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Possible caching bug
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[
"Thanks for reporting. That's a bug indeed.\r\nApparently only the `data_files` parameter is taken into account right now in `DatasetBuilder._create_builder_config` but it should also be the case for `config_kwargs` (or at least the instantiated `builder_config`)",
"Hi, does this bug be fixed? when I load JSON files, I get the same errors by the command \r\n`!python3 run.py --do_train --task qa --dataset squad-retrain-data/train-v2.0.json --output_dir ./re_trained_model/`\r\n\r\nchange the dateset to load json by refering to https://huggingface.co/docs/datasets/loading.html\r\n`dataset = datasets.load_dataset('json', data_files=args.dataset)`\r\n\r\nErrors:\r\n`Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/json/default-c1e124ad488911b8/0.0.0/45636811569ec4a6630521c18235dfbbab83b7ab572e3393c5ba68ccabe98264...\r\n`",
"```ds = load_dataset(\"csv\", data_files={'train': 'train.csv', 'test': 'test.csv'})```\r\n\r\nGives the output\r\n```Using custom data configuration default-5c8ae7c208631aca```\r\n\r\nand the code hangs there.",
"> `ds = load_dataset(\"csv\", data_files={'train': 'train.csv', 'test': 'test.csv'})`\r\n> \r\n> Gives the output `Using custom data configuration default-5c8ae7c208631aca`\r\n> \r\n> and the code hangs there.\r\n\r\nHave you solved it? I met this problem too!",
"Can you Ctrl+C to kill the process and share the stacktrace here ? It should show at which location in the code it was hanging",
"I had the same issue and solved it by downgrading the datasets version from 2.7.0 -> 2.6.1\r\npip install -q datasets==2.6.1",
"> I had the same issue and solved it by downgrading the datasets version from 2.7.0 -> 2.6.1 pip install -q datasets==2.6.1\r\n\r\nThanks, it works for me"
] | 2020-10-14T02:02:34Z
| 2022-11-22T01:45:54Z
| 2020-10-29T09:36:01Z
|
NONE
| null | null | null |
The following code with `test1.txt` containing just "🤗🤗🤗":
```
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="latin_1")
print(dataset[0])
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="utf-8")
print(dataset[0])
```
produces this output:
```
Downloading and preparing dataset text/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...
Dataset text downloaded and prepared to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.
{'text': 'ð\x9f¤\x97ð\x9f¤\x97ð\x9f¤\x97'}
Using custom data configuration default
Reusing dataset text (/home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)
{'text': 'ð\x9f¤\x97ð\x9f¤\x97ð\x9f¤\x97'}
```
Just changing the order (and deleting the temp files):
```
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="utf-8")
print(dataset[0])
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="latin_1")
print(dataset[0])
```
produces this:
```
Using custom data configuration default
Downloading and preparing dataset text/default-15600e4d83254059 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155...
Dataset text downloaded and prepared to /home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155. Subsequent calls will reuse this data.
{'text': '🤗🤗🤗'}
Using custom data configuration default
Reusing dataset text (/home/arne/.cache/huggingface/datasets/text/default-15600e4d83254059/0.0.0/52cefbb2b82b015d4253f1aeb1e6ee5591124a6491e834acfe1751f765925155)
{'text': '🤗🤗🤗'}
```
Is it intended that the cache path does not depend on the config entries?
tested with datasets==1.1.2 and python==3.8.5
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Refactorize tests to use Dataset as context manager
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[
"I find very interesting that idea of using a fixture instead!\r\n\r\nLet me rework a little bit this PR, @lhoestq.",
"@lhoestq, as this is a big refactoring, I had many problems to solve the conflicts with the master branch...\r\n\r\nTherefore, I think it is better to merge this as it is, and then to make other PRs with additional refactorings, before I get conflicts again with the master branch...",
"There are still some conflicts that prevent merging.\r\nMoreover I noticed that you added one fixture per method of the Dataset object to be mocked. The code of all these fixtures is pretty much the same, feel free to factorize them into one fixture.\r\n\r\nAlso feel free to create another branch from `master` if you don't want to fix the conflicts of this branch.\r\nLet me know if I can help you on this",
"@lhoestq, yes, the new conflicts appeared after today merge commits on master...\r\n\r\nI am definitely going to split this PR into smaller ones in order to avoid having to resolve many conflicts after each commit on master. There are lots of conflicts and these are painful to resolve."
] | 2021-04-08T11:21:04Z
| 2021-04-19T07:53:11Z
| 2021-04-19T07:53:10Z
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MEMBER
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Change `ENV_XDG_CACHE_HOME ` to `XDG_CACHE_HOME `
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Error with SquadV2 Metrics
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| 2020-06-19T08:33:41Z
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NONE
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I can't seem to import squad v2 metrics.
**squad_metric = nlp.load_metric('squad_v2')**
**This throws me an error.:**
```
ImportError Traceback (most recent call last)
<ipython-input-8-170b6a170555> in <module>
----> 1 squad_metric = nlp.load_metric('squad_v2')
~/env/lib64/python3.6/site-packages/nlp/load.py in load_metric(path, name, process_id, num_process, data_dir, experiment_id, in_memory, download_config, **metric_init_kwargs)
426 """
427 module_path = prepare_module(path, download_config=download_config, dataset=False)
--> 428 metric_cls = import_main_class(module_path, dataset=False)
429 metric = metric_cls(
430 name=name,
~/env/lib64/python3.6/site-packages/nlp/load.py in import_main_class(module_path, dataset)
55 """
56 importlib.invalidate_caches()
---> 57 module = importlib.import_module(module_path)
58
59 if dataset:
/usr/lib64/python3.6/importlib/__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib64/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib64/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib64/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~/env/lib64/python3.6/site-packages/nlp/metrics/squad_v2/a15e787c76889174874386d3def75321f0284c11730d2a57e28fe1352c9b5c7a/squad_v2.py in <module>
16
17 import nlp
---> 18 from .evaluate import evaluate
19
20 _CITATION = """\
ImportError: cannot import name 'evaluate'
```
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[] | 2022-01-12T16:07:32Z
| 2022-01-20T16:51:08Z
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Addresses the 2nd bullet point in #2520.
I'm also removing the licensing information, because I couldn't verify that it is correct.
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ignore dummy folder and dataset_infos.json
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| 2021-09-29T09:45:38Z
| 2021-09-29T09:05:38Z
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Fixes #2877
Added the `dataset_infos.json` to the ignored files list and also added check to ignore files which have parent directory as `dummy`.
Let me know if it is correct. Thanks :)
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Fix BeamWriter output Parquet file
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"_The documentation is not available anymore as the PR was closed or merged._"
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| 2022-04-05T15:00:40Z
| 2022-04-05T14:54:48Z
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Since now, the `BeamWriter` saved a Parquet file with a simplified schema, where each field value was serialized to JSON. That resulted in Parquet files larger than Arrow files.
This PR:
- writes Parquet file preserving original schema and without serialization, thus avoiding serialization overhead and resulting in a smaller output file size.
- fixes `parquet_to_arrow` function
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Can't create a dataset with `float16` features
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[
"Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types",
"Thanks for the link…. didn’t realize arrow didn’t support it yet. Should it be removed from https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Value until Arrow supports it?",
"Yes, you are right: maybe we should either remove it from our docs or add a comment explaining the issue.\r\n\r\nThe thing is that in Arrow it is partially supported: you can create `float16` values, but you can't cast them from/to other types. And current implementation of `Value` always tries to perform a cast from `float64` to `float16`.",
"Maybe we can just add a note in the `Value` documentation ?",
"Would you accept a PR to fix this? @lhoestq Do you have an idea of how hard it would be to fix?",
"I think the issue comes mostly from pyarrow not supporting `float16` completely.\r\n\r\nFor example you stil can't cast from/to `float16`\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\npa.array(range(5)).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\npa.array(range(5), pa.float32()).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from float to halffloat using function cast_half_float\r\npa.array(range(5), pa.float16())\r\n# ArrowTypeError: Expected np.float16 instance\r\npa.array(np.arange(5, dtype=np.float16())).cast(pa.float32())\r\n# ArrowNotImplementedError: Unsupported cast from halffloat to float using function cast_float\r\n```",
"Hmm it seems like we can either:\r\n1. try to fix pyarrow upstream\r\n2. half-support float16 with some workaround to make sure we don't ever do casting internally\r\n"
] | 2022-09-15T21:03:24Z
| 2023-03-22T21:40:09Z
| null |
CONTRIBUTOR
| null | null | null |
## Describe the bug
I can't create a dataset with `float16` features.
I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error.
The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?
Thanks!
## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))
import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))
import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```
## Expected results
A dataset with `float16` features is successfully created.
## Actual results
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
865 mapping = features.encode_batch(mapping)
866 mapping = {
867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
868 for col, data in mapping.items()
869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
871 if info.features is None:
872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
734 @classmethod
735 def from_pydict(cls, *args, **kwargs):
736 """
737 Construct a Table from Arrow arrays or columns
738
(...)
748 :class:`datasets.table.Table`:
749 """
--> 750 return cls(pa.Table.from_pydict(*args, **kwargs))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
192 # otherwise we can finally use the user's type
193 elif type is not None:
194 # We use cast_array_to_feature to support casting to custom types like Audio and Image
195 # Also, when trying type "string", we don't want to convert integers or floats to "string".
196 # We only do it if trying_type is False - since this is what the user asks for.
--> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
198 return out
199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762 return array.cast(pa_type)
1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
387 else:
388 options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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Add exact match metric
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| 2022-03-21T16:10:03Z
| 2022-03-21T16:05:35Z
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Adding the exact match metric and its metric card.
Note: Some of the tests have failed, but I wanted to make a PR anyway so that the rest of the code can be reviewed if anyone has time. I'll look into + work on fixing the failed tests when I'm back online after the weekend
|
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Add NewsPH_NLI dataset
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[] | 2020-12-06T04:00:31Z
| 2020-12-07T15:39:43Z
| 2020-12-07T15:39:43Z
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CONTRIBUTOR
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This PR adds the NewsPH-NLI Dataset, the first benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
Link to the paper: https://arxiv.org/pdf/2010.11574.pdf
Link to the dataset/repo: https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
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Loading big dataset raises pyarrow.lib.ArrowNotImplementedError
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[
"Hi ! It looks like an issue with PyArrow: https://issues.apache.org/jira/browse/ARROW-5030\r\n\r\nIt appears it can happen when you have parquet files with row groups larger than 2GB.\r\nI can see that your parquet files are around 10GB. It is usually advised to keep a value around the default value 500MB to avoid these issues.\r\n\r\nNote that currently the row group size is simply defined by the number of rows `datasets.config.DEFAULT_MAX_BATCH_SIZE`, so reducing this value could let you have parquet files bigger than 2GB and with row groups lower than 2GB.\r\n\r\nWould it be possible for you to re-upload the dataset with the default shard size 500MB ?",
"Hey, thanks for the reply! I've since switched to working with the locally-saved dataset (which works).\r\nMaybe it makes sense to show a warning for uploads with large shard sizes? Since the functionality completely breaks (due to the PyArrow bug).",
"Just tried uploading the same dataset with 500MB shards, I get an errors 4 hours in:\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 25%|██▍ | 358/1453 [4:40:31<14:18:00, 47.01s/it]\r\nTraceback (most recent call last):\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 344, in _inner_upload_lfs_object\r\n return _upload_lfs_object(operation=operation, lfs_batch_action=batch_action, token=token)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 391, in _upload_lfs_object\r\n lfs_upload(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 254, in lfs_upload\r\n _upload_multi_part(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/lfs.py\", line 374, in _upload_multi_part\r\n hf_raise_for_status(part_upload_res)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 301, in hf_raise_for_status\r\n raise HfHubHTTPError(str(e), response=response) from e\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py\", line 46, in __init__\r\n server_data = response.json()\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/requests/models.py\", line 899, in json\r\n return complexjson.loads(\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/__init__.py\", line 357, in loads\r\n return _default_decoder.decode(s)\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 337, in decode\r\n obj, end = self.raw_decode(s, idx=_w(s, 0).end())\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/json/decoder.py\", line 355, in raw_decode\r\n raise JSONDecodeError(\"Expecting value\", s, err.value) from None\r\njson.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"process_wit.py\", line 146, in <module>\r\n dataset.push_to_hub(FINAL_PATH, max_shard_size=\"500MB\", private=False)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1534, in push_to_hub\r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 4804, in _push_parquet_shards_to_hub\r\n _retry(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py\", line 281, in _retry\r\n return func(*func_args, **func_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2593, in upload_file\r\n commit_info = self.create_commit(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/hf_api.py\", line 2411, in create_commit\r\n upload_lfs_files(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py\", line 120, in _inner_fn\r\n return fn(*args, **kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 351, in upload_lfs_files\r\n thread_map(\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map\r\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map\r\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/tqdm/std.py\", line 1178, in __iter__\r\n for obj in iterable:\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 619, in result_iterator\r\n yield fs.pop().result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 444, in result\r\n return self.__get_result()\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/_base.py\", line 389, in __get_result\r\n raise self._exception\r\n File \"/cluster/work/cotterell/tamariucai/miniconda3/envs/torch-multimodal/lib/python3.8/concurrent/futures/thread.py\", line 57, in run\r\n result = self.fn(*self.args, **self.kwargs)\r\n File \"/cluster/home/tamariucai/.local/lib/python3.8/site-packages/huggingface_hub/_commit_api.py\", line 346, in _inner_upload_lfs_object\r\n raise RuntimeError(f\"Error while uploading '{operation.path_in_repo}' to the Hub.\") from exc\r\nRuntimeError: Error while uploading 'data/train-00358-of-01453-22a5cc8b3eb12be3.parquet' to the Hub.\r\n```\r\nLocal saves do work, however.",
"Hmmm that was probably an intermitent bug, you can resume the upload by re-running push_to_hub",
"Leaving this other error here for the record, which occurs when I load the +700GB dataset from the hub with shard sizes of 500MB:\r\n\r\n```\r\n Traceback (most recent call last): \r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py\", line 1860, in _prepare_split_single\r\n for _, table in generator:\r\n File \"/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py\", line 69, in _generate_tables\r\n for batch_idx, record_batch in enumerate(\r\n File \"pyarrow/_parquet.pyx\", line 1323, in iter_batches\r\n File \"pyarrow/error.pxi\", line 115, in pyarrow.lib.check_status\r\nOSError: Corrupt snappy compressed data.\r\n```\r\nI will probably switch back to the local big dataset or shrink it."
] | 2023-04-02T14:42:44Z
| 2023-04-11T09:17:54Z
| 2023-04-10T08:04:04Z
|
NONE
| null | null | null |
### Describe the bug
Calling `datasets.load_dataset` to load the (publicly available) dataset `theodor1289/wit` fails with `pyarrow.lib.ArrowNotImplementedError`.
### Steps to reproduce the bug
Steps to reproduce this behavior:
1. `!pip install datasets`
2. `!huggingface-cli login`
3. This step will throw the error (it might take a while as the dataset has ~170GB):
```python
from datasets import load_dataset
dataset = load_dataset("theodor1289/wit", "train", use_auth_token=True)
```
Stack trace:
```
(torch-multimodal) bash-4.2$ python test.py
Downloading and preparing dataset None/None to /cluster/work/cotterell/tamariucai/HuggingfaceDatasets/theodor1289___parquet/theodor1289--wit-7a3e984414a86a0f/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...
Downloading data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 491.68it/s]
Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 16.93it/s]
Traceback (most recent call last):
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1860, in _prepare_split_single
for _, table in generator:
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 69, in _generate_tables
for batch_idx, record_batch in enumerate(
File "pyarrow/_parquet.pyx", line 1323, in iter_batches
File "pyarrow/error.pxi", line 121, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Nested data conversions not implemented for chunked array outputs
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/cluster/work/cotterell/tamariucai/multimodal-mirror/examples/test.py", line 2, in <module>
dataset = load_dataset("theodor1289/wit", "train", use_auth_token=True)
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset
builder_instance.download_and_prepare(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare
self._download_and_prepare(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/cluster/home/tamariucai/.local/lib/python3.10/site-packages/datasets/builder.py", line 1893, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
The dataset is loaded in variable `dataset`.
### Environment info
- `datasets` version: 2.11.0
- Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.4
- Huggingface_hub version: 0.13.3
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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Unable to download PUBMED_title_abstracts_2019_baseline.jsonl.zst
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[
"Hi @ToddMorrill, thanks for reporting.\r\n\r\nThree weeks ago I contacted the team who created the Pile dataset to report this issue with their data host server: https://the-eye.eu\r\n\r\nThey told me that unfortunately, the-eye was heavily affected by the recent tornado catastrophe in the US. They hope to have their data back online asap.",
"Hi @ToddMorrill, people from the Pile team have mirrored their data in a new host server: https://mystic.the-eye.eu\r\n\r\nSee:\r\n- #3627\r\n\r\nIt should work if you update your URL.\r\n\r\nWe should also update the URL in our course material.",
"The old URL is still present in the HuggingFace course here: \r\nhttps://huggingface.co/course/chapter5/4?fw=pt\r\n\r\nI have created a PR for the Notebook here: https://github.com/huggingface/notebooks/pull/148\r\nNot sure if the HTML is in a public repo. I wasn't able to find it. ",
"Fixed the other two URLs here: \r\nhttps://github.com/mwunderlich/notebooks/pull/1",
"Both URLs are broken now\r\n`HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst`\r\nAnd\r\n`ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))`\r\n\r\n\r\n",
"I was able to find a torrent with \"The Pile\" dataset here: [The Pile An 800GB Dataset of Diverse Text for Language Modeling ](https://academictorrents.com/details/0d366035664fdf51cfbe9f733953ba325776e667)\r\n\r\nThe complete dataset is huge, so I would suggest you to download only the \"PUBMED_title_abstracts_2019_baseline.jsonl.zst\" file, which is about 7GB. You can do this by using a torrent client of your choice (I typically utilize Transmission, which is pre-installed in Ubuntu distributions).\r\n\r\n",
"@albertvillanova another issue:\r\n```\r\n15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n16 File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n17 column_names = next(iter(dataset)).keys()\r\n18 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n19 for key, example in ex_iterable:\r\n20 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n21 yield from self.generate_examples_fn(**self.kwargs)\r\n22 File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 236, in _generate_examples\r\n23 with zstd.open(open(files[subset], \"rb\"), \"rt\", encoding=\"utf-8\") as f:\r\n24 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py\", line 74, in wrapper\r\n25 return function(*args, download_config=download_config, **kwargs)\r\n26 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n27 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n28 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n29 return self.__enter__()\r\n30 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n31 f = self.fs.open(self.path, mode=mode)\r\n32 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n33 f = self._open(\r\n34 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n35 size = size or self.info(path, **kwargs)[\"size\"]\r\n36 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n37 return sync(self.loop, func, *args, **kwargs)\r\n38 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n39 raise return_result\r\n40 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n41 result[0] = await coro\r\n42 File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n43 raise FileNotFoundError(url) from exc\r\n44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst\r\n```\r\n\r\nany suggestions?",
"related: https://github.com/huggingface/datasets/issues/6144",
"this seems to work but it's rather annoying.\r\n\r\nSummary of how to make it work:\r\n1. get urls to parquet files into a list\r\n2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes)\r\n3. then it should work, print a batch of text.\r\n\r\npresudo code\r\n```python\r\nurls_hacker_news = [\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet\"\r\n]\r\n\r\n...\r\n\r\n\r\n # streaming = False\r\n from diversity.pile_subset_urls import urls_hacker_news\r\n path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news\r\n # not changing\r\n batch_size = 512\r\n today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')\r\n run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'\r\n print(f'{run_name=}')\r\n\r\n # - Init wandb\r\n debug: bool = mode == 'dryrun'\r\n run = wandb.init(mode=mode, project=\"beyond-scale\", name=run_name, save_code=True)\r\n wandb.config.update({\"num_batches\": num_batches, \"path\": path, \"name\": name, \"today\": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files})\r\n # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891\r\n print(f'{debug=}')\r\n print(f'{wandb.config=}')\r\n\r\n # -- Get probe network\r\n from datasets import load_dataset\r\n import torch\r\n from transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\n tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n if tokenizer.pad_token_id is None:\r\n tokenizer.pad_token = tokenizer.eos_token\r\n probe_network = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n device = torch.device(f\"cuda:{0}\" if torch.cuda.is_available() else \"cpu\")\r\n probe_network = probe_network.to(device)\r\n\r\n # -- Get data set\r\n def my_load_dataset(path, name):\r\n print(f'{path=} {name=} {streaming=}')\r\n if path == 'json' or path == 'bin' or path == 'csv':\r\n print(f'{data_files_prefix+name=}')\r\n return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n elif path == 'parquet':\r\n print(f'{data_files=}')\r\n return load_dataset(path, data_files=data_files, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n else:\r\n return load_dataset(path, name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n # - get data set for real now\r\n if isinstance(path, str):\r\n dataset = my_load_dataset(path, name)\r\n else:\r\n print('-- interleaving datasets')\r\n datasets = [my_load_dataset(path, name).with_format(\"torch\") for path, name in zip(path, name)]\r\n [print(f'{dataset.description=}') for dataset in datasets]\r\n dataset = interleave_datasets(datasets, probabilities)\r\n print(f'{dataset=}')\r\n batch = dataset.take(batch_size)\r\n print(f'{next(iter(batch))=}')\r\n column_names = next(iter(batch)).keys()\r\n print(f'{column_names=}')\r\n\r\n # - Prepare functions to tokenize batch\r\n def preprocess(examples):\r\n return tokenizer(examples[\"text\"], padding=\"max_length\", max_length=128, truncation=True, return_tensors=\"pt\")\r\n remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader\r\n def map(batch):\r\n return batch.map(preprocess, batched=True, remove_columns=remove_columns)\r\n tokenized_batch = map(batch)\r\n print(f'{next(iter(tokenized_batch))=}')\r\n```\r\n\r\nhttps://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681\r\n\r\nhttps://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo"
] | 2021-12-29T18:23:20Z
| 2023-08-14T23:28:48Z
| 2022-02-17T15:04:25Z
|
NONE
| null | null | null |
## Describe the bug
I am unable to download the PubMed dataset from the link provided in the [Hugging Face Course (Chapter 5 Section 4)](https://huggingface.co/course/chapter5/4?fw=pt).
https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
# This takes a few minutes to run, so go grab a tea or coffee while you wait :)
data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
pubmed_dataset
```
I also tried with `wget` as follows.
```
wget https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst
```
## Expected results
I expect to be able to download this file.
## Actual results
Traceback
```
---------------------------------------------------------------------------
timeout Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
158 try:
--> 159 conn = connection.create_connection(
160 (self._dns_host, self.port), self.timeout, **extra_kw
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
/usr/lib/python3/dist-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
timeout: timed out
During handling of the above exception, another exception occurred:
ConnectTimeoutError Traceback (most recent call last)
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
664 # Make the request on the httplib connection object.
--> 665 httplib_response = self._make_request(
666 conn,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
375 try:
--> 376 self._validate_conn(conn)
377 except (SocketTimeout, BaseSSLError) as e:
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
995 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 996 conn.connect()
997
/usr/lib/python3/dist-packages/urllib3/connection.py in connect(self)
313 # Add certificate verification
--> 314 conn = self._new_conn()
315 hostname = self.host
/usr/lib/python3/dist-packages/urllib3/connection.py in _new_conn(self)
163 except SocketTimeout:
--> 164 raise ConnectTimeoutError(
165 self,
ConnectTimeoutError: (<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)')
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
438 if not chunked:
--> 439 resp = conn.urlopen(
440 method=request.method,
/usr/lib/python3/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
718
--> 719 retries = retries.increment(
720 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
/usr/lib/python3/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
435 if new_retry.is_exhausted():
--> 436 raise MaxRetryError(_pool, url, error or ResponseError(cause))
437
MaxRetryError: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
During handling of the above exception, another exception occurred:
ConnectTimeout Traceback (most recent call last)
/tmp/ipykernel_15104/606583593.py in <module>
3 # This takes a few minutes to run, so go grab a tea or coffee while you wait :)
4 data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst"
----> 5 pubmed_dataset = load_dataset("json", data_files=data_files, split="train")
6 pubmed_dataset
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs)
1655
1656 # Create a dataset builder
-> 1657 builder_instance = load_dataset_builder(
1658 path=path,
1659 name=name,
~/.local/lib/python3.8/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs)
1492 download_config = download_config.copy() if download_config else DownloadConfig()
1493 download_config.use_auth_token = use_auth_token
-> 1494 dataset_module = dataset_module_factory(
1495 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files
1496 )
~/.local/lib/python3.8/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs)
1116 # Try packaged
1117 if path in _PACKAGED_DATASETS_MODULES:
-> 1118 return PackagedDatasetModuleFactory(
1119 path, data_files=data_files, download_config=download_config, download_mode=download_mode
1120 ).get_module()
~/.local/lib/python3.8/site-packages/datasets/load.py in get_module(self)
773 else get_patterns_locally(str(Path().resolve()))
774 )
--> 775 data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
776 module_path, hash = _PACKAGED_DATASETS_MODULES[self.name]
777 builder_kwargs = {"hash": hash, "data_files": data_files}
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
576 for key, patterns_for_key in patterns.items():
577 out[key] = (
--> 578 DataFilesList.from_local_or_remote(
579 patterns_for_key,
580 base_path=base_path,
~/.local/lib/python3.8/site-packages/datasets/data_files.py in from_local_or_remote(cls, patterns, base_path, allowed_extensions, use_auth_token)
545 base_path = base_path if base_path is not None else str(Path().resolve())
546 data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
--> 547 origin_metadata = _get_origin_metadata_locally_or_by_urls(data_files, use_auth_token=use_auth_token)
548 return cls(data_files, origin_metadata)
549
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_origin_metadata_locally_or_by_urls(data_files, max_workers, use_auth_token)
492 data_files: List[Union[Path, Url]], max_workers=64, use_auth_token: Optional[Union[bool, str]] = None
493 ) -> Tuple[str]:
--> 494 return thread_map(
495 partial(_get_single_origin_metadata_locally_or_by_urls, use_auth_token=use_auth_token),
496 data_files,
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in thread_map(fn, *iterables, **tqdm_kwargs)
92 """
93 from concurrent.futures import ThreadPoolExecutor
---> 94 return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)
95
96
~/.local/lib/python3.8/site-packages/tqdm/contrib/concurrent.py in _executor_map(PoolExecutor, fn, *iterables, **tqdm_kwargs)
74 map_args.update(chunksize=chunksize)
75 with PoolExecutor(**pool_kwargs) as ex:
---> 76 return list(tqdm_class(ex.map(fn, *iterables, **map_args), **kwargs))
77
78
~/.local/lib/python3.8/site-packages/tqdm/notebook.py in __iter__(self)
252 def __iter__(self):
253 try:
--> 254 for obj in super(tqdm_notebook, self).__iter__():
255 # return super(tqdm...) will not catch exception
256 yield obj
~/.local/lib/python3.8/site-packages/tqdm/std.py in __iter__(self)
1171 # (note: keep this check outside the loop for performance)
1172 if self.disable:
-> 1173 for obj in iterable:
1174 yield obj
1175 return
/usr/lib/python3.8/concurrent/futures/_base.py in result_iterator()
617 # Careful not to keep a reference to the popped future
618 if timeout is None:
--> 619 yield fs.pop().result()
620 else:
621 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.8/concurrent/futures/_base.py in result(self, timeout)
442 raise CancelledError()
443 elif self._state == FINISHED:
--> 444 return self.__get_result()
445 else:
446 raise TimeoutError()
/usr/lib/python3.8/concurrent/futures/_base.py in __get_result(self)
387 if self._exception:
388 try:
--> 389 raise self._exception
390 finally:
391 # Break a reference cycle with the exception in self._exception
/usr/lib/python3.8/concurrent/futures/thread.py in run(self)
55
56 try:
---> 57 result = self.fn(*self.args, **self.kwargs)
58 except BaseException as exc:
59 self.future.set_exception(exc)
~/.local/lib/python3.8/site-packages/datasets/data_files.py in _get_single_origin_metadata_locally_or_by_urls(data_file, use_auth_token)
483 if isinstance(data_file, Url):
484 data_file = str(data_file)
--> 485 return (request_etag(data_file, use_auth_token=use_auth_token),)
486 else:
487 data_file = str(data_file.resolve())
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in request_etag(url, use_auth_token)
489 def request_etag(url: str, use_auth_token: Optional[Union[str, bool]] = None) -> Optional[str]:
490 headers = get_authentication_headers_for_url(url, use_auth_token=use_auth_token)
--> 491 response = http_head(url, headers=headers, max_retries=3)
492 response.raise_for_status()
493 etag = response.headers.get("ETag") if response.ok else None
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in http_head(url, proxies, headers, cookies, allow_redirects, timeout, max_retries)
474 headers = copy.deepcopy(headers) or {}
475 headers["user-agent"] = get_datasets_user_agent(user_agent=headers.get("user-agent"))
--> 476 response = _request_with_retry(
477 method="HEAD",
478 url=url,
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
408 if tries > max_retries:
--> 409 raise err
410 else:
411 logger.info(f"{method} request to {url} timed out, retrying... [{tries/max_retries}]")
~/.local/lib/python3.8/site-packages/datasets/utils/file_utils.py in _request_with_retry(method, url, max_retries, base_wait_time, max_wait_time, timeout, **params)
403 tries += 1
404 try:
--> 405 response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
406 success = True
407 except (requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError) as err:
/usr/lib/python3/dist-packages/requests/api.py in request(method, url, **kwargs)
58 # cases, and look like a memory leak in others.
59 with sessions.Session() as session:
---> 60 return session.request(method=method, url=url, **kwargs)
61
62
/usr/lib/python3/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
531 }
532 send_kwargs.update(settings)
--> 533 resp = self.send(prep, **send_kwargs)
534
535 return resp
/usr/lib/python3/dist-packages/requests/sessions.py in send(self, request, **kwargs)
644
645 # Send the request
--> 646 r = adapter.send(request, **kwargs)
647
648 # Total elapsed time of the request (approximately)
/usr/lib/python3/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
502 # TODO: Remove this in 3.0.0: see #2811
503 if not isinstance(e.reason, NewConnectionError):
--> 504 raise ConnectTimeout(e, request=request)
505
506 if isinstance(e.reason, ResponseError):
ConnectTimeout: HTTPSConnectionPool(host='the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x7f06dd698850>, 'Connection to the-eye.eu timed out. (connect timeout=10.0)'))
```
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-5.11.0-43-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.1
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Adding CLIMATE-FEVER dataset
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"I `git rebase`ed my branch to `upstream/master` as suggested in point 7 of <https://huggingface.co/docs/datasets/share_dataset.html> and subsequently used `git pull` to be able to push to my remote branch. However, I think this messed up the history.\r\n\r\nPlease let me know if I should create a clean new PR with my changes.\r\n\r\nUpdate: I also fixed the dataset name in the Dataset Card.",
"Dear @SBrandeis , @lhoestq . I am not sure how to fix the PR with respect to the additional files that are currently included in the commits. Could you provide me with an example? Otherwise I would be happy to close/re-open another PR. Please let me know if anything is missing for the review.",
"Hi @tdiggelm, thanks for the contribution! This dataset is really awesome.\r\nI believe creating a new branch from master and opening a new PR with your changes is the simplest option since no review has been done yet. Feel free to ping us when it's done.",
"> Hi @tdiggelm, thanks for the contribution! This dataset is really awesome.\r\n> I believe creating a new branch from master and opening a new PR with your changes is the simplest option since no review has been done yet. Feel free to ping us when it's done.\r\n\r\nThank you very much for your quick reply! Will do ASAP and ping you when done.",
"closing in favor of #1623"
] | 2020-12-15T16:49:22Z
| 2020-12-22T13:43:16Z
| 2020-12-22T13:43:15Z
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This PR request the addition of the CLIMATE-FEVER dataset:
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present.
More information can be found at:
- Homepage: <http://climatefever.ai>
- Paper: <https://arxiv.org/abs/2012.00614>
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My iPhone
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| 2021-07-23T09:24:16Z
| 2021-05-03T08:17:38Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Split type not preserved when reloading the dataset
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[] | 2021-04-04T19:29:54Z
| 2021-04-19T09:08:55Z
| 2021-04-19T09:08:55Z
|
CONTRIBUTOR
| null | null | null |
A minimal reproducible example:
```python
>>> from datasets import load_dataset, Dataset
>>> dset = load_dataset("sst", split="train")
>>> dset.save_to_disk("sst")
>>> type(dset.split)
<class 'datasets.splits.NamedSplit'>
>>> dset = Dataset.load_from_disk("sst")
>>> type(dset.split) # NamedSplit expected
<class 'str'>
```
It seems like this bug was introduced in #2025.
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A descriptive name for my changes
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"I have noticed that the master branch of your fork has diverged from the one of the repo. This is probably what causes the mess in the github diff \"Files changed\".\r\n\r\nI would suggest to re-fork the `datasets` repo and recreate a new branch and a new PR. ",
"You're pretty close to having all things ready to merge !\r\nFeel free to ping me when you have a new PR",
"Closing this one in favor of #1575 "
] | 2020-12-10T06:47:24Z
| 2020-12-15T10:36:27Z
| 2020-12-15T10:36:26Z
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hind encorp resubmited
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Distributed support
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"_The documentation is not available anymore as the PR was closed or merged._",
"Alright all the tests are passing - this is ready for review",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.015146 / 0.011353 (0.003793) | 0.006683 / 0.011008 (-0.004326) | 0.125994 / 0.038508 (0.087486) | 0.041345 / 0.023109 (0.018235) | 0.378609 / 0.275898 (0.102711) | 0.483139 / 0.323480 (0.159659) | 0.009669 / 0.007986 (0.001684) | 0.005143 / 0.004328 (0.000814) | 0.092015 / 0.004250 (0.087765) | 0.052728 / 0.037052 (0.015676) | 0.397166 / 0.258489 (0.138677) | 0.465820 / 0.293841 (0.171979) | 0.051025 / 0.128546 (-0.077521) | 0.018451 / 0.075646 (-0.057196) | 0.397311 / 0.419271 (-0.021960) | 0.054842 / 0.043533 (0.011309) | 0.391203 / 0.255139 (0.136064) | 0.412743 / 0.283200 (0.129543) | 0.111356 / 0.141683 (-0.030327) | 1.697526 / 1.452155 (0.245372) | 1.795017 / 1.492716 (0.302301) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253737 / 0.018006 (0.235731) | 0.583071 / 0.000490 (0.582581) | 0.005958 / 0.000200 (0.005758) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030397 / 0.037411 (-0.007014) | 0.112242 / 0.014526 (0.097716) | 0.138807 / 0.176557 (-0.037749) | 0.209820 / 0.737135 (-0.527316) | 0.139530 / 0.296338 (-0.156808) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.574111 / 0.215209 (0.358902) | 5.623713 / 2.077655 (3.546058) | 2.416880 / 1.504120 (0.912760) | 1.951013 / 1.541195 (0.409819) | 2.124565 / 1.468490 (0.656075) | 1.268854 / 4.584777 (-3.315923) | 5.942368 / 3.745712 (2.196656) | 5.413814 / 5.269862 (0.143952) | 2.931638 / 4.565676 (-1.634038) | 0.135070 / 0.424275 (-0.289205) | 0.014290 / 0.007607 (0.006683) | 0.708384 / 0.226044 (0.482340) | 7.487994 / 2.268929 (5.219065) | 3.074210 / 55.444624 (-52.370414) | 2.380583 / 6.876477 (-4.495893) | 2.522298 / 2.142072 (0.380226) | 1.336741 / 4.805227 (-3.468486) | 0.236761 / 6.500664 (-6.263903) | 0.076592 / 0.075469 (0.001123) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.629415 / 1.841788 (-0.212373) | 19.000640 / 8.074308 (10.926332) | 21.474058 / 10.191392 (11.282666) | 0.231227 / 0.680424 (-0.449197) | 0.046213 / 0.534201 (-0.487988) | 0.565703 / 0.579283 (-0.013580) | 0.662956 / 0.434364 (0.228592) | 0.656475 / 0.540337 (0.116137) | 0.762534 / 1.386936 (-0.624402) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010952 / 0.011353 (-0.000400) | 0.006259 / 0.011008 (-0.004749) | 0.132430 / 0.038508 (0.093922) | 0.037920 / 0.023109 (0.014811) | 0.483565 / 0.275898 (0.207667) | 0.528190 / 0.323480 (0.204710) | 0.008116 / 0.007986 (0.000130) | 0.006768 / 0.004328 (0.002440) | 0.100520 / 0.004250 (0.096270) | 0.055208 / 0.037052 (0.018155) | 0.484672 / 0.258489 (0.226183) | 0.556937 / 0.293841 (0.263096) | 0.057938 / 0.128546 (-0.070609) | 0.020821 / 0.075646 (-0.054826) | 0.430735 / 0.419271 (0.011464) | 0.066317 / 0.043533 (0.022785) | 0.496652 / 0.255139 (0.241513) | 0.502004 / 0.283200 (0.218804) | 0.125403 / 0.141683 (-0.016280) | 1.833396 / 1.452155 (0.381241) | 1.974517 / 1.492716 (0.481800) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269198 / 0.018006 (0.251191) | 0.620314 / 0.000490 (0.619824) | 0.000535 / 0.000200 (0.000335) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032373 / 0.037411 (-0.005039) | 0.130043 / 0.014526 (0.115517) | 0.146217 / 0.176557 (-0.030339) | 0.200187 / 0.737135 (-0.536948) | 0.152839 / 0.296338 (-0.143499) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.677478 / 0.215209 (0.462268) | 6.678856 / 2.077655 (4.601201) | 3.025870 / 1.504120 (1.521750) | 2.678196 / 1.541195 (1.137001) | 2.740640 / 1.468490 (1.272150) | 1.237163 / 4.584777 (-3.347614) | 5.752621 / 3.745712 (2.006908) | 3.170435 / 5.269862 (-2.099427) | 2.049174 / 4.565676 (-2.516502) | 0.147663 / 0.424275 (-0.276612) | 0.016107 / 0.007607 (0.008500) | 0.849666 / 0.226044 (0.623621) | 8.395212 / 2.268929 (6.126283) | 3.741120 / 55.444624 (-51.703505) | 3.102926 / 6.876477 (-3.773550) | 3.233655 / 2.142072 (1.091583) | 1.520349 / 4.805227 (-3.284878) | 0.267159 / 6.500664 (-6.233505) | 0.083646 / 0.075469 (0.008177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640458 / 1.841788 (-0.201330) | 19.043169 / 8.074308 (10.968861) | 22.786126 / 10.191392 (12.594734) | 0.218040 / 0.680424 (-0.462384) | 0.032948 / 0.534201 (-0.501253) | 0.569574 / 0.579283 (-0.009710) | 0.658746 / 0.434364 (0.224382) | 0.650501 / 0.540337 (0.110164) | 0.730588 / 1.386936 (-0.656348) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n",
"just added a note :)",
"Hi @lhoestq ,\r\nCan you please throw some light on the following statement\r\n`If the dataset has a number of shards that is a factor of world_size (i.e. if dataset.n_shards % world_size == 0), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of world_size, skipping the other examples.`\r\n\r\nLet's assume I have 127 parquet files and world_size is 4. I was not able to fully comprehend the above statement\r\nWhat does this statement mean?\r\n`each node keeps 1 example out of world_size, skipping the other examples.`\r\nThank you!",
"If you have 128 parquet files, then `dataset.n_shards % world_size == 0`. In this case each worker can take care of 32 parquet files.\r\n\r\nOn the other hand if you have `dataset.n_shards % world_size != 0` (in your case 127 files), then we can't assign the same number of files to each worker. This is an issue because it may under-utilize your GPU at the end of your training since some workers will take longer to iterate on the dataset than others.\r\n\r\nTherefore in this case, all the workers take care of the 127 parquet files but workers will skip examples to not end up with duplicates. That's what \"each node keeps 1 example out of world_size, skipping the other examples\" means, and in your case it implies:\r\n- rank=0 will read the samples with idx=0, 4, 8 etc.\r\n- rank=1 will read the samples with idx=1, 5, 9 etc.\r\n- rank=2 will read the samples with idx=2, 6, 10 etc.\r\n- rank=3 will read the samples with idx=3, 7, 11 etc.",
"Thanks a lot @lhoestq , this helps!",
"Hi, in the case above, if we use `keep_in_memory=True` for `Dataset`, then we still need to read in n times the dataset if we use DDP on n GPUs (1 node), right? That means we need n times the memory. Is there any way to only load the data once, to save memory?",
"`Dataset` objects are memory mapped from disk so they use almost no RAM (only the current batch)\r\n\r\nAlso they are perfectly sharded using `split_dataset_by_node` so it's going to be read exactly once in total using DDP.\r\nYou can also achieve the same thing using a DistributedSampler in pytorch for DDP instead of using `split_dataset_by_node`.",
"Hi, please correct if I mistake anything: \r\n1. `Dataset` with `keep_in_memory=True` would explicitly pre-load the data into memory, instead of reading from disk via the memory map for every batch. The former way should be faster than the latter.\r\n2. When using DDP, before sending the `Dataset` object into `split_dataset_by_node` or incorporate it with `DistributedSampler`, every process still needs to pre-load the entire data into memory (when `keep_in_memory=True`) and then select the chunked indices from the loaded data. \r\n\r\nGenerally, the dilemma I'm facing is:\r\nSuppose we have a data around 120GB, and we want to use `DistributedLengthGroupedSampler` to optimize batching. When using DDP and `keep_in_memory=True`, every process loads 120GB which is not acceptable. For now, I turned off `keep_in_memory` and try to increase the number of workers for `DataLoader` to get better pipelining. \r\n\r\n**But is it possible to load 120GB once into 4 * A100 (which has around 4*120GB memory) and make each process read from this shared data from memory? Theoretically, maybe it should be faster?** ",
"Feel free to ask your questions on the [forum](https://discuss.huggingface.co/c/datasets/10) if you don't mind, this way the discussions may be useful to other people ;) "
] | 2022-12-16T17:43:47Z
| 2023-07-25T12:00:31Z
| 2023-01-16T13:33:32Z
|
MEMBER
| null | 0
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To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]:
```python
import os
from datasets.distributed import split_dataset_by_node
ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"]))
```
This works for both map-style datasets and iterable datasets.
The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`.
For map-style datasets:
Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset.
For iterable datasets:
If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`),
then the shards are evenly assigned across the nodes, which is the most optimized.
Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples.
This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data.
This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`.
TODO:
- [x] docs for usage in PyTorch
- [x] unit tests
- [x] integration tests with torch.distributed.launch
Related to https://github.com/huggingface/transformers/issues/20770
Close https://github.com/huggingface/datasets/issues/5360
|
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| 6,006
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NotADirectoryError when loading gigawords
|
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"issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence."
] | 2023-07-05T06:23:41Z
| 2023-07-05T06:31:02Z
| 2023-07-05T06:31:01Z
|
NONE
| null | null | null |
### Describe the bug
got `NotADirectoryError` whtn loading gigawords dataset
### Steps to reproduce the bug
When running
```
import datasets
datasets.load_dataset('gigaword')
```
Got the following exception:
```bash
Traceback (most recent call last): [0/1862]
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single
for key, record in generator:
File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b
64efb424b6/gigaword.py", line 115, in _generate_examples
with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper
return function(*args, use_auth_token=use_auth_token, **kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope
n
return open(main_hop, mode, *args, **kwargs)
NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e
89780c4be7599794a4f559048ec/org_data/train.src.txt'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "gigaword.py", line 38, in <module>
main()
File "gigaword.py", line 35, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data
dataset = self.load_dataset()
File "gigaword.py", line 29, in load_dataset
return datasets.load_dataset('gigaword')
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
Download and process the dataset successfully
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10
- Python version: 3.8.0
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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[
"Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile",
"> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, *args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(**inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(*args, **kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(*args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```",
"Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.",
"@albertvillanova Thanks for your recommendation. I'll do it"
] | 2023-08-08T15:32:08Z
| 2023-08-11T13:35:09Z
| 2023-08-11T13:35:09Z
|
NONE
| null | null | null |
### Describe the bug
This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib.
### Steps to reproduce the bug
I use the following code to fine-tune Falcon on my private dataset.
```python
import transformers
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
AutoConfig,
DataCollatorForSeq2Seq,
Trainer,
Seq2SeqTrainer,
HfArgumentParser,
Seq2SeqTrainingArguments,
BitsAndBytesConfig,
)
from peft import (
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
prepare_model_for_int8_training,
set_peft_model_state_dict,
)
import torch
import os
import evaluate
import functools
from datasets import load_dataset
import bitsandbytes as bnb
import logging
import json
import copy
from typing import Dict, Optional, Sequence
from dataclasses import dataclass, field
# Lora settings
LORA_R = 8
LORA_ALPHA = 16
LORA_DROPOUT= 0.05
LORA_TARGET_MODULES = ["query_key_value"]
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B")
@dataclass
class DataArguments:
data_path: str = field(default=None, metadata={"help": "Path to the training data."})
train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."})
cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."})
num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."})
@dataclass
class TrainingArguments(transformers.TrainingArguments):
# cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
)
is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."})
def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True):
result = tokenizer(
text,
truncation=True,
max_length=max_seq_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < max_seq_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
if add_eos_token and len(result["input_ids"]) >= max_seq_len:
result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id
result["attention_mask"][max_seq_len - 1] = 1
result["labels"] = result["input_ids"].copy()
return result
def main():
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
if training_args.is_lora:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
torch_dtype=torch.float16,
trust_remote_code=True,
load_in_8bit=True,
quantization_config=BitsAndBytesConfig(
load_in_8bit=True,
llm_int8_threshold=6.0
),
)
model = prepare_model_for_int8_training(model)
config = LoraConfig(
r=LORA_R,
lora_alpha=LORA_ALPHA,
target_modules=LORA_TARGET_MODULES,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
else:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
torch_dtype=torch.float16,
cache_dir=data_args.cache_path,
trust_remote_code=True,
)
model.config.use_cache = False
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
print_trainable_parameters(model)
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=data_args.cache_path,
model_max_length=training_args.model_max_length,
padding_side="left",
use_fast=True,
trust_remote_code=True,
)
tokenizer.pad_token = tokenizer.eos_token
# Load dataset
def generate_and_tokenize_prompt(sample):
input_text = sample["input"]
target_text = sample["output"] + tokenizer.eos_token
full_text = input_text + target_text
tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512)
tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512)
input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token
tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:]
return tokenized_full_text
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.eval_file is not None:
data_files["eval"] = data_args.eval_file
dataset = load_dataset(data_args.data_path, data_files=data_files)
train_dataset = dataset["train"]
eval_dataset = dataset["eval"]
train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc)
data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True)
# Evaluation metrics
def compute_metrics(eval_preds, tokenizer):
metric = evaluate.load('exact_match')
preds, labels = eval_preds
# In case the model returns more than the prediction logits
if isinstance(preds, tuple):
preds = preds[0]
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Replace -100s in the labels as we can't decode them
labels[labels == -100] = tokenizer.pad_token_id
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False)
# Some simple post-processing
decoded_preds = [pred.strip() for pred in decoded_preds]
decoded_labels = [label.strip() for label in decoded_labels]
result = metric.compute(predictions=decoded_preds, references=decoded_labels)
return {'exact_match': result['exact_match']}
compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer)
model = torch.compile(model)
# Training
trainer = Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=data_collator,
compute_metrics=compute_metrics_fn,
)
trainer.train()
trainer.save_state()
trainer.save_model(output_dir=training_args.output_dir)
tokenizer.save_pretrained(save_directory=training_args.output_dir)
if __name__ == "__main__":
main()
```
When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error:
```
Traceback (most recent call last):
File "falcon_sft.py", line 230, in <module>
main()
File "falcon_sft.py", line 223, in main
trainer.train()
File "python3.10/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop
for step, inputs in enumerate(epoch_iterator):
File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__
current_batch = next(dataloader_iter)
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__
data = self._next_data()
File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch
data = self.dataset.__getitems__(possibly_batched_index)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__
batch = self.__getitem__(keys)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__
return self._getitem(key)
File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table
_check_valid_index_key(key, size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key
_check_valid_index_key(int(max(key)), size=size)
File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key
raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
IndexError: Invalid key: 88 is out of bounds for size 0
```
So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`?
### Expected behavior
I want to use `torch.compile` in my code.
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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I_kwDODunzps5tJIfp
| 6,110
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[BUG] Dataset initialized from in-memory data does not create cache.
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[
"This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached."
] | 2023-08-01T11:58:58Z
| 2023-08-17T14:03:01Z
| 2023-08-17T14:03:00Z
|
NONE
| null | null | null |
### Describe the bug
`Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`.
### Steps to reproduce the bug
```python
# below code was run the second time so the map function can be loaded from cache if exists
from datasets import load_dataset, Dataset
dataset = load_dataset("tatsu-lab/alpaca")['train']
dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map
print(len(dataset.cache_files))
# 1
# copy the exact same data but initialize from a dictionary
memory_dataset = Dataset.from_dict({
'instruction': dataset['instruction'],
'input': dataset['input'],
'output': dataset['output'],
'text': dataset['text']})
memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map
print(len(memory_dataset.cache_files))
# Map: 100%|██████████| 52002[/52002]
# 0
```
### Expected behavior
The `map` function should create cache regardless of the method the `Dataset` was created.
### Environment info
- `datasets` version: 2.14.2
- Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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Add ALT
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[] | null |
[
"the `RemoteDatasetTest ` erros in the CI are fixed on master so it's fine",
"used `Translation ` feature type and fixed few typos as you suggested.",
"Sorry, I made a mistake. please see new PR here. https://github.com/huggingface/datasets/pull/1436"
] | 2020-12-06T11:25:30Z
| 2020-12-10T04:18:12Z
| 2020-12-10T04:18:12Z
|
CONTRIBUTOR
| null | 0
|
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ALT dataset -- https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/
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set bert_score version dependency
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[] | 2021-02-09T12:51:07Z
| 2021-02-09T14:21:48Z
| 2021-02-09T14:21:48Z
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Set the bert_score version in requirements since previous versions of bert_score will fail with datasets (closes #843)
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PR_kwDODunzps42izQG
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Document save_to_disk and push_to_hub on images and audio files
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"_The documentation is not available anymore as the PR was closed or merged._",
"Good catch, I updated the docstrings"
] | 2022-04-21T09:04:36Z
| 2022-04-22T09:55:55Z
| 2022-04-22T09:49:31Z
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Following https://github.com/huggingface/datasets/pull/4187, I explained in the documentation of `save_to_disk` and `push_to_hub` how they handle image and audio data.
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add DFKI SmartData Corpus
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[] | 2020-12-07T23:03:48Z
| 2020-12-08T17:41:23Z
| 2020-12-08T17:41:23Z
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- **Name:** DFKI SmartData Corpus
- **Description:** DFKI SmartData Corpus is a dataset of 2598 German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types.
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
- **Data:** https://github.com/DFKI-NLP/smartdata-corpus
- **Motivation:** Contains fine-grained NER labels for German.
### Checkbox
- [X] Create the dataset script `/datasets/my_dataset/my_dataset.py` using the template
- [X] Fill the `_DESCRIPTION` and `_CITATION` variables
- [X] Implement `_infos()`, `_split_generators()` and `_generate_examples()`
- [X] Make sure that the `BUILDER_CONFIGS` class attribute is filled with the different configurations of the dataset and that the `BUILDER_CONFIG_CLASS` is specified if there is a custom config class.
- [X] Generate the metadata file `dataset_infos.json` for all configurations
- [X] Generate the dummy data `dummy_data.zip` files to have the dataset script tested and that they don't weigh too much (<50KB)
- [X] Add the dataset card `README.md` using the template : fill the tags and the various paragraphs
- [X] Both tests for the real data and the dummy data pass.
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Revert container image pin in CI benchmarks
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.013736 / 0.011353 (0.002383) | 0.006253 / 0.011008 (-0.004755) | 0.127076 / 0.038508 (0.088568) | 0.040997 / 0.023109 (0.017888) | 0.394744 / 0.275898 (0.118846) | 0.454285 / 0.323480 (0.130805) | 0.009864 / 0.007986 (0.001878) | 0.005093 / 0.004328 (0.000765) | 0.098714 / 0.004250 (0.094464) | 0.044308 / 0.037052 (0.007255) | 0.421951 / 0.258489 (0.163462) | 0.462280 / 0.293841 (0.168439) | 0.059979 / 0.128546 (-0.068567) | 0.020607 / 0.075646 (-0.055039) | 0.443593 / 0.419271 (0.024321) | 0.062332 / 0.043533 (0.018799) | 0.411335 / 0.255139 (0.156196) | 0.426524 / 0.283200 (0.143324) | 0.118233 / 0.141683 (-0.023450) | 1.877681 / 1.452155 (0.425527) | 1.865271 / 1.492716 (0.372555) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234791 / 0.018006 (0.216784) | 0.557322 / 0.000490 (0.556833) | 0.000528 / 0.000200 (0.000328) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030260 / 0.037411 (-0.007151) | 0.122594 / 0.014526 (0.108068) | 0.142142 / 0.176557 (-0.034414) | 0.197098 / 0.737135 (-0.540037) | 0.150978 / 0.296338 (-0.145360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.622644 / 0.215209 (0.407435) | 6.320078 / 2.077655 (4.242423) | 2.552755 / 1.504120 (1.048635) | 2.188647 / 1.541195 (0.647453) | 2.226602 / 1.468490 (0.758112) | 1.288083 / 4.584777 (-3.296694) | 5.624143 / 3.745712 (1.878431) | 3.208382 / 5.269862 (-2.061480) | 2.115222 / 4.565676 (-2.450455) | 0.146420 / 0.424275 (-0.277856) | 0.014464 / 0.007607 (0.006857) | 0.816470 / 0.226044 (0.590425) | 7.984049 / 2.268929 (5.715120) | 3.364942 / 55.444624 (-52.079682) | 2.552306 / 6.876477 (-4.324171) | 2.664575 / 2.142072 (0.522503) | 1.556177 / 4.805227 (-3.249050) | 0.263389 / 6.500664 (-6.237275) | 0.076861 / 0.075469 (0.001391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553734 / 1.841788 (-0.288054) | 18.365029 / 8.074308 (10.290721) | 20.993993 / 10.191392 (10.802601) | 0.235642 / 0.680424 (-0.444782) | 0.047084 / 0.534201 (-0.487117) | 0.555610 / 0.579283 (-0.023673) | 0.659413 / 0.434364 (0.225049) | 0.639284 / 0.540337 (0.098947) | 0.756317 / 1.386936 (-0.630620) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014709 / 0.011353 (0.003356) | 0.006673 / 0.011008 (-0.004335) | 0.133718 / 0.038508 (0.095210) | 0.035699 / 0.023109 (0.012590) | 0.459089 / 0.275898 (0.183191) | 0.538071 / 0.323480 (0.214591) | 0.007376 / 0.007986 (-0.000610) | 0.004688 / 0.004328 (0.000360) | 0.104909 / 0.004250 (0.100659) | 0.064942 / 0.037052 (0.027890) | 0.466158 / 0.258489 (0.207669) | 0.566100 / 0.293841 (0.272259) | 0.057368 / 0.128546 (-0.071178) | 0.021572 / 0.075646 (-0.054075) | 0.413826 / 0.419271 (-0.005446) | 0.079543 / 0.043533 (0.036010) | 0.493313 / 0.255139 (0.238174) | 0.517787 / 0.283200 (0.234587) | 0.119836 / 0.141683 (-0.021847) | 1.833956 / 1.452155 (0.381801) | 2.003288 / 1.492716 (0.510572) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276013 / 0.018006 (0.258007) | 0.549194 / 0.000490 (0.548704) | 0.010939 / 0.000200 (0.010739) | 0.000129 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034983 / 0.037411 (-0.002428) | 0.131576 / 0.014526 (0.117050) | 0.140651 / 0.176557 (-0.035906) | 0.186455 / 0.737135 (-0.550681) | 0.146309 / 0.296338 (-0.150029) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675973 / 0.215209 (0.460763) | 6.821862 / 2.077655 (4.744208) | 3.090307 / 1.504120 (1.586187) | 2.710679 / 1.541195 (1.169484) | 2.891577 / 1.468490 (1.423087) | 1.306160 / 4.584777 (-3.278617) | 5.629763 / 3.745712 (1.884051) | 4.662578 / 5.269862 (-0.607283) | 2.670195 / 4.565676 (-1.895482) | 0.153867 / 0.424275 (-0.270408) | 0.016028 / 0.007607 (0.008421) | 0.878702 / 0.226044 (0.652658) | 8.801612 / 2.268929 (6.532683) | 4.005520 / 55.444624 (-51.439104) | 3.124755 / 6.876477 (-3.751721) | 3.382132 / 2.142072 (1.240060) | 1.525951 / 4.805227 (-3.279277) | 0.263350 / 6.500664 (-6.237315) | 0.079285 / 0.075469 (0.003815) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.647591 / 1.841788 (-0.194197) | 18.281646 / 8.074308 (10.207338) | 21.072142 / 10.191392 (10.880750) | 0.232236 / 0.680424 (-0.448188) | 0.026126 / 0.534201 (-0.508075) | 0.546926 / 0.579283 (-0.032357) | 0.634496 / 0.434364 (0.200132) | 0.604345 / 0.540337 (0.064007) | 0.730159 / 1.386936 (-0.656777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfe8a6aa4cd2d3d0d7067f390152d1a4aeb4c710 \"CML watermark\")\n"
] | 2023-01-17T15:59:50Z
| 2023-01-18T09:05:49Z
| 2023-01-18T06:29:06Z
|
CONTRIBUTOR
| null | 0
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Closes #5433, reverts #5432, and also:
* Uses [ghcr.io container images](https://cml.dev/doc/self-hosted-runners/#docker-images) for extra speed
* Updates `actions/checkout` to `v3` (note that `v2` is [deprecated](https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/))
* Follows the new naming convention for environment variables introduced with [iterative/cml#1272](https://github.com/iterative/cml/pull/1272)
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MDU6SXNzdWU5MjA2MzYxODY=
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SubjQA wrong boolean values in entries
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[
"Hi @arnaudstiegler, thanks for reporting. I'm investigating it.",
"@arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https://github.com/megagonlabs/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.",
"I have:\r\n- opened an issue in their repo: https://github.com/megagonlabs/SubjQA/issues/3\r\n- written an email to all the paper authors",
"Please [see my response](https://github.com/megagonlabs/SubjQA/issues/3#issuecomment-905160010). There will be a fix in a couple of days."
] | 2021-06-14T17:42:46Z
| 2021-08-25T03:52:06Z
| null |
NONE
| null | null | null |
## Describe the bug
SubjQA seems to have a boolean that's consistently wrong.
It defines:
- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).
- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)
However, `is_ques_subjective` seems to have wrong values in the entire dataset.
For instance, in the example in the dataset card, we have:
- "question_subj_level": 2
- "is_ques_subjective": false
However, according to the description, the question should be subjective since the `question_subj_level` is below 4
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[load_dataset] shard and parallelize the process
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[
"I need the same feature for distributed training",
"I think @TevenLeScao is exploring adding multiprocessing in `GeneratorBasedBuilder._prepare_split` - feel free to post updates here :)",
"Posted a PR to address the building side, still needs something to load sharded arrow files + tests",
"Closing as this feature has been implemented in #5107"
] | 2021-07-14T18:04:58Z
| 2023-11-28T19:11:41Z
| 2023-11-28T19:11:40Z
|
CONTRIBUTOR
| null | null | null |
- Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core.
- If the build crashes, everything done up to that point gets lost
Request: Shard the build over multiple arrow files, which would enable:
- much faster build by parallelizing the build process
- if the process crashed, the completed arrow files don't need to be re-built again
Thank you!
@lhoestq
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Inconsistent argument names.
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[
"Also for the `Accuracy` metric the `accuracy_score` method should have its args in the opposite order so `accuracy_score(predictions, references,,,)`.",
"Thanks for pointing this out ! 🕵🏻 \r\nPredictions and references should indeed be swapped in the docstring.\r\nHowever, the call to `accuracy_score` should not be changed, it [signature](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score) being:\r\n```\r\nsklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)\r\n```\r\n\r\nFeel free to open a PR if you want to fix this :)"
] | 2020-12-11T12:19:38Z
| 2020-12-19T15:03:39Z
| 2020-12-19T15:03:39Z
|
CONTRIBUTOR
| null | null | null |
Just find it a wee bit odd that in the transformers library `predictions` are those made by the model:
https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_utils.py#L51-L61
While in many datasets metrics they are the ground truth labels:
https://github.com/huggingface/datasets/blob/c3f53792a744ede18d748a1133b6597fdd2d8d18/metrics/accuracy/accuracy.py#L31-L40
Do you think predictions & references should be swapped? I'd be willing to do some refactoring here if you agree.
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Support for data with instance-wise dictionary as features
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"Hi ! We use the Arrow columnar format under the hood, which doesn't support such dictionaries: each field must have a fixed type and exist in each sample.\r\n\r\nInstead you can restructure your data like\r\n```\r\n{\r\n \"index\": 0,\r\n \"keys\": [\"2 * x + y >= 3\"],\r\n \"values\": [[\"2 * x + y >= 3\", \"4 * x + 2 * y >= 6\"]],\r\n }\r\n},\r\n...\r\n{\r\n \"index\": 9999,\r\n \"keys\": [\"x >= 6\"],\r\n \"values\": [[\"x >= 6\", \"x >= 0\", \"x >= -1\"]],\r\n},\r\n...\r\n```"
] | 2023-06-13T15:49:00Z
| 2023-06-14T12:13:38Z
| null |
NONE
| null | null | null |
### Feature request
I notice that when loading data instances with feature type of python dictionary, the dictionary keys would be broadcast so that every instance has the same set of keys. Please see an example in the Motivation section.
It is possible to avoid this behavior, i.e., load dictionary features as it is and do not broadcast the keys among instances? Please note that these dictionaries would have to be processed dynamically at each training iteration into strings (and tokenized).
### Motivation
I am trying to load a dataset from a json file. Each instance of the dataset has a feature that is a dictionary but its keys depend on the instance. Every two instances may have different keys. For example, imagine a dataset that contains a set of math expressions from a bunch of mutually redundant expressions:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
}
},
...
{
"index": 9999,
"feature": {
"x >= 6": ["x >= 6", "x >= 0", "x >= -1"],
...
}
},
...
```
When directly loading the dataset using `data = load_dataset("json", data_files=file_paths, split='train')`, each instance would have all the keys from other instances and None as values. That is, instance of index 0 becomes:
```
{
"index": 0,
"feature": {
"2 * x + y >= 3": ["2 * x + y >= 3", "4 * x + 2 * y >= 6"],
...
"x >= 6": None, # keys from other instances
...
}
},
```
This is not desirable. Moreover, issue would be raised if I attempt to combine two such datasets using `data = concatenate_datasets(multi_datasets)`, perhaps because their dictionary features contain different keys.
A solution I can think of is to store the dictionary features as a long string, and evaluate it later. Please kindly suggest any other solution using existing methods of datasets.
### Your contribution
N/A
|
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Add security policy to the project
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[] | 2021-09-23T08:20:55Z
| 2021-10-21T15:16:44Z
| 2021-10-21T15:16:43Z
|
MEMBER
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Add security policy to the project, as recommended by GitHub: https://docs.github.com/en/code-security/getting-started/adding-a-security-policy-to-your-repository
Close #2953.
|
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Load/Save FAISS index using fsspec
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"Hi! Sure, feel free to submit a PR. Maybe if we want to be consistent with the existing API, it would be cleaner to directly add support for `fsspec` paths in `Dataset.load_faiss_index`/`Dataset.save_faiss_index` in the same manner as it was done in `Dataset.load_from_disk`/`Dataset.save_to_disk`.",
"That's a great idea! I'll do that instead. "
] | 2023-01-16T16:08:12Z
| 2023-03-27T15:18:22Z
| 2023-03-27T15:18:22Z
|
CONTRIBUTOR
| null | null | null |
### Feature request
From what I understand `faiss` already support this [link](https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning#generic-io-support)
I would like to use a stream as input to `Dataset.load_faiss_index` and `Dataset.save_faiss_index`.
### Motivation
In my case, I'm saving faiss index in cloud storage and use `fsspec` to load them. It would be ideal if I could send the stream directly instead of copying the file locally (or mounting the bucket) and then load the index.
### Your contribution
I can submit the PR
|
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add xlrd to test package requirements
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Docs: Fix same-page haslinks
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-07-20T10:04:37Z
| 2022-07-20T17:02:33Z
| 2022-07-20T16:49:36Z
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`href="/docs/datasets/quickstart#audio"` implicitly goes to `href="/docs/datasets/{$LATEST_STABLE_VERSION}/quickstart#audio"`. Therefore, https://huggingface.co/docs/datasets/quickstart#audio #audio hashlink does not work since the new docs were not added to v2.3.2 (LATEST_STABLE_VERSION)
to preserve the version, it should be just `href="#audio"`, which will implicilty go to curren_page + #audio element
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Getting next item from IterableDataset took forever.
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[
"Hi! It can take some time to iterate over Parquet files as big as yours, convert the samples to Python, and find the first one that matches a filter predicate before yielding it...",
"Thanks @mariosasko, I figured it was the filter operation. I'm closing this issue because it is not a bug, it is the expected beheaviour."
] | 2023-04-04T09:16:17Z
| 2023-04-05T23:35:41Z
| 2023-04-05T23:35:41Z
|
NONE
| null | null | null |
### Describe the bug
I have a large dataset, about 500GB. The format of the dataset is parquet.
I then load the dataset and try to get the first item
```python
def get_one_item():
dataset = load_dataset("path/to/datafiles", split="train", cache_dir=".", streaming=True)
dataset = dataset.filter(lambda example: example['text'].startswith('Ar'))
print(next(iter(dataset)))
```
However, this function never finish. I waited ~10mins, the function was still running so I killed the process. I'm now using `line_profiler` to profile how long it would take to return one item. I'll be patient and wait for as long as it needs.
I suspect the filter operation is the reason why it took so long. Can I get some possible reasons behind this?
### Steps to reproduce the bug
Unfortunately without my data files, there is no way to reproduce this bug.
### Expected behavior
With `IteralbeDataset`, I expect the first item to be returned instantly.
### Environment info
- datasets version: 2.11.0
- python: 3.7.12
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Unable to Download Hindi Wikipedia Dataset
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[
"Currently this dataset is only available when the library is installed from source since it was added after the last release.\r\n\r\nWe pin the dataset version with the library version so that people can have a reproducible dataset and processing when pinning the library.\r\n\r\nWe'll see if we can provide access to newer datasets with a warning that they are newer than your library version, that would help in cases like yours.",
"So for now, should i try and install the library from source and then try out the same piece of code? Will it work then, considering both the versions will match then?",
"Yes",
"Hey, so i tried installing the library from source using the commands : **git clone https://github.com/huggingface/datasets**, **cd datasets** and then **pip3 install -e .**. But i still am facing the same error that file is not found. Please advise.\r\n\r\nThe Datasets library version now is 1.1.3 by installing from source as compared to the earlier 1.0.3 that i had loaded using pip command but I am still getting same error\r\n\r\n\r\n",
"Looks like the wikipedia dump for hindi at the date of 05/05/2020 is not available anymore.\r\nYou can try to load a more recent version of wikipedia\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"wikipedia\", language=\"hi\", date=\"20210101\", split=\"train\", beam_runner=\"DirectRunner\")\r\n```",
"Okay, thank you so much"
] | 2021-01-01T10:52:53Z
| 2021-01-05T10:22:12Z
| 2021-01-05T10:22:12Z
|
NONE
| null | null | null |
I used the Dataset Library in Python to load the wikipedia dataset with the Hindi Config 20200501.hi along with something called beam_runner='DirectRunner' and it keeps giving me the error that the file is not found. I have attached the screenshot of the error and the code both. Please help me to understand how to resolve this issue.
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Add tel to xtreme tatoeba
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This should fix issue #2149
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Update bucket prefix
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cc @julien-c
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MDExOlB1bGxSZXF1ZXN0NTIwMDg1NDMy
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|
amazon description fields as bullets
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| 2020-11-12T18:50:54Z
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One more minor formatting change to amazon reviews's description (in addition to #844). Just reformatting the fields to display as a bulleted list in markdown.
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add reuters21578 dataset
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new PR to add the reuters21578 dataset and fix the circle CI problems.
Fix partially:
- #353
Subsequent PR after:
- #449
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yaml error using push_to_hub with generated README.md
|
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[
"Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon."
] | 2023-08-02T18:21:21Z
| 2023-12-12T15:00:44Z
| 2023-12-12T15:00:44Z
|
NONE
| null | null | null |
### Describe the bug
When I construct a dataset with the following features:
```
features = Features(
{
"pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)),
"input_ids": Sequence(feature=Value(dtype="int64")),
"attention_mask": Sequence(Value(dtype="int64")),
"tokens": Sequence(Value(dtype="string")),
"bbox": Array2D(dtype="int64", shape=(512, 4)),
}
)
```
and run `push_to_hub`, the individual `*.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error:
```
Traceback (most recent call last):
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status
response.raise_for_status()
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module>
build_dataset()
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset
push_to_hub(dataset, "multitask_document_classification_dataset")
File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub
dataset.push_to_hub(f"looppayments/{dataset_name}", private=True)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub
HfApi(endpoint=config.HF_ENDPOINT).upload_file(
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file
commit_info = self.create_commit(
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner
return fn(self, *args, **kwargs)
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit
hf_raise_for_status(commit_resp, endpoint_name="commit")
File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e)
Bad request for commit endpoint:
Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9)
7 | - 3
8 | - 224
9 | - 224
10 | dtype: float64
--------------^
11 | - name: input_ids
12 | sequence: int64
```
My guess is that the auto-generated yaml is unable to be parsed for some reason.
### Steps to reproduce the bug
The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet:
```
from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value
from PIL import Image
from transformers import AutoProcessor
features = Features(
{
"pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)),
"input_ids": Sequence(feature=Value(dtype="int64")),
"attention_mask": Sequence(Value(dtype="int64")),
"tokens": Sequence(Value(dtype="string")),
"bbox": Array2D(dtype="int64", shape=(512, 4)),
}
)
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
def preprocess_dataset(rows):
# Get images
images = [
Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"]
]
encoding = processor(
images,
rows["tokens"],
boxes=rows["bbox"],
truncation=True,
padding="max_length",
)
encoding["tokens"] = rows["tokens"]
return encoding
dataset = dataset.map(
preprocess_dataset,
batched=True,
batch_size=5,
features=features,
)
```
### Expected behavior
Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error.
### Environment info
- `datasets` version: 2.14.2
- Platform: macOS-12.5-arm64-arm-64bit
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3
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PR_kwDODunzps4z5oq-
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|
Fix iter_archive getting reset
|
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[] | 2022-03-03T15:58:52Z
| 2022-03-03T18:06:37Z
| 2022-03-03T18:06:13Z
|
MEMBER
| null | 0
|
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The `DownloadManager.iter_archive` method currently returns an iterator - which is **empty** once you iter over it once. This means you can't pass the same archive iterator to several splits.
To fix that, I changed the ouput of `DownloadManager.iter_archive` to be an iterable that you can iterate over several times, instead of a one-time-use iterator.
The `StreamingDownloadManager.iter_archive` already returns an appropriate iterable, and the code added in this PR is inspired from the one in `streaming_download_manager.py`
|
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|
Add MATINF dataset
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[
"Hi ! sorry for the late response\r\n\r\nCould you try to rebase from master ? We changed the named of the library last week so you have to include this change in your code.\r\n\r\nCan you give me more details about the error you get when running the cli command ?\r\n\r\nNote that in case of a manual download you have to specify the directory where you downloaded the data with `--data_dir <path/to/the/directory>`",
"I fucked up the Git rebase lol. Closing it."
] | 2020-09-10T03:31:09Z
| 2023-09-24T09:50:08Z
| 2020-09-17T12:17:25Z
|
CONTRIBUTOR
| null | 0
|
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@lhoestq The command to create metadata failed. I guess it's because the zip is not downloaded from a remote address? How to solve that? Also the CI fails and I don't know how to fix that :(
|
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Text strings are split into lists of characters in xcsr dataset
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[] | 2022-09-26T11:11:50Z
| 2022-09-28T07:54:20Z
| 2022-09-28T07:54:20Z
|
MEMBER
| null | null | null |
## Describe the bug
Text strings are split into lists of characters.
Example for "X-CSQA-en":
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': ['T',
'h',
'e',
' ',
'd',
'e',
'n',
't',
'a',
'l',
' ',
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'i',
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' ',
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'd',
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'e',
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't',
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't',
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'o',
'm',
'i',
'n',
'g',
' ',
'f',
'r',
'o',
'm',
'?'],
'choices': [{'label': ['A'], 'text': ['t', 'o', 'w', 'n']},
{'label': ['B'], 'text': ['m', 'i', 'c', 'h', 'i', 'g', 'a', 'n']},
{'label': ['C'], 'text': ['h', 'o', 's', 'p', 'i', 't', 'a', 'l']},
{'label': ['D'], 'text': ['s', 'c', 'h', 'o', 'o', 'l', 's']},
{'label': ['E'],
'text': ['o',
'f',
'f',
'i',
'c',
'e',
' ',
'b',
'u',
'i',
'l',
'd',
'i',
'n',
'g']}]},
'answerKey': 'C'}
## Steps to reproduce the bug
```python
ds = load_dataset("datasets/xcsr", "X-CSQA-en", split="validation", streaming=True)
item = next(iter(ds))
item
```
## Expected results
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': 'The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from?',
'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
'text': ['town', 'michigan', 'hospital', 'schools', 'office building']}},
'answerKey': 'C'}
```
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MDExOlB1bGxSZXF1ZXN0NjM0ODk1OTU5
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Fix overflow issue in interpolation search
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[
"~~Seems like the CI failure is unrelated to this PR~~ (fixed with the merge). \r\n\r\n@lhoestq Can you please verify that everything is OK in terms of speed? Another solution is to change the offsets array dtype to np.int64 (but this doesn't scale in theory compared to Python integer which is unbound). I'm not sure why on my 64-bit machine the default numpy dtype is np.int32 tho.",
"Hi ! Thanks for the fix.\r\nUnfortunately in terms of speed this is not acceptable :/\r\nThe `get_batch_of_1024_random_rows` metric or the `benchmark_getitem_100B ` benchmark is almost at 1sec instead of a few milliseconds.\r\n\r\nWould it be possible to avoid the overflow by simply passing `dtype=np.int64` to `np.cumsum` ?\r\nOn windows machines the default is int32 unfortunately so we have to force the dtype to be int64\r\n\r\n",
"Yes, casting the array to np.int64 should work as well. Another option would be to cast the array elements (`arr[i], arr[j]`) in interpolation search to Python integers (bound only with memory) before multiplication (the error stems from this part: `(j - i) * (x - arr[i])`) when working with big values. But for now, the first option is OK for the sake of simplicity."
] | 2021-05-08T20:51:36Z
| 2021-05-10T13:29:07Z
| 2021-05-10T13:26:12Z
|
CONTRIBUTOR
| null | 0
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Fixes #2335
More info about this error can be found [here](https://stackoverflow.com/questions/53239890/why-do-i-keep-getting-this-error-runtimewarning-overflow-encountered-in-int-sc/53240100).
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PR_kwDODunzps4uj2EQ
| 3,278
|
Proposed update to the documentation for WER
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[] | 2021-11-15T23:28:31Z
| 2021-11-16T11:19:37Z
| 2021-11-16T11:19:37Z
|
CONTRIBUTOR
| null | 0
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I wanted to submit a minor update to the description of WER for your consideration.
Because of the possibility of insertions, the numerator in the WER formula can be larger than N, so the value of WER can be greater than 1.0:
```
>>> from datasets import load_metric
>>> metric = load_metric("wer")
>>> metric.compute(predictions=["hello how are you"], references=["hello"])
3.0
```
and similarly from the underlying jiwer module's `wer` function:
```
>>> from jiwer import wer
>>> wer("hello", "hello how are you")
3.0
```
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Dataset Viewer issue for shamikbose89/lancaster_newsbooks
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[
"It seems like the list of splits could not be obtained:\r\n\r\n```python\r\n>>> from datasets import get_dataset_split_names\r\n>>> get_dataset_split_names(\"shamikbose89/lancaster_newsbooks\", \"default\")\r\nUsing custom data configuration default\r\nTraceback (most recent call last):\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 354, in get_dataset_config_info\r\n for split_generator in builder._split_generators(\r\n File \"/home/slesage/.cache/huggingface/modules/datasets_modules/datasets/shamikbose89--lancaster_newsbooks/2d1c63d269bf7b9342accce0a95960b1710ab4bc774248878bd80eb96c1afaf7/lancaster_newsbooks.py\", line 73, in _split_generators\r\n data_dir = dl_manager.download_and_extract(_URL)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 916, in download_and_extract\r\n return self.extract(self.download(url_or_urls))\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 879, in extract\r\n urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py\", line 348, in map_nested\r\n return function(data_struct)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 884, in _extract\r\n protocol = _get_extraction_protocol(urlpath, use_auth_token=self.download_config.use_auth_token)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 388, in _get_extraction_protocol\r\n return _get_extraction_protocol_with_magic_number(f)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py\", line 354, in _get_extraction_protocol_with_magic_number\r\n f.seek(0)\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py\", line 684, in seek\r\n raise ValueError(\"Cannot seek streaming HTTP file\")\r\nValueError: Cannot seek streaming HTTP file\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 404, in get_dataset_split_names\r\n info = get_dataset_config_info(\r\n File \"/home/slesage/hf/datasets-server/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 359, in get_dataset_config_info\r\n raise SplitsNotFoundError(\"The split names could not be parsed from the dataset config.\") from err\r\ndatasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.\r\n```\r\n\r\nping @huggingface/datasets ",
"Oh, I removed the 'split' key from `kwargs`. I put it back in, but there's still the same error",
"It looks like the data host doesn't support http range requests, which is necessary to glob inside a ZIP archive in streaming mode. Can you try hosting the dataset elsewhere ? Or download each file separately from https://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531 ?",
"@lhoestq Thanks! That seems to have solved it. I can get the splits with the `get_dataset_split_names()` function. The dataset viewer is still not loading properly, though. The new error is\r\n```\r\nStatus code: 400\r\nException: BadZipFile\r\nMessage: File is not a zip file\r\n```\r\n\r\nPS. The dataset loads properly and can be accessed"
] | 2022-07-19T20:00:07Z
| 2022-09-08T16:47:21Z
| 2022-09-08T16:47:21Z
|
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/shamikbose89/lancaster_newsbooks
### Description
Status code: 400
Exception: ValueError
Message: Cannot seek streaming HTTP file
I am able to use the dataset loading script locally and it also runs when I'm using the one from the hub, but the viewer still doesn't load
### Owner
Yes
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[] | 2021-11-01T15:36:08Z
| 2021-11-01T15:57:03Z
| null |
CONTRIBUTOR
| null | null | null |
While running the tests, I found that the multiprocessing examples fail on Windows, or rather they do not complete: they cause a deadlock. I haven't dug deep into it, but they do not seem to work as-is. I currently have no time to tests this in detail but at least the tests seem not to run correctly (deadlocking).
## Steps to reproduce the bug
```shell
pytest tests/test_arrow_dataset.py -k "test_filter_multiprocessing"
pytest tests/test_arrow_dataset.py -k "test_map_multiprocessing"
```
## Expected results
The functionality to work on all platforms.
## Actual results
Deadlock.
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2, also tested with 3.7.9
- PyArrow version: 4.0.1
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Stream TAR-based dataset using iter_archive
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"I'm creating a new branch `stream-tar-audio` just for the audio datasets since they need https://github.com/huggingface/datasets/pull/3129 to be merged first",
"The CI fails are only related to missing sections or tags in the dataset cards - which is unrelated to this PR"
] | 2021-10-19T17:16:24Z
| 2021-11-05T17:48:49Z
| 2021-11-05T17:48:48Z
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I converted all the dataset based on TAR archive to use iter_archive instead, so that they can be streamable.
It means that around 80 datasets become streamable :)
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Fix cnn_dailymail (dm stories were ignored)
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-11T14:25:25Z
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https://github.com/huggingface/datasets/pull/4188 introduced a bug in `datasets` 2.2.0: DailyMail stories are ignored when generating the dataset.
I fixed that, and removed the google drive link (it has annoying quota limitations issues)
We can do a patch release after this is merged
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"Copy-pasted from the Slack discussion:\r\nthe annotation and language creators should be found , not unknown\r\nthe example should go under the \"Data Instances\" paragraph, not \"Data fields\"\r\ncan you remove the abstract from the citation and add it to the dataset description? More people will see that",
"@yjernite done! thanks for the feedback",
"@lhoestq not sure why it's failing tests now, I only changed cosmetics",
"You can ignores these errors\r\n```\r\n\r\n=========================== short test summary info ===========================\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_chr_en\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_ajgt_twitter_ar\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_chr_en\r\nFAILED tests/test_dataset_common.py::RemoteDatasetTest::test_load_dataset_great_code\r\n```\r\n\r\nthey're fixed on master",
"Feel free to ping me for the final review once you managed to change to ClassLabel :) ",
"Hey @lhoestq I was able to fix it !! I think the same errors appeared on circleCI and now it's hopefully ready to be merged?",
"@lhoestq done! thanks for your review ",
"merging since the CI is fixed on master"
] | 2020-12-05T16:56:42Z
| 2020-12-21T17:06:23Z
| 2020-12-21T17:06:23Z
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added restaurants reviews in Arabic for sentiment analysis tasks
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"I want to take a shot at this if possible ",
"Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas.",
"@mariosasko are there any other similar issues that I could work on? I see this has been already solved. "
] | 2023-07-07T16:38:03Z
| 2023-09-01T03:45:07Z
| null |
CONTRIBUTOR
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Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows.
We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.
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add: segmentation guide.
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks @osanseviero. Am I good to merge? ",
"I would wait for a second approval just in case :) ",
"Sure :) ",
"Merging since the images have been pushed as LFS files ([PR](https://huggingface.co/datasets/huggingface/documentation-images/discussions/8)). "
] | 2022-11-02T04:34:36Z
| 2022-11-04T18:25:57Z
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Closes #5181
I have opened a PR on Hub (https://huggingface.co/datasets/huggingface/documentation-images/discussions/5) to include the images in our central Hub repository. Once the PR is merged I will edit the image links.
I have also prepared a [Colab Notebook](https://colab.research.google.com/drive/1BMDCfOTBnyshoME5RSxn5iQy-TWeFbOA?usp=sharing) in case anyone wants to play.
- [x] Replace the image links
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exif_transpose not done to Image (PIL problem)
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[
"Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndataset = dataset.with_transform(exif_transpose_transform)\r\n```",
"This operation sets some `Image` attributes to `None` (`.format`, `.filename`, etc.), causing our tests to fail, so I think we should wait for Datasets 3.0 to make this change. In version 3.0, storing image paths will be replaced by embedding image bytes, so there will be fewer instances where we use the `.filename` attribute."
] | 2023-09-21T08:11:46Z
| 2023-09-22T14:07:52Z
| null |
NONE
| null | null | null |
### Feature request
I noticed that some of my images loaded using PIL have some metadata related to exif that can rotate them when loading.
Since the dataset.features.Image uses PIL for loading, the loaded image may be rotated (width and height will be inverted) thus for tasks as object detection and layoutLM this can create some inconsistencies (between input bboxes and input images).
For now there is no option in datasets.features.Image to specify that. We need to do the following when preparing examples (when preparing images for training, test or inference):
```
from PIL import Image, ImageOps
pil = ImageOps.exif_transpose(pil)
```
reference: https://stackoverflow.com/a/63950647/5720150
Is it possible to add this by default to the datasets.feature.Image ? or to add the option to do the ImageOps.exif_transpose?
Thank you
### Motivation
Prevent having inverted data related to exif metadata that may affect object detection tasks
### Your contribution
Changing in datasets.featrues.Image I can help with that.
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MDExOlB1bGxSZXF1ZXN0NDE4Mjc0MTU4
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Qa4mre - add dataset
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[] | 2020-05-14T22:17:51Z
| 2020-05-15T09:16:43Z
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Added dummy data test only for the first config. Will do the rest later.
I had to do add some minor hacks to an important function to make it work.
There might be a cleaner way to handle it - can you take a look @thomwolf ?
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Release: 2.10.1
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002636) | 0.004570 / 0.011008 (-0.006439) | 0.100228 / 0.038508 (0.061720) | 0.030076 / 0.023109 (0.006967) | 0.317919 / 0.275898 (0.042021) | 0.366360 / 0.323480 (0.042880) | 0.007008 / 0.007986 (-0.000978) | 0.003498 / 0.004328 (-0.000831) | 0.077607 / 0.004250 (0.073356) | 0.036106 / 0.037052 (-0.000946) | 0.314128 / 0.258489 (0.055639) | 0.351450 / 0.293841 (0.057609) | 0.033697 / 0.128546 (-0.094849) | 0.011424 / 0.075646 (-0.064222) | 0.323867 / 0.419271 (-0.095404) | 0.042073 / 0.043533 (-0.001460) | 0.304564 / 0.255139 (0.049425) | 0.334865 / 0.283200 (0.051665) | 0.087791 / 0.141683 (-0.053892) | 1.488075 / 1.452155 (0.035920) | 1.513676 / 1.492716 (0.020959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010936 / 0.018006 (-0.007070) | 0.409610 / 0.000490 (0.409121) | 0.004820 / 0.000200 (0.004620) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023931 / 0.037411 (-0.013481) | 0.096826 / 0.014526 (0.082300) | 0.105764 / 0.176557 (-0.070792) | 0.153241 / 0.737135 (-0.583895) | 0.108976 / 0.296338 (-0.187363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412833 / 0.215209 (0.197624) | 4.129735 / 2.077655 (2.052081) | 1.819049 / 1.504120 (0.314929) | 1.617411 / 1.541195 (0.076216) | 1.682353 / 1.468490 (0.213863) | 0.688987 / 4.584777 (-3.895790) | 3.388276 / 3.745712 (-0.357436) | 1.857452 / 5.269862 (-3.412410) | 1.158020 / 4.565676 (-3.407657) | 0.082161 / 0.424275 (-0.342114) | 0.012319 / 0.007607 (0.004712) | 0.523052 / 0.226044 (0.297008) | 5.237726 / 2.268929 (2.968797) | 2.275605 / 55.444624 (-53.169020) | 1.931664 / 6.876477 (-4.944813) | 1.970026 / 2.142072 (-0.172046) | 0.805240 / 4.805227 (-3.999988) | 0.148431 / 6.500664 (-6.352233) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196456 / 1.841788 (-0.645332) | 13.750113 / 8.074308 (5.675805) | 13.853543 / 10.191392 (3.662151) | 0.137892 / 0.680424 (-0.542532) | 0.028304 / 0.534201 (-0.505897) | 0.400128 / 0.579283 (-0.179155) | 0.410409 / 0.434364 (-0.023955) | 0.479165 / 0.540337 (-0.061172) | 0.575002 / 1.386936 (-0.811934) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006587 / 0.011353 (-0.004766) | 0.004526 / 0.011008 (-0.006482) | 0.075673 / 0.038508 (0.037165) | 0.027429 / 0.023109 (0.004320) | 0.341808 / 0.275898 (0.065910) | 0.379520 / 0.323480 (0.056040) | 0.004972 / 0.007986 (-0.003014) | 0.003354 / 0.004328 (-0.000975) | 0.075373 / 0.004250 (0.071123) | 0.038347 / 0.037052 (0.001294) | 0.343671 / 0.258489 (0.085181) | 0.389632 / 0.293841 (0.095791) | 0.031694 / 0.128546 (-0.096853) | 0.011458 / 0.075646 (-0.064188) | 0.084210 / 0.419271 (-0.335062) | 0.042662 / 0.043533 (-0.000871) | 0.339436 / 0.255139 (0.084297) | 0.367493 / 0.283200 (0.084294) | 0.091604 / 0.141683 (-0.050079) | 1.526762 / 1.452155 (0.074607) | 1.569110 / 1.492716 (0.076394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211496 / 0.018006 (0.193489) | 0.404868 / 0.000490 (0.404379) | 0.004267 / 0.000200 (0.004067) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025189 / 0.037411 (-0.012222) | 0.099139 / 0.014526 (0.084613) | 0.105898 / 0.176557 (-0.070659) | 0.160997 / 0.737135 (-0.576138) | 0.110158 / 0.296338 (-0.186180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444286 / 0.215209 (0.229077) | 4.445479 / 2.077655 (2.367824) | 2.118920 / 1.504120 (0.614800) | 1.908296 / 1.541195 (0.367102) | 1.947211 / 1.468490 (0.478721) | 0.704850 / 4.584777 (-3.879927) | 3.395990 / 3.745712 (-0.349723) | 1.892529 / 5.269862 (-3.377332) | 1.172190 / 4.565676 (-3.393486) | 0.084235 / 0.424275 (-0.340040) | 0.012588 / 0.007607 (0.004981) | 0.546962 / 0.226044 (0.320918) | 5.475842 / 2.268929 (3.206913) | 2.575280 / 55.444624 (-52.869344) | 2.245658 / 6.876477 (-4.630818) | 2.274767 / 2.142072 (0.132695) | 0.813755 / 4.805227 (-3.991473) | 0.151927 / 6.500664 (-6.348737) | 0.067167 / 0.075469 (-0.008302) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267666 / 1.841788 (-0.574122) | 13.658905 / 8.074308 (5.584597) | 13.207249 / 10.191392 (3.015857) | 0.128590 / 0.680424 (-0.551833) | 0.016531 / 0.534201 (-0.517670) | 0.385050 / 0.579283 (-0.194233) | 0.388945 / 0.434364 (-0.045419) | 0.472378 / 0.540337 (-0.067959) | 0.568929 / 1.386936 (-0.818007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87cd5f7f7fda60d0f91f50424bcc3f327fe0d059 \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009339 / 0.011353 (-0.002014) | 0.005197 / 0.011008 (-0.005811) | 0.100698 / 0.038508 (0.062190) | 0.035484 / 0.023109 (0.012375) | 0.299030 / 0.275898 (0.023132) | 0.366603 / 0.323480 (0.043124) | 0.007909 / 0.007986 (-0.000077) | 0.005683 / 0.004328 (0.001355) | 0.077719 / 0.004250 (0.073469) | 0.042147 / 0.037052 (0.005094) | 0.310174 / 0.258489 (0.051685) | 0.342720 / 0.293841 (0.048879) | 0.039679 / 0.128546 (-0.088867) | 0.012042 / 0.075646 (-0.063605) | 0.335663 / 0.419271 (-0.083609) | 0.051137 / 0.043533 (0.007604) | 0.298218 / 0.255139 (0.043079) | 0.316398 / 0.283200 (0.033198) | 0.108906 / 0.141683 (-0.032776) | 1.422823 / 1.452155 (-0.029331) | 1.472955 / 1.492716 (-0.019761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205845 / 0.018006 (0.187839) | 0.445942 / 0.000490 (0.445453) | 0.003553 / 0.000200 (0.003353) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025506 / 0.037411 (-0.011906) | 0.107494 / 0.014526 (0.092969) | 0.116226 / 0.176557 (-0.060331) | 0.157313 / 0.737135 (-0.579822) | 0.123822 / 0.296338 (-0.172516) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400908 / 0.215209 (0.185699) | 3.980232 / 2.077655 (1.902578) | 1.805410 / 1.504120 (0.301290) | 1.615698 / 1.541195 (0.074503) | 1.677213 / 1.468490 (0.208723) | 0.697882 / 4.584777 (-3.886895) | 3.752781 / 3.745712 (0.007069) | 2.076062 / 5.269862 (-3.193800) | 1.446909 / 4.565676 (-3.118768) | 0.084572 / 0.424275 (-0.339703) | 0.011917 / 0.007607 (0.004310) | 0.511815 / 0.226044 (0.285771) | 5.121487 / 2.268929 (2.852558) | 2.277642 / 55.444624 (-53.166982) | 1.930393 / 6.876477 (-4.946084) | 1.965855 / 2.142072 (-0.176218) | 0.843391 / 4.805227 (-3.961837) | 0.163581 / 6.500664 (-6.337083) | 0.062547 / 0.075469 (-0.012922) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223930 / 1.841788 (-0.617858) | 14.354466 / 8.074308 (6.280158) | 14.015159 / 10.191392 (3.823767) | 0.148658 / 0.680424 (-0.531766) | 0.028469 / 0.534201 (-0.505732) | 0.437614 / 0.579283 (-0.141669) | 0.435452 / 0.434364 (0.001089) | 0.523623 / 0.540337 (-0.016715) | 0.625109 / 1.386936 (-0.761827) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006917 / 0.011353 (-0.004436) | 0.005080 / 0.011008 (-0.005928) | 0.075806 / 0.038508 (0.037298) | 0.032402 / 0.023109 (0.009293) | 0.331105 / 0.275898 (0.055207) | 0.361226 / 0.323480 (0.037746) | 0.005694 / 0.007986 (-0.002292) | 0.003810 / 0.004328 (-0.000518) | 0.076886 / 0.004250 (0.072635) | 0.046158 / 0.037052 (0.009106) | 0.338791 / 0.258489 (0.080302) | 0.385733 / 0.293841 (0.091892) | 0.035590 / 0.128546 (-0.092956) | 0.011997 / 0.075646 (-0.063649) | 0.087854 / 0.419271 (-0.331417) | 0.048985 / 0.043533 (0.005452) | 0.331248 / 0.255139 (0.076109) | 0.354633 / 0.283200 (0.071434) | 0.101609 / 0.141683 (-0.040074) | 1.496899 / 1.452155 (0.044745) | 1.570469 / 1.492716 (0.077753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180871 / 0.018006 (0.162865) | 0.449417 / 0.000490 (0.448928) | 0.004300 / 0.000200 (0.004100) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029054 / 0.037411 (-0.008358) | 0.110888 / 0.014526 (0.096362) | 0.121736 / 0.176557 (-0.054821) | 0.172563 / 0.737135 (-0.564572) | 0.126565 / 0.296338 (-0.169773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419545 / 0.215209 (0.204336) | 4.193685 / 2.077655 (2.116031) | 2.049967 / 1.504120 (0.545847) | 1.855038 / 1.541195 (0.313843) | 1.899822 / 1.468490 (0.431332) | 0.709123 / 4.584777 (-3.875654) | 3.795939 / 3.745712 (0.050227) | 2.076055 / 5.269862 (-3.193807) | 1.335864 / 4.565676 (-3.229812) | 0.085555 / 0.424275 (-0.338720) | 0.012197 / 0.007607 (0.004590) | 0.516164 / 0.226044 (0.290119) | 5.158983 / 2.268929 (2.890054) | 2.445581 / 55.444624 (-52.999044) | 2.122256 / 6.876477 (-4.754221) | 2.160011 / 2.142072 (0.017939) | 0.840251 / 4.805227 (-3.964976) | 0.165924 / 6.500664 (-6.334740) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285292 / 1.841788 (-0.556495) | 14.561084 / 8.074308 (6.486776) | 12.899269 / 10.191392 (2.707877) | 0.185657 / 0.680424 (-0.494767) | 0.017866 / 0.534201 (-0.516335) | 0.425365 / 0.579283 (-0.153918) | 0.427183 / 0.434364 (-0.007181) | 0.529773 / 0.540337 (-0.010564) | 0.642061 / 1.386936 (-0.744875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0628013d009dd5150e8a1c1a4ac9d93887b88a76 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008995 / 0.011353 (-0.002357) | 0.004540 / 0.011008 (-0.006469) | 0.099675 / 0.038508 (0.061167) | 0.030338 / 0.023109 (0.007229) | 0.307167 / 0.275898 (0.031269) | 0.338789 / 0.323480 (0.015309) | 0.007293 / 0.007986 (-0.000692) | 0.004681 / 0.004328 (0.000352) | 0.077475 / 0.004250 (0.073225) | 0.036399 / 0.037052 (-0.000654) | 0.304615 / 0.258489 (0.046126) | 0.351611 / 0.293841 (0.057770) | 0.034449 / 0.128546 (-0.094097) | 0.011565 / 0.075646 (-0.064082) | 0.322765 / 0.419271 (-0.096506) | 0.041971 / 0.043533 (-0.001562) | 0.307492 / 0.255139 (0.052354) | 0.327240 / 0.283200 (0.044040) | 0.087110 / 0.141683 (-0.054573) | 1.484600 / 1.452155 (0.032445) | 1.536651 / 1.492716 (0.043934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185876 / 0.018006 (0.167869) | 0.404276 / 0.000490 (0.403787) | 0.001592 / 0.000200 (0.001392) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023272 / 0.037411 (-0.014139) | 0.096273 / 0.014526 (0.081747) | 0.105400 / 0.176557 (-0.071157) | 0.149720 / 0.737135 (-0.587416) | 0.107807 / 0.296338 (-0.188532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420072 / 0.215209 (0.204863) | 4.184108 / 2.077655 (2.106454) | 1.880690 / 1.504120 (0.376570) | 1.673103 / 1.541195 (0.131909) | 1.715792 / 1.468490 (0.247302) | 0.695771 / 4.584777 (-3.889006) | 3.450224 / 3.745712 (-0.295488) | 2.999218 / 5.269862 (-2.270644) | 1.585571 / 4.565676 (-2.980106) | 0.082105 / 0.424275 (-0.342170) | 0.012453 / 0.007607 (0.004846) | 0.528538 / 0.226044 (0.302494) | 5.287951 / 2.268929 (3.019023) | 2.289127 / 55.444624 (-53.155497) | 1.956503 / 6.876477 (-4.919974) | 2.004498 / 2.142072 (-0.137575) | 0.813547 / 4.805227 (-3.991681) | 0.151574 / 6.500664 (-6.349090) | 0.063763 / 0.075469 (-0.011706) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239125 / 1.841788 (-0.602662) | 13.627676 / 8.074308 (5.553368) | 13.747815 / 10.191392 (3.556423) | 0.157745 / 0.680424 (-0.522679) | 0.028590 / 0.534201 (-0.505611) | 0.397472 / 0.579283 (-0.181811) | 0.405925 / 0.434364 (-0.028439) | 0.477942 / 0.540337 (-0.062396) | 0.572379 / 1.386936 (-0.814557) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006637 / 0.011353 (-0.004716) | 0.004657 / 0.011008 (-0.006351) | 0.082056 / 0.038508 (0.043548) | 0.027974 / 0.023109 (0.004865) | 0.342887 / 0.275898 (0.066989) | 0.375938 / 0.323480 (0.052458) | 0.004958 / 0.007986 (-0.003028) | 0.004738 / 0.004328 (0.000409) | 0.080449 / 0.004250 (0.076198) | 0.038138 / 0.037052 (0.001085) | 0.345636 / 0.258489 (0.087147) | 0.385992 / 0.293841 (0.092151) | 0.033265 / 0.128546 (-0.095281) | 0.011965 / 0.075646 (-0.063681) | 0.091441 / 0.419271 (-0.327830) | 0.051407 / 0.043533 (0.007874) | 0.353758 / 0.255139 (0.098619) | 0.372118 / 0.283200 (0.088919) | 0.093947 / 0.141683 (-0.047735) | 1.468197 / 1.452155 (0.016042) | 1.554677 / 1.492716 (0.061960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222034 / 0.018006 (0.204027) | 0.403658 / 0.000490 (0.403169) | 0.003242 / 0.000200 (0.003042) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025335 / 0.037411 (-0.012076) | 0.100404 / 0.014526 (0.085878) | 0.107858 / 0.176557 (-0.068698) | 0.156115 / 0.737135 (-0.581021) | 0.113967 / 0.296338 (-0.182372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437567 / 0.215209 (0.222358) | 4.362486 / 2.077655 (2.284832) | 2.067315 / 1.504120 (0.563195) | 1.857669 / 1.541195 (0.316475) | 1.926380 / 1.468490 (0.457890) | 0.703905 / 4.584777 (-3.880872) | 3.437139 / 3.745712 (-0.308573) | 3.051931 / 5.269862 (-2.217930) | 1.356494 / 4.565676 (-3.209182) | 0.083679 / 0.424275 (-0.340596) | 0.012507 / 0.007607 (0.004900) | 0.539572 / 0.226044 (0.313528) | 5.405790 / 2.268929 (3.136861) | 2.532769 / 55.444624 (-52.911855) | 2.181950 / 6.876477 (-4.694527) | 2.212627 / 2.142072 (0.070554) | 0.807468 / 4.805227 (-3.997759) | 0.152146 / 6.500664 (-6.348518) | 0.068891 / 0.075469 (-0.006578) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286972 / 1.841788 (-0.554816) | 13.987186 / 8.074308 (5.912878) | 13.115065 / 10.191392 (2.923673) | 0.162143 / 0.680424 (-0.518281) | 0.016767 / 0.534201 (-0.517434) | 0.384766 / 0.579283 (-0.194517) | 0.397438 / 0.434364 (-0.036926) | 0.470850 / 0.540337 (-0.069487) | 0.562216 / 1.386936 (-0.824720) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010877 / 0.011353 (-0.000476) | 0.005739 / 0.011008 (-0.005269) | 0.118542 / 0.038508 (0.080034) | 0.042266 / 0.023109 (0.019157) | 0.359317 / 0.275898 (0.083419) | 0.412995 / 0.323480 (0.089515) | 0.009158 / 0.007986 (0.001173) | 0.006343 / 0.004328 (0.002014) | 0.089587 / 0.004250 (0.085336) | 0.047899 / 0.037052 (0.010847) | 0.358745 / 0.258489 (0.100256) | 0.421316 / 0.293841 (0.127476) | 0.044540 / 0.128546 (-0.084006) | 0.013872 / 0.075646 (-0.061774) | 0.399856 / 0.419271 (-0.019415) | 0.056484 / 0.043533 (0.012951) | 0.356922 / 0.255139 (0.101783) | 0.385598 / 0.283200 (0.102398) | 0.116039 / 0.141683 (-0.025644) | 1.726095 / 1.452155 (0.273940) | 1.888643 / 1.492716 (0.395927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269517 / 0.018006 (0.251511) | 0.511204 / 0.000490 (0.510714) | 0.001906 / 0.000200 (0.001706) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031133 / 0.037411 (-0.006278) | 0.128513 / 0.014526 (0.113987) | 0.139639 / 0.176557 (-0.036918) | 0.189778 / 0.737135 (-0.547358) | 0.145219 / 0.296338 (-0.151120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486693 / 0.215209 (0.271484) | 4.851999 / 2.077655 (2.774344) | 2.255334 / 1.504120 (0.751214) | 2.052271 / 1.541195 (0.511077) | 2.143262 / 1.468490 (0.674772) | 0.835765 / 4.584777 (-3.749012) | 4.451280 / 3.745712 (0.705568) | 2.534392 / 5.269862 (-2.735469) | 1.747817 / 4.565676 (-2.817859) | 0.101186 / 0.424275 (-0.323089) | 0.014281 / 0.007607 (0.006674) | 0.616164 / 0.226044 (0.390120) | 6.161789 / 2.268929 (3.892860) | 2.815347 / 55.444624 (-52.629277) | 2.408305 / 6.876477 (-4.468172) | 2.508240 / 2.142072 (0.366167) | 1.017709 / 4.805227 (-3.787519) | 0.198272 / 6.500664 (-6.302392) | 0.075663 / 0.075469 (0.000194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.435501 / 1.841788 (-0.406287) | 18.149581 / 8.074308 (10.075273) | 16.619011 / 10.191392 (6.427619) | 0.205080 / 0.680424 (-0.475344) | 0.033780 / 0.534201 (-0.500421) | 0.515768 / 0.579283 (-0.063515) | 0.542628 / 0.434364 (0.108264) | 0.634067 / 0.540337 (0.093730) | 0.757841 / 1.386936 (-0.629095) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008541 / 0.011353 (-0.002812) | 0.005733 / 0.011008 (-0.005275) | 0.089859 / 0.038508 (0.051351) | 0.039379 / 0.023109 (0.016270) | 0.402037 / 0.275898 (0.126139) | 0.454046 / 0.323480 (0.130566) | 0.006652 / 0.007986 (-0.001334) | 0.004555 / 0.004328 (0.000227) | 0.087651 / 0.004250 (0.083401) | 0.054934 / 0.037052 (0.017881) | 0.404468 / 0.258489 (0.145979) | 0.467127 / 0.293841 (0.173286) | 0.042034 / 0.128546 (-0.086512) | 0.014225 / 0.075646 (-0.061421) | 0.103281 / 0.419271 (-0.315990) | 0.057767 / 0.043533 (0.014234) | 0.396391 / 0.255139 (0.141252) | 0.429364 / 0.283200 (0.146165) | 0.120193 / 0.141683 (-0.021489) | 1.794029 / 1.452155 (0.341875) | 1.875431 / 1.492716 (0.382714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325707 / 0.018006 (0.307701) | 0.503841 / 0.000490 (0.503351) | 0.010224 / 0.000200 (0.010024) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035289 / 0.037411 (-0.002123) | 0.139018 / 0.014526 (0.124492) | 0.145112 / 0.176557 (-0.031445) | 0.202616 / 0.737135 (-0.534519) | 0.152975 / 0.296338 (-0.143363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493110 / 0.215209 (0.277901) | 4.885713 / 2.077655 (2.808058) | 2.344417 / 1.504120 (0.840297) | 2.135734 / 1.541195 (0.594540) | 2.254118 / 1.468490 (0.785628) | 0.811516 / 4.584777 (-3.773261) | 4.484454 / 3.745712 (0.738742) | 2.459913 / 5.269862 (-2.809948) | 1.553106 / 4.565676 (-3.012570) | 0.100943 / 0.424275 (-0.323332) | 0.014848 / 0.007607 (0.007241) | 0.626214 / 0.226044 (0.400170) | 6.206925 / 2.268929 (3.937997) | 2.986549 / 55.444624 (-52.458076) | 2.521895 / 6.876477 (-4.354582) | 2.610917 / 2.142072 (0.468845) | 0.998496 / 4.805227 (-3.806731) | 0.199405 / 6.500664 (-6.301260) | 0.077355 / 0.075469 (0.001886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.525135 / 1.841788 (-0.316653) | 18.708407 / 8.074308 (10.634099) | 16.049482 / 10.191392 (5.858090) | 0.170986 / 0.680424 (-0.509437) | 0.021090 / 0.534201 (-0.513111) | 0.511734 / 0.579283 (-0.067549) | 0.495507 / 0.434364 (0.061143) | 0.628578 / 0.540337 (0.088241) | 0.749546 / 1.386936 (-0.637390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n"
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| 2023-02-28T18:16:27Z
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Addition of py_ast dataset
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@lhoestq as discussed in PR #1195
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Adding OPUS MultiUN
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Adding UnMulti
http://www.euromatrixplus.net/multi-un/
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Cannot load the blog_authorship_corpus due to codec errors
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"Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...",
"Hi @izaskr, thanks again for having reported this issue.\r\n\r\nAfter investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:\r\n- the `NonMatchingSplitsSizesError`\r\n- the `UnicodeDecodeError`\r\n\r\nOnce the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`.",
"@albertvillanova \r\nCan you shed light on how this fix works?\r\n\r\nWe're experiencing a similar issue. \r\n\r\nIf we run several runs (eg in a Wandb sweep) the first run \"works\" but then we get `NonMatchingSplitsSizesError`\r\n\r\n| run num | actual train examples # | expected example # | recorded example # |\r\n| ------- | -------------- | ----------------- | -------- |\r\n| 1 | 100 | 100 | 100 |\r\n| 2 | 102 | 100 | 102 |\r\n| 3 | 100 | 100 | 202 | \r\n| 4 | 40 | 100 | 40 |\r\n| 5 | 40 | 100 | 40 |\r\n| 6 | 40 | 100 | 40 | \r\n\r\n\r\nThe second through the nth all crash with \r\n\r\n```\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]\r\n\r\n```"
] | 2021-07-20T10:13:20Z
| 2021-07-21T17:02:21Z
| 2021-07-21T13:11:58Z
|
NONE
| null | null | null |
## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
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Bug in caching 2 datasets both with the same builder class name
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[
"Hi @NouamaneTazi, thanks for reporting.\r\n\r\nPlease note that both datasets are cached in the same directory because their loading builder classes have the same name: `class MTOP(datasets.GeneratorBasedBuilder)`.\r\n\r\nYou should name their builder classes differently, e.g.:\r\n- `MtopDomain`\r\n- `MtopIntent`",
"Hi @NouamaneTazi, please note that after our fix:\r\n- #4388\r\n\r\nwe do not consider the class name anymore, but the name of the file where the loading builder class is implemented. "
] | 2022-05-20T18:18:03Z
| 2022-06-02T08:18:37Z
| 2022-05-25T05:16:15Z
|
MEMBER
| null | null | null |
## Describe the bug
The two datasets `mteb/mtop_intent` and `mteb/mtop_domain `use both the same cache folder `.cache/huggingface/datasets/mteb___mtop`. So if you first load `mteb/mtop_intent` then datasets will not load `mteb/mtop_domain`.
If you delete this cache folder and flip the order how you load the two datasets , you will get the opposite datasets loaded (difference is here in terms of the label and label_text).
## Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("mteb/mtop_intent", "en")
print(dataset['train'][0])
dataset = datasets.load_dataset("mteb/mtop_domain", "en")
print(dataset['train'][0])
```
## Expected results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_intent/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop_domain/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 0, 'label_text': 'messaging'}
```
## Actual results
```
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 920.14it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
Reusing dataset mtop (/home/nouamane/.cache/huggingface/datasets/mteb___mtop/en/0.0.0/f930e32a294fed424f70263d8802390e350fff17862266e5fc156175c07d9c35)
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 1307.59it/s]
{'id': 3232343436343136, 'text': 'Has Angelika Kratzer video messaged me?', 'label': 1, 'label_text': 'GET_MESSAGE'}
```
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- Platform: macOS-12.1-arm64-arm-64bit
- Python version: 3.9.12
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- Pandas version: 1.4.2
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PR_kwDODunzps4xcZMD
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Make streamable the BnL Historical Newspapers dataset
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[] | 2022-01-22T14:52:36Z
| 2022-02-04T14:05:23Z
| 2022-02-04T14:05:21Z
|
MEMBER
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I've refactored the code in order to make the dataset streamable and to avoid it takes too long:
- I've used `iter_files`
Close #3615
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I_kwDODunzps5yd0nH
| 6,271
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Overwriting Split overwrites data but not metadata, corrupting dataset
|
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[] | 2023-09-30T22:37:31Z
| 2023-10-16T13:30:50Z
| 2023-10-16T13:30:50Z
|
NONE
| null | null | null |
### Describe the bug
I want to be able to overwrite/update/delete splits in my dataset. Currently the only way to do is to manually go into the dataset and delete the split. If I try to overwrite programmatically I end up in an error state and (somewhat) corrupting the dataset. Read below.
**Current Behavior**
When I push to an existing split I get this error:
`ValueError: Split complexRoofLocation_01Apr2023_to_31May2023test already present`
This seems to suggest that the library doesn't support overwriting splits.
**Potential Bug**
What’s strange is that datasets, despite the operation erroring out with the ValueError above, does, in fact, overwrite the split:
`Pushing dataset shards to the dataset hub: 100% [.....................] 1/1 [00:00<00:00, 55.04it/s]`
Even though you got an error message and your code fails, your dataset is now changed. That seems like a bug. Either don't change the dataset, or don't throw the error and allow the script to proceed.
Additional Bug
While it overwrites the split, it doesn’t overwrite the split’s information. Because of this when you pull down the dataset you may end up getting a `NonMatchingSplitsSizesError` if the size of the dataset during the overwrite is different. For example, my original split had 5 rows, but on my overwrite, I only had 4. Then when I try to download the dataset, I get a `NonMatchingSplitsSizesError` because the dataset's data.json states there’s 5 but only 4 exist in the split.
Expected Behavior
This corrupts the dataset rendering it unusable (until you take manual intervention). Either the library should let the overwrite happen (which it does but should also update the metadata) or it shouldn’t do anything.
### Steps to reproduce the bug
[Colab Notebook](https://colab.research.google.com/drive/1bqVkD06Ngs9MQNdSk_ygCG6y1UqXA4pC?usp=sharing)
### Expected behavior
The split should be overwritten and I should be able to use the new version of the dataset without issue.
### Environment info
- `datasets` version: 2.14.5
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
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Replace AssertionErrors with more meaningful errors
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"Hi, can I pick up this issue?",
"#self-assign",
"Looks like the top-level `datasource` directory was removed when https://github.com/huggingface/datasets/pull/4974 was merged, so there are 3 source files to fix."
] | 2022-10-05T14:03:55Z
| 2022-10-07T14:33:11Z
| 2022-10-07T14:33:11Z
|
CONTRIBUTOR
| null | null | null |
Replace the AssertionErrors with more meaningful errors such as ValueError, TypeError, etc.
The files with AssertionErrors that need to be replaced:
```
src/datasets/arrow_reader.py
src/datasets/builder.py
src/datasets/utils/version.py
```
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Arrow dataset builder to be able to load and stream Arrow datasets
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq tips applied. Thanks for a review. :smile: It's a lot of fun to improve this project. ",
"Let's add some documentation in a subsequent PR :)\r\n\r\nIn particular @mariosasko and I think it's important to note to users that local arrow data are copied to cache according to the way load_dataset works, but if they want they can use Dataset.from_file instead",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006384 / 0.011353 (-0.004969) | 0.003788 / 0.011008 (-0.007220) | 0.098524 / 0.038508 (0.060016) | 0.031786 / 0.023109 (0.008677) | 0.307799 / 0.275898 (0.031901) | 0.337329 / 0.323480 (0.013849) | 0.003650 / 0.007986 (-0.004336) | 0.003731 / 0.004328 (-0.000598) | 0.076816 / 0.004250 (0.072566) | 0.041888 / 0.037052 (0.004835) | 0.310702 / 0.258489 (0.052213) | 0.343846 / 0.293841 (0.050005) | 0.027841 / 0.128546 (-0.100705) | 0.008312 / 0.075646 (-0.067334) | 0.320230 / 0.419271 (-0.099042) | 0.047378 / 0.043533 (0.003845) | 0.308683 / 0.255139 (0.053544) | 0.335129 / 0.283200 (0.051930) | 0.096294 / 0.141683 (-0.045389) | 1.485521 / 1.452155 (0.033366) | 1.559868 / 1.492716 (0.067152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197376 / 0.018006 (0.179370) | 0.430461 / 0.000490 (0.429972) | 0.004152 / 0.000200 (0.003953) | 0.000068 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023660 / 0.037411 (-0.013751) | 0.103128 / 0.014526 (0.088602) | 0.107549 / 0.176557 (-0.069008) | 0.175934 / 0.737135 (-0.561201) | 0.112210 / 0.296338 (-0.184129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415804 / 0.215209 (0.200595) | 4.216333 / 2.077655 (2.138679) | 1.910354 / 1.504120 (0.406234) | 1.712689 / 1.541195 (0.171494) | 1.754705 / 1.468490 (0.286215) | 0.554647 / 4.584777 (-4.030130) | 3.393592 / 3.745712 (-0.352120) | 1.737504 / 5.269862 (-3.532358) | 1.021213 / 4.565676 (-3.544464) | 0.066908 / 0.424275 (-0.357367) | 0.011446 / 0.007607 (0.003839) | 0.524630 / 0.226044 (0.298585) | 5.243005 / 2.268929 (2.974077) | 2.349685 / 55.444624 (-53.094939) | 2.027457 / 6.876477 (-4.849020) | 2.131053 / 2.142072 (-0.011020) | 0.669070 / 4.805227 (-4.136157) | 0.136317 / 6.500664 (-6.364347) | 0.065924 / 0.075469 (-0.009545) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254102 / 1.841788 (-0.587686) | 13.790492 / 8.074308 (5.716184) | 14.197772 / 10.191392 (4.006380) | 0.143989 / 0.680424 (-0.536434) | 0.016577 / 0.534201 (-0.517624) | 0.375437 / 0.579283 (-0.203846) | 0.398995 / 0.434364 (-0.035369) | 0.445287 / 0.540337 (-0.095050) | 0.538632 / 1.386936 (-0.848304) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006251 / 0.011353 (-0.005101) | 0.004019 / 0.011008 (-0.006989) | 0.077985 / 0.038508 (0.039477) | 0.028705 / 0.023109 (0.005596) | 0.417360 / 0.275898 (0.141462) | 0.463964 / 0.323480 (0.140484) | 0.003489 / 0.007986 (-0.004497) | 0.003032 / 0.004328 (-0.001296) | 0.077953 / 0.004250 (0.073702) | 0.040104 / 0.037052 (0.003051) | 0.405242 / 0.258489 (0.146753) | 0.475029 / 0.293841 (0.181188) | 0.028113 / 0.128546 (-0.100433) | 0.008610 / 0.075646 (-0.067036) | 0.084847 / 0.419271 (-0.334424) | 0.048227 / 0.043533 (0.004694) | 0.417235 / 0.255139 (0.162096) | 0.450470 / 0.283200 (0.167270) | 0.096978 / 0.141683 (-0.044705) | 1.514688 / 1.452155 (0.062533) | 1.560205 / 1.492716 (0.067488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235125 / 0.018006 (0.217119) | 0.409904 / 0.000490 (0.409414) | 0.002474 / 0.000200 (0.002275) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025152 / 0.037411 (-0.012259) | 0.103517 / 0.014526 (0.088991) | 0.110154 / 0.176557 (-0.066402) | 0.161431 / 0.737135 (-0.575704) | 0.114891 / 0.296338 (-0.181448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456077 / 0.215209 (0.240868) | 4.541171 / 2.077655 (2.463517) | 2.297912 / 1.504120 (0.793792) | 2.079337 / 1.541195 (0.538143) | 2.121291 / 1.468490 (0.652801) | 0.560172 / 4.584777 (-4.024605) | 3.421122 / 3.745712 (-0.324590) | 1.764675 / 5.269862 (-3.505186) | 1.043482 / 4.565676 (-3.522195) | 0.067652 / 0.424275 (-0.356623) | 0.011181 / 0.007607 (0.003574) | 0.557232 / 0.226044 (0.331188) | 5.607851 / 2.268929 (3.338922) | 2.783715 / 55.444624 (-52.660909) | 2.380943 / 6.876477 (-4.495534) | 2.378316 / 2.142072 (0.236244) | 0.674356 / 4.805227 (-4.130871) | 0.135912 / 6.500664 (-6.364752) | 0.067009 / 0.075469 (-0.008460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.309002 / 1.841788 (-0.532786) | 14.464073 / 8.074308 (6.389765) | 14.418727 / 10.191392 (4.227335) | 0.148486 / 0.680424 (-0.531938) | 0.016650 / 0.534201 (-0.517551) | 0.368786 / 0.579283 (-0.210497) | 0.395026 / 0.434364 (-0.039338) | 0.433565 / 0.540337 (-0.106772) | 0.526603 / 1.386936 (-0.860333) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#443fc92700b4f9e12421e8082e205535314a67d5 \"CML watermark\")\n"
] | 2023-06-12T14:21:49Z
| 2023-06-13T17:36:02Z
| 2023-06-13T17:29:01Z
|
CONTRIBUTOR
| null | 0
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This adds a Arrow dataset builder to be able to load and stream from already preprocessed Arrow files.
It's related to https://github.com/huggingface/datasets/issues/3035
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Use old url for conll2003
|
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[] | 2022-01-19T13:56:49Z
| 2022-01-19T14:16:28Z
| 2022-01-19T14:16:28Z
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MEMBER
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As reported in https://github.com/huggingface/datasets/issues/3582 the CoNLL2003 data files are not available in the master branch of the repo that used to host them.
For now we can use the URL from an older commit to access the data files
|
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Adjusting chunk size for streaming datasets
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"Hi ! Data streaming uses `fsspec` to read the data files progressively. IIRC the block size for buffering is 5MiB by default. So every time you finish iterating over a block, it downloads the next one. You can still try to increase the `fsspec` block size for buffering if it can help. To do so you just need to increase `fsspec.spec.AbstractBufferedFile.DEFAULT_BLOCK_SIZE `\r\n\r\nCurrently this is unfortunately done in a single thread, so it blocks the processing to download and uncompress the next block. At one point it would be nice to be able to do that in parallel !",
"Hi! Thanks for the help, I will try it :)"
] | 2021-12-28T21:17:53Z
| 2022-05-06T16:29:05Z
| 2022-05-06T16:29:05Z
|
CONTRIBUTOR
| null | null | null |
**Is your feature request related to a problem? Please describe.**
I want to use mc4 which I cannot save locally, so I stream it. However, I want to process the entire dataset and filter some documents from it. With the current chunk size of around 1000 documents (right?) I hit a performance bottleneck because of the frequent decompressing.
**Describe the solution you'd like**
I would appreciate a parameter in the load_dataset function, that allows me to set the chunksize myself (to a value like 100'000 in my case). Like that, I hope to improve the processing time.
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Fix streaming tar files from canonical datasets
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"In case it's relevant for this PR, I'm finding that I cannot stream the `bookcorpus` dataset (using the `master` branch of `datasets`), which is a `.tar.bz2` file:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nbooks_dataset_streamed = load_dataset(\"bookcorpus\", split=\"train\", streaming=True)\r\n# Throws a 404 HTTP error\r\nnext(iter(books_dataset_streamed))\r\n```\r\n\r\nThe full stack trace is:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\n<ipython-input-11-5ebbbe110b13> in <module>()\r\n----> 1 next(iter(books_dataset_streamed))\r\n\r\n11 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in __iter__(self)\r\n 339 \r\n 340 def __iter__(self):\r\n--> 341 for key, example in self._iter():\r\n 342 if self.features:\r\n 343 # we encode the example for ClassLabel feature types for example\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in _iter(self)\r\n 336 else:\r\n 337 ex_iterable = self._ex_iterable\r\n--> 338 yield from ex_iterable\r\n 339 \r\n 340 def __iter__(self):\r\n\r\n/usr/local/lib/python3.7/dist-packages/datasets/iterable_dataset.py in __iter__(self)\r\n 76 \r\n 77 def __iter__(self):\r\n---> 78 for key, example in self.generate_examples_fn(**self.kwargs):\r\n 79 yield key, example\r\n 80 \r\n\r\n/root/.cache/huggingface/modules/datasets_modules/datasets/bookcorpus/44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700/bookcorpus.py in _generate_examples(self, directory)\r\n 98 for txt_file in files:\r\n 99 with open(txt_file, mode=\"r\", encoding=\"utf-8\") as f:\r\n--> 100 for line in f:\r\n 101 yield _id, {\"text\": line.strip()}\r\n 102 _id += 1\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/implementations/http.py in read(self, length)\r\n 496 else:\r\n 497 length = min(self.size - self.loc, length)\r\n--> 498 return super().read(length)\r\n 499 \r\n 500 async def async_fetch_all(self):\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/spec.py in read(self, length)\r\n 1481 # don't even bother calling fetch\r\n 1482 return b\"\"\r\n-> 1483 out = self.cache._fetch(self.loc, self.loc + length)\r\n 1484 self.loc += len(out)\r\n 1485 return out\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/caching.py in _fetch(self, start, end)\r\n 374 ):\r\n 375 # First read, or extending both before and after\r\n--> 376 self.cache = self.fetcher(start, bend)\r\n 377 self.start = start\r\n 378 elif start < self.start:\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in wrapper(*args, **kwargs)\r\n 86 def wrapper(*args, **kwargs):\r\n 87 self = obj or args[0]\r\n---> 88 return sync(self.loop, func, *args, **kwargs)\r\n 89 \r\n 90 return wrapper\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in sync(loop, func, timeout, *args, **kwargs)\r\n 67 raise FSTimeoutError\r\n 68 if isinstance(result[0], BaseException):\r\n---> 69 raise result[0]\r\n 70 return result[0]\r\n 71 \r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/asyn.py in _runner(event, coro, result, timeout)\r\n 23 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 24 try:\r\n---> 25 result[0] = await coro\r\n 26 except Exception as ex:\r\n 27 result[0] = ex\r\n\r\n/usr/local/lib/python3.7/dist-packages/fsspec/implementations/http.py in async_fetch_range(self, start, end)\r\n 535 # range request outside file\r\n 536 return b\"\"\r\n--> 537 r.raise_for_status()\r\n 538 if r.status == 206:\r\n 539 # partial content, as expected\r\n\r\n/usr/local/lib/python3.7/dist-packages/aiohttp/client_reqrep.py in raise_for_status(self)\r\n 1003 status=self.status,\r\n 1004 message=self.reason,\r\n-> 1005 headers=self.headers,\r\n 1006 )\r\n 1007 \r\n\r\nClientResponseError: 404, message='Not Found', url=URL('https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2/books_large_p1.txt')\r\n```\r\n\r\nLet me know if this is unrelated and I'll open a separate issue :)\r\n\r\nEnvironment info:\r\n\r\n```\r\n- `datasets` version: 1.11.1.dev0\r\n- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic\r\n- Python version: 3.7.11\r\n- PyArrow version: 3.0.0\r\n```",
"@lewtun: `.tar.compression-extension` files are not supported yet. That is the objective of this PR.",
"> @lewtun: `.tar.compression-extension` files are not supported yet. That is the objective of this PR.\r\n\r\nthanks for the context and the great work on the streaming features (right now i'm writing the streaming section of the HF course, so am acting like a beta tester 😄)",
"@lewtun this PR fixes previous issue with xjoin:\r\n\r\nGiven:\r\n```python\r\nxjoin(\r\n \"https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2\",\r\n \"books_large_p1.txt\"\r\n)\r\n```\r\n\r\n- Before it gave: \r\n `\"https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2/books_large_p1.txt\"`\r\n thus raising the 404 error\r\n\r\n- Now it gives:\r\n `tar://books_large_p1.txt::https://storage.googleapis.com/huggingface-nlp/datasets/bookcorpus/bookcorpus.tar.bz2`\r\n (this is the expected format for `fsspec`) and additionally passes the parameter `compression=\"bz2\"`.\r\n See: https://github.com/huggingface/datasets/pull/2806/files#diff-97bb2d08db65ce3b679aefc43cadad76d053c1e58ecc315e49b80873d0fbdabeR15",
"closing in favor of #3066 "
] | 2021-08-16T11:10:28Z
| 2021-10-13T09:04:03Z
| 2021-10-13T09:04:02Z
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Previous PR #2800 implemented support to stream remote tar files when passing the parameter `data_files`: they required a glob string `"*"`.
However, this glob string creates an error when streaming canonical datasets (with a `join` after the `open`).
This PR fixes this issue and allows streaming tar files both from:
- canonical datasets scripts and
- data files.
This PR also adds support for compressed tar files: `.tar.gz`, `.tar.bz2`,...
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PR_kwDODunzps41Ad_L
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Support streaming scan dataset
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-25T10:11:28Z
| 2022-03-25T12:08:55Z
| 2022-03-25T12:03:52Z
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MDExOlB1bGxSZXF1ZXN0NTMxNzU5MjA0
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add sharc dataset
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[] | 2020-12-03T12:57:23Z
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This PR adds the ShARC dataset.
More info:
https://sharc-data.github.io/index.html
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Spark docs
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010480 / 0.011353 (-0.000872) | 0.006743 / 0.011008 (-0.004265) | 0.126503 / 0.038508 (0.087995) | 0.036918 / 0.023109 (0.013808) | 0.387372 / 0.275898 (0.111474) | 0.456930 / 0.323480 (0.133450) | 0.008038 / 0.007986 (0.000052) | 0.005082 / 0.004328 (0.000753) | 0.093312 / 0.004250 (0.089062) | 0.065440 / 0.037052 (0.028387) | 0.378172 / 0.258489 (0.119683) | 0.430049 / 0.293841 (0.136208) | 0.054372 / 0.128546 (-0.074174) | 0.021875 / 0.075646 (-0.053772) | 0.441722 / 0.419271 (0.022450) | 0.063716 / 0.043533 (0.020183) | 0.375718 / 0.255139 (0.120579) | 0.413688 / 0.283200 (0.130488) | 0.122583 / 0.141683 (-0.019100) | 1.835992 / 1.452155 (0.383838) | 1.915862 / 1.492716 (0.423145) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275305 / 0.018006 (0.257299) | 0.617170 / 0.000490 (0.616680) | 0.006467 / 0.000200 (0.006267) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031057 / 0.037411 (-0.006354) | 0.135178 / 0.014526 (0.120653) | 0.139265 / 0.176557 (-0.037292) | 0.221597 / 0.737135 (-0.515538) | 0.147632 / 0.296338 (-0.148706) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.640621 / 0.215209 (0.425411) | 6.354359 / 2.077655 (4.276704) | 2.748945 / 1.504120 (1.244825) | 2.396637 / 1.541195 (0.855442) | 2.395193 / 1.468490 (0.926703) | 1.209604 / 4.584777 (-3.375173) | 5.626901 / 3.745712 (1.881189) | 3.300941 / 5.269862 (-1.968920) | 2.123598 / 4.565676 (-2.442078) | 0.144270 / 0.424275 (-0.280005) | 0.015114 / 0.007607 (0.007507) | 0.812352 / 0.226044 (0.586307) | 8.024250 / 2.268929 (5.755322) | 3.557589 / 55.444624 (-51.887036) | 2.840632 / 6.876477 (-4.035845) | 3.152319 / 2.142072 (1.010246) | 1.447232 / 4.805227 (-3.357995) | 0.251740 / 6.500664 (-6.248924) | 0.083725 / 0.075469 (0.008256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.568032 / 1.841788 (-0.273755) | 18.463860 / 8.074308 (10.389552) | 21.217395 / 10.191392 (11.026003) | 0.228457 / 0.680424 (-0.451967) | 0.031398 / 0.534201 (-0.502803) | 0.547627 / 0.579283 (-0.031656) | 0.642921 / 0.434364 (0.208557) | 0.687857 / 0.540337 (0.147520) | 0.800940 / 1.386936 (-0.585996) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009933 / 0.011353 (-0.001420) | 0.006065 / 0.011008 (-0.004943) | 0.102556 / 0.038508 (0.064048) | 0.034646 / 0.023109 (0.011537) | 0.437951 / 0.275898 (0.162053) | 0.482439 / 0.323480 (0.158959) | 0.007715 / 0.007986 (-0.000271) | 0.007426 / 0.004328 (0.003098) | 0.096427 / 0.004250 (0.092177) | 0.052983 / 0.037052 (0.015930) | 0.464533 / 0.258489 (0.206044) | 0.484848 / 0.293841 (0.191007) | 0.050415 / 0.128546 (-0.078131) | 0.021001 / 0.075646 (-0.054645) | 0.121214 / 0.419271 (-0.298058) | 0.061658 / 0.043533 (0.018125) | 0.431898 / 0.255139 (0.176759) | 0.482106 / 0.283200 (0.198907) | 0.128524 / 0.141683 (-0.013159) | 1.775714 / 1.452155 (0.323559) | 1.904738 / 1.492716 (0.412021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287641 / 0.018006 (0.269635) | 0.600667 / 0.000490 (0.600178) | 0.005097 / 0.000200 (0.004897) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032836 / 0.037411 (-0.004575) | 0.133114 / 0.014526 (0.118588) | 0.150874 / 0.176557 (-0.025683) | 0.217069 / 0.737135 (-0.520066) | 0.160387 / 0.296338 (-0.135951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668444 / 0.215209 (0.453235) | 6.240015 / 2.077655 (4.162360) | 2.808661 / 1.504120 (1.304542) | 2.336550 / 1.541195 (0.795356) | 2.538973 / 1.468490 (1.070483) | 1.189292 / 4.584777 (-3.395485) | 5.781028 / 3.745712 (2.035315) | 3.149895 / 5.269862 (-2.119967) | 2.130646 / 4.565676 (-2.435030) | 0.144944 / 0.424275 (-0.279331) | 0.014650 / 0.007607 (0.007043) | 0.792313 / 0.226044 (0.566269) | 7.933108 / 2.268929 (5.664180) | 3.527527 / 55.444624 (-51.917098) | 2.864271 / 6.876477 (-4.012205) | 3.098330 / 2.142072 (0.956258) | 1.421208 / 4.805227 (-3.384019) | 0.255638 / 6.500664 (-6.245026) | 0.086971 / 0.075469 (0.011502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585317 / 1.841788 (-0.256471) | 18.643133 / 8.074308 (10.568825) | 21.921256 / 10.191392 (11.729864) | 0.215493 / 0.680424 (-0.464931) | 0.028348 / 0.534201 (-0.505853) | 0.556925 / 0.579283 (-0.022358) | 0.631480 / 0.434364 (0.197116) | 0.654026 / 0.540337 (0.113689) | 0.799727 / 1.386936 (-0.587209) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#62520514b524b5904c7e4f0beddab1971212a96a \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006516 / 0.011353 (-0.004837) | 0.004500 / 0.011008 (-0.006509) | 0.097639 / 0.038508 (0.059131) | 0.028336 / 0.023109 (0.005227) | 0.377263 / 0.275898 (0.101365) | 0.409209 / 0.323480 (0.085729) | 0.004832 / 0.007986 (-0.003154) | 0.004629 / 0.004328 (0.000301) | 0.075046 / 0.004250 (0.070795) | 0.034080 / 0.037052 (-0.002972) | 0.377565 / 0.258489 (0.119076) | 0.419204 / 0.293841 (0.125363) | 0.030343 / 0.128546 (-0.098203) | 0.011465 / 0.075646 (-0.064182) | 0.322777 / 0.419271 (-0.096494) | 0.043774 / 0.043533 (0.000241) | 0.375808 / 0.255139 (0.120669) | 0.402665 / 0.283200 (0.119465) | 0.086811 / 0.141683 (-0.054872) | 1.518686 / 1.452155 (0.066531) | 1.540381 / 1.492716 (0.047664) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197730 / 0.018006 (0.179724) | 0.409285 / 0.000490 (0.408795) | 0.004739 / 0.000200 (0.004539) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022974 / 0.037411 (-0.014437) | 0.096843 / 0.014526 (0.082317) | 0.103241 / 0.176557 (-0.073316) | 0.163691 / 0.737135 (-0.573444) | 0.107905 / 0.296338 (-0.188433) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.449408 / 0.215209 (0.234199) | 4.501375 / 2.077655 (2.423720) | 2.181491 / 1.504120 (0.677371) | 1.986153 / 1.541195 (0.444958) | 2.024735 / 1.468490 (0.556245) | 0.695368 / 4.584777 (-3.889409) | 3.416912 / 3.745712 (-0.328800) | 1.893343 / 5.269862 (-3.376519) | 1.275535 / 4.565676 (-3.290142) | 0.082772 / 0.424275 (-0.341503) | 0.012365 / 0.007607 (0.004758) | 0.553859 / 0.226044 (0.327814) | 5.540014 / 2.268929 (3.271085) | 2.634298 / 55.444624 (-52.810326) | 2.286686 / 6.876477 (-4.589790) | 2.384402 / 2.142072 (0.242330) | 0.806413 / 4.805227 (-3.998814) | 0.151757 / 6.500664 (-6.348907) | 0.067155 / 0.075469 (-0.008314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198776 / 1.841788 (-0.643012) | 13.517434 / 8.074308 (5.443126) | 13.926300 / 10.191392 (3.734908) | 0.141887 / 0.680424 (-0.538537) | 0.016571 / 0.534201 (-0.517630) | 0.383179 / 0.579283 (-0.196104) | 0.395189 / 0.434364 (-0.039175) | 0.479635 / 0.540337 (-0.060702) | 0.570576 / 1.386936 (-0.816360) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006691 / 0.011353 (-0.004662) | 0.004634 / 0.011008 (-0.006375) | 0.077087 / 0.038508 (0.038579) | 0.028281 / 0.023109 (0.005172) | 0.340108 / 0.275898 (0.064210) | 0.370611 / 0.323480 (0.047131) | 0.004997 / 0.007986 (-0.002988) | 0.003336 / 0.004328 (-0.000992) | 0.074814 / 0.004250 (0.070563) | 0.039001 / 0.037052 (0.001948) | 0.344225 / 0.258489 (0.085736) | 0.380621 / 0.293841 (0.086780) | 0.030858 / 0.128546 (-0.097689) | 0.011623 / 0.075646 (-0.064023) | 0.085016 / 0.419271 (-0.334256) | 0.042378 / 0.043533 (-0.001155) | 0.341428 / 0.255139 (0.086289) | 0.364823 / 0.283200 (0.081624) | 0.096695 / 0.141683 (-0.044988) | 1.527683 / 1.452155 (0.075528) | 1.585361 / 1.492716 (0.092645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.184280 / 0.018006 (0.166274) | 0.397845 / 0.000490 (0.397355) | 0.004415 / 0.000200 (0.004215) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024296 / 0.037411 (-0.013115) | 0.101053 / 0.014526 (0.086527) | 0.108968 / 0.176557 (-0.067589) | 0.155732 / 0.737135 (-0.581403) | 0.112604 / 0.296338 (-0.183735) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440819 / 0.215209 (0.225609) | 4.394017 / 2.077655 (2.316363) | 2.092456 / 1.504120 (0.588336) | 1.880186 / 1.541195 (0.338991) | 1.918035 / 1.468490 (0.449545) | 0.698059 / 4.584777 (-3.886718) | 3.422598 / 3.745712 (-0.323114) | 1.860465 / 5.269862 (-3.409396) | 1.157788 / 4.565676 (-3.407889) | 0.083566 / 0.424275 (-0.340709) | 0.012440 / 0.007607 (0.004832) | 0.549526 / 0.226044 (0.323481) | 5.500623 / 2.268929 (3.231694) | 2.546980 / 55.444624 (-52.897644) | 2.199527 / 6.876477 (-4.676949) | 2.297276 / 2.142072 (0.155203) | 0.801580 / 4.805227 (-4.003648) | 0.151842 / 6.500664 (-6.348822) | 0.067165 / 0.075469 (-0.008305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.329097 / 1.841788 (-0.512691) | 13.830354 / 8.074308 (5.756046) | 14.155250 / 10.191392 (3.963858) | 0.144517 / 0.680424 (-0.535907) | 0.016738 / 0.534201 (-0.517463) | 0.379337 / 0.579283 (-0.199946) | 0.391382 / 0.434364 (-0.042982) | 0.459153 / 0.540337 (-0.081184) | 0.547287 / 1.386936 (-0.839649) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2efb0289c887ec60d54e0715cd85c111cb45f9ee \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007176 / 0.011353 (-0.004177) | 0.005125 / 0.011008 (-0.005883) | 0.096060 / 0.038508 (0.057552) | 0.033262 / 0.023109 (0.010152) | 0.311461 / 0.275898 (0.035563) | 0.340673 / 0.323480 (0.017193) | 0.005700 / 0.007986 (-0.002286) | 0.005223 / 0.004328 (0.000894) | 0.072812 / 0.004250 (0.068561) | 0.042078 / 0.037052 (0.005025) | 0.320042 / 0.258489 (0.061553) | 0.346539 / 0.293841 (0.052698) | 0.035284 / 0.128546 (-0.093262) | 0.012021 / 0.075646 (-0.063625) | 0.331555 / 0.419271 (-0.087717) | 0.051058 / 0.043533 (0.007525) | 0.303001 / 0.255139 (0.047862) | 0.328431 / 0.283200 (0.045231) | 0.100954 / 0.141683 (-0.040729) | 1.407445 / 1.452155 (-0.044710) | 1.512826 / 1.492716 (0.020110) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216442 / 0.018006 (0.198436) | 0.446298 / 0.000490 (0.445809) | 0.004701 / 0.000200 (0.004501) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028088 / 0.037411 (-0.009324) | 0.108669 / 0.014526 (0.094144) | 0.119597 / 0.176557 (-0.056960) | 0.178249 / 0.737135 (-0.558886) | 0.123914 / 0.296338 (-0.172424) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413437 / 0.215209 (0.198228) | 4.136602 / 2.077655 (2.058947) | 1.875872 / 1.504120 (0.371752) | 1.680783 / 1.541195 (0.139588) | 1.757059 / 1.468490 (0.288569) | 0.711080 / 4.584777 (-3.873697) | 3.791701 / 3.745712 (0.045989) | 2.111612 / 5.269862 (-3.158250) | 1.351204 / 4.565676 (-3.214473) | 0.086477 / 0.424275 (-0.337798) | 0.012359 / 0.007607 (0.004752) | 0.504984 / 0.226044 (0.278940) | 5.040456 / 2.268929 (2.771527) | 2.266946 / 55.444624 (-53.177679) | 1.957827 / 6.876477 (-4.918650) | 2.120490 / 2.142072 (-0.021583) | 0.856148 / 4.805227 (-3.949079) | 0.172414 / 6.500664 (-6.328250) | 0.066833 / 0.075469 (-0.008636) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198163 / 1.841788 (-0.643625) | 14.944930 / 8.074308 (6.870622) | 14.317196 / 10.191392 (4.125804) | 0.166104 / 0.680424 (-0.514320) | 0.017443 / 0.534201 (-0.516758) | 0.423025 / 0.579283 (-0.156258) | 0.437476 / 0.434364 (0.003112) | 0.500156 / 0.540337 (-0.040181) | 0.606226 / 1.386936 (-0.780710) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007417 / 0.011353 (-0.003936) | 0.005143 / 0.011008 (-0.005865) | 0.076401 / 0.038508 (0.037893) | 0.034818 / 0.023109 (0.011709) | 0.339633 / 0.275898 (0.063735) | 0.373839 / 0.323480 (0.050359) | 0.006004 / 0.007986 (-0.001982) | 0.005403 / 0.004328 (0.001075) | 0.074150 / 0.004250 (0.069899) | 0.050489 / 0.037052 (0.013436) | 0.343357 / 0.258489 (0.084868) | 0.377009 / 0.293841 (0.083168) | 0.035921 / 0.128546 (-0.092625) | 0.012197 / 0.075646 (-0.063449) | 0.087992 / 0.419271 (-0.331279) | 0.049452 / 0.043533 (0.005919) | 0.340495 / 0.255139 (0.085356) | 0.360277 / 0.283200 (0.077077) | 0.111114 / 0.141683 (-0.030569) | 1.463888 / 1.452155 (0.011734) | 1.548320 / 1.492716 (0.055604) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228437 / 0.018006 (0.210431) | 0.445120 / 0.000490 (0.444631) | 0.000392 / 0.000200 (0.000192) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029965 / 0.037411 (-0.007446) | 0.113484 / 0.014526 (0.098958) | 0.125249 / 0.176557 (-0.051308) | 0.177201 / 0.737135 (-0.559934) | 0.128750 / 0.296338 (-0.167589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420089 / 0.215209 (0.204880) | 4.195772 / 2.077655 (2.118117) | 2.021539 / 1.504120 (0.517419) | 1.825118 / 1.541195 (0.283924) | 1.904090 / 1.468490 (0.435600) | 0.716276 / 4.584777 (-3.868501) | 3.742257 / 3.745712 (-0.003455) | 3.368880 / 5.269862 (-1.900981) | 1.728285 / 4.565676 (-2.837392) | 0.087656 / 0.424275 (-0.336619) | 0.012263 / 0.007607 (0.004656) | 0.524321 / 0.226044 (0.298277) | 5.217610 / 2.268929 (2.948682) | 2.474670 / 55.444624 (-52.969955) | 2.135452 / 6.876477 (-4.741025) | 2.292578 / 2.142072 (0.150505) | 0.852109 / 4.805227 (-3.953119) | 0.172031 / 6.500664 (-6.328633) | 0.065230 / 0.075469 (-0.010240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260494 / 1.841788 (-0.581293) | 15.019167 / 8.074308 (6.944859) | 14.647586 / 10.191392 (4.456193) | 0.170578 / 0.680424 (-0.509846) | 0.017619 / 0.534201 (-0.516582) | 0.423116 / 0.579283 (-0.156167) | 0.426680 / 0.434364 (-0.007684) | 0.519563 / 0.540337 (-0.020775) | 0.619335 / 1.386936 (-0.767601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e210dc20c19b5e6af05df9ca6e82984dfb42465f \"CML watermark\")\n"
] | 2023-04-26T17:39:43Z
| 2023-04-27T16:41:50Z
| 2023-04-27T16:34:45Z
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Added a "Use with Spark" doc page to document `Dataset.from_spark` following https://github.com/huggingface/datasets/pull/5701
cc @maddiedawson
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NonMatchingChecksumError when attempting to download GLUE
|
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"Hi :)\r\n\r\nI think your issue may be related to the older `nlp` library. I was able to download `glue` with the latest version of `datasets`. Can you try updating with:\r\n\r\n```py\r\npip install -U datasets\r\n```\r\n\r\nThen you can download:\r\n\r\n```py\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"glue\", \"rte\")\r\n```",
"This appears to work. Thank you!\n\nOn Wed, Apr 27, 2022, 1:18 PM Steven Liu ***@***.***> wrote:\n\n> Hi :)\n>\n> I think your issue may be related to the older nlp library. I was able to\n> download glue with the latest version of datasets. Can you try updating\n> with:\n>\n> pip install -U datasets\n>\n> Then you can download:\n>\n> from datasets import load_datasetds = load_dataset(\"glue\", \"rte\")\n>\n> —\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/issues/4241#issuecomment-1111267650>,\n> or unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/ACJUEKLUP2EL7ES3RRWJRPTVHFZHBANCNFSM5UPJBYXA>\n> .\n> You are receiving this because you authored the thread.Message ID:\n> ***@***.***>\n>\n"
] | 2022-04-27T14:14:21Z
| 2022-04-28T07:45:27Z
| 2022-04-28T07:45:27Z
|
NONE
| null | null | null |
## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
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Dataset Viewer issue for loubnabnl/humaneval-x
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[
"It's a bug! Thanks for reporting, I'm looking at it",
"Fixed."
] | 2022-09-21T09:06:17Z
| 2022-09-21T11:49:49Z
| 2022-09-21T11:49:49Z
|
NONE
| null | null | null |
### Link
https://huggingface.co/datasets/loubnabnl/humaneval-x/viewer/
### Description
The dataset has subsets but the viewer gets stuck in the default subset even when I select another one (the data loading of the subsets works fine)
### Owner
Yes
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Adding an Elastic Search index to a Dataset
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[
"Hi, is this bug deterministic in your poetry env ? I mean, does it always stop at 90% or is it random ?\r\n\r\nAlso, can you try using another version of Elasticsearch ? Maybe there's an issue with the one of you poetry env",
"I face similar issue with oscar dataset on remote ealsticsearch instance. It was mainly due to timeout of batch indexing requests and I solve these by adding large request_timeout param in `search.py`\r\n\r\n```\r\n for ok, action in es.helpers.streaming_bulk(\r\n client=self.es_client,\r\n index=index_name,\r\n actions=passage_generator(),\r\n request_timeout=3600,\r\n )\r\n ```",
"Hi @MotzWanted - are there any errors in the Elasticsearch cluster logs? Since it works in your local environment and the cluster versions are different between your poetry env and your local env, it is possible that it is some difference in the cluster - either settings or the cluster being under a different load etc that has this effect, so it would be useful to see if any errors are thrown in the cluster's logs when you try to ingest. \r\nWhich elasticsearch client method is the function `add_elasticsearch_index` from your code using under the hood? Is it `helpers.bulk` or is the indexing performed using something else? You can try adding a timeout to the indexing method to see if this helps. Also, you mention that it stops at around 90% - do you know if the timeout/hanging happens always when a particular document is being indexed or does it happen randomly at around 90% completeness but on different documents?"
] | 2021-09-09T12:21:39Z
| 2021-10-20T18:57:11Z
| null |
NONE
| null | null | null |
## Describe the bug
When trying to index documents from the squad dataset, the connection to ElasticSearch seems to break:
Reusing dataset squad (/Users/andreasmotz/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)
90%|████████████████████████████████████████████▉ | 9501/10570 [00:01<00:00, 6335.61docs/s]
No error is thrown, but the indexing breaks ~90%.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
from elasticsearch import Elasticsearch
es = Elasticsearch()
squad = load_dataset('squad', split='validation')
index_name = "corpus"
es_config = {
"settings": {
"number_of_shards": 1,
"analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
},
"mappings": {
"properties": {
"idx" : {"type" : "keyword"},
"title" : {"type" : "keyword"},
"text": {
"type": "text",
"analyzer": "standard",
"similarity": "BM25"
},
}
},
}
class IndexBuilder:
"""
Elastic search indexing of a corpus
"""
def __init__(
self,
*args,
#corpus : None,
dataset : squad,
index_name = str,
query = str,
config = dict,
**kwargs,
):
#instantiate HuggingFace dataset
self.dataset = dataset
#instantiate ElasticSearch config
self.config = config
self.es = Elasticsearch()
self.index_name = index_name
self.query = query
def elastic_index(self):
print(self.es.info)
self.es.indices.delete(index=self.index_name, ignore=[400, 404])
search_index = self.dataset.add_elasticsearch_index(column='context', host='localhost', port='9200', es_index_name=self.index_name, es_index_config=self.config)
return search_index
def exact_match_method(self, index):
scores, retrieved_examples = index.get_nearest_examples('context', query=self.query, k=1)
return scores, retrieved_examples
if __name__ == "__main__":
print(type(squad))
Index = IndexBuilder(dataset=squad, index_name='corpus_index', query='Where was Chopin born?', config=es_config)
search_index = Index.elastic_index()
scores, examples = Index.exact_match_method(search_index)
print(scores, examples)
for name in squad.column_names:
print(type(squad[name]))
```
## Environment info
We run the code in Poetry. This might be the issue, since the script runs successfully in our local environment.
Poetry:
- Python version: 3.8
- PyArrow: 4.0.1
- Elasticsearch: 7.13.4
- datasets: 1.10.2
Local:
- Python version: 3.8
- PyArrow: 3.0.0
- Elasticsearch: 7.7.1
- datasets: 1.7.0
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User token is printed out!
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[
"Indeed, this is not a good practice. I've opened a PR that removes the token value from the (deprecation) warning."
] | 2023-11-14T10:01:34Z
| 2023-11-14T22:19:46Z
| 2023-11-14T22:19:46Z
|
NONE
| null | null | null |
This line prints user token on command line! Is it safe?
https://github.com/huggingface/datasets/blob/12ebe695b4748c5a26e08b44ed51955f74f5801d/src/datasets/load.py#L2091
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|
Fix ArrayXD YAML conversion
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6168). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009350 / 0.011353 (-0.002003) | 0.005658 / 0.011008 (-0.005350) | 0.123173 / 0.038508 (0.084664) | 0.096354 / 0.023109 (0.073244) | 0.464398 / 0.275898 (0.188500) | 0.544455 / 0.323480 (0.220975) | 0.007337 / 0.007986 (-0.000648) | 0.004424 / 0.004328 (0.000096) | 0.089715 / 0.004250 (0.085465) | 0.072462 / 0.037052 (0.035410) | 0.460601 / 0.258489 (0.202112) | 0.544384 / 0.293841 (0.250543) | 0.052994 / 0.128546 (-0.075552) | 0.014459 / 0.075646 (-0.061187) | 0.464368 / 0.419271 (0.045096) | 0.072889 / 0.043533 (0.029356) | 0.471387 / 0.255139 (0.216248) | 0.560982 / 0.283200 (0.277783) | 0.041398 / 0.141683 (-0.100285) | 1.964688 / 1.452155 (0.512533) | 2.240727 / 1.492716 (0.748011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308524 / 0.018006 (0.290518) | 0.669306 / 0.000490 (0.668816) | 0.006644 / 0.000200 (0.006444) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037395 / 0.037411 (-0.000016) | 0.111303 / 0.014526 (0.096777) | 0.158988 / 0.176557 (-0.017569) | 0.236155 / 0.737135 (-0.500980) | 0.134775 / 0.296338 (-0.161564) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.648830 / 0.215209 (0.433621) | 6.614794 / 2.077655 (4.537139) | 2.867526 / 1.504120 (1.363407) | 2.472967 / 1.541195 (0.931772) | 2.488419 / 1.468490 (1.019929) | 0.915785 / 4.584777 (-3.668992) | 6.010754 / 3.745712 (2.265042) | 5.468873 / 5.269862 (0.199011) | 3.446535 / 4.565676 (-1.119141) | 0.118592 / 0.424275 (-0.305684) | 0.012005 / 0.007607 (0.004398) | 0.808467 / 0.226044 (0.582423) | 8.152122 / 2.268929 (5.883193) | 3.751282 / 55.444624 (-51.693342) | 3.009569 / 6.876477 (-3.866908) | 3.282613 / 2.142072 (1.140540) | 1.152727 / 4.805227 (-3.652500) | 0.240224 / 6.500664 (-6.260440) | 0.097871 / 0.075469 (0.022402) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.824944 / 1.841788 (-0.016843) | 27.840842 / 8.074308 (19.766533) | 24.368669 / 10.191392 (14.177277) | 0.260621 / 0.680424 (-0.419803) | 0.033730 / 0.534201 (-0.500471) | 0.552494 / 0.579283 (-0.026789) | 0.666921 / 0.434364 (0.232557) | 0.648812 / 0.540337 (0.108475) | 0.912602 / 1.386936 (-0.474334) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011688 / 0.011353 (0.000335) | 0.005794 / 0.011008 (-0.005215) | 0.093466 / 0.038508 (0.054958) | 0.102583 / 0.023109 (0.079474) | 0.593572 / 0.275898 (0.317674) | 0.614351 / 0.323480 (0.290871) | 0.007006 / 0.007986 (-0.000980) | 0.005557 / 0.004328 (0.001229) | 0.087779 / 0.004250 (0.083529) | 0.072639 / 0.037052 (0.035586) | 0.577464 / 0.258489 (0.318975) | 0.628240 / 0.293841 (0.334399) | 0.053876 / 0.128546 (-0.074670) | 0.015383 / 0.075646 (-0.060263) | 0.110633 / 0.419271 (-0.308639) | 0.067467 / 0.043533 (0.023934) | 0.613457 / 0.255139 (0.358318) | 0.604939 / 0.283200 (0.321739) | 0.041738 / 0.141683 (-0.099945) | 1.967167 / 1.452155 (0.515012) | 2.121009 / 1.492716 (0.628293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.449937 / 0.018006 (0.431930) | 0.694410 / 0.000490 (0.693921) | 0.064051 / 0.000200 (0.063851) | 0.000810 / 0.000054 (0.000756) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.045138 / 0.037411 (0.007727) | 0.116831 / 0.014526 (0.102306) | 0.131906 / 0.176557 (-0.044651) | 0.202421 / 0.737135 (-0.534714) | 0.132568 / 0.296338 (-0.163770) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.698046 / 0.215209 (0.482837) | 7.112591 / 2.077655 (5.034936) | 3.332679 / 1.504120 (1.828559) | 2.946384 / 1.541195 (1.405189) | 3.074484 / 1.468490 (1.605994) | 0.970917 / 4.584777 (-3.613859) | 6.143506 / 3.745712 (2.397794) | 5.572496 / 5.269862 (0.302634) | 3.602673 / 4.565676 (-0.963004) | 0.115068 / 0.424275 (-0.309207) | 0.009971 / 0.007607 (0.002364) | 0.891090 / 0.226044 (0.665046) | 8.761788 / 2.268929 (6.492859) | 4.362685 / 55.444624 (-51.081939) | 3.612893 / 6.876477 (-3.263583) | 3.797948 / 2.142072 (1.655876) | 1.202890 / 4.805227 (-3.602337) | 0.238120 / 6.500664 (-6.262544) | 0.095612 / 0.075469 (0.020143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.958880 / 1.841788 (0.117092) | 28.216454 / 8.074308 (20.142146) | 25.361424 / 10.191392 (15.170032) | 0.308203 / 0.680424 (-0.372221) | 0.032903 / 0.534201 (-0.501298) | 0.539714 / 0.579283 (-0.039569) | 0.688278 / 0.434364 (0.253914) | 0.644818 / 0.540337 (0.104481) | 0.905694 / 1.386936 (-0.481242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5289345e5b23548fee680a0bbc047c0b9a5ee8c \"CML watermark\")\n",
"Maybe convert all the tuples by default instead of hardcoding a logic specific to ArrayXD ?",
"@mariosasko Have you been able to fix this issue ? we're having quite a rough time updating our dataset lately",
"@lhoestq Does it look good now?",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005519 / 0.011353 (-0.005834) | 0.003482 / 0.011008 (-0.007527) | 0.064904 / 0.038508 (0.026396) | 0.052399 / 0.023109 (0.029289) | 0.247238 / 0.275898 (-0.028660) | 0.273426 / 0.323480 (-0.050054) | 0.003102 / 0.007986 (-0.004884) | 0.003420 / 0.004328 (-0.000908) | 0.048029 / 0.004250 (0.043779) | 0.039378 / 0.037052 (0.002326) | 0.253809 / 0.258489 (-0.004680) | 0.287483 / 0.293841 (-0.006358) | 0.028096 / 0.128546 (-0.100450) | 0.010806 / 0.075646 (-0.064841) | 0.207799 / 0.419271 (-0.211472) | 0.035861 / 0.043533 (-0.007672) | 0.251912 / 0.255139 (-0.003227) | 0.278877 / 0.283200 (-0.004323) | 0.019498 / 0.141683 (-0.122185) | 1.104916 / 1.452155 (-0.347238) | 1.157376 / 1.492716 (-0.335340) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093051 / 0.018006 (0.075045) | 0.303331 / 0.000490 (0.302841) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018052 / 0.037411 (-0.019359) | 0.060597 / 0.014526 (0.046071) | 0.074033 / 0.176557 (-0.102524) | 0.120966 / 0.737135 (-0.616169) | 0.075012 / 0.296338 (-0.221326) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283922 / 0.215209 (0.068713) | 2.805445 / 2.077655 (0.727791) | 1.498292 / 1.504120 (-0.005828) | 1.371686 / 1.541195 (-0.169509) | 1.402074 / 1.468490 (-0.066416) | 0.567231 / 4.584777 (-4.017546) | 2.393291 / 3.745712 (-1.352422) | 2.800329 / 5.269862 (-2.469533) | 1.789197 / 4.565676 (-2.776479) | 0.063620 / 0.424275 (-0.360655) | 0.005008 / 0.007607 (-0.002600) | 0.338929 / 0.226044 (0.112884) | 3.292122 / 2.268929 (1.023193) | 1.813313 / 55.444624 (-53.631311) | 1.557122 / 6.876477 (-5.319354) | 1.576395 / 2.142072 (-0.565677) | 0.666714 / 4.805227 (-4.138513) | 0.118253 / 6.500664 (-6.382411) | 0.042633 / 0.075469 (-0.032836) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950678 / 1.841788 (-0.891110) | 11.589806 / 8.074308 (3.515498) | 10.436701 / 10.191392 (0.245309) | 0.141048 / 0.680424 (-0.539376) | 0.014766 / 0.534201 (-0.519435) | 0.298359 / 0.579283 (-0.280924) | 0.268850 / 0.434364 (-0.165514) | 0.340242 / 0.540337 (-0.200095) | 0.451447 / 1.386936 (-0.935489) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005405 / 0.011353 (-0.005948) | 0.003545 / 0.011008 (-0.007464) | 0.048959 / 0.038508 (0.010451) | 0.056565 / 0.023109 (0.033455) | 0.274289 / 0.275898 (-0.001609) | 0.296565 / 0.323480 (-0.026915) | 0.004790 / 0.007986 (-0.003196) | 0.002772 / 0.004328 (-0.001557) | 0.048605 / 0.004250 (0.044354) | 0.040676 / 0.037052 (0.003624) | 0.279949 / 0.258489 (0.021460) | 0.312816 / 0.293841 (0.018976) | 0.029605 / 0.128546 (-0.098941) | 0.010799 / 0.075646 (-0.064848) | 0.056941 / 0.419271 (-0.362331) | 0.034518 / 0.043533 (-0.009014) | 0.277193 / 0.255139 (0.022054) | 0.292334 / 0.283200 (0.009134) | 0.018836 / 0.141683 (-0.122847) | 1.145228 / 1.452155 (-0.306927) | 1.198958 / 1.492716 (-0.293758) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093618 / 0.018006 (0.075612) | 0.303687 / 0.000490 (0.303197) | 0.000234 / 0.000200 (0.000034) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021347 / 0.037411 (-0.016065) | 0.067811 / 0.014526 (0.053286) | 0.080631 / 0.176557 (-0.095926) | 0.119289 / 0.737135 (-0.617846) | 0.082085 / 0.296338 (-0.214254) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.293374 / 0.215209 (0.078165) | 2.864516 / 2.077655 (0.786861) | 1.611042 / 1.504120 (0.106922) | 1.466124 / 1.541195 (-0.075071) | 1.480509 / 1.468490 (0.012019) | 0.569463 / 4.584777 (-4.015314) | 2.448181 / 3.745712 (-1.297531) | 2.841732 / 5.269862 (-2.428130) | 1.754458 / 4.565676 (-2.811219) | 0.063771 / 0.424275 (-0.360505) | 0.004976 / 0.007607 (-0.002631) | 0.346094 / 0.226044 (0.120050) | 3.440090 / 2.268929 (1.171162) | 1.961862 / 55.444624 (-53.482763) | 1.675780 / 6.876477 (-5.200697) | 1.679676 / 2.142072 (-0.462396) | 0.641063 / 4.805227 (-4.164164) | 0.116268 / 6.500664 (-6.384396) | 0.041767 / 0.075469 (-0.033702) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980286 / 1.841788 (-0.861502) | 12.055227 / 8.074308 (3.980919) | 10.685417 / 10.191392 (0.494025) | 0.140842 / 0.680424 (-0.539582) | 0.015413 / 0.534201 (-0.518788) | 0.286939 / 0.579283 (-0.292344) | 0.278796 / 0.434364 (-0.155568) | 0.326740 / 0.540337 (-0.213597) | 0.574516 / 1.386936 (-0.812421) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53b8c2d9d3714f4a56d74637c0840a90cbfb7aa0 \"CML watermark\")\n"
] | 2023-08-22T17:02:54Z
| 2023-12-12T15:06:59Z
| 2023-12-12T15:00:43Z
|
CONTRIBUTOR
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Replace the `shape` tuple with a list in the `ArrayXD` YAML conversion.
Fix #6112
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| 153
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Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations
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"As @yoavgo suggested, there should be the possibility to call a function like nlp.bib that outputs all bibtex ref from the datasets and models actually used and eventually nlp.bib.forreadme that would output the same info + versions numbers so they can be included in a readme.md file.",
"Actually, double checking with @mariamabarham, we already have this feature I think.\r\n\r\nIt's like this currently:\r\n```python\r\n>>> from nlp import load_dataset\r\n>>> \r\n>>> dataset = load_dataset('glue', 'cola', split='train')\r\n>>> print(dataset.info.citation)\r\n@article{warstadt2018neural,\r\n title={Neural Network Acceptability Judgments},\r\n author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\r\n journal={arXiv preprint arXiv:1805.12471},\r\n year={2018}\r\n}\r\n@inproceedings{wang2019glue,\r\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\r\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\r\n note={In the Proceedings of ICLR.},\r\n year={2019}\r\n}\r\n\r\nNote that each GLUE dataset has its own citation. Please see the source to see\r\nthe correct citation for each contained dataset.\r\n```\r\n\r\nWhat do you think @dseddah?",
"Looks good but why would there be a difference between the ref in the source and the one to be printed? ",
"Yes, I think we should remove this warning @mariamabarham.\r\n\r\nIt's probably a relic of tfds which didn't have the same way to access citations. "
] | 2020-05-18T07:24:22Z
| 2020-05-18T21:18:16Z
| null |
MEMBER
| null | null | null |
Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself.
Let's take GLUE as an example:
The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.
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MDExOlB1bGxSZXF1ZXN0NTI5MDkyOTc3
| 913
|
My new dataset PEC
|
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[
"How to resolve these failed checks?",
"Thanks for adding this one :) \r\n\r\nTo fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8\r\nTo fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.\r\nFor example : `encoding=\"utf-8\"`\r\nTo fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset\r\n\r\n",
"Could you also add a dataset card ? you can find a template here : https://github.com/huggingface/datasets/blob/master/templates/README.md\r\n\r\nThat would be awesome",
"> Thanks for adding this one :)\r\n> \r\n> To fix the check_code_quality, please run `make style` with the latest version of black, isort, flake8\r\n> To fix the test_no_encoding_on_file_open, make sure to specify the encoding each time you call `open()` on a text file.\r\n> For example : `encoding=\"utf-8\"`\r\n> To fix the test_load_dataset_pec , you must add the dummy_data.zip file. It is used to test the dataset script and make sure it runs fine. To add it, please refer to the steps in https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-add-a-dataset\r\n\r\nThank you for the detailed suggestion.\r\n\r\nI have added dummy_data but it still failed the DistributedDatasetTest check. My dataset has a central file (containing a python dict) that needs to be accessed by each data example. Is it because the central file cannot be distributed (which would lead to a partial dictionary)?\r\n\r\nSpecifically, the central file contains a dictionary of speakers with their attributes. Each data example is also associated with a speaker. As of now, I keep the central file and data files separately. If I remove the central file by appending the speaker attributes to each data example, then there would be lots of redundancy because there are lots of duplicate speakers in the data files.",
"The `DistributedDatasetTest` fail and the changes of this PR are not related, there was just a bug in the CI. You can ignore it",
"> Really cool thanks !\r\n> \r\n> Could you make the dummy files smaller ? For example by reducing the size of persona.txt ?\r\n> I also left a comment about the files concatenation. It would be cool to replace that with simple iterations through the different files.\r\n> \r\n> Then once this is done, you can add a dataset card using the template guide here : https://github.com/huggingface/datasets/blob/master/templates/README_guide.md\r\n> If some fields can't be filled, just leave `[N/A]`\r\n\r\nSmall change: if you don't have the information for a field, please leave `[More Information Needed]` rather than `[N/A]`\r\n\r\nThe full information can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#manually-tag-the-dataset-and-write-the-dataset-card)"
] | 2020-11-29T11:10:37Z
| 2020-12-01T10:41:53Z
| 2020-12-01T10:41:53Z
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A new dataset PEC published in EMNLP 2020.
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PR_kwDODunzps4zPahh
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Expose method and fix param
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A fix + expose a new method, following https://github.com/huggingface/datasets/pull/3670
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Question/problem with dataset labels
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[
"It seems that I get parsing errors for various fields in my data. For example now I get this:\r\n```\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 523, in <module>\r\n main()\r\n File \"../../../models/tr-4.3.2/run_puppets.py\", line 249, in main\r\n datasets = load_dataset(\"csv\", data_files=data_files)\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/load.py\", line 740, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py\", line 572, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py\", line 650, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py\", line 1028, in _prepare_split\r\n writer.write_table(table)\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/arrow_writer.py\", line 292, in write_table\r\n pa_table = pa_table.cast(self._schema)\r\n File \"pyarrow/table.pxi\", line 1311, in pyarrow.lib.Table.cast\r\n File \"pyarrow/table.pxi\", line 265, in pyarrow.lib.ChunkedArray.cast\r\n File \"/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/pyarrow/compute.py\", line 87, in cast\r\n return call_function(\"cast\", [arr], options)\r\n File \"pyarrow/_compute.pyx\", line 298, in pyarrow._compute.call_function\r\n File \"pyarrow/_compute.pyx\", line 192, in pyarrow._compute.Function.call\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 84, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Failed to parse string: https://www.netgalley.com/catalog/book/121872\r\n```",
"Not sure if this helps, this is how I load my files (as in the sample scripts on transformers):\r\n\r\n```\r\n if data_args.train_file.endswith(\".csv\"):\r\n # Loading a dataset from local csv files\r\n datasets = load_dataset(\"csv\", data_files=data_files)\r\n```",
"Since this worked out of the box in a few examples before, I wonder if it's some quoting issue or something else. ",
"Hi @ioana-blue,\r\nCan you share a sample from your .csv? A dummy where you get this error will also help.\r\n\r\nI tried this csv:\r\n```csv\r\nfeature,label\r\n1.2,not nurse\r\n1.3,nurse\r\n1.5,surgeon\r\n```\r\nand the following snippet:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nd = load_dataset(\"csv\",data_files=['test.csv'])\r\n\r\nprint(d)\r\nprint(d['train']['label'])\r\n```\r\nand this works perfectly fine for me:\r\n```sh\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['feature', 'label'],\r\n num_rows: 3\r\n })\r\n})\r\n['not nurse', 'nurse', 'surgeon']\r\n```\r\nI'm sure your csv is more complicated than this one. But it is hard to tell where the issue might be without looking at a sample.",
"I've had versions where it worked fain. For this dataset, I had all kind of parsing issues that I couldn't understand. What I ended up doing is strip all the columns that I didn't need and also make the label 0/1. \r\n\r\nI think one line that may have caused a problem was the csv version of this:\r\n\r\n```crawl-data/CC-MAIN-2017-47/segments/1510934806225.78/wet/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job. ^M ('Rose', '', 'Blakey') journalist F 38 journalist https://www.netgalley.com/catalog/book/121872 _ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```\r\n\r\nThe error I got in this case is this one: https://github.com/huggingface/datasets/issues/1989#issuecomment-790842771\r\n\r\nNote, this line was part of a much larger file and until this line I guess it was working fine. ",
"Hi @ioana-blue,\r\n\r\nWhat is the separator you're using for the csv? I see there are only two commas in the given line, but they don't seem like appropriate points. Also, is this a string part of one line, or an entire line? There should also be a label, right?",
"Sorry for the confusion, the sample above was from a tsv that was used to derive the csv. Let me construct the csv again (I had remove it). \r\n\r\nThis is the line in the csv - this is the whole line:\r\n```crawl-data/CC-MAIN-2017-47/segments/1510934806225.78/wet/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https://www.netgalley.com/catalog/book/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.```",
"Hi,\r\nJust in case you want to use tsv directly, you can use the separator argument while loading the dataset.\r\n```python\r\nd = load_dataset(\"csv\",data_files=['test.csv'],sep=\"\\t\")\r\n```\r\n\r\nAdditionally, I don't face the issues with the following csv (same as the one you provided):\r\n\r\n```sh\r\nlink1,text1,info1,info2,info3,info4,info5,link2,text2,text3\r\ncrawl-data/CC-MAIN-2017-47/segments/1510934806225.78/wet/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz,Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead,\"('Rose', '', 'Blakey')\",journalist,F,38,journalist,https://www.netgalley.com/catalog/book/121872,_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job., She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.\r\n```\r\nOutput after loading:\r\n```sh\r\n{'link1': 'crawl-data/CC-MAIN-2017-47/segments/1510934806225.78/wet/CC-MAIN-20171120203833-20171120223833-00571.warc.wet.gz', 'text1': 'Rose Blakey is an aspiring journalist. She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead', 'info1': \"('Rose', '', 'Blakey')\", 'info2': 'journalist', 'info3': 'F', 'info4': 38, 'info5': 'journalist', 'link2': 'https://www.netgalley.com/catalog/book/121872', 'text2': '_ is desperate to escape the from the small Australian town in which _ lives. Rejection after rejection mean _ is stuck in what _ sees as a dead-end waitressing job.', 'text3': ' She is desperate to escape the from the small Australian town in which she lives. Rejection after rejection mean she is stuck in what she sees as a dead-end waitressing job.'}\r\n```\r\nCan you check once if the tsv works for you directly using the separator argument? The conversion from tsv to csv could create issues, I'm only guessing though.",
"thanks for the tip. very strange :/ I'll check my datasets version as well. \r\n\r\nI will have more similar experiments soon so I'll let you know if I manage to get rid of this. ",
"No problem at all. I thought I'd be able to solve this but I'm unable to replicate the issue :/"
] | 2021-03-04T17:06:53Z
| 2023-07-24T14:39:33Z
| 2023-07-24T14:39:33Z
|
NONE
| null | null | null |
Hi, I'm using a dataset with two labels "nurse" and "not nurse". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are "nurse" and "surgeon".
This is the trace I get:
```
File "../../../models/tr-4.3.2/run_puppets.py", line 523, in <module>
main()
File "../../../models/tr-4.3.2/run_puppets.py", line 249, in main
datasets = load_dataset("csv", data_files=data_files)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 572, in download_and_prepare
self._download_and_prepare(
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 650, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 1028, in _prepare_split
writer.write_table(table)
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/arrow_writer.py", line 292, in write_table
pa_table = pa_table.cast(self._schema)
File "pyarrow/table.pxi", line 1311, in pyarrow.lib.Table.cast
File "pyarrow/table.pxi", line 265, in pyarrow.lib.ChunkedArray.cast
File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/pyarrow/compute.py", line 87, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 298, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 192, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Failed to parse string: not nurse
```
Any ideas how to fix this? For now, I'll probably make them numeric.
|
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Add Pandas as format type
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| 2020-04-27T18:07:50Z
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As detailed in the title ^^
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MDExOlB1bGxSZXF1ZXN0NjU4MDU2MjI3
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Add copyright info for wiki_lingua dataset
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"Build fails but this change should not be the reason...",
"rebased on master",
"rebased on master"
] | 2021-05-31T07:22:52Z
| 2021-06-04T10:22:33Z
| 2021-06-04T10:22:33Z
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PR_kwDODunzps41VIMJ
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|
Add support for metadata files to `imagefolder`
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"_The documentation is not available anymore as the PR was closed or merged._",
"Love it !\r\n\r\n+1 to using JSON Lines rather than CSV. I've also seen image datasets for which JSON Lines was used.\r\n\r\nA `file_name` column sounds good as well, and it means we could reuse the same name for audio. And ok to check the metadata file by default :)\r\n\r\nYou suggested to name the file infos.json - since we already have a datasets_infos.json file, maybe it would be nice to have a name for the metadata/annotations that doesn't contain \"info\" ? (e.g. metadata.json, annotations.json, labels.json)",
"@lhoestq I've addressed your comments and my TODOs. Additionally, I've updated `encode_nested_example`/`decode_nested_example` to support null values in place of a dictionary (if it's not top-level) since JSON Lines also supports this. ",
"@lhoestq Sure, feel free to add more tests if you have the time. ",
"I created a dedicated test file for `imagefolder`, moved some existing tests there from `test_packaged_modules.py`, and added an end-to-end test of `imagefolder` with metadata. I tested for train split only, and for two splits train and test.\r\n\r\nLet me know if the test looks ok to you. I'll add similar tests but with the other structures we support on tuesday",
"Thanks a lot for working on this! The test looks great :). ",
"Added a test for archives. Will also add a test when the metadata file is not named correctly, and see if we can raise an informative error"
] | 2022-03-30T17:47:51Z
| 2022-05-03T12:49:00Z
| 2022-05-03T12:42:16Z
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This PR adds support for metadata files to `imagefolder` to add an ability to specify image fields other than `image` and `label`, which are inferred from the directory structure in the loaded dataset.
To be parsed as an image metadata file, a file should be named `"info.csv"` and should have the following structure:
```
image_id,some_col1_name,some_col2_name
rel/path/to/image1.jpg,image1_col1_value,image1_col2_value
rel/path/to/image2.jpg,image2_col1_value,image2_col2_value
...
```
This is how the resolution works:
```
- path/to/imagefolder/directory
- info.csv
- 10.jpg # referenced as 10.jpg in "info.csv"
- Cat
- 0.jpg # referenced as Cat/0.jpg in "info.csv"
- 1.jpg # referenced as Cat/1.jpg in "info.csv"
- Dog
- 0.jpg # referenced as Dog/0.jpg in "info.csv"
- 1.jpg # referenced as Dog/1.jpg in "info.csv"
```
Open questions:
1. IMO it makes more sense to store image metadata as JSON Lines than CSV. CSV is sufficient for textual metadata but not the best for representing bounding boxes, for instance. Also, JSON Lines is more strict, which is good in this case (CSV supports various delimiters, the header line is optional, etc., so it's easier to enforce rules on JSON Lines that it's on CSV)
2. A better name for the `image_id` column, which contains image identifiers? Maybe `image_file` or `image_filename`?
3. WDYT about making `with_metadata=True` the default behavior if the loaded repo/directory contains an `info.csv` file?
An example repository: https://huggingface.co/datasets/mariosasko/PetImages. Can be loaded by installing `datasets` from the PR branch and running `load_dataset("mariosasko/PetImages", with_metadata=True)`.
cc: @abhishekkrthakur (this PR should address https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF)
TODOs:
- [x] Test
- [x] Metadata file nesting
```
- path/to/imagefolder/directory
- info.csv
- 10.jpg
- Cat
- info.csv # should have higher precedence in this directory than the top-level info.csv, but we choose the first "eligible" metadata file currently
- 0.jpg
- 1.jpg
```
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PR_kwDODunzps46bo2h
| 4,579
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Support streaming cfq dataset
|
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"_The documentation is not available anymore as the PR was closed or merged._",
"@lhoestq I've been refactoring a little the code:\r\n- Use less RAM by loading only the required samples: only if its index is in the splits file\r\n- Start yielding \"earlier\" in streaming mode: for each `split_idx`:\r\n - either yield from buffer\r\n - or iterate over samples and either yield or buffer the sample\r\n \r\n The speed gain obviously depends on how the indexes are sorted in the split file:\r\n - Best case: indices are [1, 2, 3]\r\n - Worst case (no speed gain): indices are [3, 1, 2] or [3, 2, 1]\r\n\r\nLet me know what you think.",
"I have to update the dummy data so that it aligns with the real data (inside the archive, the samples file `dataset.json` is the last member).",
"There is an issue when testing `test_load_dataset_cfq` with dummy data:\r\n- `MockDownloadManager.iter_archive` yields FIRST `'cfq/dataset.json'`\r\n- [`Streaming`]`DownloadManager.iter_archive` yields LAST `'cfq/dataset.json'` when using real data tar.gz archive\r\n\r\nNote that this issue arises only with dummy data: loading the real dataset works smoothly for all configurations: I recreated the `dataset_infos.json` file to check it (it generated the same file).",
"This PR should be merged first:\r\n- #4611",
"Impressive, thank you ! :o \r\n\r\nfeel free to merge master into this branch, now that the files order is respected. You can merge if the CI is green :)"
] | 2022-06-27T17:11:23Z
| 2022-07-04T19:35:01Z
| 2022-07-04T19:23:57Z
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Support streaming cfq dataset.
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New datasets
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Don't compute checksums if not necessary in `datasets-cli test`
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008550 / 0.011353 (-0.002803) | 0.004476 / 0.011008 (-0.006532) | 0.100902 / 0.038508 (0.062394) | 0.029684 / 0.023109 (0.006575) | 0.308081 / 0.275898 (0.032183) | 0.363435 / 0.323480 (0.039955) | 0.006987 / 0.007986 (-0.000999) | 0.003401 / 0.004328 (-0.000927) | 0.078218 / 0.004250 (0.073967) | 0.036657 / 0.037052 (-0.000395) | 0.319670 / 0.258489 (0.061181) | 0.349952 / 0.293841 (0.056111) | 0.033416 / 0.128546 (-0.095130) | 0.011511 / 0.075646 (-0.064135) | 0.323888 / 0.419271 (-0.095384) | 0.042429 / 0.043533 (-0.001104) | 0.307310 / 0.255139 (0.052171) | 0.329459 / 0.283200 (0.046259) | 0.085209 / 0.141683 (-0.056474) | 1.475893 / 1.452155 (0.023739) | 1.502782 / 1.492716 (0.010065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200137 / 0.018006 (0.182131) | 0.411269 / 0.000490 (0.410780) | 0.000415 / 0.000200 (0.000215) | 0.000061 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022626 / 0.037411 (-0.014785) | 0.097045 / 0.014526 (0.082519) | 0.102955 / 0.176557 (-0.073602) | 0.148411 / 0.737135 (-0.588725) | 0.107238 / 0.296338 (-0.189100) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421683 / 0.215209 (0.206474) | 4.203031 / 2.077655 (2.125376) | 1.908232 / 1.504120 (0.404112) | 1.698867 / 1.541195 (0.157672) | 1.743561 / 1.468490 (0.275071) | 0.693199 / 4.584777 (-3.891578) | 3.361022 / 3.745712 (-0.384690) | 2.989610 / 5.269862 (-2.280251) | 1.533036 / 4.565676 (-3.032641) | 0.082675 / 0.424275 (-0.341601) | 0.012419 / 0.007607 (0.004812) | 0.531543 / 0.226044 (0.305499) | 5.330595 / 2.268929 (3.061666) | 2.347519 / 55.444624 (-53.097105) | 1.975672 / 6.876477 (-4.900804) | 2.039541 / 2.142072 (-0.102532) | 0.810281 / 4.805227 (-3.994946) | 0.148917 / 6.500664 (-6.351747) | 0.065441 / 0.075469 (-0.010028) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266213 / 1.841788 (-0.575574) | 13.628106 / 8.074308 (5.553798) | 13.852191 / 10.191392 (3.660799) | 0.149004 / 0.680424 (-0.531420) | 0.028549 / 0.534201 (-0.505652) | 0.399824 / 0.579283 (-0.179459) | 0.401231 / 0.434364 (-0.033133) | 0.473251 / 0.540337 (-0.067086) | 0.561094 / 1.386936 (-0.825842) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006669 / 0.011353 (-0.004684) | 0.004477 / 0.011008 (-0.006532) | 0.077514 / 0.038508 (0.039006) | 0.027489 / 0.023109 (0.004380) | 0.341935 / 0.275898 (0.066037) | 0.377392 / 0.323480 (0.053912) | 0.004947 / 0.007986 (-0.003039) | 0.004600 / 0.004328 (0.000271) | 0.075938 / 0.004250 (0.071687) | 0.039586 / 0.037052 (0.002534) | 0.344966 / 0.258489 (0.086477) | 0.392181 / 0.293841 (0.098340) | 0.031838 / 0.128546 (-0.096708) | 0.011572 / 0.075646 (-0.064075) | 0.085811 / 0.419271 (-0.333461) | 0.042250 / 0.043533 (-0.001283) | 0.345605 / 0.255139 (0.090466) | 0.367814 / 0.283200 (0.084615) | 0.090683 / 0.141683 (-0.051000) | 1.483168 / 1.452155 (0.031014) | 1.559724 / 1.492716 (0.067008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235655 / 0.018006 (0.217649) | 0.399016 / 0.000490 (0.398527) | 0.003096 / 0.000200 (0.002896) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024454 / 0.037411 (-0.012957) | 0.100710 / 0.014526 (0.086185) | 0.107950 / 0.176557 (-0.068606) | 0.161560 / 0.737135 (-0.575576) | 0.111840 / 0.296338 (-0.184498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441362 / 0.215209 (0.226153) | 4.428105 / 2.077655 (2.350450) | 2.074501 / 1.504120 (0.570381) | 1.866672 / 1.541195 (0.325477) | 1.928266 / 1.468490 (0.459776) | 0.703561 / 4.584777 (-3.881216) | 3.396537 / 3.745712 (-0.349175) | 3.047369 / 5.269862 (-2.222492) | 1.595133 / 4.565676 (-2.970543) | 0.084028 / 0.424275 (-0.340247) | 0.012349 / 0.007607 (0.004741) | 0.539354 / 0.226044 (0.313310) | 5.401535 / 2.268929 (3.132606) | 2.499874 / 55.444624 (-52.944750) | 2.161406 / 6.876477 (-4.715071) | 2.197385 / 2.142072 (0.055313) | 0.810864 / 4.805227 (-3.994363) | 0.152277 / 6.500664 (-6.348387) | 0.067266 / 0.075469 (-0.008203) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280900 / 1.841788 (-0.560887) | 13.815731 / 8.074308 (5.741423) | 13.007438 / 10.191392 (2.816046) | 0.129711 / 0.680424 (-0.550713) | 0.016852 / 0.534201 (-0.517349) | 0.380775 / 0.579283 (-0.198508) | 0.384143 / 0.434364 (-0.050221) | 0.459954 / 0.540337 (-0.080383) | 0.549335 / 1.386936 (-0.837601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8805d67bd81ce48f481d5c1e56b84e6ebcaa2b2b \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009570 / 0.011353 (-0.001783) | 0.005219 / 0.011008 (-0.005789) | 0.098472 / 0.038508 (0.059964) | 0.035429 / 0.023109 (0.012320) | 0.303086 / 0.275898 (0.027188) | 0.365926 / 0.323480 (0.042446) | 0.008797 / 0.007986 (0.000811) | 0.004220 / 0.004328 (-0.000108) | 0.076670 / 0.004250 (0.072419) | 0.045596 / 0.037052 (0.008543) | 0.309476 / 0.258489 (0.050987) | 0.343958 / 0.293841 (0.050117) | 0.038741 / 0.128546 (-0.089805) | 0.011990 / 0.075646 (-0.063657) | 0.332326 / 0.419271 (-0.086945) | 0.048897 / 0.043533 (0.005364) | 0.296002 / 0.255139 (0.040863) | 0.322048 / 0.283200 (0.038849) | 0.104403 / 0.141683 (-0.037280) | 1.461777 / 1.452155 (0.009622) | 1.516362 / 1.492716 (0.023645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201565 / 0.018006 (0.183559) | 0.435781 / 0.000490 (0.435291) | 0.004215 / 0.000200 (0.004015) | 0.000282 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027272 / 0.037411 (-0.010139) | 0.106157 / 0.014526 (0.091631) | 0.116948 / 0.176557 (-0.059609) | 0.160404 / 0.737135 (-0.576731) | 0.122518 / 0.296338 (-0.173820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397721 / 0.215209 (0.182512) | 3.966433 / 2.077655 (1.888778) | 1.755410 / 1.504120 (0.251290) | 1.566480 / 1.541195 (0.025285) | 1.623684 / 1.468490 (0.155194) | 0.696820 / 4.584777 (-3.887957) | 3.750437 / 3.745712 (0.004725) | 2.105875 / 5.269862 (-3.163986) | 1.442026 / 4.565676 (-3.123650) | 0.085026 / 0.424275 (-0.339249) | 0.012239 / 0.007607 (0.004632) | 0.502613 / 0.226044 (0.276569) | 5.049016 / 2.268929 (2.780087) | 2.314499 / 55.444624 (-53.130126) | 1.967943 / 6.876477 (-4.908534) | 2.033507 / 2.142072 (-0.108565) | 0.861908 / 4.805227 (-3.943319) | 0.167784 / 6.500664 (-6.332880) | 0.063022 / 0.075469 (-0.012447) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210434 / 1.841788 (-0.631353) | 14.979319 / 8.074308 (6.905011) | 14.095263 / 10.191392 (3.903871) | 0.174203 / 0.680424 (-0.506221) | 0.028547 / 0.534201 (-0.505654) | 0.442509 / 0.579283 (-0.136774) | 0.445811 / 0.434364 (0.011447) | 0.531313 / 0.540337 (-0.009024) | 0.636541 / 1.386936 (-0.750395) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007341 / 0.011353 (-0.004012) | 0.005197 / 0.011008 (-0.005811) | 0.075413 / 0.038508 (0.036905) | 0.033261 / 0.023109 (0.010152) | 0.339596 / 0.275898 (0.063698) | 0.376051 / 0.323480 (0.052571) | 0.005827 / 0.007986 (-0.002159) | 0.005473 / 0.004328 (0.001144) | 0.074851 / 0.004250 (0.070600) | 0.049059 / 0.037052 (0.012007) | 0.357182 / 0.258489 (0.098693) | 0.384589 / 0.293841 (0.090748) | 0.037122 / 0.128546 (-0.091424) | 0.012298 / 0.075646 (-0.063348) | 0.088191 / 0.419271 (-0.331081) | 0.052002 / 0.043533 (0.008469) | 0.343216 / 0.255139 (0.088077) | 0.364534 / 0.283200 (0.081334) | 0.105462 / 0.141683 (-0.036221) | 1.486717 / 1.452155 (0.034562) | 1.584725 / 1.492716 (0.092009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199210 / 0.018006 (0.181203) | 0.439069 / 0.000490 (0.438580) | 0.000436 / 0.000200 (0.000236) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029931 / 0.037411 (-0.007480) | 0.109564 / 0.014526 (0.095038) | 0.122284 / 0.176557 (-0.054273) | 0.170819 / 0.737135 (-0.566317) | 0.125886 / 0.296338 (-0.170452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422724 / 0.215209 (0.207515) | 4.210304 / 2.077655 (2.132650) | 2.001481 / 1.504120 (0.497361) | 1.810818 / 1.541195 (0.269623) | 1.901367 / 1.468490 (0.432877) | 0.686004 / 4.584777 (-3.898773) | 3.768850 / 3.745712 (0.023138) | 2.079501 / 5.269862 (-3.190360) | 1.326970 / 4.565676 (-3.238706) | 0.085991 / 0.424275 (-0.338284) | 0.012298 / 0.007607 (0.004690) | 0.526878 / 0.226044 (0.300833) | 5.267241 / 2.268929 (2.998312) | 2.451781 / 55.444624 (-52.992843) | 2.109143 / 6.876477 (-4.767333) | 2.185426 / 2.142072 (0.043353) | 0.830165 / 4.805227 (-3.975063) | 0.166167 / 6.500664 (-6.334497) | 0.064077 / 0.075469 (-0.011392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270430 / 1.841788 (-0.571358) | 14.844852 / 8.074308 (6.770544) | 13.196672 / 10.191392 (3.005280) | 0.162853 / 0.680424 (-0.517571) | 0.017727 / 0.534201 (-0.516474) | 0.424803 / 0.579283 (-0.154480) | 0.439970 / 0.434364 (0.005606) | 0.530691 / 0.540337 (-0.009647) | 0.630474 / 1.386936 (-0.756462) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24fb01b720ef4203d4ae6225f43cba912b1f6d55 \"CML watermark\")\n"
] | 2023-03-02T16:42:39Z
| 2023-03-03T15:45:32Z
| 2023-03-03T15:38:28Z
|
MEMBER
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we only need them if there exists a `dataset_infos.json`
|
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| 1,804,358,679
|
I_kwDODunzps5rjFQX
| 6,032
|
DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info
|
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"`HfApi` comes from the `huggingface_hub` package. You can use [this](https://huggingface.co/docs/huggingface_hub/v0.16.3/en/package_reference/utilities#huggingface_hub.configure_http_backend) utility to change the `huggingface_hub`'s `Session` proxies (see the example).\r\n\r\nWe plan to implement https://github.com/huggingface/datasets/issues/5080 and make this behavior more consistent eventually.",
"> this\r\n\r\nThanks. I will try `huggingface_hub.configure_http_backend` to change session's config.",
"@mariosasko are you saying if I do the following:\r\n\r\n```\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = {\r\n \"https\": \"127.0.0.1:8887\",\r\n \"http\": \"127.0.0.1:8887\",\r\n }\r\n session.verify = \"/etc/ssl/certs/ca-certificates.crt\"\r\n return session\r\n\r\n# Set it as the default session factory\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\n\r\nwhich works nicely with transformer library:\r\n\r\n```\r\ndef download_gpt_2_model():\r\n tokenizer = GPT2Tokenizer.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n text = \"Replace me by any text you'd like.\"\r\n encoded_input = tokenizer(text, return_tensors=\"pt\")\r\n print(encoded_input)\r\n\r\n model = GPT2Model.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n output = model(**encoded_input)\r\n```\r\n\r\nshould work for datasets library as well ?\r\n\r\nIn my case if I just do:\r\n\r\n```\r\ndef download_sts12_sts_dataset():\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n\r\n```\r\nI am getting:\r\n`ConnectionError: Couldn't reach https://huggingface.co/datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f429e87a3a0>, 'Connection to huggingface.co timed out. (connect timeout=100)'))\")))`\r\n\r\nwhich is typical when the proxy server is not defined. Looks like what is set in configure_http_backend(backend_factory=backend_factory) is ignore.\r\n\r\nIf I use env variable instead, it is working \r\n```\r\ndef download_sts12_sts_dataset():\r\n\r\n os.environ[\"https_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"http_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"REQUESTS_CA_BUNDLE\"] = \"/etc/ssl/certs/ca-certificates.crt\"\r\n\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n```\r\n\r\nShould I add something ?\r\n\r\nI am using `huggingface_hub 0.15.1`, `datasets 2.13.0`, `transformers 4.30.2`",
"`huggingface_hub.configure_http_backend` works for `transformers` because they only use the `huggingface_hub` lib for downloads. Our download logic is a bit more complex (e.g., we also support downloading non-Hub files), so we are not aligned with them yet. In the meantime, it's best to use the env vars.",
"@mariosasko I fully understand that the logic for dataset is different. I see 2 issues with the current implementation of the env variables:\r\n\r\n- having the same https_proxy/http_prox/no_proxy env variables for all tools is not good in some case. For example I have 2 differents proxy server. In 2019 we had discussion with the Tensorflow teams and they recommended to do the following: TFDS_HTTP_PROXY, TFDS_HTTPS_PROXY ...\r\n- with recent version of requests, it is not possible to deactivate TLS interception (verify=false) by using env variable. This is useful to debug things and in some case TLS is not working and you need to ignore verifying the SSL certificate (probably not recommended) \r\n\r\nOne of the best way is to able to pass our requests.Session() directly\r\n```\r\nimport openai\r\nsession = requests.Session()\r\nsession.cert = CERT\r\nsession.verify = False\r\nopenai.requestssession = session\r\n```\r\n\r\nMy 2 cents in this discussion"
] | 2023-07-14T07:22:55Z
| 2023-09-11T13:50:41Z
| null |
NONE
| null | null | null |
### Describe the bug
```python
download_config = DownloadConfig(proxies={'https': '<my proxy>'})
builder = load_dataset_builder(..., download_config=download_config)
```
But, when getting the dataset_info from HfApi, the http requests not using the proxies.
### Steps to reproduce the bug
1. Setup proxies in DownloadConfig.
2. Call `load_dataset_build` with download_config.
3. Inspect the call stack in HfApi.dataset_info.
### Expected behavior
DownloadConfig.proxies works for getting dataset_info.
### Environment info
https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00
Python 3.11.4
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|
Add accuracy, precision, recall and F1 metrics
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[] | 2020-11-10T13:50:35Z
| 2020-11-11T19:23:48Z
| 2020-11-11T19:23:43Z
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This PR adds several single metrics, namely:
- Accuracy
- Precision
- Recall
- F1
They all uses under the hood the sklearn metrics of the same name. They allow different useful features when training a multilabel/multiclass model:
- have a macro/micro/per label/weighted/binary/per sample score
- score only the selected labels (usually what we call the positive labels) and ignore the negative ones. For example in case of a Named Entity Recognition task, positive labels are (`PERSON`, `LOCATION` or `ORGANIZATION`) and the negative one is `O`.
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add reuters21578 dataset
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Reopen a PR this the merge.
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Add concurrent loading of shards to datasets.load_from_disk
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[
"If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This parameter is kinda legacy.\r\n\r\nHave you been able to run the benchmark on a fresh node ? The speed up doesn't seem that big in your first report",
"I got some fresh nodes with the 32 threads I'm loading the dataset with around 315 seconds (without any preloading). Sequentially, it used to take around 1865 seconds. \r\nOk I'll roll back the changes and switch to `tqdm.contrib.concurrent.thread_map` without the `num_proc` parameter. ",
"I switched to `tqdm.contrib.concurrent.thread_map` the code looks much simpler now.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6464). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | 2023-12-01T13:13:53Z
| 2023-12-07T12:47:02Z
| null |
NONE
| null | 0
|
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In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads.
- Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252).
- I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config.
- I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`.
### Tests on luster file system (on a shared partial node):
Loading 1231 shards of ~2GBs.
The files were pre-loaded in another process before the script runs (couldn't get a fresh node).
```python
import logging
from time import perf_counter
import datasets
logger = datasets.logging.get_logger(__name__)
datasets.logging.set_verbosity_info()
logging.basicConfig(level=logging.DEBUG, format="%(message)s")
class catchtime:
# context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time
def __init__(self, debug_print="Time", logger=logger):
self.debug_print = debug_print
self.logger = logger
def __enter__(self):
self.start = perf_counter()
return self
def __exit__(self, type, value, traceback):
self.time = perf_counter() - self.start
readout = f"{self.debug_print}: {self.time:.3f} seconds"
self.logger.info(readout)
dataset_path=""
# warmup
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=16
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=32
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=32)
# num_proc=1
with catchtime("Loading in conseq", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=1)
```
#### Run 1
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s]
Loading in parallel: 88.690 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s]
Loading in parallel: 109.339 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s]
Loading in parallel: 66.931 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s]
Loading in conseq: 309.792 seconds
```
#### Run 2
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s]
Loading in parallel: 98.831 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s]
Loading in parallel: 121.669 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s]
Loading in parallel: 68.192 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s]
Loading in conseq: 319.759 seconds
```
#### Run 3
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s]
Loading in parallel: 96.936 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s]
Loading in parallel: 120.761 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s]
Loading in parallel: 68.666 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s]
Loading in conseq: 335.777 seconds
```
fix #2252
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Trouble loading image folder with additional features - metadata file ignored
|
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[
"I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)"
] | 2023-11-22T11:01:35Z
| 2023-11-24T17:13:03Z
| 2023-11-24T17:13:03Z
|
NONE
| null | null | null |
### Describe the bug
Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions.
When loading a local image folder with captions using `datasets==2.13.0`
```
from datasets import load_dataset
data = load_dataset(<image_folder_path>)
data.column_names
```
yields
`{'train': ['image', 'prompt']}`
but when using `datasets==2.15.0`
yeilds
`{'train': ['image']}`
Putting the images and `metadata.jsonl` file into a nested `train` folder **or** loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and
yields
`{'train': ['image', 'prompt']}`
### Steps to reproduce the bug
1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt"
2. run:
```
from datasets import load_dataset
data = load_dataset("<image_folder_path>")
data.column_names
```
### Expected behavior
`{'train': ['image', 'prompt']}`
### Environment info
- `datasets` version: 2.15.0
- Platform: Linux-5.15.120+-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.19.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.6.0
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I_kwDODunzps5zdQtm
| 6,292
|
how to load the image of dtype float32 or float64
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"Hi! Can you provide a code that reproduces the issue?\r\n\r\nAlso, which version of `datasets` are you using? You can check this by running `python -c \"import datasets; print(datasets.__version__)\"` inside the env. We added support for \"float images\" in `datasets 2.9`."
] | 2023-10-11T07:27:16Z
| 2023-10-11T13:19:11Z
| null |
NONE
| null | null | null |
_FEATURES = datasets.Features(
{
"image": datasets.Image(),
"text": datasets.Value("string"),
},
)
The datasets builder seems only support the unit8 data. How to load the float dtype data?
|
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MDExOlB1bGxSZXF1ZXN0NDkwNzk4MDc0
| 658
|
Fix squad metric's Features
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"Closing this one in favor of #670 \r\n\r\nThanks again for reporting the issue and proposing this fix !\r\nLet me know if you have other remarks"
] | 2020-09-22T09:09:52Z
| 2020-09-29T15:58:30Z
| 2020-09-29T15:58:30Z
|
NONE
| null | 0
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Resolves issue [657](https://github.com/huggingface/datasets/issues/657).
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Synchronize table metadata with features
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[
"See PR #2274 "
] | 2021-04-27T15:55:13Z
| 2022-06-01T17:13:21Z
| 2022-06-01T17:13:21Z
|
MEMBER
| null | null | null |
**Is your feature request related to a problem? Please describe.**
As pointed out in this [comment](https://github.com/huggingface/datasets/pull/2145#discussion_r621326767):
> Metadata stored in the schema is just a redundant information regarding the feature types.
It is used when calling Dataset.from_file to know which feature types to use.
These metadata are stored in the schema of the pyarrow table by using `update_metadata_with_features`.
However this something that's almost never tested properly.
**Describe the solution you'd like**
We should find a way to always make sure that the metadata (in `self.data.schema.metadata`) are synced with the actual feature types (in `self.info.features`).
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Adding ro sts dataset
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[
"@lhoestq thank you very much for the quick review and useful comments! \r\n\r\nI have tried to address them all, and a few comments that you left for ro_sts I have applied to the ro_sts_parallel as well (in read-me: fixed source_datasets, links to homepage, repository, leaderboard, thanks to me message, in ro_sts_parallel.py changed to camel case as well). In the ro_sts_parallel I have changed the order on the languages, also in the example, as you said order doesn't matter, but just to have them listed in the readme in the same order.\r\n\r\nI have commented above on why we would like to keep them as separate datasets, hope it makes sense.\r\n\r\nIf there is anything else I should change please let me know.\r\n\r\nThanks again!",
"@lhoestq I tried to adjust the ro_sts_parallel, locally when I run the tests they are passing, but somewhere it has the old name of rosts-parallel-ro-en which I am trying to change to ro_sts_parallel. I don't think I have left anything related to rosts-parallel-ro-en, but when the dataset_infos.json is regenerated it adds it. Could you please help me out, how can I fix this? Thanks in advance!",
"Great, thanks for all your help! "
] | 2021-03-03T10:08:53Z
| 2021-03-05T10:00:14Z
| 2021-03-05T09:33:55Z
|
CONTRIBUTOR
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Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset
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Fixed check for pyarrow
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[
"Great, thanks!"
] | 2020-07-25T00:16:53Z
| 2020-07-25T06:36:34Z
| 2020-07-25T06:36:34Z
|
CONTRIBUTOR
| null | 0
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Fix check for pyarrow in __init__.py. Previously would raise an error for pyarrow >= 1.0.0
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Best practices for training tokenizers with nlp
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[
"Docs that explain how to train a tokenizer with `datasets` are available here: https://huggingface.co/docs/tokenizers/training_from_memory#using-the-datasets-library"
] | 2020-08-29T12:06:49Z
| 2022-10-04T17:28:04Z
| 2022-10-04T17:28:04Z
|
NONE
| null | null | null |
Hi, thank you for developing this library.
What do you think are the best practices for training tokenizers using `nlp`? In the document and examples, I could only find pre-trained tokenizers used.
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|
Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist
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[
"I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now",
"Closing this one.\r\nFeel free to re-open if you still have issues"
] | 2020-11-02T12:36:35Z
| 2020-11-10T14:05:02Z
| 2020-11-10T14:05:02Z
|
NONE
| null | null | null |
I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error.
```sh
git clone https://github.com/huggingface/datasets
cd datasets
virtualenv venv -p python3 --system-site-packages
source venv/bin/activate
pip install -e ".[dev]"
```
Python 3.7.7
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Doc metrics
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[] | 2020-09-07T10:15:24Z
| 2020-09-10T13:06:11Z
| 2020-09-10T13:06:10Z
|
MEMBER
| null | 0
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Adding documentation on metrics loading/using/sharing
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I_kwDODunzps5AkMto
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JSONDecodeError with HuggingFace dataset viewer
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[
"Hi ! I think the issue comes from the dataset_infos.json file: it has the \"flat\" field twice.\r\n\r\nCan you try deleting this file and regenerating it please ?",
"Thanks! That fixed that, but now I am getting:\r\nServer Error\r\nStatus code: 400\r\nException: KeyError\r\nMessage: 'feature'\r\n\r\nI checked the dataset_infos.json and pubmed_neg.py script, I don't use 'feature' anywhere as a key. Is the dataset viewer expecting that I do?",
"It seems that the `feature` key is missing from some feature type definition in your dataset_infos.json:\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n\t\t\t}\r\n```\r\nThey should be\r\n```json\r\n\t\t\t\"tokens\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\"\r\n \"feature\": {\"dtype\": \"string\", \"id\": null, \"_type\": \"Value\"}\r\n\t\t\t},\r\n\t\t\t\"tags\": {\r\n\t\t\t\t\"dtype\": \"list\",\r\n\t\t\t\t\"id\": null,\r\n\t\t\t\t\"_type\": \"Sequence\",\r\n \"feature\": {\"num_classes\": 5, \"names\": [\"-\", \"S\", \"H\", \"N\", \"C\"], \"names_file\": null, \"id\": null, \"_type\": \"ClassLabel\"}\r\n\t\t\t}\r\n```\r\n\r\nNote that you can generate the dataset_infos.json automatically to avoid mistakes:\r\n```bash\r\ndatasets-cli test ./path/to/dataset --save_infos\r\n```"
] | 2021-12-17T12:52:41Z
| 2022-02-24T09:10:26Z
| 2022-02-24T09:10:26Z
|
NONE
| null | null | null |
## Dataset viewer issue for 'pubmed_neg'
**Link:** https://huggingface.co/datasets/IGESML/pubmed_neg
I am getting the error:
Status code: 400
Exception: JSONDecodeError
Message: Expecting property name enclosed in double quotes: line 61 column 2 (char 1202)
I have checked all files - I am not using single quotes anywhere. Not sure what is causing this issue.
Am I the one who added this dataset ? Yes
|
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I_kwDODunzps5A_0sf
| 3,503
|
Batched in filter throws error
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[] | 2021-12-29T12:01:04Z
| 2022-01-04T10:24:27Z
| 2022-01-04T10:24:27Z
|
CONTRIBUTOR
| null | null | null |
I hope this is really a bug, I could not find it among the open issues
## Describe the bug
using `batched=False` in DataSet.filter throws error
```python
TypeError: filter() got an unexpected keyword argument 'batched'
```
but in the docs it is lister as an argument.
## Steps to reproduce the bug
```python
task = "mnli"
max_length = 128
tokenizer = AutoTokenizer.from_pretrained("./pretrained_models/pretrained_models_drozd/sl250.m.gsic.titech.ac.jp:8000/21.11.17_06.30.32_roberta-base_a0057/checkpoints/smpl_400M/hf/")
dataset = load_dataset("glue", task)
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mnli-mm": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
##### tokenization_parameters
sentence1_key, sentence2_key = task_to_keys[task]
def preprocess_function(examples, max_length):
if sentence2_key is None:
return tokenizer(
examples[sentence1_key], truncation=True, max_length=max_length
)
return tokenizer(
examples[sentence1_key],
examples[sentence2_key],
truncation=False,
padding="max_length",
max_length=max_length,
)
encoded_dataset = dataset.map(
lambda x: preprocess_function(x, max_length=max_length), batched=False
)
encoded_dataset.filter(lambda x: len(x['input_ids']) <= max_length, batched=False)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.16.1, 1.17.0
- Platform: ubuntu
- Python version: 3.8.12
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|
Update ted_talks_iwslt license to include ND
|
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[
"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-08-12T16:14:52Z
| 2022-08-14T11:15:22Z
| 2022-08-14T11:00:22Z
|
CONTRIBUTOR
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|
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Excerpt from the paper's abstract: "Aside from its cultural and social relevance, this content, which is published under the Creative Commons BY-NC-ND license, also represents a precious language resource for the machine translation research community"
|
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