "Also see: https://github.com/common-voice/common-voice-bundler/issues/15" ]
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## Adding a Dataset - **Name:** CommonVoice mid-2021 release - **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220). - **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812 - **Data:** https://commonvoice.mozilla.org/en/datasets - **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
https://api.github.com/repos/huggingface/datasets/issues/2730/timeline
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2,729
Fix IndexError while loading Arabic Billion Words dataset
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Catch `IndexError` and ignore that record. Close #2727.
https://api.github.com/repos/huggingface/datasets/issues/2729/timeline
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Concurrent use of same dataset (already downloaded)
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[ "Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded file.", "If i have two jobs that use the same dataset. I got :\r\n\r\n\r\n File \"compute_measures.py\", line 181, in <module>\r\n train_loader, val_loader, test_loader = get_dataloader(args)\r\n File \"/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py\", line 69, in get_dataloader\r\n dataset_train = load_dataset('paws', \"labeled_final\", split='train', download_mode=\"reuse_cache_if_exists\")\r\n File \"/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py\", line 748, in load_dataset\r\n use_auth_token=use_auth_token,\r\n File \"/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py\", line 582, in download_and_prepare\r\n self._save_info()\r\n File \"/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py\", line 690, in _save_info\r\n self.info.write_to_directory(self._cache_dir)\r\n File \"/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/info.py\", line 195, in write_to_directory\r\n with open(os.path.join(dataset_info_dir, config.LICENSE_FILENAME), \"wb\") as f:\r\nFileNotFoundError: [Errno 2] No such file or directory: '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/LICENSE'", "You can probably have a solution much faster than me (first time I use the library). But I suspect some write function are used when loading the dataset from cache.", "I have the same issue:\r\n```\r\nTraceback (most recent call last):\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py\", line 652, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py\", line 1040, in _prepare_split\r\n with ArrowWriter(features=self.info.features, path=fpath) as writer:\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/arrow_writer.py\", line 192, in __init__\r\n self.stream = pa.OSFile(self._path, \"wb\")\r\n File \"pyarrow/io.pxi\", line 829, in pyarrow.lib.OSFile.__cinit__\r\n File \"pyarrow/io.pxi\", line 844, in pyarrow.lib.OSFile._open_writable\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 97, in pyarrow.lib.check_status\r\nFileNotFoundError: [Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\nDuring handling of the above exception, another exception occurred:\r\nTraceback (most recent call last):\r\n File \"/dccstor/tslm/elron/tslm-gen/train.py\", line 510, in <module>\r\n main()\r\n File \"/dccstor/tslm/elron/tslm-gen/train.py\", line 246, in main\r\n datasets = prepare_dataset(dataset_args, logger)\r\n File \"/dccstor/tslm/elron/tslm-gen/data.py\", line 157, in prepare_dataset\r\n datasets = load_dataset(extension, data_files=data_files, split=dataset_split, cache_dir=dataset_args.dataset_cache_dir, na_filter=False, download_mode=dataset_args.dataset_generate_mode)\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/load.py\", line 742, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py\", line 574, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py\", line 654, in _download_and_prepare\r\n raise OSError(\r\nOSError: Cannot find data file. \r\nOriginal error:\r\n[Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory\r\n```" ]
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## Describe the bug When launching several jobs at the same time loading the same dataset trigger some errors see (last comments). ## Steps to reproduce the bug export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do export OUTPUT_DIR=${MODEL}_${TASK_NAME} sbatch --job-name=${OUTPUT_DIR} \ --gres=gpu:1 \ --no-requeue \ --cpus-per-task=10 \ --hint=nomultithread \ --time=1:00:00 \ --output=jobinfo/${OUTPUT_DIR}_%j.out \ --error=jobinfo/${OUTPUT_DIR}_%j.err \ --qos=qos_gpu-t4 \ --wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL" done done ```python # Sample code to reproduce the bug dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists") dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True).select(list(range(args.filter))) dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists") dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True).select(list(range(args.filter, args.filter + 5000))) dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists") dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True) ``` ## Expected results I believe I am doing something wrong with the objects. ## Actual results Traceback (most recent call last): File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split check_duplicates=True, File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__ self.stream = pa.OSFile(self._path, "wb") File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__ File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory During handling of the above exception, another exception occurred: Traceback (most recent call last): File "compute_measures.py", line 181, in <module> train_loader, val_loader, test_loader = get_dataloader(args) File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists") File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset use_auth_token=use_auth_token, File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare + str(e) OSError: Cannot find data file. Original error: [Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets==1.8.0 - Platform: linux (jeanzay) - Python version: pyarrow==2.0.0 - PyArrow version: 3.7.8
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Error in loading the Arabic Billion Words Corpus
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[ "I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:\r\nFor the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:\r\n```\r\n<Techreen>\r\n <ID>TRN_ARB_0248167</ID>\r\n <URL>http://tishreen.news.sy/tishreen/public/read/248240</URL>\r\n <Headline>Removed, because the original articles was in English</Headline>\r\n</Techreen>\r\n```\r\n\r\nand all the 288 faulty records in the `Almustaqbal` config look like:\r\n```\r\n<Almustaqbal>\r\n <ID>MTL_ARB_0028398</ID>\r\n \r\n <URL>http://www.almustaqbal.com/v4/article.aspx?type=NP&ArticleID=179015</URL>\r\n <Headline> Removed because it is not available in the original site</Headline>\r\n</Almustaqbal>\r\n```\r\n\r\nso the error is happening because the articles were removed and so the associated records lack the `Text` tag.\r\n\r\nIn this case, I think we just need to catch the `IndexError` and ignore (pass) it.\r\n", "Thanks @M-Salti for reporting this issue and for your investigation.\r\n\r\nIndeed, those `IndexError` should be catched and the corresponding record should be ignored.\r\n\r\nI'm opening a Pull Request to fix it." ]
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CONTRIBUTOR
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## Describe the bug I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset. ## Steps to reproduce the bug ```python load_dataset("arabic_billion_words", "Techreen") load_dataset("arabic_billion_words", "Almustaqbal") ``` ## Expected results The datasets load succefully. ## Actual results ```python _extract_tags(self, sample, tag) 139 if len(out) > 0: 140 break --> 141 return out[0] 142 143 def _clean_text(self, text): IndexError: list index out of range ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.10.2 - Platform: Ubuntu 18.04.5 LTS - Python version: 3.7.11 - PyArrow version: 3.0.0
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Typo fix `tokenize_exemple`
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There is a small typo in the main README.md
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Pass use_auth_token to request_etags
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Fix #2724.
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404 Error when loading remote data files from private repo
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[ "I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/35b5e4bc0cb2ed896e40f3eb2a4aa3de1cb1a6c5/src/datasets/builder.py#L160-L160", "Yes, I remember having properly implemented that: \r\n- https://github.com/huggingface/datasets/commit/7a9c62f7cef9ecc293f629f859d4375a6bd26dc8#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R160\r\n- https://github.com/huggingface/datasets/pull/2628/commits/6350a03b4b830339a745f7b1da46ece784ca734c\r\n\r\nBut a subsequent refactoring accidentally removed it...", "I have opened a PR to fix it @lewtun." ]
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## Describe the bug When loading remote data files from a private repo, a 404 error is raised. ## Steps to reproduce the bug ```python url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset") dset = load_dataset("json", data_files=url, use_auth_token=True) # HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl ``` ## Expected results Load dataset. ## Actual results 404 Error.
https://api.github.com/repos/huggingface/datasets/issues/2724/timeline
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Fix en subset by modifying dataset_info with correct validation infos
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- Related to: #2682 We correct the values of `en` subset concerning the expected validation values (both `num_bytes` and `num_examples`. Instead of having: `{"name": "validation", "num_bytes": 828589180707, "num_examples": 364868892, "dataset_name": "c4"}` We replace with correct values: `{"name": "validation", "num_bytes": 825767266, "num_examples": 364608, "dataset_name": "c4"}` There are still issues with validation with other subsets, but I can't download all the files, unzip to check for the correct number of bytes. (If you have a fast way to obtain those values for other subsets, I can do this in this PR ... otherwise I can't spend those resources)
https://api.github.com/repos/huggingface/datasets/issues/2723/timeline
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2,722
Missing cache file
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[ "This could be solved by going to the glue/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset.", "Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset" ]
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Strangely missing cache file after I restart my program again. `glue_dataset = datasets.load_dataset('glue', 'sst2')` `FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
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954,238,230
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2,721
Deal with the bad check in test_load.py
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[ "Hi ! I did a change for this test already in #2662 :\r\n\r\nhttps://github.com/huggingface/datasets/blob/00686c46b7aaf6bfcd4102cec300a3c031284a5a/tests/test_load.py#L312-L316\r\n\r\n(though I have to change the variable name `m_combined_path` to `m_url` or something)\r\n\r\nI guess it's ok to remove this check for now :)" ]
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This PR removes a check that's been added in #2684. My intention with this check was to capture an URL in the error message, but instead, it captures a substring of the previous regex match in the test function. Another option would be to replace this check with: ```python m_paths = re.findall(r"\S*_dummy/_dummy.py\b", str(exc_info.value)) # on Linux this will match an URL as well as a local_path due to different os.sep, so take the last element (an URL always comes last in the list) assert len(m_paths) > 0 and is_remote_url(m_paths[-1]) # is_remote_url comes from datasets.utils.file_utils ``` @lhoestq Let me know which one of these two approaches (delete or replace) do you prefer?
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fix: πŸ› fix two typos
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Use ETag in streaming mode to detect resource updates
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**Is your feature request related to a problem? Please describe.** I want to cache data I generate from processing a dataset I've loaded in streaming mode, but I've currently no way to know if the remote data has been updated or not, thus I don't know when to invalidate my cache. **Describe the solution you'd like** Take the ETag of the data files into account and provide it (directly or through a hash) to give a signal that I can invalidate my cache. **Describe alternatives you've considered** None
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New documentation structure
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[ "I just did some minor changes + added some content in these sections: share, about arrow, about cache\r\n\r\nFeel free to mark this PR as ready for review ! :)", "I just separated the `Share` How-to page into three pages: share, dataset_script and dataset_card.\r\n\r\nThis way in the share page we can explain in more details how to share a community or a canonical dataset - focus in their differences and the steps to upload them.\r\n\r\nAlso given that making a dataset script or a dataset card both require several steps, I feel like it's better to have dedicated pages for them.\r\n\r\nLet me know what you think @stevhliu and others. We can still revert this change if you feel like it was better with everything in the same place.", "I just added some minor changes to match the style, fix typos, etc. Great work on the conceptual guides, I learned a lot from them and I'm sure they will help a lot of other people too!\r\n\r\nI am fine with splitting `Share` into three separate pages. I think this probably makes it easier for users to navigate, instead of having to scroll up and down on a really long single page.", "Thanks a lot for all the suggestions ! I'm doing the final changes based on the remaining comments, then we can merge and release v1.12 of `datasets` and the new documentation ^^", "Alright I think I took all the suggestions and comments into account :)\r\nThanks everyone for the help !" ]
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Organize Datasets documentation into four documentation types to improve clarity and discoverability of content. **Content to add in the very short term (feel free to add anything I'm missing):** - A discussion on why Datasets uses Arrow that includes some context and background about why we use Arrow. Would also be great to talk about Datasets speed and performance here, and if you can share any benchmarking/tests you did, that would be awesome! Finally, a discussion about how memory-mapping frees the user from RAM constraints would be very helpful. - Explain why you would want to disable or override verifications when loading a dataset. - If possible, include a code sample of when the number of elements in the field of an output dictionary aren’t the same as the other fields in the output dictionary (taken from the [note](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset) here).
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Fix shuffle on IterableDataset that disables batching in case any functions were mapped
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Made a very minor change to fix the issue#2716. Added the missing argument in the constructor call. As discussed in the bug report, the change is made to prevent the `shuffle` method call from resetting the value of `batched` attribute in `MappedExamplesIterable` Fix #2716.
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Calling shuffle on IterableDataset will disable batching in case any functions were mapped
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[ "Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)", "Have raised the PR [here](https://github.com/huggingface/datasets/pull/2717)", "Fixed by #2717." ]
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When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False` I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is `self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`. To remedy the problem we can change this line to `self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
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Update PAN-X data URL in XTREME dataset
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[ "Merging since the CI is just about missing infos in the dataset card" ]
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Related to #2710, #2691.
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add more precise information for size
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[ "We already have this information in the dataset_infos.json files of each dataset.\r\nMaybe we can parse these files in the backend to return their content with the endpoint at huggingface.co/api/datasets\r\n\r\nFor now if you want to access this info you have to load the json for each dataset. For example:\r\n- for a dataset on github like `squad` \r\n- https://raw.githubusercontent.com/huggingface/datasets/master/datasets/squad/dataset_infos.json\r\n- for a community dataset on the hub like `lhoestq/squad`:\r\n https://huggingface.co/datasets/lhoestq/squad/resolve/main/dataset_infos.json" ]
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For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets.
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Enumerate all ner_tags values in WNUT 17 dataset
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This PR does: - Enumerate all ner_tags in dataset card Data Fields section - Add all metadata tags to dataset card Close #2709.
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Update WikiANN data URL
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[ "We have to update the URL in the XTREME benchmark as well:\r\n\r\nhttps://github.com/huggingface/datasets/blob/0dfc639cec450ed8762a997789a2ed63e63cdcf2/datasets/xtreme/xtreme.py#L411-L411\r\n\r\n" ]
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WikiANN data source URL is no longer accessible: 404 error from Dropbox. We have decided to host it at Hugging Face. This PR updates the data source URL, the metadata JSON file and the dataset card. Close #2691.
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Missing documentation for wnut_17 (ner_tags)
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[ "Hi @maxpel, thanks for reporting this issue.\r\n\r\nIndeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.\r\n\r\nAs the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`. \r\n\r\nEach of these entity types has 2 possible IOB2 format tags: \r\n- `B-`: to indicate that the token is the beginning of an entity name, and the \r\n- `I-`: to indicate that the token is inside an entity name. \r\n\r\nAdditionally, there is the standalone IOB2 tag \r\n- `O`: that indicates that the token belongs to no named entity. \r\n\r\nIn total there are 13 possible tags, which correspond to the following integer numbers:\r\n\r\n0. `O`\r\n1. `B-corporation`\r\n2. `I-corporation`\r\n3. `B-creative-work`\r\n4. `I-creative-work`\r\n5. `B-group`\r\n6. `I-group`\r\n7. `B-location`\r\n8. `I-location`\r\n9. `B-person`\r\n10. `I-person`\r\n11. `B-product`\r\n12. `I-product`" ]
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On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases: `ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).` I trained a model with the data and it gives me 13 classes: ``` "id2label": { "0": 0, "1": 1, "2": 2, "3": 3, "4": 4, "5": 5, "6": 6, "7": 7, "8": 8, "9": 9, "10": 10, "11": 11, "12": 12 } "label2id": { "0": 0, "1": 1, "10": 10, "11": 11, "12": 12, "2": 2, "3": 3, "4": 4, "5": 5, "6": 6, "7": 7, "8": 8, "9": 9 } ``` The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match: ``` 1. person 2. location (including GPE, facility) 3. corporation 4. product (tangible goods, or well-defined services) 5. creative-work (song, movie, book and so on) 6. group (subsuming music band, sports team, and non-corporate organisations) ``` I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me. Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner!
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QASC: incomplete training set
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[ "Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],\r\n num_rows: 8134\r\n})\r\n\r\nIn [11]: ds[\"train\"].shape\r\nOut[11]: (8134, 8)\r\n```\r\nand the content of the last 5 examples is:\r\n```ipython\r\nIn [12]: for i in range(8129, 8134):\r\n ...: print(json.dumps(ds[\"train\"][i]))\r\n ...:\r\n{\"id\": \"3KAKFY4PGU1LGXM77JAK2700NGCI3X\", \"question\": \"Chitin can be used for protection by whom?\", \"choices\": {\"text\": [\"Fungi\", \"People\", \"Man\", \"Fish\", \"trees\", \"Dogs\", \"animal\", \"Birds\"], \"label\": [\"A\", \"B\",\r\n \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"D\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish scales are also composed of chitin.\", \"combinedfact\": \"Chitin can be used for prote\r\nction by fish.\", \"formatted_question\": \"Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds\"}\r\n{\"id\": \"336YQZE83VDAQVZ26HW59X51JZ9M5M\", \"question\": \"Which type of animal uses plates for protection?\", \"choices\": {\"text\": [\"squids\", \"reptiles\", \"sea urchins\", \"fish\", \"amphibians\", \"Frogs\", \"mammals\", \"salm\r\non\"], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"B\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Reptiles have scales or plates.\", \"combinedfact\": \"Reptiles use\r\n their plates for protection.\", \"formatted_question\": \"Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon\"}\r\n{\"id\": \"3WZ36BJEV3FGS66VGOOUYX0LN8GTBU\", \"question\": \"What are used for protection by fish?\", \"choices\": {\"text\": [\"scales\", \"fins\", \"streams.\", \"coral\", \"gills\", \"Collagen\", \"mussels\", \"whiskers\"], \"label\": [\"\r\nA\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"A\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are backboned aquatic animals.\", \"combinedfact\": \"scales are used for prote\r\nction by fish \", \"formatted_question\": \"What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers\"}\r\n{\"id\": \"3Z2R0DQ0JHDKFAO2706OYIXGNA4E28\", \"question\": \"What are pangolins covered in?\", \"choices\": {\"text\": [\"tunicates\", \"Echinoids\", \"shells\", \"exoskeleton\", \"blastoids\", \"barrel-shaped\", \"protection\", \"white\"\r\n], \"label\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"G\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Pangolins have an elongate and tapering body covered above with ov\r\nerlapping scales.\", \"combinedfact\": \"Pangolins are covered in overlapping protection.\", \"formatted_question\": \"What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids\r\n (F) barrel-shaped (G) protection (H) white\"}\r\n{\"id\": \"3PMBY0YE272GIWPNWIF8IH5RBHVC9S\", \"question\": \"What are covered with protection?\", \"choices\": {\"text\": [\"apples\", \"trees\", \"coral\", \"clams\", \"roses\", \"wings\", \"hats\", \"fish\"], \"label\": [\"A\", \"B\", \"C\", \"D\r\n\", \"E\", \"F\", \"G\", \"H\"]}, \"answerKey\": \"H\", \"fact1\": \"scales are used for protection by scaled animals\", \"fact2\": \"Fish are covered with scales.\", \"combinedfact\": \"Fish are covered with protection\", \"formatted_q\r\nuestion\": \"What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish\"}\r\n```\r\n\r\nCould you please load again your dataset and print its shape, like this:\r\n```python\r\nds = load_dataset(\"qasc\", split=\"train)\r\nprint(ds.shape)\r\n```\r\nand confirm which is your output?", "Hmm .... it must have been a mistake on my side. Sorry for the hassle! " ]
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CONTRIBUTOR
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## Describe the bug The training instances are not loaded properly. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("qasc", script_version='1.10.2') def load_instances(split): instances = dataset[split] print(f"split: {split} - size: {len(instances)}") for x in instances: print(json.dumps(x)) load_instances('test') load_instances('validation') load_instances('train') ``` ## results For test and validation, we can see the examples in the output (which is good!): ``` split: test - size: 920 {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"} {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"} {"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"} ... ``` However, only a few instances are loaded for the training split, which is not correct. ## Environment info - `datasets` version: '1.10.2' - Platform: MaxOS - Python version:3.7 - PyArrow version: 3.0.0
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404 Not Found Error when loading LAMA dataset
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[ "Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)", "Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the latest version: `pip install -U datasets`?\r\n\r\nThanks @stevhliu for the hint to fix this! ;)", "@stevhliu @albertvillanova updating to the latest version of datasets did in fact fix this issue. Thanks a lot for your help!" ]
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The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download: Steps to Reproduce: 1. `from datasets import load_dataset` 2. `dataset = load_dataset('lama', 'trex')`. Results: `FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py`
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404 not found error on loading WIKIANN dataset
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[ "Hi @ronbutan, thanks for reporting.\r\n\r\nYou are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.\r\n\r\nWe have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https://github.com/huggingface/datasets/issues/2691#issuecomment-885463027\r\n\r\nI close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates." ]
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## Describe the bug Unable to retreive wikiann English dataset ## Steps to reproduce the bug ```python from datasets import list_datasets, load_dataset, list_metrics, load_metric WIKIANN = load_dataset("wikiann","en") ``` ## Expected results Colab notebook should display successful download status ## Actual results FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1 ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.10.1 - Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.11 - PyArrow version: 3.0.0
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Fix pick default config name message
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The error message to tell which config name to load is not displayed. This is because in the code it was considering the config kwargs to be non-empty, which is a special case for custom configs created on the fly. It appears after this change: https://github.com/huggingface/datasets/pull/2659 I fixed that by making the config kwargs empty by default, even if default parameters are passed Fix https://github.com/huggingface/datasets/issues/2703
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2,703
Bad message when config name is missing
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When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name. However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message: ```python import datasets datasets.load_dataset("glue") ``` raises ```python AttributeError: 'BuilderConfig' object has no attribute 'text_features' ``` instead of ```python ValueError: Config name is missing. Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax'] Example of usage: `load_dataset('glue', 'cola')` ```
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Update BibTeX entry
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Update BibTeX entry.
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https://api.github.com/repos/huggingface/datasets/issues/2701
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950,422,403
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2,701
Fix download_mode docstrings
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Fix `download_mode` docstrings.
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from datasets import Dataset is failing
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[ "Hi @kswamy15, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`" ]
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug from datasets import Dataset ``` ## Expected results A clear and concise description of the expected results. ## Actual results Specify the actual results or traceback. /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>() 25 import posixpath 26 import requests ---> 27 from tqdm.contrib.concurrent import thread_map 28 29 from .. import __version__, config, utils ModuleNotFoundError: No module named 'tqdm.contrib.concurrent' --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: latest version as of 07/21/2021 - Platform: Google Colab - Python version: 3.7 - PyArrow version:
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950,221,226
MDU6SXNzdWU5NTAyMjEyMjY=
2,699
cannot combine splits merging and streaming?
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[ "Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations" ]
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this does not work: `dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)` with error: `ValueError: Bad split: train+validation. Available splits: ['train', 'validation']` these work: `dataset = datasets.load_dataset('mc4','iw',split='train+validation')` `dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)` `dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)` i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits
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Ignore empty batch when writing
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This prevents an schema update with unknown column types, as reported in #2644. This is my first attempt at fixing the issue. I tested the following: - First batch returned by a batched map operation is empty. - An intermediate batch is empty. - `python -m unittest tests.test_arrow_writer` passes. However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable.
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Fix import on Colab
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[ "@lhoestq @albertvillanova - It might be a good idea to have a patch release after this gets merged (presumably tomorrow morning when you're around). The Colab issue linked to this PR is a pretty big blocker. " ]
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Fix #2695, fix #2700.
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Add support for disable_progress_bar on Windows
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[ "The CI failure seems unrelated to this PR (probably has something to do with Transformers)." ]
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This PR is a continuation of #2667 and adds support for `utils.disable_progress_bar()` on Windows when using multiprocessing. This [answer](https://stackoverflow.com/a/6596695/14095927) on SO explains it nicely why the current approach (with calling `utils.is_progress_bar_enabled()` inside `Dataset._map_single`) would not work on Windows.
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Cannot import load_dataset on Colab
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[ "I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n 3 \r\n 4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n 6 from datasets import load_dataset, load_metric\r\n 7 from dataclasses import dataclass\r\n\r\n7 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()\r\n 25 import posixpath\r\n 26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n 28 \r\n 29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n```", "@phosseini \r\nI think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\nFor now I just downgraded to 1.9.0 and it is working fine.", "> @phosseini\r\n> I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\n> For now I just downgraded to 1.9.0 and it is working fine.\r\n\r\nSame here, downgraded to 1.9.0 for now and works fine.", "Hi, \r\n\r\nupdating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:\r\n```\r\n!pip install tqdm --upgrade\r\n```", "Hi @bayartsogt-ya and @phosseini, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library: \r\n```\r\n!pip install -U tqdm\r\n```" ]
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## Describe the bug Got tqdm concurrent module not found error during importing load_dataset from datasets. ## Steps to reproduce the bug Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error On colab: ```python !pip install datasets from datasets import load_dataset ``` ## Expected results Works without error ## Actual results Specify the actual results or traceback. ``` ModuleNotFoundError Traceback (most recent call last) <ipython-input-2-8cc7de4c69eb> in <module>() ----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value 2 from sklearn.metrics import mean_squared_error /usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>() 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>() 40 from tqdm.auto import tqdm 41 ---> 42 from datasets.tasks.text_classification import TextClassification 43 44 from . import config, utils /usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>() 1 from typing import Optional 2 ----> 3 from ..utils.logging import get_logger 4 from .automatic_speech_recognition import AutomaticSpeechRecognition 5 from .base import TaskTemplate /usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>() 19 20 from . import logging ---> 21 from .download_manager import DownloadManager, GenerateMode 22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed 23 from .mock_download_manager import MockDownloadManager /usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>() 24 25 from .. import config ---> 26 from .file_utils import ( 27 DownloadConfig, 28 cached_path, /usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>() 25 import posixpath 26 import requests ---> 27 from tqdm.contrib.concurrent import thread_map 28 29 from .. import __version__, config, utils ModuleNotFoundError: No module named 'tqdm.contrib.concurrent' ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.10.0 - Platform: Colab - Python version: 3.7.11 - PyArrow version: 3.0.0
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fix: πŸ› change string format to allow copy/paste to work in bash
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Before: copy/paste resulted in an error because the square bracket characters `[]` are special characters in bash
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Fix OSCAR Esperanto
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The Esperanto part (original) of OSCAR has the wrong number of examples: ```python from datasets import load_dataset raw_datasets = load_dataset("oscar", "unshuffled_original_eo") ``` raises ```python NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=314188336, num_examples=121171, dataset_name='oscar'), 'recorded': SplitInfo(name='train', num_bytes=314064514, num_examples=121168, dataset_name='oscar')}] ``` I updated the number of expected examples in dataset_infos.json cc @sgugger
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xtreme / pan-x cannot be downloaded
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[ "Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.", "Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bjve/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case?", "Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.\r\n\r\nAnyway, I have opened an issue in the GitHub repository responsible for the original dataset: https://github.com/afshinrahimi/mmner/issues/4\r\nI have also contacted the maintainer by email.\r\n\r\nI'll keep you informed with their answer.", "Reply from the author/maintainer: \r\n> Will fix the issue and let you know during the weekend.", "The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset." ]
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## Describe the bug Dataset xtreme / pan-x cannot be loaded Seems related to https://github.com/huggingface/datasets/pull/2326 ## Steps to reproduce the bug ```python dataset = load_dataset("xtreme", "PAN-X.fr") ``` ## Expected results Load the dataset ## Actual results ``` FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1 ``` ## Environment info - `datasets` version: 1.9.0 - Platform: macOS-11.4-x86_64-i386-64bit - Python version: 3.8.11 - PyArrow version: 4.0.1
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[ "Thanks for all the comments and for the corrections in the docs !\r\n\r\nAbout all the points you mentioned:\r\n\r\n> * the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)\r\n\r\nYes good idea\r\n\r\n> * \"If you’d like to play with the examples, you must install it from source.\" in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these \"examples\"?)\r\n\r\nIt refers to examples scripts inside the git repository of the library, see the `examples` folder in the `transformers` repo.\r\nWe don't have examples yet in the git repo of `datasets` as in transformers. So currently there are no examples. Maybe we can just remove this sentence from the docs for now\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html: \"or AWS bucket if it’s not already stored in the library\". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the \"AWS bucket\" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.\r\n\r\nThis is outdated and must be replaced by\r\n```\r\nor from the Hugging Face Hub if it’s not already stored in the library\r\n```\r\n\r\n> * example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by [Enable auto-download for PAN-X / Wikiann domain in XTREMEΒ #2326](https://github.com/huggingface/datasets/pull/2326). Also: see [xtreme / pan-x cannot be downloadedΒ #2691](https://github.com/huggingface/datasets/issues/2691) for a bug on this specific dataset.\r\n\r\nWe can replace the `XTREME` `PANX` dataste by `matinf` instead for example\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says \"After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:\", but the following example does not show how to use `data_dir`\r\n\r\nLet's add `data_dir=\"path/to/your/downloaded/data\"` for example\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.\r\n\r\nCurrently there's no documentation for the CSV loader config. Maybe we can add the docstrings to the `CsvConfig` class to explain the parameters and how it works, and then redirect to the doc of this class in this section of the documentation.\r\n\r\n> * in the API reference (docstrings) I would prefer \"SOURCE\" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)\r\n\r\nThis is the same as in `transformers`, not sure if this is a big issue\r\n\r\n> * it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)\r\n\r\nThe function `disable_progress_bar` should definitely be in the docs, thanks. We can add it to the logging methods\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, \"The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:\", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name (\"line-delimited JSON\"? \"JSON Lines\" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)\r\n\r\nYes good idea !\r\n\r\n> * in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)\r\n\r\nSure why not. Moreover the csv loader now supports remote files so you could just run the code pass an an URL to the sample csv file.\r\n\r\n> * the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.\r\n\r\nThis can be used for distributed processing or just to use a percentage of the data. We can definitely give example of use cases\r\n\r\n> * the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc.\r\n\r\n`training_args` comes from `transformers`, it's a practical way to define all your arguments to train a model. Maybe we can just import it from `transformers` and use it with the default values\r\n\r\n" ]
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Some comments here: - the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file) - "If you’d like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?) - in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if it’s not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html. - example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset. - in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir` - in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries. - in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset) - it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html) - in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?) - in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try) - the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why. - the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc.
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cannot save the dataset to disk after rename_column
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[ "Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.\r\n\r\nTherefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same).", "Ok, thanks for clearing it up :)" ]
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## Describe the bug If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk` ## Steps to reproduce the bug ```python # Sample code to reproduce the bug In [1]: from datasets import Dataset, load_from_disk In [5]: dataset=Dataset.from_dict({'foo': [0]}) In [7]: dataset.save_to_disk('foo') In [8]: dataset=load_from_disk('foo') In [10]: dataset=dataset.rename_column('foo', 'bar') In [11]: dataset.save_to_disk('foo') --------------------------------------------------------------------------- PermissionError Traceback (most recent call last) <ipython-input-11-a3bc0d4fc339> in <module> ----> 1 dataset.save_to_disk('foo') /mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path , fs) 597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths: 598 raise PermissionError( --> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself." 600 ) 601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths: PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself. ``` N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.8.0 - Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core - Python version: 3.7.10 - PyArrow version: 3.0.0
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hebrew language codes he and iw should be treated as aliases
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[ "Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datasets/catalog/c4).", "For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https://github.com/huggingface/datasets/commit/38288087b1b02f97586e0346e8f28f4960f1fd37\r\n\r\nOnce the website is updated, mC4 will be listed in https://huggingface.co/datasets?filter=languages:he\r\n\r\n" ]
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https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability.
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Minor documentation fix
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Currently, [Writing a dataset loading script](https://huggingface.co/docs/datasets/add_dataset.html) page has a small error. A link to `matinf` dataset in [_Dataset scripts of reference_](https://huggingface.co/docs/datasets/add_dataset.html#dataset-scripts-of-reference) section actually leads to `xsquad`, instead. This PR fixes that.
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Fix bad config ids that name cache directories
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`data_dir=None` was considered a dataset config parameter, hence creating a special config_id for all dataset being loaded. Since the config_id is used to name the cache directories, this leaded to datasets being regenerated for users. I fixed this by ignoring the value of `data_dir` when it's `None` when computing the config_id. I also added a test to make sure the cache directories are not unexpectedly renamed in the future. Fix https://github.com/huggingface/datasets/issues/2683
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Fix Blog Authorship Corpus dataset
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[ "Normally, I'm expecting errors from the validation of the README file... πŸ˜… ", "That is:\r\n```\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_cards.py::test_changed_dataset_card[blog_authorship_corpus]\r\n==== 1 failed, 3182 passed, 2763 skipped, 16 warnings in 201.23s (0:03:21) =====\r\n```", "@lhoestq, apart from the dataset card, everything is OK with this PR: I tested it locally." ]
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This PR: - Update the JSON metadata file, which previously was raising a `NonMatchingSplitsSizesError` - Fix the codec of the data files (`latin_1` instead of `utf-8`), which previously was raising ` UnicodeDecodeError` for some files Close #2679.
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Print absolute local paths in load_dataset error messages
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Use absolute local paths in the error messages of `load_dataset` as per @stas00's suggestion in https://github.com/huggingface/datasets/pull/2500#issuecomment-874891223
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Cache directories changed due to recent changes in how config kwargs are handled
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MEMBER
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Since #2659 I can see weird cache directory names with hashes in the config id, even though no additional config kwargs are passed. For example: ```python from datasets import load_dataset_builder c4_builder = load_dataset_builder("c4", "en") print(c4_builder.cache_dir) # /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/... # instead of # /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/... ``` This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets cc @stas00 this is what you experienced a few days ago
https://api.github.com/repos/huggingface/datasets/issues/2683/timeline
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Fix c4 expected files
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Some files were not registered in the list of expected files to download Fix https://github.com/huggingface/datasets/issues/2677
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5 duplicate datasets
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[ "Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion ", "Thanks for the antecedents. I close." ]
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CONTRIBUTOR
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## Describe the bug In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are: - https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch <img width="838" alt="Capture d’écran 2021-07-20 aΜ€ 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png"> - https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2 - https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual - https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive - https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli Possible solutions: - don't fix (it works) - for each pair of duplicate datasets, remove one, and create an alias to the other. ## Steps to reproduce the bug Visit the Paperswithcode links, and look at the "Dataset Loaders" section ## Expected results There should only be one reference to a Hugging Face dataset loader ## Actual results Two Hugging Face dataset loaders
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feat: 🎸 add paperswithcode id for qasper dataset
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The reverse reference exists on paperswithcode: https://paperswithcode.com/dataset/qasper
https://api.github.com/repos/huggingface/datasets/issues/2680/timeline
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Cannot load the blog_authorship_corpus due to codec errors
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[ "Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...", "Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.", "@albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1 | 100 | 100 | 100 |\r\n| 2 | 102 | 100 | 102 |\r\n| 3 | 100 | 100 | 202 | \r\n| 4 | 40 | 100 | 40 |\r\n| 5 | 40 | 100 | 40 |\r\n| 6 | 40 | 100 | 40 | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```" ]
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## Describe the bug A codec error is raised while loading the blog_authorship_corpus. ## Steps to reproduce the bug ``` from datasets import load_dataset raw_datasets = load_dataset("blog_authorship_corpus") ``` ## Expected results Loading the dataset without errors. ## Actual results An error similar to the one below was raised for (what seems like) every XML file. /home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset builder_instance.download_and_prepare( File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare self._download_and_prepare( File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits raise NonMatchingSplitsSizesError(str(bad_splits)) datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyArrow version: 4.0.1
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Import Error in Kaggle notebook
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[ "This looks like an issue with PyArrow. Did you try reinstalling it ?", "@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.\r\n```\r\nInstall Trace of datasets:\r\n\r\nCollecting datasets\r\n Downloading datasets-1.9.0-py3-none-any.whl (262 kB)\r\n |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 262 kB 834 kB/s eta 0:00:01\r\nRequirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)\r\nCollecting pyarrow!=4.0.0,>=1.0.0\r\n Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)\r\n |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 21.8 MB 6.2 MB/s eta 0:00:01\r\nRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)\r\nRequirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)\r\nRequirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)\r\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)\r\nRequirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)\r\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)\r\nRequirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)\r\nCollecting xxhash\r\n Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)\r\n |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 243 kB 23.7 MB/s eta 0:00:01\r\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)\r\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)\r\nRequirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)\r\nRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)\r\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)\r\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)\r\nRequirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)\r\nRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)\r\nRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)\r\nRequirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)\r\nRequirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)\r\nRequirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)\r\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\r\nInstalling collected packages: xxhash, pyarrow, datasets\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 4.0.0\r\n Uninstalling pyarrow-4.0.0:\r\n Successfully uninstalled pyarrow-4.0.0\r\nSuccessfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2\r\nWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\r\n```", "You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.\r\n\r\nIf it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail", "> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.\r\n> \r\n> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail\r\n\r\nIt works after restarting.\r\nMy bad, I forgot to restart the notebook. Sorry for the trouble!" ]
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## Describe the bug Not able to import datasets library in kaggle notebooks ## Steps to reproduce the bug ```python !pip install datasets import datasets ``` ## Expected results No such error ## Actual results ``` ImportError Traceback (most recent call last) <ipython-input-9-652e886d387f> in <module> ----> 1 import datasets /opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter /opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module> 36 import pandas as pd 37 import pyarrow as pa ---> 38 import pyarrow.compute as pc 39 from multiprocess import Pool, RLock 40 from tqdm.auto import tqdm /opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module> 16 # under the License. 17 ---> 18 from pyarrow._compute import ( # noqa 19 Function, 20 FunctionOptions, ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Kaggle - Python version: 3.7.10 - PyArrow version: 4.0.1
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948,429,788
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2,677
Error when downloading C4
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[ "Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)", "Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error", "@lhoestq thank you for such a quick feedback!" ]
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Hi, I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive: `datasets.load_dataset('c4', 'en')` Is this a bug or do I have some configurations missing on my server? Thanks! <img width="1014" alt="Π‘Π½ΠΈΠΌΠΎΠΊ экрана 2021-07-20 Π² 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png">
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947,734,909
MDExOlB1bGxSZXF1ZXN0NjkyNjc2NTg5
2,676
Increase json reader block_size automatically
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Currently some files can't be read with the default parameters of the JSON lines reader. For example this one: https://huggingface.co/datasets/thomwolf/codeparrot/resolve/main/file-000000000006.json.gz raises a pyarrow error: ```python ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?) ``` The block size that is used is the default one by pyarrow (related to this [jira issue](https://issues.apache.org/jira/browse/ARROW-9612)). To fix this issue I changed the block_size to increase automatically if there is a straddling issue when parsing a batch of json lines. By default the value is `chunksize // 32` in order to leverage multithreading, and it doubles every time a straddling issue occurs. The block_size is then reset for each file. cc @thomwolf @albertvillanova
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947,657,732
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Parallelize ETag requests
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Since https://github.com/huggingface/datasets/pull/2628 we use the ETag or the remote data files to compute the directory in the cache where a dataset is saved. This is useful in order to reload the dataset from the cache only if the remote files haven't changed. In this I made the ETag requests parallel using multithreading. There is also a tqdm progress bar that shows up if there are more than 16 data files.
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947,338,202
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2,674
Fix sacrebleu parameter name
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DONE: - Fix parameter name: `smooth` to `smooth_method`. - Improve kwargs description. - Align docs on using a metric. - Add example of passing additional arguments in using metrics. Related to #2669.
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Fix potential DuplicatedKeysError in SQuAD
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DONE: - Fix potential DiplicatedKeysError by ensuring keys are unique. - Align examples in the docs with SQuAD code. We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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2,672
Fix potential DuplicatedKeysError in LibriSpeech
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DONE: - Fix unnecessary path join. - Fix potential DiplicatedKeysError by ensuring keys are unique. We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique).
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Mesinesp development and training data sets have been added.
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[ "It'll be new pull request with new commits." ]
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https://zenodo.org/search?page=1&size=20&q=mesinesp, Mesinesp has Medical Semantic Indexed records in Spanish. Indexing is done using DeCS codes, a sort of Spanish equivalent to MeSH terms. The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records. The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) training set has a total of 369,368 records.
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Using sharding to parallelize indexing
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**Is your feature request related to a problem? Please describe.** Creating an elasticsearch index on large dataset could be quite and cannot be parallelized on shard (the index creation is colliding) **Describe the solution you'd like** When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data. Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset. **Describe alternatives you've considered** Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25). **Additional context** The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset.
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Metric kwargs are not passed to underlying external metric f1_score
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[ "Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weighted\", \"binary\"} or None, default=\"binary\"`.\r\n\r\nSecond, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\nSo for example, if you would like to calculate the macro-averaged F1 score, you should use:\r\n```python\r\nimport datasets\r\n\r\nf1 = datasets.load_metric(\"f1\", keep_in_memory=True)\r\nf1.add_batch(predictions=[0,2,3], references=[1, 2, 3])\r\nf1.compute(average=\"macro\")\r\n```", "Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself." ]
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CONTRIBUTOR
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## Describe the bug When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so. ## Steps to reproduce the bug ```python import datasets f1 = datasets.load_metric("f1", keep_in_memory=True, average="min") f1.add_batch(predictions=[0,2,3], references=[1, 2, 3]) f1.compute() ``` ## Expected results No error, because `average="min"` should be passed correctly to f1_score in sklearn. ## Actual results ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute "f1": f1_score( File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score return fbeta_score(y_true, y_pred, beta=1, labels=labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score _, _, f, _ = precision_recall_fscore_support(y_true, y_pred, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support labels = _check_set_wise_labels(y_true, y_pred, average, labels, File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels raise ValueError("Target is %s but average='binary'. Please " ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted']. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.9.2 - PyArrow version: 4.0.1
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Add Russian SuperGLUE
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[ "Added the missing label classes and their explanations (to the best of my understanding)", "Thanks a lot ! Once the last comment about the label names is addressed we can merge :)" ]
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Hi, This adds the [Russian SuperGLUE](https://russiansuperglue.com/) dataset. For the most part I reused the code for the original SuperGLUE, although there are some relatively minor differences in the structure that I accounted for.
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Use tqdm from tqdm_utils
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[ "The current CI failure is due to modifications in the dataset script.", "Merging since the CI is only failing because of dataset card issues, which is unrelated to this PR" ]
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This PR replaces `tqdm` from the `tqdm` lib with `tqdm` from `datasets.utils.tqdm_utils`. With this change, it's possible to disable progress bars just by calling `disable_progress_bar`. Note this doesn't work on Windows when using multiprocessing due to how global variables are shared between processes. Currently, there is no easy way to disable progress bars in a multiprocess setting on Windows (patching logging with `datasets.utils.logging.get_verbosity = lambda: datasets.utils.logging.NOTSET` doesn't seem to work as well), so adding support for this is a future goal. Additionally, this PR adds a unit ("ba" for batches) to the bar printed by `Dataset.to_json` (this change is motivated by https://github.com/huggingface/datasets/issues/2657).
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Adds CodeClippy dataset [WIP]
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CodeClippy is an opensource code dataset scrapped from github during flax-jax-community-week https://the-eye.eu/public/AI/training_data/code_clippy_data/
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Adds APPS dataset to the hub [WIP]
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A loading script for [APPS dataset](https://github.com/hendrycks/apps)
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[`to_json`] add multi-proc sharding support
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[ "Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:\r\n```\r\nresult = list(\r\n pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])\r\n )\r\n```\r\n`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient=\"records\", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file? \r\n\r\nI think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this?", "Followed up in https://github.com/huggingface/datasets/pull/2747" ]
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As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR. I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards? I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process. I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs. The code I was using: ``` from multiprocessing import cpu_count, Process, Queue [...] filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count()) DATASET_NAME = "oscar" SHARDS = 10 def process_shard(idx): print(f"Sharding {idx}") ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True) # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling print(f"Saving {DATASET_NAME}-{idx}.jsonl") ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False) queue = Queue() processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)] for p in processes: p.start() for p in processes: p.join() ``` Thank you! @lhoestq
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Load Dataset from the Hub (NO DATASET SCRIPT)
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[ "This is ready for review now :)\r\n\r\nI would love to have some feedback on the changes in load.py @albertvillanova. There are many changes so if you have questions let me know, especially on the `resolve_data_files` functions and on the changes in `prepare_module`.\r\n\r\nAnd @thomwolf if you want to take a look at the documentation, feel free to share your suggestions :)", "I took your comments into account thanks !\r\nAnd I made `aiohttp` a required dependency :)", "Just updated the documentation :)\r\n[share_datasets.html](https://45532-250213286-gh.circle-artifacts.com/0/docs/_build/html/share_dataset.html)\r\n\r\nLet me know if you have some comments", "Merging this one :) \r\n\r\nWe can try to integrate the changes in the docs to #2718 @stevhliu !", "Baked this into the [docs](https://44335-250213286-gh.circle-artifacts.com/0/docs/_build/html/loading.html#hugging-face-hub) already, let me know if there is anything else I should add! :)" ]
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## Load the data from any Dataset repository on the Hub This PR adds support for loading datasets from any dataset repository on the hub, without requiring any dataset script. As a user it's now possible to create a repo and upload some csv/json/text/parquet files, and then be able to load the data in one line. Here is an example with the `allenai/c4` repository that contains a lot of compressed json lines files: ```python from datasets import load_dataset data_files = {"train": "en/c4-train.*.json.gz"} c4 = load_dataset("allenai/c4", data_files=data_files, split="train", streaming=True) print(c4.n_shards) # 1024 print(next(iter(c4))) # {'text': 'Beginners BBQ Class Takin...'} ``` By default it loads all the files, but as shown in the example you can choose the ones you want with unix style patterns. Of course it's still possible to use dataset scripts since they offer the most flexibility. ## Implementation details It uses `huggingface_hub` to list the files in a dataset repository. If you provide a path to a local directory instead of a repository name, it works the same way but it uses `glob`. Depending on the data files available, or passed in the `data_files` parameter, one of the available builders will be used among the csv, json, text and parquet builders. Because of this, it's not possible to load both csv and json files at once. In this case you have to load them separately and then concatenate the two datasets for example. ## TODO - [x] tests - [x] docs - [x] when huggingface_hub gets a new release, update the CI and the setup.py Close https://github.com/huggingface/datasets/issues/2629
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Add SD task for SUPERB
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[ "I make a summary about our discussion with @lewtun and @Narsil on the agreed schema for this dataset and the additional steps required to generate the 2D array labels:\r\n- The labels for this dataset are a 2D array:\r\n Given an example:\r\n ```python\r\n {\"record_id\": record_id, \"file\": file, \"start\": start, \"end\": end, \"speakers\": [...]}\r\n ```\r\n the labels are a 2D array of shape `(num_frames, num_speakers)` where `num_frames = end - start` and `num_speakers = 2`.\r\n- In order to avoid a too large dataset (too large disk space), `datasets` does not store the 2D array label. Instead, we store a compact form:\r\n ```\r\n \"speakers\": [\r\n {\"speaker_id\": speaker_0_id, \"start\": start_0_speaker_0, \"end\": end_0_speaker_0},\r\n {\"speaker_id\": speaker_0_id, \"start\": start_1_speaker_0, \"end\": end_1_speaker_0},\r\n {\"speaker_id\": speaker_1_id, \"start\": start_0_speaker_1, \"end\": end_0_speaker_1},\r\n ],\r\n ```\r\n - Once loaded the dataset, an additional step is required to generate the 2D array label from this compact form\r\n - This additional step should be a modified version of the s3prl method `_get_labeled_speech`:\r\n - Original s3prl `_get_labeled_speech` includes 2 functionalities: reading the audio file and transforming it into an array, and generating the label 2D array; I think we should separate these 2 functionalities\r\n - Original s3prl `_get_labeled_speech` performs 2 steps to generate the labels:\r\n - Transform start/end seconds (float) into frame numbers (int): I have already done this step to generate the dataset\r\n - Generate the 2D array label from the frame numbers\r\n\r\nI also ping @osanseviero and @lhoestq to include them in the loop.", "Here I would like to discuss (and agree) one of the decisions I made, as I'm not completely satisfied with it: to transform the seconds (float) into frame numbers (int) to generate this dataset.\r\n\r\n- A priori, the most natural and general choice would be to preserve the seconds (float), because:\r\n - this is the way the raw data comes from\r\n - the transformation into frame numbers depends on the sample rate, frame_shift and subsampling\r\n\r\nHowever, I finally decided to transform seconds into frame numbers because:\r\n- for SUPERB, sampling rate, frame_shift and subsampling are fixed (`rate = 16_000`, `frame_shift = 160`, `subsampling = 1`)\r\n- it makes easier the post-processing, as labels are generated from sample numbers: labels are a 2D array of shape `(num_frames, num_speakers)`\r\n- the number of examples depends on the number of frames:\r\n - if an example has more than 2_000 frames, then it is split into 2 examples. This is the case for `record_id = \"7859-102521-0017_3983-5371-0014\"`, which has 2_452 frames and it is split into 2 examples:\r\n ```\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 0, \"end\": 2_000,...},\r\n {\"record_id\": \"7859-102521-0017_3983-5371-0014\", \"start\"= 2_000, \"end\": 2_452,...},\r\n ```\r\n\r\nAs I told you, I'm not totally convinced of this decision, and I would really appreciate your opinion.\r\n\r\ncc: @lewtun @Narsil @osanseviero @lhoestq ", "It makes total sense to prepare the data to be in a format that can actually be used for model training and evaluation. That's one of the roles of this lib :)\r\n\r\nSo for me it's ok to use frames as a unit instead of seconds. Just pinging @patrickvonplaten in case he has ever played with such audio tasks and has some advice. For the context: the task is to classify which speaker is speaking, let us know if you are aware of any convenient/standard format for this.\r\n\r\nAlso I'm not sure why you have to split an example if it's longer that 2,000 frames ?", "> Also I'm not sure why you have to split an example if it's longer that 2,000 frames ?\r\n\r\nIt is a convention in SUPERB benchmark.", "Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n- one to generate the 2D array labels\r\n- one to load the audio file into an array, but taking into account start/end to cut the audio\r\n\r\nIs there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...", "You could add an example of usage in the dataset card, as it is done for other audio datasets", "@albertvillanova this simple function can be edited simply to add the start/stop cuts \r\n\r\nhttps://github.com/huggingface/transformers/blob/master/src/transformers/pipelines/automatic_speech_recognition.py#L29 ", "Does this function work on windows ?", "Windows ? What is it ? (Not sure not able to test, it's directly calling ffmpeg binary, so depending on the setup it could but can't say for sure without testing)\r\n", "It's one of the OS we're supposed to support :P (for the better and for the worse)", "> Note that if we agree to leave the dataset as it is now, 2 additional custom functions must be used:\r\n> \r\n> * one to generate the 2D array labels\r\n> * one to load the audio file into an array, but taking into account start/end to cut the audio\r\n> \r\n> Is there a way we can give these functions ready to be used? Or should we leave this entirely to the end user? This is not trivial...\r\n\r\n+1 on providing the necessary functions on the dataset card. aside from that, the current implementation looks great from my perspective!" ]
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Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization). TODO: - [x] Generate the LibriMix corpus - [x] Prepare the corpus for diarization - [x] Upload these files to the superb-data repo - [x] Transcribe the corresponding s3prl processing of these files into our superb loading script - [x] README: tags + description sections - ~~Add DER metric~~ (we leave the DER metric for a follow-up PR) Related to #2619. Close #2653. cc: @lewtun
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Move checks from _map_single to map
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[ "@lhoestq This one has been open for a while. Could you please take a look?", "@lhoestq Ready for the final review!", "I forgot to update the signature of `DatasetDict.map`, so did that now." ]
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The goal of this PR is to remove duplicated checks in the `map` logic to execute them only once whenever possible (`fn_kwargs`, `input_columns`, ...). Additionally, this PR improves the consistency (to align it with `input_columns`) of the `remove_columns` check by adding support for a single string value, which is then wrapped into a list.
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Allow dataset config kwargs to be None
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Close https://github.com/huggingface/datasets/issues/2658 The dataset config kwargs that were set to None we simply ignored. This was an issue when None has some meaning for certain parameters of certain builders, like the `sep` parameter of the "csv" builder that allows to infer to separator. cc @SBrandeis
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Can't pass `sep=None` to load_dataset("csv", ...) to infer the separator via pandas.read_csv
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When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator. Related to https://github.com/huggingface/datasets/pull/2656 cc @SBrandeis
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`to_json` reporting enhancements
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While using `to_json` 2 things came to mind that would have made the experience easier on the user: 1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently. 2. It took me a while to make sense of the reported numbers: ``` 22%|β–ˆβ–ˆβ– | 1536/7076 [12:30:57<44:09:42, 28.70s/it] ``` So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.: ``` 22%|β–ˆβ–ˆβ– | 15360K/70760K [12:30:57<44:09:42, 28.70s/it] ``` or ``` 22%|β–ˆβ–ˆβ– | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it] ``` (while of course remaining friendly to small datasets) I forget if tqdm lets you add a magnitude identifier to the running count. Thank you!
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Change `from_csv` default arguments
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[ "This is not the default in pandas right ?\r\nWe try to align our CSV loader with the pandas API.\r\n\r\nMoreover according to their documentation, the python parser is used when sep is None, which might not be the fastest one.\r\n\r\nMaybe users could just specify `sep=None` themselves ?\r\nIn this case we should add some documentation about this" ]
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Passing `sep=None` to pandas's `read_csv` lets pandas guess the CSV file's separator This PR allows users to use this pandas's feature by passing `sep=None` to `Dataset.from_csv`: ```python Dataset.from_csv( ..., sep=None ) ```
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Allow the selection of multiple columns at once
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[ "Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something.", "Hi! Sorry for the delay.\r\n\r\nIn this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.\r\n\r\nMy issue is that my dataset is too big for memory if I load everything into pandas." ]
1,626,355,845,000
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**Is your feature request related to a problem? Please describe.** Similar to pandas, it would be great if we could select multiple columns at once. **Describe the solution you'd like** ```python my_dataset = ... # Has columns ['idx', 'sentence', 'label'] idx, label = my_dataset[['idx', 'label']] ``` **Describe alternatives you've considered** we can do `[dataset[col] for col in ('idx', 'label')]` **Additional context** This is of course very minor.
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Give a user feedback if the dataset he loads is streamable or not
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[ "#self-assign", "I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet\r\n```\r\n" ]
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MEMBER
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**Is your feature request related to a problem? Please describe.** I would love to know if a `dataset` is with the current implementation streamable or not. **Describe the solution you'd like** We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive. **Describe alternatives you've considered** Add a new metadata tag for "streaming"
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Add SD task for SUPERB
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[ "Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n\r\nThen we can use the URLs for the files to load the data in `superb`'s dataset loading script.\r\n\r\nFor consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :)", "@lewtun @lhoestq: \r\n\r\nI have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur rttm segments spk2utt utt2spk wav.scp\r\n\r\nNext steps:\r\n- Upload these files to the superb-data repo\r\n- Transcribe the corresponding s3prl processing of these files into our superb loading script\r\n\r\nNote that processing of these files is a bit more intricate than usual datasets: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/diarization/dataset.py#L233\r\n\r\n" ]
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Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization). Steps: - [x] Generate the LibriMix corpus - [x] Prepare the corpus for diarization - [x] Upload these files to the superb-data repo - [x] Transcribe the corresponding s3prl processing of these files into our superb loading script - [ ] README: tags + description sections Related to #2619. cc: @lewtun
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Fix logging docstring
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Remove "no tqdm bars" from the docstring in the logging module to align it with the changes introduced in #2534.
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Setting log level higher than warning does not suppress progress bar
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[ "Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```", "Thank you, it worked :)", "See https://github.com/huggingface/datasets/issues/2528 for reference", "Note also that you can disable the progress bar with\r\n\r\n```python\r\nfrom datasets.utils import disable_progress_bar\r\ndisable_progress_bar()\r\n```\r\n\r\nSee https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84" ]
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## Describe the bug I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well). According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0. I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work. ## Steps to reproduce the bug ```python import datasets from datasets.utils.logging import set_verbosity_error set_verbosity_error() def dummy_map(batch): return batch common_voice_train = datasets.load_dataset("common_voice", "de", split="train") common_voice_test = datasets.load_dataset("common_voice", "de", split="test") common_voice_train.map(dummy_map) ``` ## Expected results - The progress bar for `.map` call won't be shown ## Actual results - The progress bar for `.map` is still shown ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.9.0 - Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.5 - PyArrow version: 4.0.1
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[load_dataset] shard and parallelize the process
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- Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core. - If the build crashes, everything done up to that point gets lost Request: Shard the build over multiple arrow files, which would enable: - much faster build by parallelizing the build process - if the process crashed, the completed arrow files don't need to be re-built again Thank you! @lhoestq
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adding progress bar / ETA for `load_dataset`
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Please consider: ``` Downloading and preparing dataset oscar/unshuffled_deduplicated_en (download: 462.40 GiB, generated: 1.18 TiB, post-processed: Unknown size, total: 1.63 TiB) to cache/oscar/unshuffled_deduplicated_en/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2... HF google storage unreachable. Downloading and preparing it from source ``` and no indication whatsoever of whether things work well or when it'll be done. It's important to have an estimated completion time for when doing slurm jobs since some instances have a cap on run-time. I think for this particular job it sat for 30min in total silence and then after 30min it started generating: ``` 897850 examples [07:24, 10286.71 examples/s] ``` which is already great! Request: 1. ETA - knowing how many hours to allocate for a slurm job 2. progress bar - helps to know things are working and aren't stuck and where we are at. Thank you! @lhoestq
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