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[ "Thanks for finishing it @albertvillanova " ]

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[ "the bug code locate in ：\r\n if data_args.task_name is not None:\r\n # Downloading and loading a dataset from the hub.\r\n datasets = load_dataset(\"glue\", data_args.task_name, cache_dir=model_args.cache_dir)", "Hi @jinec,\r\n\r\nFrom time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com\r\n\r\nNormally, it should work if you wait a little and then retry.\r\n\r\nCould you please confirm if the problem persists?", "cannot connect，even by Web browser，please check that there is some problems。", "I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...", "> I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...\r\n\r\nI can not access https://raw.githubusercontent.com/huggingface/datasets either, I am in China", "Finally i can access it, by the superfast software. Thanks", "> Finally i can access it, by the superfast software. Thanks\r\n\r\nExcuse me, I have the same problem as you, could you please tell me how to solve it？" ]

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[ "thanks a lot for implementing this @anton-l !!\r\n\r\ni won't have time to review this while i'm away, so happy for @albertvillanova and @patrickvonplaten to decide when to merge :)", "@albertvillanova thanks! Everything should be ready now :)", "> The _background_noise_/_silence_ audio files are much longer than others, so they require some sort of slicing for downstream training. I decided to leave the implementation of that up to the users, since TFDS and s3prl take different approaches (either slicing wavs deterministically, or subsampling randomly at runtime)\r\n\r\n@anton-l I was thinking that maybe we could give some hints in the dataset card (in a Usage section); something similar as for diarization: https://github.com/huggingface/datasets/blob/master/datasets/superb/README.md#example-of-usage\r\nNote that for diarization it is not yet finished: we have to test it and then provide an end-to-end example: https://github.com/huggingface/datasets/pull/2661/files#r680224909 ", "@albertvillanova yeah, I'm not sure how to best implement it in pure `datasets` yet. It's something like this, where `sample_noise()` needs to be called from a pytorch batch collator or other framework-specific variant:\r\n\r\n```python\r\ndef map_to_array(example):\r\n import soundfile as sf\r\n\r\n speech_array, sample_rate = sf.read(example[\"file\"])\r\n example[\"speech\"] = speech_array\r\n example[\"sample_rate\"] = sample_rate\r\n return example\r\n\r\n\r\ndef sample_noise(example):\r\n # Use a version of this function in a stateless way to extract random 1 sec slices of background noise\r\n # on each epoch\r\n from random import randint\r\n\r\n # _silence_ audios are longer than 1 sec\r\n if example[\"label\"] == \"_silence_\":\r\n random_offset = randint(0, len(example[\"speech\"]) - example[\"sample_rate\"] - 1)\r\n example[\"speech\"] = example[\"speech\"][random_offset : random_offset + example[\"sample_rate\"]]\r\n\r\n return example\r\n```", "I see... Yes, not trivial indeed. Maybe for the moment you could add those functions above to the README (as it is the case for now in diarization)? What do you think?" ]

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[ "I'm guessing tests are failling, because this was pushed before https://github.com/huggingface/datasets/pull/2779 was merged? cc @albertvillanova ", "Hi @thomasw21, yes you are right: those failing tests were fixed with #2779.\r\n\r\nWould you mind to merge current upstream master branch and push again?\r\n```\r\ngit checkout sequential_map_when_cached\r\ngit fetch upstream master\r\ngit merge upstream/master\r\ngit push -u origin sequential_map_when_cached\r\n```", "Thanks for working on this ! I'm sure we can figure something out ;)\r\n\r\nCurrently `map` starts a process to apply the map function on each shard. If the shard has already been processed, then the process that has been spawned loads the processed shard from the cache and returns it.\r\n\r\nI think we should be able to simply not start a process if a shard is already processed and cached.\r\nThis way:\r\n- you won't need to specify `sequential=True`\r\n- it won't create new processes if the dataset is already processed and cached\r\n- it will properly reload each processed shard that is cached\r\n\r\nTo know if we have to start a new process for a shard you can use the function `update_fingerprint` from fingerprint.py to know the expected fingerprint of the processed shard.\r\nThen, if the shard has already been processed, there will be a cache file named `cached-<new_fingerprint>.arrow` and you can load it with\r\n```\r\nDataset.from_file(path_to_cache_file, info=self.info, split=self.split)\r\n```\r\n\r\nLet me know if that makes sense !", "Yes that makes total sense, I tried to initially do that, except the way fingerprint is handled doesn't allow to easily manipulate such a field. Typically the fingerprinting is handled in `@fingerprint_transform` which has a bunch of params that aren't quite easy to extract. Those params are used to manipulate args, kwargs in fancy ways in order to finally obtain a dictionary used for fingerprint. I could duplicate everything, but this look like a very risky thing to do. I'll take a look if I can make something work with `inspect` if I can make a very simple wrapper.\r\n\r\nA much more simpler solution I think is adding an optional `shard: Optional[int] = None` parameter. If None, we use the number of proc as the number of shards, otherwise we pass down the expected number of shards and use either sequential/multiprocessing (with arbitrary number of workers) to load the shards? This would allow the weird case where one wants a large number of shards with a limited amount of processes. Not the smartest thing to do, but it's not an absurd behaviour. Would this be acceptable?", "@lhoestq friendly ping as I feel it's up for review.", "The CI error is unrelated to the changes of this PR - it looks like an SSL issue with conda" ]

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[ "Hi ! I think the name `common_voice_7` is fine :)\r\nMoreover if the dataset_infos.json is missing I'm pretty sure you don't need to specify `ignore_verifications=True`", "Hi, how about to add a new parameter \"version\" in the function load_dataset, something like: \r\n`load_dataset(\"common_voice\", \"lg\", version=\"7.0\") `\r\nThis is to avoid creating a new common_voice_? dataset (with almost the same code) every time \r\nMozilla updates their Common Voice dataset.\r\n" ]

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[ "Hi,\r\n\r\nthe `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"tweets_hate_speech_detection\")['train'].select(range(128))\r\nds = ds.flatten_indices()\r\nds = ds.add_column('ones', [1]*128)\r\n```", "Thanks for the question @lvwerra. And thanks for the answer @mariosasko. ^^" ]

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[ "Hi,\r\n\r\nThe `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:\r\n```\r\npip uninstall bert-score\r\npip install \"bert-score<0.3.10\"\r\n```" ]

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[ "Hi @danshirron, \r\n`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration.", "@bhavitvyamalik @lhoestq , just tried the above and got:\r\n>>> a=datasets.load_dataset('c4','en.realnewslike')\r\nDownloading: 3.29kB [00:00, 1.66MB/s] \r\nDownloading: 2.40MB [00:00, 12.6MB/s] \r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py\", line 819, in load_dataset\r\n builder_instance = load_dataset_builder(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py\", line 701, in load_dataset_builder\r\n builder_instance: DatasetBuilder = builder_cls(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 1049, in __init__\r\n super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 268, in __init__\r\n self.config, self.config_id = self._create_builder_config(\r\n File \"/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py\", line 360, in _create_builder_config\r\n raise ValueError(\r\nValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']\r\n>>> \r\n\r\ndatasets version is 1.11.0\r\n", "I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`. \r\n\r\nI tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up!", "It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks" ]

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[ "Hi @JulesBelveze, thanks for your question.\r\n\r\nNote that 🤗 `datasets` internally store their data in Apache Arrow format.\r\n\r\nHowever, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).\r\n\r\nIf you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:\r\n```python\r\nconcat_dataset.set_format(type=\"torch\")\r\n```\r\n\r\nYou have detailed information in our docs:\r\n- [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)\r\n- [Dataset.set_format()](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format)", "Thanks @albertvillanova it indeed did the job 😃 \r\nThanks for your answer!" ]

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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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[ "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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[ "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. ", "Hello, @Mehrad0711; Hi, @albertvillanova !\r\nI am the maintainer of the `dfki/few-nerd\" dataset script, sorry for the very late reply and hope this message finds you well!\r\nWe should use\r\n```\r\ndataset = load_dataset(\"dfki-nlp/few-nerd\", name=\"supervised\")\r\n```\r\ninstead of not specifying the \"name\" argument, where name is from `[\"supervised\", \"inter\", \"intra\"]`. Otherwise the method just treats \"supervised\" as `split`, which we reserve after specifying the name, since for each name, there are three splits: train, dev and test.\r\n\r\nAlso we use Tsinghua server source to download data files since it is the official source referred in the paper where the dataset is released (even though it is cc-by-sa-4.0 licensed, means we can copy the data anywhere after mentioning the license\r\n). Sometimes the server just runs down due to high pressure, kinda weird (we encountered the same server problem serveral times a month when we conducted experiments on Few-NERD XD). I tried the script just now and it works perfectly!\r\n```\r\n>> dataset\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 131767\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 18824\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 37648\r\n })\r\n})\r\n>>> dataset[\"train\"]\r\nDataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 131767\r\n})\r\n>>> dataset[\"train\"][0]\r\n{'id': '0', 'tokens': ['Paul', 'International', 'airport', '.'], 'ner_tags': [0, 0, 0, 0], 'fine_ner_tags': [0, 0, 0, 0]}\r\n```\r\nAnyways if you cannot stand the pain with the server and its slow download speed, you can also download the `dfki/few-nerd.py` script from HF and change the `_URLs` to your personal drive (after you once successfully download the data and upload to your cloud drive), and then load the .py script locally.\r\n\r\nHope this reply can still be any help. If you still have problems with it, feel free to ask here and I am glad to help!\r\nBest wishes.", "Hi @chen-yuxuan, thanks for your answer.\r\n\r\nJust a few comments:\r\n\r\n- Please, note that as we use `datasets.load_dataset` implementation, we can pass the configuration name as the second positional argument (no need to pass explicitly `name=`) and it downloads the 3 splits:\r\n```python\r\n In [4]: ds = load_dataset(\"dfki-nlp/few-nerd\", \"supervised\")\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.5k/11.5k [00:00<00:00, 2.85MB/s]\r\nDownloading and preparing dataset few_nerd/supervised to .cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255...\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6M/14.6M [01:16<00:00, 190kB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.9M/11.9M [01:14<00:00, 160kB/s]\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12.0M/12.0M [01:04<00:00, 186kB/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [03:58<00:00, 79.45s/it]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3.11it/s]\r\n```\r\n\r\n- On the other hand, please note that your script does not work on Windows machines, because you call `open()` without passing the encoding parameter:\r\n```\r\n~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\dfki-nlp___few-nerd\\e40882b71f037a4a1f232025899170fbe8113cd2f4a26dddd2add7222a077255\\few-nerd.py in <genexpr>(.0)\r\n 276 assert filepath[-4:] == \".txt\"\r\n 277\r\n--> 278 num_lines = sum(1 for _ in open(filepath))\r\n 279 id = 0\r\n 280\r\n\r\n.venv\\lib\\encodings\\cp1252.py in decode(self, input, final)\r\n 21 class IncrementalDecoder(codecs.IncrementalDecoder):\r\n 22 def decode(self, input, final=False):\r\n---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]\r\n 24\r\n 25 class StreamWriter(Codec,codecs.StreamWriter):\r\n\r\nUnicodeDecodeError: 'charmap' codec can't decode byte 0x8d in position 5238: character maps to <undefined>\r\n```\r\n\r\nIf you would like your script to be usable on Windows machines, you should pass `encoding=\"utf-8\"` to every `open()` function:\r\n- line 278: `num_lines = sum(1 for _ in open(filepath, encoding=\"utf-8\"))`\r\n- line 281: `with open(filepath, \"r\", encoding=\"utf-8\")`", "Thank you @albertvillanova for your detailed feedback!\r\n\r\n> no need to pass explicitly `name=`\r\n\r\nGood catch! I thought `split` stands before `name` in the argument list... but now it is all clear to me, sounds cool! Thanks for the explanation.\r\n\r\nAnyways in our old code it still looks bit confusing if we only want one split but the function downloads all, so to allow efficient downloading, I optimized the code a bit so that only the specified split data is downloaded. now we get\r\n```\r\n>>> x = load_dataset(\"dfki-nlp/few-nerd\", \"supervised\")\r\nDownloading and preparing dataset few_nerd/supervised to /home/user/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885...\r\nDownloading: 100%|███████████████████████████████████████████████████████████████████| 14.6M/14.6M [01:01<00:00, 238kB/s]\r\n100%|██████████████████████████████████████████████████████████████████████| 3359329/3359329 [00:12<00:00, 275462.84it/s]\r\n100%|████████████████████████████████████████████████████████████████████████| 482037/482037 [00:01<00:00, 278633.64it/s]\r\n100%|████████████████████████████████████████████████████████████████████████| 958765/958765 [00:03<00:00, 267472.83it/s]\r\nDataset few_nerd downloaded and prepared to /home/user/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/8e7ab598946cd5b395dcec6ea239123c8dff5b58b8e1c03b0c595b540248a885. Subsequent calls will reuse this data.\r\n```\r\nwhere only one progress bar indicates downloading, and the three others just indicate pre-processing for the train, dev, test set.\r\n\r\nFor the encoding issue, I have made corresponding changes for the two lines you pointed out. However, I have no windows machine at hand, I would really appreciate it if you could help test on your end.\r\n\r\nAll the updates are uploaded to HF under `dfki-nlp` account where I am working for. \r\nThank you again for your kind help!\r\n", "Hi @chen-yuxuan,\r\n\r\nI have tested on Windows and now it works perfectly, after the fixing of the encoding issue:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"dfki-nlp/few-nerd\", \"supervised\")\r\nDownloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 11.5k/11.5k [00:00<?, ?B/s]\r\nDownloading and preparing dataset few_nerd/supervised to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511...\r\n100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3359329/3359329 [00:25<00:00, 129427.23it/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 482037/482037 [00:03<00:00, 134513.66it/s]\r\n100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 958765/958765 [00:06<00:00, 143152.35it/s]\r\nDataset few_nerd downloaded and prepared to C:\\Users\\username\\.cache\\huggingface\\datasets\\dfki-nlp___few_nerd\\supervised\\0.0.0\\e1ceeaee82073fea12206e4461c7cfcd67e68c8f3ebeca179bddcacee00c4511. Subsequent calls will reuse this data.765 [00:06<00:00, 139045.03it/s]\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 174.71it/s]\r\n\r\nIn [3]: ds\r\nOut[3]:\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 131767\r\n })\r\n validation: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 18824\r\n })\r\n test: Dataset({\r\n features: ['id', 'tokens', 'ner_tags', 'fine_ner_tags'],\r\n num_rows: 37648\r\n })\r\n})\r\n```" ]

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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.", "@lhoestq Unfortunately, I discovered a problem with the test data sets on the competion page (train and dev is fine). They still contain NONE labels for each of the emotions, for example for English: http://saifmohammad.com/WebDocs/AIT-2018/AIT2018-DATA/AIT2018-TEST-DATA/semeval2018englishtestfiles/2018-E-c-En-test.zip\r\nLuckily, a zip file with all data of the competition contains the correct labels also for the test set:\r\nhttp://saifmohammad.com/WebDocs/AIT-2018/AIT2018-DATA/SemEval2018-Task1-all-data.zip\r\nWhat's the best way to correct this?", "Hi ! I think we can edit the sem_eval_2018_task_1.py file to use this URL instead, and maybe update the `os.path.join` calls to the new paths to the text data in the new ZIP file. Would you like to try to make this work ?" ]

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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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[ "maybe we should rather attempt to download a `Range` from the server and see if it works?" ]

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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.", "> 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\n> I'm reopening this Issue and making a Pull Request to fix it.\r\n\r\nHow did you solve him" ]

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[ "@lhoestq, lütfen bu PR'ı gözden geçirebilir misiniz?", "> Thanks ! This looks all good now :)\r\n\r\nThanks" ]

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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\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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[ "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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[ "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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[ "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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[ "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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[ "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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[ "Merging since the CI is just about missing infos in the dataset card" ]

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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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[ "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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[ "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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[ "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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[ "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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[ "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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[ "@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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[ "The CI failure seems unrelated to this PR (probably has something to do with Transformers)." ]

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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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