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[ "Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646))\r\n\r\nThat means it keeps the permissions specified by the `tempfile.NamedTemporaryFile` object, i.e. `-rw-------` instead of `-rw-r--r--`. Improving this could be a nice first contribution to the library :)", "Hi @lhoestq,\r\nI looked into this and yes you're right. The `NamedTemporaryFile` is always created with mode 0600, which prevents group from reading the file. Should we change the permissions of `tmp_file.name` [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1871) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1590), post creation to 0644 inorder for group and others to read it?", "Good idea :) we could even update the permissions after the file has been moved by shutil.move [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1899) and [here](https://github.com/huggingface/datasets/blob/f6b8251eb975f66a568356d2a40d86442c03beb9/src/datasets/arrow_dataset.py#L1646) actually.\r\nApparently they set the default 0600 for temporary files for security reasons, so let's update the umask only after the file has been moved", "Would it be possible to actually set the umask based on a user provided argument? For example, a popular usecase my team has is using a shared file-system for processing datasets. This may involve writing/deleting other files, or changing filenames, which a -rw-r--r-- wouldn't fix. ", "Note that you can get the cache files of a dataset with the `cache_files` attributes.\r\nThen you can `chmod` those files and all the other cache files in the same directory.\r\n\r\nMoreover we can probably keep the same permissions after each transform. This way you just need to set the permissions once after doing `load_dataset` for example, and then all the new transformed cached files will have the same permissions.\r\nWhat do you think ?", "This means we'll check the permissions of other `cache_files` already created for a dataset before setting permissions for new `cache_files`?", "You can just check the permission of `dataset.cache_files[0]` imo", "> This way you just need to set the permissions once after doing load_dataset for example, and then all the new transformed cached files will have the same permissions.\r\n\r\nI was referring to this. Ensuring that newly generated `cache_files` have the same permissions", "Yes exactly\r\n\r\nI imagine users can first do `load_dataset`, then chmod on the arrow files. After that all the new cache files could have the same permissions as the first arrow files. Opinions on this ?", "Sounds nice but I feel this is a sub-part of the approach mentioned by @siddk. Instead of letting the user set new permissions by itself first and then making sure newly generated files have same permissions why don't we ask the user initially only what they want? What are your thoughts?", "Yes sounds good. Should this be a parameter in `load_dataset` ? Or an env variable ? Or use the value of `os.umask` ?", "Ideally it should be a parameter in `load_dataset` but I'm not sure how important it is for the users (considering only important things should go into `load_dataset` parameters)", "I think it's fairly important; for context, our team uses a shared file-system where many folks run experiments based on datasets that are cached by other users.\r\n\r\nFor example, I might start a training run, downloading a dataset. Then, a couple of days later, a collaborator using the same repository might want to use the same dataset on the same shared filesystem, but won't be able to under the default permissions.\r\n\r\nBeing able to specify directly in the top-level `load_dataset()` call seems important, but an equally valid option would be to just inherit from the running user's `umask` (this should probably be the default anyway).\r\n\r\nSo basically, argument that takes a custom set of permissions, and by default, use the running user's umask!", "Maybe let's start by defaulting to the user's umask !\r\nDo you want to give it a try @bhavitvyamalik ?", "Yeah sure! Instead of using default `0o644` should I first extract umask of current user and then use `os.umask` on it? We can do it inside `Dataset` class so that all folders/files created during the call use running user's umask\r\n\r\n", "You can get the umask using `os.umask` and then I guess you can just use `os.chmod` as in your previous PR, but with the right permissions depending on the umask.", "FWIW, we have this issue with other caches - e.g. `transformers` model files. So probably will need to backport this into `transformers` as well.\r\n\r\nthanks @thomwolf for the pointer.", "Hi @stas00,\r\nFor this should we use the same umask code in the respective model directory inside `TRANSFORMERS_CACHE`?", "That sounds very right to me, @bhavitvyamalik " ]

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[ "@lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.", "Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n> \r\n> The bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.\r\n\r\nYou're right: \"_\" should be added to the list of labels, and the examples must be sequences of tokens, not singles tokens.\r\n", "@lhoestq Can you please label this issue with the \"good first issue\" label? I'm not sure I'll find time to fix this.\r\n\r\nTo resolve it, the user should:\r\n1. add `\"_\"` to the list of labels\r\n2. transform the udpos subset to the conll format (I think the preprocessing logic can be borrowed from [the original repo](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204))\r\n3. update the dummy data\r\n4. update the dataset info\r\n5. [optional] add info about the data fields structure of the udpos subset to the dataset readme", "I tried fixing this issue, but its working fine in the dev version : \"1.6.2.dev0\"\r\n\r\nI think somebody already fixed it. ", "Hi,\r\n\r\nafter #2326, the lines with pos tags equal to `\"_\"` are filtered out when generating the dataset, so this fixes the KeyError described above. However, the udpos subset should be in the conll format i.e. it should yield sequences of tokens and not single tokens, so it would be great to see this fixed (feel free to borrow the logic from [here](https://github.com/google-research/xtreme/blob/58a76a0d02458c4b3b6a742d3fd4ffaca80ff0de/utils_preprocess.py#L187-L204) if you decide to work on this). ", "Closed by #2466." ]

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[ "I thought at first that the multiproc test was not relevant now that we do stuff only in memory, but I think there's something that's actually broken, my tiny benchmark on bookcorpus runs forever (2hrs+) when I add `num_proc=4` as a kwarg, will investigate 👀 \r\n\r\nI'm not familiar with the caching you describe for `.map`, I'll look it up.", "turns out the multi proc issue is also on master, I won't fix it in this PR but opened #2071 to track the problem.", "tracemalloc outputs from this script:\r\n\r\n```python\r\nimport logging\r\nimport sys\r\nimport time\r\nimport tracemalloc\r\n\r\nfrom datasets import load_dataset, set_caching_enabled\r\n\r\n\r\nif __name__ == \"__main__\":\r\n set_caching_enabled(False)\r\n logging.basicConfig(level=logging.DEBUG)\r\n\r\n tracemalloc.start()\r\n bc = load_dataset(\"bookcorpus\")\r\n\r\n now = time.time()\r\n try:\r\n snapshot1 = tracemalloc.take_snapshot()\r\n bc[\"train\"].filter(lambda x: len(x[\"text\"]) < 64, num_proc=int(sys.argv[1]))\r\n except Exception as e:\r\n print(f\"cancelled: {e}\")\r\n exit(1)\r\n snapshot2 = tracemalloc.take_snapshot()\r\n tracemalloc.stop()\r\n elapsed = time.time() - now\r\n\r\n print(elapsed)\r\n top_stats = snapshot2.compare_to(snapshot1, \"lineno\")\r\n\r\n print(\"[ Top 10 differences ]\")\r\n for stat in top_stats[:10]:\r\n print(stat)\r\n\r\n```\r\n\r\n\r\nThis branch:\r\n\r\n```\r\n ssh://theo@35.205.12.130:22/home/theo/.local/share/miniconda3/envs/datasets/bin/python -u benchmark_filter.py 1\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.amazonaws.com:443\r\n DEBUG:urllib3.connectionpool:https://s3.amazonaws.com:443 \"HEAD /datasets.huggingface.co/datasets/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/dataset_infos.json HTTP/1.1\" 200 0\r\n WARNING:datasets.builder:Reusing dataset bookcorpus (/home/theo/.cache/huggingface/datasets/bookcorpus/plain_text/1.0.0/af844be26c089fb64810e9f2cd841954fd8bd596d6ddd26326e4c70e2b8c96fc)\r\n 0%| | 0/74005 [00:00<?, ?ba/s]2021-03-23 10:23:20.051255: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n 2021-03-23 10:23:20.051304: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\r\n 100%|████████████████████████████████████| 74005/74005 [12:16<00:00, 100.54ba/s]\r\n 815.6356580257416\r\n [ Top 10 differences ]\r\n <frozen importlib._bootstrap_external>:580: size=38.0 MiB (+33.7 MiB), count=326226 (+307928), average=122 B\r\n <frozen importlib._bootstrap>:219: size=7643 KiB (+7553 KiB), count=26372 (+25473), average=297 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/torch/__init__.py:427: size=1291 KiB (+1291 KiB), count=5924 (+5924), average=223 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/abc.py:85: size=1039 KiB (+1026 KiB), count=3428 (+3384), average=310 B\r\n <frozen importlib._bootstrap_external>:64: size=917 KiB (+891 KiB), count=5300 (+5132), average=177 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/collections/__init__.py:456: size=720 KiB (+709 KiB), count=3403 (+3349), average=217 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_export.py:346: size=607 KiB (+607 KiB), count=3962 (+3962), average=157 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/linecache.py:137: size=998 KiB (+487 KiB), count=9551 (+4517), average=107 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_decorator.py:241: size=367 KiB (+367 KiB), count=5225 (+5225), average=72 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/decorator_utils.py:114: size=359 KiB (+359 KiB), count=330 (+330), average=1114 B\r\n```\r\n\r\nOn master:\r\n```\r\n ssh://theo@35.205.12.130:22/home/theo/.local/share/miniconda3/envs/datasets/bin/python -u benchmark_filter.py 1\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): s3.amazonaws.com:443\r\n DEBUG:urllib3.connectionpool:https://s3.amazonaws.com:443 \"HEAD /datasets.huggingface.co/datasets/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/bookcorpus.py HTTP/1.1\" 200 0\r\n DEBUG:urllib3.connectionpool:Starting new HTTPS connection (1): raw.githubusercontent.com:443\r\n DEBUG:urllib3.connectionpool:https://raw.githubusercontent.com:443 \"HEAD /huggingface/datasets/master/datasets/bookcorpus/dataset_infos.json HTTP/1.1\" 200 0\r\n WARNING:datasets.builder:Reusing dataset bookcorpus (/home/theo/.cache/huggingface/datasets/bookcorpus/plain_text/1.0.0/af844be26c089fb64810e9f2cd841954fd8bd596d6ddd26326e4c70e2b8c96fc)\r\n 0%| | 0/74005 [00:00<?, ?ba/s]2021-03-23 12:26:47.219622: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\r\n 2021-03-23 12:26:47.219669: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\r\n DEBUG:tensorflow:Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\r\n 100%|███████████████████████████████████| 74005/74005 [1:02:17<00:00, 19.80ba/s]\r\n 3738.870892047882\r\n [ Top 10 differences ]\r\n <frozen importlib._bootstrap_external>:580: size=38.0 MiB (+33.7 MiB), count=326221 (+307919), average=122 B\r\n <frozen importlib._bootstrap>:219: size=7648 KiB (+7557 KiB), count=26455 (+25555), average=296 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/torch/__init__.py:427: size=1291 KiB (+1291 KiB), count=5924 (+5924), average=223 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/abc.py:85: size=1039 KiB (+1026 KiB), count=3429 (+3385), average=310 B\r\n <frozen importlib._bootstrap_external>:64: size=917 KiB (+891 KiB), count=5300 (+5132), average=177 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/collections/__init__.py:456: size=720 KiB (+709 KiB), count=3403 (+3349), average=217 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_export.py:346: size=607 KiB (+607 KiB), count=3962 (+3962), average=157 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/linecache.py:137: size=1000 KiB (+489 KiB), count=9569 (+4535), average=107 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/tf_decorator.py:241: size=367 KiB (+367 KiB), count=5225 (+5225), average=72 B\r\n /home/theo/.local/share/miniconda3/envs/datasets/lib/python3.8/site-packages/tensorflow/python/util/decorator_utils.py:114: size=359 KiB (+359 KiB), count=330 (+330), average=1114 B\r\n```\r\n\r\nI'm not concluding much, it seems nothing is really happening to memory on `pyarrow::Table.filter`? ", "Cool ! Maybe it increases the memory a bit but what's brought in memory is not the resulting Table but something else (not sure what though).\r\nWhat's the length of the resulting dataset ?\r\nYou can also take a look at `pyarrow.total_allocated_memory()` to show how much memory is being used by pyarrow", "```diff\r\ndiff --git a/benchmarks/benchmark_filter.py b/benchmarks/benchmark_filter.py\r\nindex 4b9efd4e..a862c204 100644\r\n--- a/benchmarks/benchmark_filter.py\r\n+++ b/benchmarks/benchmark_filter.py\r\n@@ -1,6 +1,9 @@\r\n import logging\r\n import sys\r\n import time\r\n+import tracemalloc\r\n+\r\n+import pyarrow as pa\r\n \r\n from datasets import load_dataset, set_caching_enabled\r\n \r\n@@ -9,13 +12,28 @@ if __name__ == \"__main__\":\r\n set_caching_enabled(False)\r\n logging.basicConfig(level=logging.DEBUG)\r\n \r\n+ tracemalloc.start()\r\n bc = load_dataset(\"bookcorpus\")\r\n \r\n now = time.time()\r\n try:\r\n+ snapshot1 = tracemalloc.take_snapshot()\r\n+ pamem1 = pa.total_allocated_bytes()\r\n bc[\"train\"].filter(lambda x: len(x[\"text\"]) < 64, num_proc=int(sys.argv[1]))\r\n+ pamem2 = pa.total_allocated_bytes()\r\n+ snapshot2 = tracemalloc.take_snapshot()\r\n except Exception as e:\r\n print(f\"cancelled: {e}\")\r\n+ exit(1)\r\n+ tracemalloc.stop()\r\n elapsed = time.time() - now\r\n \r\n print(elapsed)\r\n+ top_stats = snapshot2.compare_to(snapshot1, \"lineno\")\r\n+\r\n+ print(\"[ Top 10 differences ]\")\r\n+ for stat in top_stats[:10]:\r\n+ print(stat)\r\n+\r\n+ print(\"[ pyarrow reporting ]\")\r\n+ print(f\"before: ({pamem1}) after: ({pamem2})\")\r\n```\r\n\r\nthis yields 0-0, does not seem like a good tool 😛 and the documentation is [quite mysterious.](https://arrow.apache.org/docs/python/generated/pyarrow.total_allocated_bytes.html)", "Personally if I use your script to benchmark on this branch\r\n```python\r\nbc = load_dataset(\"bookcorpus\", split=\"train[:1%]\")\r\nbc = bc.filter(lambda x: len(x[\"text\"]) < 64)\r\n```\r\n\r\nthen I get\r\n```\r\n[ pyarrow reporting ]\r\nbefore: (0) after: (15300672)\r\n```\r\n\r\nMaybe you got 0-0 because the filter output is directly garbage collected, since you didn't do\r\n```python\r\nbc[\"train\"] = bc[\"train\"].filter(...)\r\n```\r\nCan you try again on your side just to make sure ?\r\n\r\nEven if the documentation doesn't say much, `pa.total_allocated_bytes` if pretty useful, and also very consistent.\r\nIt tracks the number of bytes used for arrow data.", "> Maybe you got 0-0 because the filter output is directly garbage collected, since you didn't do\r\n> \r\n> ```python\r\n> bc[\"train\"] = bc[\"train\"].filter(...)\r\n> ```\r\nNice catch! I get 1.74GB for this branch", "Looks like we may need to write the filtered table on the disk then.\r\n\r\nThe other option is to slice the table to keep only the good rows and concatenate them but this is too slow at the moment since slicing is O(n) until #1803 is fixed. I'll work on this issue this afternoon", "From investigation it looks like the lib's `Table.filter` cannot send its output to memorymap, asked a question on the mailing list, see [here](https://lists.apache.org/thread.html/r8cd8591ce83a967eb0097a7f31785ac2f3ee95ea371c8c5beb0720ad%40%3Cuser.arrow.apache.org%3E)", "closing in favor of #2836 " ]

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[ "@skyprince999 as you authored the PR for this dataset, any comments?", "This has been fixed in #2064 by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)" ]

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[ "@lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks ", "Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers import MT5TokenizerFast\r\n\r\ndef get_tokenized_dataset(dataset_name, dataset_config_name, tokenizer):\r\n datasets = load_dataset(dataset_name, dataset_config_name, script_version=\"master\")\r\n column_names = datasets[\"train\"].column_names\r\n text_column_name = \"translation\"\r\n def process_dataset(datasets):\r\n def process_function(examples):\r\n lang = \"fr\"\r\n return {\"src_texts\": [example[lang] for example in examples[text_column_name]]}\r\n datasets = datasets.map(\r\n process_function,\r\n batched=True,\r\n num_proc=None,\r\n remove_columns=column_names,\r\n load_from_cache_file=True,\r\n )\r\n return datasets\r\n datasets = process_dataset(datasets)\r\n text_column_name = \"src_texts\"\r\n column_names = [\"src_texts\"]\r\n def tokenize_function(examples):\r\n return tokenizer(examples[text_column_name], return_special_tokens_mask=True)\r\n tokenized_datasets = datasets.map(\r\n tokenize_function,\r\n batched=True,\r\n num_proc=None,\r\n remove_columns=column_names,\r\n load_from_cache_file=True\r\n )\r\n\r\nif __name__ == \"__main__\":\r\n tokenizer_kwargs = {\r\n \"cache_dir\": None,\r\n \"use_fast\": True,\r\n \"revision\": \"main\",\r\n \"use_auth_token\": None\r\n }\r\n tokenizer = MT5TokenizerFast.from_pretrained(\"google/mt5-small\", **tokenizer_kwargs)\r\n get_tokenized_dataset(dataset_name=\"opus100\", dataset_config_name=\"en-fr\", tokenizer=tokenizer)\r\n~ \r\n```", "as per https://github.com/huggingface/tokenizers/issues/626 this looks like to be the tokenizer bug, I therefore, reported it there https://github.com/huggingface/tokenizers/issues/626 and I am closing this one." ]

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[ "Hi\r\nYou can rename the arrow file and update the name in `state.json`", "I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n dataset_with_embedding =csv_dataset.map(\r\n partial(self.embed, ctx_encoder=ctx_encoder, ctx_tokenizer=self.context_tokenizer),\r\n batched=True,\r\n batch_size=1,\r\n features=new_features,\r\n cache_file_name=cache_arrow_path,\r\n load_from_cache_file=False\r\n )\r\n```\r\nSo here we set a cache_file_name , after this it uses the same file name when saving again and again. ", "I'm not sure I understand your issue, can you elaborate ?\r\n\r\n`cache_file_name` is indeed an argument you can set to specify the cache file that will be used for the processed dataset. By default the file is named with something like `cache-<fingerprint>.arrow` where the fingerprint is a hash.", "Let's say I am updating a set of embedding in a dataset that is around 40GB inside a training loop every 500 steps (Ex: calculating the embeddings in updated ctx_encoder in RAG and saving it to the passage path). So when we use **dataset_object.save_to_disk('passage_path_directory')** it will save the new dataset object every time with a random file name, especially when we do some transformations to dataset objects such as map or shards. This way, we keep collecting unwanted files that will eventually eat up all the disk space. \r\n\r\nBut if we can save the dataset object every time by a single name like **data_shard_1.arrow**, it will automatically remove the previous file and save the new one in the same directory. I found the above-mentioned code snippet useful to complete this task. \r\n\r\nIs this clear?" ]

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[ "The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.", "This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)", "Thanks @lhoestq and @matt-peters ", "I am closing this issue since its fixed!" ]

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[ "Hi @lhoestq,\r\n\r\nShould I remove the 160 configurations? Is it too much?\r\n\r\nEDIT:\r\nCan you also check the task category? I'm not sure if there is an appropriate tag for the same.", "Thanks for the changes !\r\n\r\n> Should I remove the 160 configurations? Is it too much?\r\n\r\nYea 160 configuration is a lot.\r\nMaybe this dataset can work with parameters `type` and `task_no` ?\r\nYou can just remove the configuration in BUILDER_CONFIGS to only keep a few ones.\r\nAlso feel free to add an example in the dataset card of how to load the other configurations\r\n```\r\nload_dataset(\"babi_qa\", type=\"hn\", task_no=\"qa1\")\r\n```\r\nfor example, and with a list of the possible combinations.\r\n\r\n> Can you also check the task category? I'm not sure if there is an appropriate tag for the same.\r\n\r\nIt looks appropriate, thanks :)", "Hi @lhoestq \r\n\r\nI'm unable to test it locally using:\r\n```python\r\nload_dataset(\"datasets/babi_qa\", type=\"hn\", task_no=\"qa1\")\r\n```\r\nIt raises an error:\r\n```python\r\nTypeError: __init__() got an unexpected keyword argument 'type'\r\n```\r\nWill this be possible only after merging? Or am I missing something here?", "Can you try adding this class attribute to `BabiQa` ?\r\n```python\r\nBUILDER_CONFIG_CLASS = BabiQaConfig\r\n```\r\nThis should fix the TypeError issue you got", "My bad. Thanks a lot!", "Hi @lhoestq \r\n\r\nI have added the changes. Only the \"qa1\" task for each category is included. Also, I haven't removed the size categories and other description because I think it will still be useful. I have updated the line in README showing the example.\r\n\r\nThanks,\r\nGunjan", "Hi @lhoestq,\r\n\r\nDoes this look good now?" ]

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[ "Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```", "Ty!" ]

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[ "Hi @lhoestq,\r\n\r\nI have added changes from review.", "Thanks for approving :)" ]

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[ "@lhoestq - We could simply use the \"general\" json dataset for this no? ", "Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path/to/your/train_data.json\", \"test\": \"path/to/your/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\ntest_dataset = load_dataset(\"json\", data_files=data_files, split=\"test\")\r\n```\r\n\r\nYou just need to make sure that the data contain the paths to the audio files.\r\nIf not, feel free to use `.map()` to add them.", "Many thanks! that was what I was looking for. " ]

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[ "LGTM, thanks for fixing." ]

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[ "Hello. All aforementioned changes have been made. I've also re-run black on miam.py. :-)", "I will run isort again. Hopefully it resolves the current check_code_quality test failure.", "Once the review period is over, feel free to open a PR to add all the missing information ;)", "Hi! I will follow up right now with one more pull request as I have new anonymous citation information to include." ]

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[ "I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?", "Hi,\r\n I am running the add_faiss_index during the training process of the RAG from the master process (rank 0). But at the exact moment, I do not run any other process since I do it in every 5000 training steps. \r\n \r\n I think what you say is correct. It depends on the number of CPU cores. I did an experiment to compare the time taken to finish the add_faiss_index process on use_own_knowleldge_dataset.py vs the training loop thing. The training loop thing takes 40 mins more. It might be natural right? \r\n \r\n \r\n at the moment it uses around 40 cores of a 96 core machine (I am fine-tuning the entire process). ", "Can you try to set the number of threads manually ?\r\nIf you set the same number of threads for both the `use_own_knowledge_dataset.py` and RAG training, it should take the same amount of time.\r\nYou can see how to set the number of thread in the faiss wiki: https://github.com/facebookresearch/faiss/wiki/Threads-and-asynchronous-calls", "Ok, I will report the details too soon. I am the first one on the list and currently add_index being computed for the 3rd time in the loop. Actually seems like the time is taken to complete each interaction is the same, but around 1 hour more compared to running it without the training loop. A the moment this takes 5hrs and 30 mins. If there is any way to faster the process, an end-to-end rag will be perfect. So I will also try out with different thread numbers too. \r\n\r\n\r\n", "@lhoestq on a different note, I read about using Faiss-GPU, but the documentation says we should use it when the dataset has the ability to fit into the GPU memory. Although this might work, in the long-term this is not that practical for me.\r\n\r\nhttps://github.com/matsui528/faiss_tips", "@lhoestq \r\n\r\nHi, I executed the **use_own_dataset.py** script independently and ask a few of my friends to run their programs in the HPC machine at the same time. \r\n\r\n Once there are so many other processes are running the add_index function gets slows down naturally. So basically the speed of the add_index depends entirely on the number of CPU processes. Then I set the number of threads as you have mentioned and got actually the same time for RAG training and independat running. So you are correct! :) \r\n\r\n \r\n Then I added this [issue in Faiss repostiary](https://github.com/facebookresearch/faiss/issues/1767). I got an answer saying our current **IndexHNSWFlat** can get slow for 30 million vectors and it would be better to use alternatives. What do you think?", "It's a matter of tradeoffs.\r\nHSNW is fast at query time but takes some time to build.\r\nA flat index is flat to build but is \"slow\" at query time.\r\nAn IVF index is probably a good choice for you: fast building and fast queries (but still slower queries than HSNW).\r\n\r\nNote that for an IVF index you would need to have an `nprobe` parameter (number of cells to visit for one query, there are `nlist` in total) that is not too small in order to have good retrieval accuracy, but not too big otherwise the queries will take too much time. From the faiss documentation:\r\n> The nprobe parameter is always a way of adjusting the tradeoff between speed and accuracy of the result. Setting nprobe = nlist gives the same result as the brute-force search (but slower).\r\n\r\nFrom my experience with indexes on DPR embeddings, setting nprobe around 1/4 of nlist gives really good retrieval accuracy and there's no need to have a value higher than that (or you would need to brute-force in order to see a difference).", "@lhoestq \r\n\r\nThanks a lot for sharing all this prior knowledge. \r\n\r\nJust asking what would be a good nlist of parameters for 30 million embeddings?", "When IVF is used alone, nlist should be between `4*sqrt(n)` and `16*sqrt(n)`.\r\nFor more details take a look at [this section of the Faiss wiki](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset)", "Thanks a lot. I was lost with calling the index from class and using faiss_index_factory. ", "@lhoestq Thanks a lot for the help you have given to solve this issue. As per my experiments, IVF index suits well for my case and it is a lot faster. The use of this can make the entire RAG end-to-end trainable lot faster. So I will close this issue. Will do the final PR soon. " ]

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[ "Not sure why CI isn't triggered.\r\n\r\n@lhoestq Can you please help me with this? ", "I don't know how to trigger it manually, but an empty commit should do the job" ]

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[ "Hi @lhoestq,\r\n\r\nI have added changes from the review.", "Thanks for approving @lhoestq " ]

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[ "@lhoestq But we don't perform conversion to a `NamedSplit` if `_split` is not a string which means it **will** be a `ReadInstruction` after reloading.", "Yes right ! I read it wrong.\r\nPerfect then" ]

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[ "Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no longer have such restrictions.", "Sure, thanks for the fast reply!\r\n\r\nFor dataset A: `[{'filename': 'drive/MyDrive/data_target_task/dataset_a/train/cache-4797266bf4db1eb7.arrow'}]`\r\nFor dataset B: `[]`\r\n\r\nNo clue why for B it returns nothing. `PATH_DATA_CLS_B` is exactly the same in `save_to_disk` and `load_from_disk`... Also I can verify that the folder physically exists under 'drive/MyDrive/data_target_task/dataset_b/'", "In the next release you'll be able to concatenate any kinds of dataset (either from memory or from disk).\r\n\r\nFor now I'd suggest you to flatten the indices of the A and B datasets. This will remove the indices mapping and you will be able to concatenate them. You can flatten the indices with\r\n```python\r\ndataset = dataset.flatten_indices()\r\n```", "Indeed this works. Not the most elegant solution, but it does the trick. Thanks a lot! " ]

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[ "Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications\r\n```", "Fixed by #2041, thanks again @songfeng !" ]

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[ "The error you got is minor and appeared in the last version of pyarrow, we'll fix the CI to take this into account. You can ignore it" ]

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[ "Solved!" ]

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[ "Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only chance to be able training the models at scale and I am grateful for your help.\r\n\r\n", "Hi @dorost1234,\r\nTry installing this library first, `pip install 'apache-beam[gcp]' --use-feature=2020-resolver` followed by loading dataset like this using beam runner.\r\n\r\n`dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\n I also read in error stack trace that:\r\n\r\n> Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc.\r\n\r\nWorked perfectly fine after this (Ignore these warnings)\r\n\r\n\r\n\r\n", "For wikipedia dataset, looks like the files it's looking for are no longer available. For `bg`, I checked [here](https://dumps.wikimedia.org/bgwiki/). For this I think `dataset_infos.json` for this dataset has to made again? You'll have to load this dataset also using beam runner.\r\n\r\n", "Hello @dorost1234,\r\n\r\nIndeed, Wikipedia datasets need a lot of preprocessing and this is done using Apache Beam. That is the reason why it is required that you install Apache Beam in order to preform this preprocessing.\r\n\r\nFor some specific default parameters (English Wikipedia), Hugging Face has already preprocessed the dataset for you (and it is stored in the cloud). That is the reason why you do not get the error for English: the preprocessing is already done by HF and you just get the preprocessed dataset; Apache Beam is not required in that case.", "Hi\nI really appreciate if huggingface can kindly provide preprocessed\ndatasets, processing these datasets require sufficiently large resources\nand I do not have unfortunately access to, and perhaps many others too.\nthanks\n\nOn Fri, Mar 12, 2021 at 9:04 AM Albert Villanova del Moral <\n***@***.***> wrote:\n\n> Hello @dorost1234 <https://github.com/dorost1234>,\n>\n> Indeed, Wikipedia datasets need a lot of preprocessing and this is done\n> using Apache Beam. That is the reason why it is required that you install\n> Apache Beam in order to preform this preprocessing.\n>\n> For some specific default parameters (English Wikipedia), Hugging Face has\n> already preprocessed the dataset for you (and it is stored in the cloud).\n> That is the reason why you do not get the error for English: the\n> preprocessing is already done by HF and you just get the preprocessed\n> dataset; Apache Beam is not required in that case.\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-797310899>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMXACFQZAGMK4VGXRETTDHDI3ANCNFSM4ZA5R2UA>\n> .\n>\n", "Hi everyone\r\nthanks for the helpful pointers, I did it as @bhavitvyamalik suggested, for me this freezes on this command for several hours, \r\n\r\n`Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /users/dara/cache/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n`\r\n\r\nDo you know how long this takes? Any specific requirements the machine should have? like very large memory or so? @lhoestq \r\n\r\nthanks \r\n\r\n\r\n", "HI @dorost1234, \r\nThe dataset size is 631.84 MiB so depending on your internet speed it'll take some time. You can monitor your internet speed meanwhile to see if it's downloading the dataset or not (use `nload` if you're using linux/mac to monitor the same). In my case it took around 3-4 mins. Since they haven't used `download_and_extract` here that's why there's no download progress bar.", "Hi\r\nthanks, my internet speed should be good, but this really freezes for me, this is how I try to get this dataset:\r\n\r\n`from datasets import load_dataset\r\ndataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')`\r\n\r\nthe output I see if different also from what you see after writing this command:\r\n\r\n`Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /users/dara/cache/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...`\r\n\r\ndo you have any idea why it might get freezed? anything I am missing @lhoestq @bhavitvyamalik. Do I need maybe to set anything special for apache-beam? \r\n\r\nthanks a lot \r\n\r\nOn Tue, Mar 16, 2021 at 9:03 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> HI @dorost1234 <https://github.com/dorost1234>,\r\n> The dataset size is 631.84 MiB so depending on your internet speed it'll\r\n> take some time. You can monitor your internet speed meanwhile to see if\r\n> it's downloading the dataset or not (use nload if you're using linux/mac\r\n> to monitor the same). In my case it took around 3-4 mins. Since they\r\n> haven't used download_and_extract here that's why there's no download\r\n> progress bar.\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-800044303>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMQIHNNLM2LGG6QKZ73TD4GDJANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n", "I tried this on another machine (followed the same procedure I've mentioned above). This is what it shows (during the freeze period) for me:\r\n```\r\n>>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\nDownloading: 5.26kB [00:00, 1.23MB/s] \r\nDownloading: 1.40kB [00:00, 327kB/s] \r\nDownloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\nWARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https://developers.google.com/accounts/docs/application-default-credentials for more information.\r\nConnecting anonymously.\r\nWARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n```\r\nAfter around 10 minutes, here's the loading of dataset:\r\n```\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.42s/sources]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.12sources/s]\r\n100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.14sources/s]\r\nDataset wiki40b downloaded and prepared to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n```", "Hi\r\nI honestly also now tried on another machine and nothing shows up after\r\nhours of waiting. Are you sure you have not set any specific setting? maybe\r\ngoogle cloud which seems it is used here, needs some credential setting?\r\nthanks for any suggestions on this\r\n\r\nOn Tue, Mar 16, 2021 at 10:02 AM Bhavitvya Malik ***@***.***>\r\nwrote:\r\n\r\n> I tried this on another machine (followed the same procedure I've\r\n> mentioned above). This is what it shows (during the freeze period) for me:\r\n>\r\n> >>> dataset = load_dataset(\"wiki40b\", \"cs\", beam_runner='DirectRunner')\r\n> Downloading: 5.26kB [00:00, 1.23MB/s]\r\n> Downloading: 1.40kB [00:00, 327kB/s]\r\n> Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...\r\n> WARNING:apache_beam.internal.gcp.auth:Unable to find default credentials to use: The Application Default Credentials are not available. They are available if running in Google Compute Engine. Otherwise, the environment variable GOOGLE_APPLICATION_CREDENTIALS must be defined pointing to a file defining the credentials. See https://developers.google.com/accounts/docs/application-default-credentials for more information.\r\n> Connecting anonymously.\r\n> WARNING:apache_beam.io.tfrecordio:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\r\n>\r\n> After around 10 minutes, here's the loading of dataset:\r\n>\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:16<00:00, 16.42s/sources]\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.12sources/s]\r\n> 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1.14sources/s]\r\n> Dataset wiki40b downloaded and prepared to /home/bhavitvya/.cache/huggingface/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f. Subsequent calls will reuse this data.\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/2035#issuecomment-800081772>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMX6A2ZTRZUIIZVFRCDTD4NC3ANCNFSM4ZA5R2UA>\r\n> .\r\n>\r\n" ]

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[ "Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?", "OK! I'll send it later." ]

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[ "I am wondering why only one test of \"keep_in_memory=True\" fails, when there are many other tests that test the same and it happens only in pyarrow_1..." ]

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[ "In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r\n```python\r\ndataset2 = load_from_disk(dataset_filename)\r\n```\r\nwhere `dataset_filename` is the place where you saved you dataset with the embeddings in the first place.", "Ok in that case HF should fix their misleading example at https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index \r\n\r\nI copy-pasted it here.\r\n\r\n> When you are done with your queries you can save your index on disk:\r\n> \r\n> ```python\r\n> ds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n> ```\r\n> Then reload it later:\r\n> \r\n> ```python\r\n> ds = load_dataset('crime_and_punish', split='train[:100]')\r\n> ds.load_faiss_index('embeddings', 'my_index.faiss')\r\n> ```", "Hi !\r\n\r\nThe code of the example is valid.\r\nAn index is a search engine, it's not considered a column of a dataset.\r\nWhen you do `ds.load_faiss_index(\"embeddings\", 'my_index.faiss')`, it attaches an index named \"embeddings\" to the dataset but it doesn't re-add the \"embeddings\" column. You can list the indexes of a dataset by using `ds.list_indexes()`.\r\n\r\nIf I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nThis looks misleading indeed, and we should add a note to make it more explicit that it doesn't store the column that was used to build the index.\r\n\r\nFeel free to open a PR to suggest an improvement on the documentation if you want to contribute :)", "> If I understand correctly by reading this example you thought that it was re-adding the \"embeddings\" column.\r\nYes. I was trying to use the dataset in RAG and it complained that the dataset didn't have the right columns. No problems when loading the dataset with `load_from_disk` and then doing `load_faiss_index`\r\n\r\nWhat I learned was\r\n1. column and index are different\r\n2. loading the index does not create a column\r\n3. the column is not needed to be able to use the index\r\n4. RAG needs both the embeddings column and the index\r\n\r\nIf I can come up with a way to articulate this in the right spot in the docs, I'll open a PR" ]

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[ "@lhoestq I think I have addressed all your comments. ", "Thanks! @lhoestq Let me know if you want me to address anything to get this merged. ", "It's all good thanks ;)\r\nmerging" ]

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[ "Hi,\r\n\r\nActually, the error occurs due to these two lines:\r\n```python\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n```\r\n`Dataset.rename_column` doesn't update the `_format_columns` attribute, previously defined by `Dataset.set_format`, with a new column name which is why this new column is missing in the output.", "Hi @mariosasko,\n\nThanks for opening a PR on this :)\nWhy does the old name also disappear?", "I just merged a @mariosasko 's PR that fixes this issue.\r\nIf it happens again, feel free to re-open :)" ]

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[ "There is one more thing I would love to see. Let's say we iteratively keep updating a data source that loaded from **load_dataset** or **load_from_disk**. Now we need to save it to the same location by overriding the previous file inorder to save the disk space. At the moment **save_to_disk** can not assign a name. So I do not see an easy way to override the previous files. @lhoestq is this possible?\r\n\r\n\r\n\r\np.s one last thing?\r\n\r\nIs there a way to flush out any connection to a data source loaded from **load_from_disk** or **load_dataset** methods? At the moment I suspect when we use any of those functions, it will always keep a pointer although we override it again with a new version of the dataset source. This is really useful in an iterative process. \r\n\r\n", "> There is one more thing I would love to see. Let's say we iteratively keep updating a data source that loaded from **load_dataset** or **load_from_disk**. Now we need to save it to the same location by overriding the previous file inorder to save the disk space. At the moment **save_to_disk** can not assign a name. So I do not see an easy way to override the previous files. @lhoestq is this possible?\r\n\r\nIn the new save_to_disk, the filename of the arrow file is fixed: `dataset.arrow`.\r\nThis way is will be overwritten if you save your dataset again\r\n\r\n> Is there a way to flush out any connection to a data source loaded from **load_from_disk** or **load_dataset** methods? At the moment I suspect when we use any of those functions, it will always keep a pointer although we override it again with a new version of the dataset source. This is really useful in an iterative process.\r\n\r\nIf you update an arrow file, then you must reload it with `load_from_disk` for example in order to have the updated data.\r\nDoes that answer the question ? How does this \"pointer\" behavior manifest exactly on your side ?", "Apparently the usage of the compute layer of pyarrow requires pyarrow>=1.0.0 (otherwise there are some issues on windows with file permissions when doing dataset concatenation).\r\n\r\nI'll bump the pyarrow requirement from, 0.17.1 to 1.0.0", "\r\n> If you update an arrow file, then you must reload it with `load_from_disk` for example in order to have the updated data.\r\n> Does that answer the question? How does this \"pointer\" behavior manifest exactly on your side?\r\n\r\nYes, I checked this behavior.. if we update the .arrow file it kind of flushes out the previous one. So your solution is perfect <3. ", "Sorry for spamming, there's a a bug that only happens on the CI so I have to re-run it several times", "Alright I finally added all the tests I wanted !\r\nI also fixed all the bugs and now all the tests are passing :)\r\n\r\nLet me know if you have comments.\r\n\r\nI also noticed that two methods in pyarrow seem to bring some data in memory even for a memory mapped table: filter and cast:\r\n- for filter I took a look at the C++ code on the arrow's side and found [this part](https://github.com/apache/arrow/blob/55c8d74d5556b25238fb2028e9fb97290ea24684/cpp/src/arrow/compute/kernels/vector_selection.cc#L93-L160) that \"builds\" the array during filter. It seems to indicate that it allocates new memory for the filtered array but not 100% sure.\r\n- regarding cast I noticed that it happens when changing the precision of an array of integers. Not sure if there are other cases.\r\n\r\n\r\nMaybe we'll need to investigate this a bit for your PR on improving `filter` @theo-m , since we don't want to fill the users memory.", "> Maybe we'll need to investigate this a bit for your PR on improving `filter` @theo-m , since we don't want to fill the users memory.\r\n\r\nI'm a bit unclear on this, I thought the point of the refactor was to use `Table.filter` to speed up our own `.filter` and stop using `.map` that offloaded too much stuff on disk. \r\nAt some point I recall we decided to use `keep_in_memory=True` as the expectations were that it would be hard to fill the memory?", "> I'm a bit unclear on this, I thought the point of the refactor was to use Table.filter to speed up our own .filter and stop using .map that offloaded too much stuff on disk.\r\n> At some point I recall we decided to use keep_in_memory=True as the expectations were that it would be hard to fill the memory?\r\n\r\nYes it's ok to have the mask in memory, but not the full table. I was not aware that the table returned by filter could actually be in memory (it's not part of the pyarrow documentation afaik).\r\nTo be more specific I noticed that every time you call `filter`, the pyarrow total allocated memory increases.\r\nI haven't checked on a big dataset though, but it would be nice to see how much memory it uses with respect to the size of the dataset.", "I have addressed your comments @theo-m @albertvillanova ! Thanks for the suggestions", "I totally agree with you. I would have loved to use inheritance instead.\r\nHowever because `pa.Table` is a cython class without proper initialization methods (you can't call `__init__` for example): you can't instantiate a subclass of `pa.Table` in python.\r\nTo be more specific, you actually can try to instantiate a subclass of `pa.Table` with no data BUT this is not a valid table so you get an error.\r\nAnd since `pa.Table` objects are immutable you can't even set the data in `__new__` or `__init__`.\r\n\r\nEDIT: one could make a new cython class that inherits from `pa.Table` with proper initialization methods, so that we can inherit from this class instead in python. We can do that in the future if we plan to use cython in `datasets`.\r\n(see: https://arrow.apache.org/docs/python/extending.html)", "@lhoestq, but in which cases you would like to instantiate directly either `InMemoryTable` or `MemoryMappedTable`? You normally use one of their `from_xxx` class methods...", "Yes I was thinking of these cases. The issue is that they return `pa.Table` objects even from a subclass of `pa.Table`", "That is indeed a weird behavior...", "I guess that in this case, the best approach is as you did, using composition over inheritance...\r\n\r\nhttps://github.com/apache/arrow/pull/5322", "@lhoestq I think you forgot to add the new classes to the docs?", "Yes you're right, let me add them" ]

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[ "Thanks for noticing !\r\n#2020 fixed this earlier today though ^^'\r\n\r\nClosing this one" ]

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[ "Hi ! Thanks for updating XQUAD :)\r\n\r\nThe slow test is failing though since there's no dummy data nor metadata in dataset_infos.json for the romanian configuration.\r\n\r\nCould you please generate the dummy data with\r\n```\r\ndatasets-cli dummy_data ./datasets/xquad --auto_generate --json_field data\r\n```\r\nThis will update all the dummy data files, and also add the new one for the romanian configuration.\r\n\r\n\r\nYou can also update the metadata with\r\n```\r\ndatasets-cli test ./datasets/xquad --name xquad.ro --save_infos\r\n```\r\nThis will update the dataset_infos.json file with the metadata of the romanian config :)\r\n\r\nThanks in advance !", "Hello Quentin, and thanks for your help.\r\n\r\nI found that running\r\n\r\n```python\r\ndatasets-cli test ./datasets/xquad --name xquad.ro --save_infos\r\n```\r\n\r\nwas not enough to pass the slow tests, because it was not adding the new `xquad.ro.json` checksum to the other configs infos and becuase of that an `UnexpectedDownloadedFile` error was being thrown, so instead I used:\r\n\r\n```python\r\ndatasets-cli test ./datasets/xquad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n`--ignore_verifications` was necessary to bypass the same `UnexpectedDownloadedFile` error.\r\n\r\nAdditionally, I deleted `dummy_data_copy.zip` and the `copy.sh` script because they both seem now unnecessary.\r\n\r\nThe slow tests for both the real and dummy data now pass successfully, so I hope that I didn't mess anything up :)\r\n", "You're right, you needed the `--ignore_verifications` flag !\r\nThanks for updating them :)\r\n\r\nAlthough I just noticed that the new dummy_data.zip files are quite big (170KB each) because they contain the json files of all the languages, while only one json file per language is necessary. Could you remove the unnecessary json files to reduce the size of the dummy_data.zip files if you don't mind ?", "Done. I created a script (`remove_unnecessary_langs.sh`) to automate the process.\r\n" ]

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[ "Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__` is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use the named splits API (for now):\r\n```python\r\ntrain_ds, test_ds = load_dataset(\r\n path='csv', \r\n delimiter='\\t', \r\n data_files=text_files, \r\n split=['train[:90%]', 'train[-10%:]'],\r\n)\r\n\r\ntrain_ds = train_ds.rename_column('sentence', 'text')\r\n```", "This has been fixed in #2043 , thanks @mariosasko \r\nThe fix is available on master and we'll do a new release soon :)\r\n\r\nfeel free to re-open if you still have issues" ]

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[ "Hi,\r\n\r\nCan you give us a minimal reproducible example? This [part](https://huggingface.co/docs/datasets/master/processing.html#controling-the-cache-behavior) of the docs explains how to control caching." ]

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[ "@lhoestq Maybe a script or even a test in `test_dataset_common.py` that verifies that a dataset script meets some set of quality standards (print calls and todos from the dataset script template are not present, etc.) could be added?", "Yes definitely !" ]

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[ "Link to the card: https://github.com/mcmillanmajora/datasets/blob/md-gender-card/datasets/md_gender_bias/README.md", "dataset card* @sgugger :p ", "Ahah that's what I wanted to say @lhoestq, thanks for fixing. Not used to review the Datasets side ;-)" ]

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[ "What's the issue exactly ?\r\n\r\nGiven an `upstream` remote repository with url `https://github.com/huggingface/datasets.git`, you can totally rebase from `upstream/master`.\r\n\r\nIt's mentioned at the beginning how to add the `upstream` remote repository\r\n\r\nhttps://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L10-L14", "~~What difference is there with the default `origin` remote that is set when you clone the repo?~~ I've just understood that this applies to **forks** of the repo 🤡 " ]

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[ "I'm not able to reproduce on my side.\r\nCan you provide the full stacktrace please ?\r\nWhat version of `python` and `dill` do you have ? Which OS are you using ?", "```\r\nco_filename = '<ipython-input-2-e0383a102aae>', returned_obj = [0]\r\n \r\n def create_ipython_func(co_filename, returned_obj):\r\n def func():\r\n return returned_obj\r\n \r\n code = func.__code__\r\n> code = CodeType(*[getattr(code, k) if k != \"co_filename\" else co_filename for k in code_args])\r\nE TypeError: an integer is required (got type bytes)\r\n\r\ntests/test_caching.py:152: TypeError\r\n```\r\n\r\nPython 3.8.8 \r\ndill==0.3.1.1\r\n", "I managed to reproduce. This comes from the CodeType init signature that is different in python 3.8.8\r\nI opened a PR to fix this test\r\nThanks !" ]

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[ "Hi @theo-m !\r\n\r\nA few lines above this line, you'll find that the `_split_generators` method returns a list of `SplitGenerator`s objects:\r\n\r\n```python\r\ndatasets.SplitGenerator(\r\n name=datasets.Split.VALIDATION,\r\n # These kwargs will be passed to _generate_examples\r\n gen_kwargs={\r\n \"filepath\": os.path.join(data_dir, \"dev.jsonl\"),\r\n \"split\": \"dev\",\r\n },\r\n),\r\n```\r\n\r\nNotice the `gen_kwargs` argument passed to the constructor of `SplitGenerator`: this dict will be unpacked as keyword arguments to pass to the `_generat_examples` method (in this case the `filepath` and `split` arguments).\r\n\r\nLet me know if that helps!", "Oh ok I hadn't made the connection between those two, will offer a tweak to the comment and the template then - thanks!" ]

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[ "What do yo think of the documentation btw ?\r\nWhat parts would you like to see improved ?", "I like how concise and straightforward the docs are.\r\n\r\nFew things that would further improve the docs IMO:\r\n* the usage example of `Dataset.formatted_as` in https://huggingface.co/docs/datasets/master/processing.html\r\n* the \"Open in Colab\" button would be nice where it makes sense (we can borrow this from the transformers project + link to HF Forum)" ]

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[ "So maybe a summary here: \r\nIf I could fit a large model with batch_size = X into memory, is there a way I could train this model for huge datasets with keeping setting the same? thanks ", "The `datastets` library doesn't load datasets into memory. Therefore you can load a dataset that is terabytes big without filling up your RAM.\r\n\r\nThe only thing that's loaded into memory during training is the batch used in the training step.\r\nSo as long as your model works with batch_size = X, then you can load an even bigger dataset and it will work as well with the same batch_size.\r\n\r\nNote that you still have to take into account that some batches take more memory than others, depending on the texts lengths. If it works for a batch with batch_size = X and with texts of maximum length, then it will work for all batches.\r\n\r\nIn your case I guess that there are a few long sentences in the dataset. For those long sentences you get a memory error on your GPU because they're too long. By passing `max_train_samples` you may have taken a subset of the dataset that only contain short sentences. That's probably why in your case it worked only when you set `max_train_samples`.\r\nI'd suggest you to reduce the batch size so that the batches with long sentences can be loaded on the GPU.\r\n\r\nLet me know if that helps or if you have other questions" ]

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[ "Adding to the previous information, I think `torch.utils.data.DataLoader` is doing some conversion. \r\nWhat I tried:\r\n```python\r\ntrain_dataset = load_dataset('mnist')\r\n```\r\nI don't use any `map` or `set_format` or any `transform`. I use this directly, and try to load batches using the `DataLoader` with batch size 2, I get an output like this for the `image`:\r\n\r\n```\r\n[[tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor([0, 0]), tensor...\r\n```\r\nFor `label`, it works fine:\r\n```\r\ntensor([7, 6])\r\n```\r\nNote that I didn't specify conversion to torch tensors anywhere.\r\n\r\nBasically, there are two problems here:\r\n1. `dataset.map` doesn't return tensor type objects, even though it uses the transforms, the grayscale conversion in transform was done, but the output was lists only.\r\n2. The `DataLoader` performs its own conversion, which may be not desired.\r\n\r\nI understand that we can't change `DataLoader` because it is a torch functionality, however, is there a way we can handle image data to allow using it with torch `DataLoader` and `torchvision` properly?\r\n\r\nI think if the `image` was a torch tensor (N,H,W,C), or a list of torch tensors (H,W,C), before it is passed to `DataLoader`, then we might not face this issue. ", "What's the feature types of your new dataset after `.map` ?\r\n\r\nCan you try with adding `features=` in the `.map` call in order to set the \"image\" feature type to `Array2D` ?\r\nThe default feature type is lists of lists, we've not implemented shape verification to use ArrayXD instead of nested lists yet", "Hi @lhoestq\r\n\r\nRaw feature types are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000 #(type, len)\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'int'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nInside the `prepare_feature` method with batch size 100000 , after processing, they are like this:\r\n\r\nInside Prepare Train Features\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter map, the feature type are like this:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\n\r\nAfter dataloader with batch size 2, the batch features are like this:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n<hr>\r\n\r\nWhen I was setting the format of `train_dataset` to 'torch' after mapping - \r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nCorresponding DataLoader batch:\r\n```\r\nFrom DataLoader batch features\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nI will check with features and get back.\r\n\r\n\r\n\r\n", "Hi @lhoestq\r\n\r\n# Using Array3D\r\nI tried this:\r\n```python\r\nfeatures = datasets.Features({\r\n \"image\": datasets.Array3D(shape=(1,28,28),dtype=\"float32\"),\r\n \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n })\r\ntrain_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n```\r\nand it didn't fix the issue.\r\n\r\nDuring the `prepare_train_features:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter the `map`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'float'>\r\nLabel:\r\n<class 'list'> 60000\r\n<class 'int'>\r\n```\r\nFrom the DataLoader batch:\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\nIt is the same as before.\r\n\r\n---\r\n\r\nUsing `datasets.Sequence(datasets.Array2D(shape=(28,28),dtype=\"float32\"))` gave an error during `map`:\r\n\r\n```python\r\nArrowNotImplementedError Traceback (most recent call last)\r\n<ipython-input-95-d28e69289084> in <module>()\r\n 3 \"label\": datasets.features.ClassLabel(names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]),\r\n 4 })\r\n----> 5 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)\r\n\r\n15 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)\r\n 446 num_proc=num_proc,\r\n 447 )\r\n--> 448 for k, dataset in self.items()\r\n 449 }\r\n 450 )\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/dataset_dict.py in <dictcomp>(.0)\r\n 446 num_proc=num_proc,\r\n 447 )\r\n--> 448 for k, dataset in self.items()\r\n 449 }\r\n 450 )\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)\r\n 1307 fn_kwargs=fn_kwargs,\r\n 1308 new_fingerprint=new_fingerprint,\r\n-> 1309 update_data=update_data,\r\n 1310 )\r\n 1311 else:\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)\r\n 202 }\r\n 203 # apply actual function\r\n--> 204 out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n 205 datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [out]\r\n 206 # re-apply format to the output\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)\r\n 335 # Call actual function\r\n 336 \r\n--> 337 out = func(self, *args, **kwargs)\r\n 338 \r\n 339 # Update fingerprint of in-place transforms + update in-place history of transforms\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, update_data)\r\n 1580 if update_data:\r\n 1581 batch = cast_to_python_objects(batch)\r\n-> 1582 writer.write_batch(batch)\r\n 1583 if update_data:\r\n 1584 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)\r\n 274 typed_sequence = TypedSequence(batch_examples[col], type=col_type, try_type=col_try_type)\r\n 275 typed_sequence_examples[col] = typed_sequence\r\n--> 276 pa_table = pa.Table.from_pydict(typed_sequence_examples)\r\n 277 self.write_table(pa_table, writer_batch_size)\r\n 278 \r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pydict()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in __arrow_array__(self, type)\r\n 95 out = pa.ExtensionArray.from_storage(type, pa.array(self.data, type.storage_dtype))\r\n 96 else:\r\n---> 97 out = pa.array(self.data, type=type)\r\n 98 if trying_type and out[0].as_py() != self.data[0]:\r\n 99 raise TypeError(\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowNotImplementedError: extension\r\n```", "# Convert raw tensors to torch format\r\nStrangely, converting to torch tensors works perfectly on `raw_dataset`:\r\n```python\r\nraw_dataset.set_format('torch',columns=['image','label'])\r\n```\r\nTypes:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nUsing this for transforms:\r\n```python\r\ndef prepare_features(examples):\r\n images = []\r\n labels = []\r\n for example_idx, example in enumerate(examples[\"image\"]):\r\n if transform is not None:\r\n images.append(transform(\r\n examples[\"image\"][example_idx].numpy()\r\n ))\r\n else:\r\n images.append(examples[\"image\"][example_idx].numpy())\r\n labels.append(examples[\"label\"][example_idx])\r\n output = {\"label\":labels, \"image\":images}\r\n return output\r\n```\r\n\r\nInside `prepare_train_features`:\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batch:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n\r\n## Using `torch` format:\r\n```\r\nImage:\r\n<class 'list'> 60000\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\nDataLoader batches:\r\n\r\n```\r\nImage:\r\n<class 'list'> 1\r\n<class 'list'> 28\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\n---\r\n## Using the features - `Array3D`:\r\n\r\n```\r\nImage:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'list'> 10000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter `map`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 60000\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nAfter DataLoader `batch`:\r\n```\r\nImage:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'> 1\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'> 28\r\n<class 'torch.Tensor'>\r\nLabel:\r\n<class 'torch.Tensor'> 2\r\n<class 'torch.Tensor'>\r\n```\r\n\r\nThe last one works perfectly.\r\n\r\n\r\n\r\nI wonder why this worked, and others didn't.\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "Concluding, the way it works right now is:\r\n\r\n1. Converting raw dataset to `torch` format.\r\n2. Use the transform and apply using `map`, ensure the returned values are tensors. \r\n3. When mapping, use `features` with `image` being `Array3D` type.", "What the dataset returns depends on the feature type.\r\nFor a feature type that is Sequence(Sequence(Sequence(Value(\"uint8\")))), a dataset formatted as \"torch\" return lists of lists of tensors. This is because the lists lengths may vary.\r\nFor a feature type that is Array3D on the other hand it returns one tensor. This is because the size of the tensor is fixed and defined bu the Array3D type.", "Okay, that makes sense.\r\nRaw images are list of Array2D, hence we get a single tensor when `set_format` is used. But, why should I need to convert the raw images to `torch` format when `map` does this internally?\r\n\r\nUsing `Array3D` did not work with `map` when raw images weren't `set_format`ted to torch type.", "I understand that `map` needs to know what kind of output tensors are expected, and thus converting the raw dataset to `torch` format is necessary. Closing the issue since it is resolved." ]

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[ "@lhoestq all the changes requested are implemented. Thank you for your time and feedback :)" ]

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[ "This is expected to show this message to the user via stdout.\r\nThis way the users see it directly and can cancel the downloading if they want to.\r\nCould you elaborate why it would be better to have it in stderr instead of stdout ?", "@lhoestq, sorry for the late reply\r\n\r\nI completely understand why you decided to output a message that is always shown. The only problem is that the message is printed to the `stdout`. For example, if the user runs `python run_glue.py > log_file`, it will redirect `stdout` to the file named `log_file`, and the message will not be shown to the user.\r\n\r\nInstead, we should print this message to `stderr`. Even in the case of `python run_glue.py > log_file` only `stdout` is being redirected and so the message is always shown." ]

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[ "@lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit." ]

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[ "Hi @itsLuisa !\r\n\r\nCould you give us more information about the error you're getting, please?\r\nA copy-paste of the Traceback would be nice to get a better understanding of what is wrong :) ", "Hello @SBrandeis , this is it:\r\n```\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 537, in incomplete_dir\r\n yield tmp_dir\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 578, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 656, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 982, in _prepare_split\r\n num_examples, num_bytes = writer.finalize()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 297, in finalize\r\n self.write_on_file()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 230, in write_on_file\r\n pa_array = pa.array(typed_sequence)\r\n File \"pyarrow\\array.pxi\", line 222, in pyarrow.lib.array\r\n File \"pyarrow\\array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\arrow_writer.py\", line 97, in __arrow_array__\r\n out = pa.array(self.data, type=type)\r\n File \"pyarrow\\array.pxi\", line 305, in pyarrow.lib.array\r\n File \"pyarrow\\array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow\\error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow\\error.pxi\", line 107, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Expected bytes, got a 'list' object\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"C:/Users/Luisa/Documents/Uni/WS 2020,21/Neural Networks/Final_Project/NN_Project/data_loading.py\", line 122, in <module>\r\n main()\r\n File \"C:/Users/Luisa/Documents/Uni/WS 2020,21/Neural Networks/Final_Project/NN_Project/data_loading.py\", line 111, in main\r\n dataset = datasets.load_dataset(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\load.py\", line 740, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 586, in download_and_prepare\r\n self._save_info()\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\datasets\\builder.py\", line 543, in incomplete_dir\r\n shutil.rmtree(tmp_dir)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 740, in rmtree\r\n return _rmtree_unsafe(path, onerror)\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 618, in _rmtree_unsafe\r\n onerror(os.unlink, fullname, sys.exc_info())\r\n File \"C:\\Users\\Luisa\\AppData\\Local\\Programs\\Python\\Python38\\lib\\shutil.py\", line 616, in _rmtree_unsafe\r\n os.unlink(fullname)\r\nPermissionError: [WinError 32] Der Prozess kann nicht auf die Datei zugreifen, da sie von einem anderen Prozess verwendet wird: 'C:\\\\Users\\\\Luisa\\\\.cache\\\\huggingface\\\\datasets\\\\sample\\\\default-20ee7d51a6a9454f\\\\0.0.0\\\\5fc4c3a355ea77ab446bd31fca5082437600b8364d29b2b95264048bd1f398b1.incomplete\\\\sample-train.arrow'\r\n\r\nProcess finished with exit code 1\r\n```", "Hi @itsLuisa, thanks for sharing the Traceback.\r\n\r\nYou are defining the \"id\" field as a `string` feature:\r\n```python\r\nclass Sample(datasets.GeneratorBasedBuilder):\r\n ...\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n features=datasets.Features(\r\n {\r\n \"id\": datasets.Value(\"string\"),\r\n # ^^ here\r\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\r\n \"pos_tags\": datasets.Sequence(datasets.features.ClassLabel(names=[...])),\r\n[...]\r\n```\r\n\r\nBut in the `_generate_examples`, the \"id\" field is a list:\r\n```python\r\nids = list()\r\n```\r\n\r\nChanging:\r\n```python\r\n\"id\": datasets.Value(\"string\"),\r\n```\r\nInto:\r\n```python\r\n\"id\": datasets.Sequence(datasets.Value(\"string\")),\r\n```\r\n\r\nShould fix your issue.\r\n\r\nLet me know if this helps!", "It seems to be working now, thanks a lot for the help, @SBrandeis !", "Glad to hear it!\r\nI'm closing the issue" ]

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[ "Hi @lhoestq,\r\n\r\nI have added the changes from the review." ]

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[ "Nice catch! Yes I didn't check the actual data, instead I was just looking for the `if line.startswith(\"-DOCSTART-\")` pattern." ]

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[ "Thanks for reporting! We'll fix that as soon as possible", "Actually soundfile is not a dependency of this dataset.\r\nThe error comes from a bug that was fixed in this commit: https://github.com/huggingface/datasets/pull/1767/commits/c304e63629f4453367de2fd42883a78768055532\r\nBasically the library used to consider the `import soundfile` in the docstring as a dependency, while it's just here as a code example.\r\n\r\nUpdating the viewer to the latest version of `datasets` should fix this issue\r\n" ]

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[ "cc @lhoestq @vrindaprabhu", "Failing `run (push)` is unrelated -> merging", "Thanks for fixing this, it was affecting my runs for https://github.com/huggingface/transformers/pull/10581/", "I am seeing this very late! Sorry for the blunder everyone! :(" ]

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[ "@lhoestq I really appreciate if you could help me providiing processed datasets, I do not really have access to enough resources to run the apache-beam and need to run the codes on these datasets. Only en/de/fr currently works, but I need all the languages more or less. thanks ", "Hi @dorost1234, I think I can help you a little. I’ve processed some Wikipedia datasets (Spanish inclusive) using the HF/datasets library during recent research.\r\n\r\n@lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\n", "Thank you so much @jonatasgrosman , I greatly appreciate your help with them. \r\nYes, I unfortunately does not have access to a good resource and need it for my\r\nresearch. I greatly appreciate @lhoestq your help with uploading the processed datasets in huggingface datasets. This would be really helpful for some users like me with not access to high-memory GPU resources.\r\n\r\nthank you both so much again.\r\n\r\nOn Sat, Mar 6, 2021 at 12:55 AM Jonatas Grosman <notifications@github.com>\r\nwrote:\r\n\r\n> Hi @dorost1234 <https://github.com/dorost1234>, I think I can help you a\r\n> little. I’ve processed some Wikipedia datasets (Spanish inclusive) using\r\n> the HF/datasets library during recent research.\r\n>\r\n> @lhoestq <https://github.com/lhoestq> Could you help me to upload these\r\n> preprocessed datasets to Huggingface's repositories? To be more precise,\r\n> I've built datasets from the following languages using the 20201201 dumps:\r\n> Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish.\r\n> Process these datasets have high costs that most of the community can't\r\n> afford. I think these preprocessed datasets I have could be helpful for\r\n> someone without access to high-resource machines to process Wikipedia's\r\n> dumps like @dorost1234 <https://github.com/dorost1234>\r\n>\r\n> —\r\n> You are receiving this because you were mentioned.\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/1994#issuecomment-791798195>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AS37NMWMK5GFJFU3ACCJFUDTCFVNZANCNFSM4YUZIF4A>\r\n> .\r\n>\r\n", "Hi @dorost1234, so sorry, but looking at my files here, I figure out that I've preprocessed files using the HF/datasets for all the languages previously listed by me (Portuguese, Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my tests I've used the [wikicorpus](https://www.cs.upc.edu/~nlp/wikicorpus/) instead).\r\n\r\nOnly with the Spanish Wikipedia's dump, I had the same `KeyError: '000nbsp'` problem already reported here https://github.com/huggingface/datasets/issues/577\r\n\r\nSo nowadays, even with access to a high resource machine, you couldn't be able to get Wikipedia's Spanish data using the HF/datasets :(\r\n\r\n\r\n\r\n\r\n", "Thanks a lot for the information and help. This would be great to have\nthese datasets.\n@lhoestq <https://github.com/lhoestq> Do you know a way I could get\nsmaller amount of these data like 1 GBtype of each language to deal with\ncomputatioanl requirements? thanks\n\nOn Sat, Mar 6, 2021 at 5:36 PM Jonatas Grosman <notifications@github.com>\nwrote:\n\n> Hi @dorost1234 <https://github.com/dorost1234>, so sorry, but looking at\n> my files here, I figure out that I've preprocessed files using the\n> HF/datasets for all the languages previously listed by me (Portuguese,\n> Russian, French, Japanese, Chinese, and Turkish) except the Spanish (on my\n> tests I've used the wikicorpus <https://www.cs.upc.edu/~nlp/wikicorpus/>\n> instead).\n>\n> Only with the Spanish Wikipedia's dump, I had the same KeyError: '000nbsp'\n> problem already reported here #577\n> <https://github.com/huggingface/datasets/issues/577>\n>\n> So nowadays, even with access to a high resource machine, you couldn't be\n> able to get Wikipedia's Spanish data using the HF/datasets :(\n>\n> —\n> You are receiving this because you were mentioned.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/1994#issuecomment-791985546>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AS37NMWMO7WOHWLOROPD6Q3TCJKXPANCNFSM4YUZIF4A>\n> .\n>\n", "Hi ! As mentioned above the Spanish configuration have parsing issues from `mwparserfromhell`. I haven't tested with the latest `mwparserfromhell` >=0.6 though. Which version of `mwparserfromhell` are you using ?\r\n\r\n> @lhoestq Could you help me to upload these preprocessed datasets to Huggingface's repositories? To be more precise, I've built datasets from the following languages using the 20201201 dumps: Spanish, Portuguese, Russian, French, Japanese, Chinese, and Turkish. Process these datasets have high costs that most of the community can't afford. I think these preprocessed datasets I have could be helpful for someone without access to high-resource machines to process Wikipedia's dumps like @dorost1234\r\n\r\nThat would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\n> Do you know a way I could get smaller amount of these data like 1 GBtype of each language to deal with computatioanl requirements? thanks\r\n\r\nI'd suggest to copy the [wikipedia.py](https://github.com/huggingface/datasets/blob/master/datasets/wikipedia/wikipedia.py) to a new script `custom_wikipedia.py` and modify it to only download and process only a subset of the raw data files.\r\nYou can for example replace [this line](https://github.com/huggingface/datasets/blob/64e59fc45ca2134218b3e42e83fddddbe840ff74/datasets/wikipedia/wikipedia.py#L446) by:\r\n```python\r\n if total_bytes >= (1 << 30): # stop if the total amount of data is >= 1GB\r\n break\r\n else:\r\n xml_urls.append(_base_url(lang) + fname)\r\n```\r\n\r\nThen you can load your custom wikipedia dataset with\r\n```python\r\nload_dataset(\"path/to/my/custom_wikipedia.py\", f\"{date}.{language}\")\r\n```", "Hi @lhoestq!\r\n\r\n> Hi ! As mentioned above the Spanish configuration have parsing issues from mwparserfromhell. I haven't tested with the latest mwparserfromhell >=0.6 though. Which version of mwparserfromhell are you using ?\r\n\r\nI'm using the latest mwparserfromhell version (0.6)\r\n\r\n> That would be awesome ! Feel free to ping me on slack so we can put the processed wikipedia files on google storage with the other ones we've already preprocessed.\r\n\r\nI'll ping you there 👍 ", "Thank you so much @jonatasgrosman and @lhoestq this would be a great help. I am really thankful to you both and to wonderful Huggingface dataset library allowing us to train models at scale." ]

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