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**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/liweili/c4_200m)*
*Server Error*
```
Status code: 404
Exception: Status404Error
Message: Not found. Maybe the cache is missing, or maybe the ressource does not exist.
```
Am I the one who added this dataset ? Yes
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} | **Fix #3257**
Replaced _.format()_ and _%_ by f-strings in the following modules :
- [x] **tests**
- [x] **metrics**
- [x] **benchmarks**
- [x] **utils**
- [x] **templates**
Will follow in the next PR the modules left :
- [ ] **src**
Module **datasets** will not be edited as asked by @mariosasko
PS : black and isort applied to files
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] | null | 2 | "2021-11-13T12:04:17Z" | "2021-11-16T11:23:53Z" | "2021-11-16T11:21:58Z" | NONE | null | null | null | ## Describe the bug
Checksum failed when downloads kilt_tasks_wow. See error output for details.
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('kilt_tasks','wow')
```
## Expected results
Download successful
## Actual results
```
Downloading and preparing dataset kilt_tasks/wow (download: 72.07 MiB, generated: 61.82 MiB, post-processed: Unknown size, total: 133.89 MiB) to /root/.cache/huggingface/datasets/kilt_tasks/wow/1.0.0/57dc8b2431e76637e0c6ef79689ca4af61ed3a330e2e0cd62c8971465a35db3a...
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 5121.25it/s]
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 3/3 [00:00<00:00, 1527.42it/s]
Traceback (most recent call last):
File "kilt_wow.py", line 30, in <module>
main()
File "kilt_wow.py", line 27, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "kilt_wow.py", line 21, in load_dataset
return datasets.load_dataset('kilt_tasks','wow')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 679, in _download_and_prepare
verify_checksums(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['http://dl.fbaipublicfiles.com/KILT/wow-train-kilt.jsonl', 'http://dl.fbaipublicfiles.com/KILT/wow-dev-kilt.jsonl']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
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- WikiAuto Manual
The original manual datasets with the following downloading URL in this [repository](https://github.com/chaojiang06/wiki-auto) was [deleted](https://github.com/chaojiang06/wiki-auto/commit/0af9b066f2b4e02726fb8a9be49283c0ad25367f) by the author.
```
https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
The downloading URL for jeopardy may move from
```
http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
to
```
https://drive.google.com/file/d/0BwT5wj_P7BKXb2hfM3d2RHU1ckE/view?resourcekey=0-1abK4cJq-mqxFoSg86ieIg
```
- definite_pronoun_resolution
The following downloading URL for definite_pronoun_resolution cannot be reached for some reasons.
```
http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Steps to reproduce the bug
```python
import datasets
datasets.load_datasets('wiki_auto','manual')
datasets.load_datasets('jeopardy')
datasets.load_datasets('definite_pronoun_resolution')
```
## Expected results
Download successfully
## Actual results
- WikiAuto Manual
```
Downloading and preparing dataset wiki_auto/manual (download: 151.65 MiB, generated: 155.97 MiB, post-processed: Unknown size, total: 307.61 MiB) to /root/.cache/huggingface/datasets/wiki_auto/manual/1.0.0/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8...
0%| | 0/3 [00:00<?, ?it/s]Traceback (most recent call last):
File "wiki_auto.py", line 43, in <module>
main()
File "wiki_auto.py", line 40, in main
train, dev, test = dataset.generate_k_shot_data(k=16, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 24, in generate_k_shot_data
dataset = self.load_dataset()
File "wiki_auto.py", line 34, in load_dataset
return datasets.load_dataset('wiki_auto', 'manual')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wiki_auto/5ffdd9fc62422d29bd02675fb9606f77c1251ee17169ac10b143ce07ef2f4db8/wiki_auto.py", line 193, in _split_generators
data_dir = dl_manager.download_and_extract(my_urls)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 592, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/train.tsv
```
- jeopardy
```
Using custom data configuration default
Downloading and preparing dataset jeopardy/default (download: 12.13 MiB, generated: 34.46 MiB, post-processed: Unknown size, total: 46.59 MiB) to /root/.cache/huggingface/datasets/jeopardy/default/0.1.0/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810...
Traceback (most recent call last):
File "jeopardy.py", line 45, in <module>
main()
File "jeopardy.py", line 42, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "jeopardy.py", line 36, in load_dataset
return datasets.load_dataset("jeopardy")
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/jeopardy/25ee3e4a73755e637b8810f6493fd36e4523dea3ca8a540529d0a6e24c7f9810/jeopardy.py", line 72, in _split_generators
filepath = dl_manager.download_and_extract(_DATA_URL)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://skeeto.s3.amazonaws.com/share/JEOPARDY_QUESTIONS1.json.gz
```
- definite_pronoun_resolution
```
Downloading and preparing dataset definite_pronoun_resolution/plain_text (download: 222.12 KiB, generated: 239.12 KiB, post-processed: Unknown size, total: 461.24 KiB) to /root/.cache/huggingface/datasets/definite_pronoun_resolution/plain_text/1.0.0/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff...
0%| | 0/2 [00:00<?, ?it/s]Traceback (most recent call last):
File "definite_pronoun_resolution.py", line 37, in <module>
main()
File "definite_pronoun_resolution.py", line 34, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/workspace/projects/CrossFit/tasks/fewshot_gym_dataset.py", line 79, in generate_k_shot_data
dataset = self.load_dataset()
File "definite_pronoun_resolution.py", line 28, in load_dataset
return datasets.load_dataset('definite_pronoun_resolution')
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/definite_pronoun_resolution/35a1dfd4fba4afb8ba226cbbb65ac7cef0dd3cf9302d8f803740f05d2f16ceff/definite_pronoun_resolution.py", line 76, in _split_generators
files = dl_manager.download_and_extract(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 216, in map_nested
mapped = [
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 217, in <listcomp>
_single_map_nested((function, obj, types, None, True))
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 152, in _single_map_nested
return function(data_struct)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "/opt/conda/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.hlt.utdallas.edu/~vince/data/emnlp12/train.c.txt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-4.15.0-161-generic-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyArrow version: 4.0.1
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
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] | closed | false | null | [] | null | 0 | "2021-11-13T05:46:06Z" | "2021-11-13T13:31:47Z" | "2021-11-13T13:31:47Z" | 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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**Link:** [link](https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows)
I tried adding both a script (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/Scifi_TV_Shows.py) and some dummy examples (https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/tree/main/dummy), but the viewer still has a 404 error ("Not found. Maybe the cache is missing, or maybe the ressource does not exist."). I'm not sure what to try next. Thanks in advance!
Am I the one who added this dataset? Yes
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* allows 403 status code in HEAD requests to S3 buckets to fix the connection error in the Scielo dataset (instead of `url`, uses `response.url` to check the URL of the final endpoint)
* makes the Scielo dataset streamable
Fixes #3255. | {
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https://api.github.com/repos/huggingface/datasets/issues/3259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3259/comments | https://api.github.com/repos/huggingface/datasets/issues/3259/events | https://github.com/huggingface/datasets/pull/3259 | 1,052,189,775 | PR_kwDODunzps4ud5W3 | 3,259 | Updating details of IRC disentanglement data | {
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} | I was pleasantly surprised to find that someone had already added my dataset to the huggingface library, but some details were missing or incorrect. This PR fixes the documentation. | {
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] | open | false | null | [] | null | 0 | "2021-11-12T17:14:59Z" | "2021-11-12T17:14:59Z" | null | MEMBER | null | null | null | `load_from_disk` downloads the dataset to a temporary directory without checking if the dataset has already been downloaded once.
It would be nice to have some sort of caching for datasets downloaded this way. This could leverage the fingerprint of the dataset that was saved in the `state.json` file. | {
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> **NOTE FOR CONTRIBUTORS**: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under `datasets/*`. | {
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} | I have replaced only a single assert in text_classification.py along with a unit test to verify an exception is raised based on https://github.com/huggingface/datasets/issues/3171 .
I would like to first understand the code contribution workflow. So keeping the change to a single file rather than making too many changes. Once this gets approved, I will look into the rest.
Thanks. | {
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] | null | 0 | "2021-11-12T09:57:14Z" | "2021-11-16T17:55:22Z" | "2021-11-16T17:55:22Z" | NONE | null | null | null | ## Describe the bug
I get `ConnectionError` when I am trying to load the SciELO dataset.
When I try the URL with `requests` I get:
```
>>> requests.head("https://ndownloader.figstatic.com/files/14019287")
<Response [302]>
```
And as far as I understand redirections in `datasets` are not supported for downloads.
https://github.com/huggingface/datasets/blob/807341d0db0728073ab605c812c67f927d148f38/datasets/scielo/scielo.py#L45
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("scielo", "en-es")
```
## Expected results
Download SciELO dataset and load Dataset object
## Actual results
```
Downloading and preparing dataset scielo/en-es (download: 21.90 MiB, generated: 68.45 MiB, post-processed: Unknown size, total: 90.35 MiB) to /Users/test/.cache/huggingface/datasets/scielo/en-es/1.0.0/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e...
Traceback (most recent call last):
File "scielo.py", line 3, in <module>
dataset = load_dataset("scielo", "en-es")
File "../lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "../lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "../lib/python3.8/site-packages/datasets/builder.py", line 675, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/test/.cache/huggingface/modules/datasets_modules/datasets/scielo/7e05d55a20257efeb9925ff5de65bd4884fc6ddb6d765f1ea3e8860449d90e0e/scielo.py", line 77, in _split_generators
data_dir = dl_manager.download_and_extract(_URLS[self.config.name])
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 284, in download_and_extract
return self.extract(self.download(url_or_urls))
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 196, in download
downloaded_path_or_paths = map_nested(
File "../lib/python3.8/site-packages/datasets/utils/py_utils.py", line 206, in map_nested
return function(data_struct)
File "../lib/python3.8/site-packages/datasets/utils/download_manager.py", line 217, in _download
return cached_path(url_or_filename, download_config=download_config)
File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 295, in cached_path
output_path = get_from_cache(
File "../lib/python3.8/site-packages/datasets/utils/file_utils.py", line 594, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://ndownloader.figstatic.com/files/14019287
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.12
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3253 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3253/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3253/comments | https://api.github.com/repos/huggingface/datasets/issues/3253/events | https://github.com/huggingface/datasets/issues/3253 | 1,051,308,972 | I_kwDODunzps4-qbOs | 3,253 | `GeneratorBasedBuilder` does not support `None` values | {
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] | closed | false | null | [] | null | 1 | "2021-11-11T19:51:21Z" | "2021-12-09T14:26:58Z" | "2021-12-09T14:26:58Z" | NONE | null | null | null | ## Describe the bug
`GeneratorBasedBuilder` does not support `None` values.
## Steps to reproduce the bug
See [this repository](https://github.com/pavel-lexyr/huggingface-datasets-bug-reproduction) for minimal reproduction.
## Expected results
Dataset is initialized with a `None` value in the `value` column.
## Actual results
```
Traceback (most recent call last):
File "main.py", line 3, in <module>
datasets.load_dataset("./bad-data")
File ".../datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File ".../datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File ".../datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File ".../datasets/builder.py", line 1103, in _prepare_split
example = self.info.features.encode_example(record)
File ".../datasets/features/features.py", line 1033, in encode_example
return encode_nested_example(self, example)
File ".../datasets/features/features.py", line 808, in encode_nested_example
return {
File ".../datasets/features/features.py", line 809, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File ".../datasets/features/features.py", line 855, in encode_nested_example
return schema.encode_example(obj)
File ".../datasets/features/features.py", line 299, in encode_example
return float(value)
TypeError: float() argument must be a string or a number, not 'NoneType'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.4.0-81-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 6.0.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/3252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3252/comments | https://api.github.com/repos/huggingface/datasets/issues/3252/events | https://github.com/huggingface/datasets/pull/3252 | 1,051,124,749 | PR_kwDODunzps4uagoy | 3,252 | Fix failing CER metric test in CI after update | {
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https://api.github.com/repos/huggingface/datasets/issues/3250 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3250/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3250/comments | https://api.github.com/repos/huggingface/datasets/issues/3250/events | https://github.com/huggingface/datasets/pull/3250 | 1,050,541,348 | PR_kwDODunzps4uYmkr | 3,250 | Add ETHICS dataset | {
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From https://arxiv.org/abs/2008.02275 | {
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https://api.github.com/repos/huggingface/datasets/issues/3249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3249/comments | https://api.github.com/repos/huggingface/datasets/issues/3249/events | https://github.com/huggingface/datasets/pull/3249 | 1,050,193,138 | PR_kwDODunzps4uXeea | 3,249 | Fix streaming for id_newspapers_2018 | {
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} | To be compatible with streaming, this dataset must use `dl_manager.iter_archive` since the data are in a .tgz file | {
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https://api.github.com/repos/huggingface/datasets/issues/3248 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3248/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3248/comments | https://api.github.com/repos/huggingface/datasets/issues/3248/events | https://github.com/huggingface/datasets/pull/3248 | 1,050,171,082 | PR_kwDODunzps4uXZzU | 3,248 | Stream from Google Drive and other hosts | {
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} | Streaming from Google Drive is a bit more challenging than the other host we've been supporting:
- the download URL must be updated to add the confirm token obtained by HEAD request
- it requires to use cookies to keep the connection alive
- the URL doesn't tell any information about whether the file is compressed or not
Therefore I did two things:
- I added a step for URL and headers/cookies preparation in the StreamingDownloadManager
- I added automatic compression type inference by reading the [magic number](https://en.wikipedia.org/wiki/List_of_file_signatures)
This allows to do do fancy things like
```python
from datasets.utils.streaming_download_manager import StreamingDownloadManager, xopen, xjoin, xglob
# zip file containing a train.tsv file
url = "https://drive.google.com/uc?export=download&id=1k92sUfpHxKq8PXWRr7Y5aNHXwOCNUmqh"
extracted = StreamingDownloadManager().download_and_extract(url)
for inner_file in xglob(xjoin(extracted, "*.tsv")):
with xopen(inner_file) as f:
# streaming starts here
for line in f:
print(line)
```
This should make around 80 datasets streamable. It concerns those hosted on Google Drive but also any dataset for which the URL doesn't give any information about compression. Here is the full list:
```
amazon_polarity, ami, arabic_billion_words, ascent_kb, asset, big_patent, billsum, capes, cmrc2018, cnn_dailymail,
code_x_glue_cc_code_completion_token, code_x_glue_cc_code_refinement, code_x_glue_cc_code_to_code_trans,
code_x_glue_tt_text_to_text, conll2002, craigslist_bargains, dbpedia_14, docred, ehealth_kd, emo, euronews, germeval_14,
gigaword, grail_qa, great_code, has_part, head_qa, health_fact, hope_edi, id_newspapers_2018,
igbo_english_machine_translation, irc_disentangle, jfleg, jnlpba, journalists_questions, kor_ner, linnaeus, med_hop, mrqa,
mt_eng_vietnamese, multi_news, norwegian_ner, offcombr, offenseval_dravidian, para_pat, peoples_daily_ner, pn_summary,
poleval2019_mt, pubmed_qa, qangaroo, reddit_tifu, refresd, ro_sts_parallel, russian_super_glue, samsum, sberquad, scielo,
search_qa, species_800, spider, squad_adversarial, tamilmixsentiment, tashkeela, ted_talks_iwslt, trec, turk, turkish_ner,
twi_text_c3, universal_morphologies, web_of_science, weibo_ner, wiki_bio, wiki_hop, wiki_lingua, wiki_summary, wili_2018,
wisesight1000, wnut_17, yahoo_answers_topics, yelp_review_full, yoruba_text_c3
```
Some of them may not work if the host doesn't support HTTP range requests for example
Fix https://github.com/huggingface/datasets/issues/2742
Fix https://github.com/huggingface/datasets/issues/3188 | {
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https://api.github.com/repos/huggingface/datasets/issues/3247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3247/comments | https://api.github.com/repos/huggingface/datasets/issues/3247/events | https://github.com/huggingface/datasets/issues/3247 | 1,049,699,088 | I_kwDODunzps4-kSMQ | 3,247 | Loading big json dataset raises pyarrow.lib.ArrowNotImplementedError | {
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] | closed | false | null | [] | null | 3 | "2021-11-10T11:17:59Z" | "2022-04-10T14:05:57Z" | "2022-04-10T14:05:57Z" | NONE | null | null | null | ## Describe the bug
When trying to create a dataset from a json file with around 25MB, the following error is raised `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
Splitting the big file into smaller ones and then loading it with the `load_dataset` method did also not work.
Creating a pandas dataframe from it and then loading it with `Dataset.from_pandas` works
## Steps to reproduce the bug
```python
load_dataset("json", data_files="test.json")
```
test.json ~25MB
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
...
```
working.json ~160bytes
```json
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
{"a": {"b": 7, "c": 6}}
{"a": {"c": 8, "b": 5}}
```
## Expected results
It should load the dataset from the json file without error.
## Actual results
It raises Exception `pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct`
```
Traceback (most recent call last):
File "/Users/m/workspace/xxx/project/main.py", line 60, in <module>
dataset = load_dataset("json", data_files="result.json")
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/load.py", line 1627, in load_dataset
builder_instance.download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/datasets/arrow_writer.py", line 428, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1685, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 630, in pyarrow.lib._sanitize_arrays
File "pyarrow/array.pxi", line 338, in pyarrow.lib.asarray
File "pyarrow/table.pxi", line 304, in pyarrow.lib.ChunkedArray.cast
File "/opt/homebrew/Caskroom/miniforge/base/envs/xxx/lib/python3.9/site-packages/pyarrow/compute.py", line 309, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 528, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 327, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 120, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Unsupported cast from struct<b: int64, c: int64> to struct using function cast_struct
```
## Environment info
- `datasets` version: 1.14.0
- Platform: macOS-12.0.1-arm64-arm-64bit
- Python version: 3.9.7
- PyArrow version: 6.0.0
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In practice, it prevents anyone from using methods like `shuffle` on a dataset loaded this way, because it can't write the shuffled indices in a directory that doesn't exist anymore.
In this PR I switch to using `get_temporary_cache_files_directory()` and I update the tests.
cc @mariosasko since you worked on `get_temporary_cache_files_directory()` | {
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Following command gives an ConnectionError.
## Steps to reproduce the bug
```python
disaster = load_dataset('disaster_response_messages')
```
## Error
```
ConnectionError: Couldn't reach https://datasets.appen.com/appen_datasets/disaster_response_data/disaster_response_messages_training.csv
```
## Expected results
It should load dataset without an error
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
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] | open | false | null | [] | null | 0 | "2021-11-09T09:11:00Z" | "2021-11-09T09:11:00Z" | null | NONE | null | null | null | ## Describe the bug
When downloaded from macine 1 the dataset is downloaded and parsed correctly.
When downloaded from machine two (which has a different cache directory),
the following script:
import datasets
from datasets import load_dataset
raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload')
gives the following error:
**Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17...
Downloading: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 348M/348M [00:24<00:00, 14.0MB/s]
Traceback (most recent call last):
File ".../why_mismatch.py", line 3, in <module>
File "/opt/conda/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/opt/conda/lib/python3.8/site-packages/datasets/builder.py", line 709, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/opt/conda/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=1601790302, num_examples=349342, dataset_name='arabic_billion_words'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='arabic_billion_words')}]**
Note that the package versions of datasets (1.15.1) and rarfile (4.0) are identical.
## Steps to reproduce the bug
import datasets
from datasets import load_dataset
raw_dataset_elkhair_1 = load_dataset('arabic_billion_words', 'Alittihad', split="train",download_mode='force_redownload')
# Sample code to reproduce the bug
## Expected results
Downloading and preparing dataset arabic_billion_words/Alittihad (download: 332.13 MiB, generated: 1.49 GiB, post-processed: Unknown size, total: 1.82 GiB) to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17...
Downloading: 100%|βββββββββββββββββββββββββββ| 348M/348M [00:22<00:00, 15.8MB/s]
Dataset arabic_billion_words downloaded and prepared to .../.cache/huggingface/datasets/arabic_billion_words/Alittihad/1.1.0/687a1f963284c8a766558661375ea8f7ab3fa3633f8cd9c9f42a53ebe83bfe17. Subsequent calls will reuse this data.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
Machine 1:
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
Machine 2 (the bugged one)
- `datasets` version: 1.15.1
- Platform: Linux-4.4.0-210-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 6.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3238/comments | https://api.github.com/repos/huggingface/datasets/issues/3238/events | https://github.com/huggingface/datasets/issues/3238 | 1,048,226,086 | I_kwDODunzps4-eqkm | 3,238 | Reuters21578 Couldn't reach | {
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] | closed | false | null | [] | null | 2 | "2021-11-09T06:08:56Z" | "2021-11-11T00:02:57Z" | "2021-11-11T00:02:57Z" | NONE | null | null | null | ``## Adding a Dataset
- **Name:** *Reuters21578*
- **Description:** *ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz*
- **Data:** *https://huggingface.co/datasets/reuters21578*
`from datasets import load_dataset`
`dataset = load_dataset("reuters21578", 'ModLewis')`
ConnectionError: Couldn't reach https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz
And I try to request the link as follow:
`import requests`
`requests.head('https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz')`
SSLError: HTTPSConnectionPool(host='kdd.ics.uci.edu', port=443): Max retries exceeded with url: /databases/reuters21578/reuters21578.tar.gz (Caused by SSLError(SSLError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)'),))
This problem likes #575
What should I do ?
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] | null | 2 | "2021-11-09T04:06:52Z" | "2022-02-14T15:45:11Z" | "2021-11-09T13:49:28Z" | NONE | null | null | null | ## Describe the bug
Descriptions of the wikitext datasests are wrong.
## Steps to reproduce the bug
Please see: https://github.com/huggingface/datasets/blob/f6dcafce996f39b6a4bbe3a9833287346f4a4b68/datasets/wikitext/wikitext.py#L50
## Expected results
The descriptions for raw-v1 and v1 should be switched. | {
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Loading of datasets changed in https://github.com/huggingface/datasets/pull/3110 returns no examples:
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 0
})
})
```
## Steps to reproduce the bug
Load any of the datasets that were changed in https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper")
# The problem only started with the commit of #3110
load_dataset("qasper", revision="b6469baa22c174b3906c631802a7016fedea6780")
```
## Expected results
```python
DatasetDict({
train: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 888
})
validation: Dataset({
features: ['id', 'title', 'abstract', 'full_text', 'qas'],
num_rows: 281
})
})
```
Which can be received when specifying revision of the commit before https://github.com/huggingface/datasets/pull/3110:
```python
from datasets import load_dataset
load_dataset("qasper", revision="acfe2abda1ca79f0ce5c1896aa83b4b78af76b7d")
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.2.dev0 (master)
- Python version: 3.8.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/3235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3235/comments | https://api.github.com/repos/huggingface/datasets/issues/3235/events | https://github.com/huggingface/datasets/pull/3235 | 1,047,808,263 | PR_kwDODunzps4uPr9Z | 3,235 | Addd options to use updated bleurt checkpoints | {
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} | Adds options to use newer recommended checkpoint (as of 2021/10/8) bleurt-20 and its distilled versions.
Updated checkpoints are described in https://github.com/google-research/bleurt/blob/master/checkpoints.md#the-recommended-checkpoint-bleurt-20
This change won't affect the default behavior of metrics/bleurt. It only adds option to load newer checkpoints as
`datasets.load_metric('bleurt', 'bleurt-20')`
`bluert-20` generates scores roughly between 0 and 1, which wasn't the case for the previous checkpoints. | {
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https://api.github.com/repos/huggingface/datasets/issues/3234 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3234/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3234/comments | https://api.github.com/repos/huggingface/datasets/issues/3234/events | https://github.com/huggingface/datasets/pull/3234 | 1,047,634,236 | PR_kwDODunzps4uPHRk | 3,234 | Avoid PyArrow type optimization if it fails | {
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Fix #2206 | {
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The download Link of the Xsum dataset provided in the repository is [Link](http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz). It seems not able to download.
## Steps to reproduce the bug
```python
load_dataset('xsum')
```
## Actual results
``` python
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz
```
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} | By grouping tests by test file, we make sure that all the tests in `test_load.py` are sent to the same worker.
Therefore, the fixture `hf_token` will be called only once (and from the same worker).
Related to: #3200.
Fix #3219. | {
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https://api.github.com/repos/huggingface/datasets/issues/3230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3230/comments | https://api.github.com/repos/huggingface/datasets/issues/3230/events | https://github.com/huggingface/datasets/pull/3230 | 1,047,135,583 | PR_kwDODunzps4uNfEd | 3,230 | Add full tagset to conll2003 README | {
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} | Even though it is possible to manually get the tagset list with
```python
dset.features[field_name].feature.names
```
I think it is useful to have an overview of the used tagset on the dataset card. This is particularly useful in light of the **dataset viewer**: the tags are encoded, so it is not immediately obvious what they are for a given sample. Adding a label-int mapping should make it easier for visitors to get a grasp of what they mean.
From user-experience perspective, I would urge the full tagsets to always be available in the README's but I understand that that would take a lot of work, probably. Perhaps it can be automated?
closes #3189 | {
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} | Currently the BibTeX citation parsed from the CITATION file has wrong URL (it shows the repo URL instead of the proceedings paper URL):
```
@inproceedings{Lhoest_Datasets_A_Community_2021,
author = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaőko, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément},
booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
month = {11},
pages = {175--184},
publisher = {Association for Computational Linguistics},
title = {{Datasets: A Community Library for Natural Language Processing}},
url = {https://github.com/huggingface/datasets},
year = {2021}
}
``` | {
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When a json file contains a `text` field that is larger than the block_size, the JSON dataset builder fails.
## Steps to reproduce the bug
Create a folder that contains the following:
```
.
βββ testdata
βΒ Β βββ mydata.json
βββ test.py
```
Please download [this file](https://github.com/huggingface/datasets/files/7491797/mydata.txt) as `mydata.json`. (The error does not occur in JSON files with shorter text, but it is reproducible when the text is long as in the file I provide)
:exclamation: :exclamation: GitHub doesn't allow me to upload JSON so this file is a TXT, and you should rename it to `.json`!
`test.py` simply contains:
```python
from datasets import load_dataset
my_dataset = load_dataset("testdata")
```
To reproduce the error, simply run
```
python test.py
```
## Expected results
The data should load correctly without error.
## Actual results
The dataset builder fails with:
```
Using custom data configuration testdata-d490389b8ab4fd82
Downloading and preparing dataset json/testdata to /home/junshern.chan/.cache/huggingface/datasets/json/testdata-d490389b8ab4fd82/0.0.0/3333a8af0db9764dfcff43a42ff26228f0f2e267f0d8a0a294452d188beadb34...
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 2264.74it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 447.01it/s]
Failed to read file '/home/junshern.chan/hf-json-bug/testdata/mydata.json' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Missing a name for object member. in row 0
Traceback (most recent call last):
File "test.py", line 28, in <module>
my_dataset = load_dataset("testdata")
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/builder.py", line 1156, in _prepare_split
for key, table in utils.tqdm(
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/tqdm/std.py", line 1168, in __iter__
for obj in iterable:
File "/home/junshern.chan/.casio/miniconda/envs/hf-json-bug/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 146, in _generate_tables
raise ValueError(
ValueError: Not able to read records in the JSON file at /home/junshern.chan/hf-json-bug/testdata/mydata.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['text']. Select the correct one and provide it as `field='XXX'` to the dataset loading method.
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.15.1
- Platform: Linux-5.8.0-63-generic-x86_64-with-glibc2.17
- Python version: 3.8.12
- PyArrow version: 6.0.0
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} | This is a continuation of the now closed PR in https://github.com/huggingface/datasets/pull/3206. The discussion there has shaped a new approach to do this.
In this PR, behavior of `pklregister` and `Pickler` is extended. Earlier, users were already able to register custom pickle functions. That is useful if they have objects that are not easily picklable with default methods. When one registers a custom function to a type, an object of that type will be pickled with the given function by `Pickler` which looks up the type in its `dispatch` table. The downside of this method, and of `pickle` in general, is that it is limited to direct type-matching and does not allow sub-classes. In many, default, cases that is not an issue. But when you are using external libraries where classes (e.g. parsers, models) are sub-classed this is not ideal.
```python
from datasets.fingerprint import Hasher
from datasets.utils.py_utils import pklregister
class BaseParser:
pass
class EnglishParser(BaseParser):
pass
@pklregister(BaseParser)
def custom_pkl_func(pickler, obj):
print(f"Called the custom pickle function for type {type(obj)}!")
# do something with the obj and ultimately save with the pickler
base = BaseParser()
en = EnglishParser()
# Hasher.hash uses the Pickler behind the scenes
# `custom_pkl_func` called for base
Hasher.hash(base)
# `custom_pkl_func` not called for en :-(
Hasher.hash(en)
```
In the example above we'd want to sub-class `EnglishParser` to be handled in the same way as its super-class `BaseParser`. This PR solves that by allowing for a keyword-argument `allow_subclasses` in `pklregister` (default: `False`).
```python
@pklregister(BaseParser, allow_subclasses=True)
```
When this option is enabled, we not only save the function in `Pickler.dispatch` but also save it in a custom table `Pickler.subclass_dispatch` **which allows us to dynamically add sub-classes of that class to the real dispatch table**. Then, if we want to pickle an object `obj` with `Pickler.dump()` (which ultimately will call `Pickler.save()`) we _first_ check whether any of the object's super-classes exist in `Pickler.sublcass_dispatch` and get the related custom pickle function. If we find one, we add the type of `obj` alongside the function to `Pickler.dispatch`. All of this happens at the start of the call to `Pickler.save()`. _Only then_ dill.Pickler's `save` will be called, which in turn will call `pickle._Pickler.save` which handles everything. Here, the `Pickler.dispatch` table will be used to look up custom pickler functions - and it now also includes the function for `obj`, which was copied from its super-class, which we added at the very start of our custom `Pickler.save()`.
For edge cases and, especially, for testing, a contextmanager class `TempPickleRegistry` is included that resets the pickle registry on exit to its previous state.
```python
with TempPickleRegistry():
@pklregister(MyObjClass)
def pickle_registry_test_false(pickler, obj):
pickler.save(obj.fancy_method())
some_obj = MyObjClass()
dumps(some_obj)
# `MyObjClass` is in Pickler.dispatch
# ... `MyObjClass` is _not_ in Pickler.dispatch anymore
```
closes https://github.com/huggingface/datasets/issues/3178
To Do
====
- [x] Write tests
- [ ] Write documentation/examples? | {
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} | This PR adds documentation for the `Audio` feature. It describes:
- The difference between loading `path` and `audio`, as well as use-cases/best practices for each of them.
- Resampling audio files with `cast_column`, and then calling `ds[0]["audio"]` to automatically decode and resample to the desired sampling rate.
- Resampling with `map`.
Preview [here](https://52969-250213286-gh.circle-artifacts.com/0/docs/_build/html/audio_process.html), let me know if I'm missing anything! | {
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} | As discussed in https://github.com/huggingface/datasets/issues/3027 we should automatically infer what file is supposed to go to what split automatically, based on filenames.
I added the support for different kinds of patterns, for both dataset repositories and local directories:
```
Input structure:
my_dataset_repository/
βββ README.md
βββ dataset.csv
Output patterns:
{"train": ["*"]}
```
```
Input structure:
my_dataset_repository/
βββ README.md
βββ train.csv
βββ test.csv
my_dataset_repository/
βββ README.md
βββ data/
βββ train.csv
βββ test.csv
my_dataset_repository/
βββ README.md
βββ train_0.csv
βββ train_1.csv
βββ train_2.csv
βββ train_3.csv
βββ test_0.csv
βββ test_1.csv
Output patterns:
{"train": ["*train*"], "test": ["*test*"]}
```
```
Input structure:
my_dataset_repository/
βββ README.md
βββ data/
βββ train/
β βββ shard_0.csv
β βββ shard_1.csv
β βββ shard_2.csv
β βββ shard_3.csv
βββ test/
βββ shard_0.csv
βββ shard_1.csv
Output patterns:
{"train": ["*train*/*", "*train*/**/*"], "test": ["*test*/*", "*test*/**/*"]}
```
and also this pattern that allows to have custom split names, and that is the structure used by #3098 for `push_to_hub` (cc @LysandreJik ):
```
Input structure:
my_dataset_repository/
βββ README.md
βββ data/
βββ train-00000-of-00003.csv
βββ train-00001-of-00003.csv
βββ train-00002-of-00003.csv
βββ test-00000-of-00001.csv
βββ random-00000-of-00003.csv
βββ random-00001-of-00003.csv
βββ random-00002-of-00003.csv
Output patterns:
{
"train": ["data/train-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
"test": ["data/test-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
"random": ["data/random-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*"],
}
```
You can check the documentation about structuring your repository [here](https://52640-250213286-gh.circle-artifacts.com/0/docs/_build/html/repository_structure.html). cc @stevhliu
Fix https://github.com/huggingface/datasets/issues/3027
Fix https://github.com/huggingface/datasets/issues/3212
In the future we can also add support for dataset configurations. | {
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CC: @julien-c
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From time to time, there appear Invalid Token errors with private datasets:
- https://app.circleci.com/pipelines/github/huggingface/datasets/8520/workflows/d44629f2-4749-40f8-a657-50931d0b3434/jobs/52534
```
____________ ERROR at setup of test_load_streaming_private_dataset _____________
ValueError: Invalid token passed!
____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
ValueError: Invalid token passed!
=========================== short test summary info ============================
ERROR tests/test_load.py::test_load_streaming_private_dataset - ValueError: I...
ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
```
- https://app.circleci.com/pipelines/github/huggingface/datasets/8557/workflows/a8383181-ba6d-4487-9d0a-f750b6dcb936/jobs/52763
```
____ ERROR at setup of test_load_streaming_private_dataset_with_zipped_data ____
[gw1] linux -- Python 3.6.15 /home/circleci/.pyenv/versions/3.6.15/bin/python3.6
hf_api = <huggingface_hub.hf_api.HfApi object at 0x7f4899bab908>
hf_token = 'vgNbyuaLNEBuGbgCEtSBCOcPjZnngJufHkTaZvHwkXKGkHpjBPwmLQuJVXRxBuaRzNlGjlMpYRPbthfHPFWXaaEDTLiqTTecYENxukRYVAAdpeApIUPxcgsowadkTkPj'
zip_csv_path = PosixPath('/tmp/pytest-of-circleci/pytest-0/popen-gw1/data16/dataset.csv.zip')
@pytest.fixture(scope="session")
def hf_private_dataset_repo_zipped_txt_data_(hf_api: HfApi, hf_token, zip_csv_path):
repo_name = "repo_zipped_txt_data-{}".format(int(time.time() * 10e3))
hf_api.create_repo(token=hf_token, name=repo_name, repo_type="dataset", private=True)
repo_id = f"{USER}/{repo_name}"
hf_api.upload_file(
token=hf_token,
path_or_fileobj=str(zip_csv_path),
path_in_repo="data.zip",
repo_id=repo_id,
> repo_type="dataset",
)
tests/hub_fixtures.py:68:
...
ValueError: Invalid token passed!
=========================== short test summary info ============================
ERROR tests/test_load.py::test_load_streaming_private_dataset_with_zipped_data
```
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Fix #3217. | {
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```
datasets/riddle_sense/riddle_sense.py:36:21: W291 trailing whitespace
``` | {
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- https://github.com/tensorflow/tensorflow/commit/c1867f3bfdd1042f694df7a9870be51ba80543cb
- https://pypi.org/project/tensorflow/2.6.2/
with the patch:
- tensorflow/tensorflow#52927
we can remove the temporary fix we introduced in:
- #3208
Fix #3209. | {
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] | open | false | null | [] | null | 0 | "2021-11-04T15:59:58Z" | "2021-12-08T12:00:30Z" | null | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** *ACAV100M*
- **Description:** *contains 100 million videos with high audio-visual correspondence, ideal for self-supervised video representation learning.*
- **Paper:** *https://arxiv.org/abs/2101.10803*
- **Data:** *https://github.com/sangho-vision/acav100m*
- **Motivation:** *The largest dataset (to date) for audio-visual learning.*
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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This could lead to counter-intuitive results if, for example, the data files are sorted by date or similar since they would appear in different order in the `Dataset`.
The straightforward solution is to sort the list of files alphabetically before loading them.
cc @lhoestq
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} | Currently the `disable_nullable` parameter is not consistent across all dataset transforms. For example it is `False` in `map` but `True` in `flatten_indices`.
This creates unexpected behaviors like this
```python
from datasets import Dataset, concatenate_datasets
d1 = Dataset.from_dict({"a": [0, 1, 2, 3]})
d2 = d1.filter(lambda x: x["a"] < 2).flatten_indices()
d1.data.schema == d2.data.schema # False
```
This can cause issues when concatenating datasets for example.
For consistency I set `disable_nullable` to `False` in `flatten_indices` and I fixed some docstrings
cc @SBrandeis | {
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- #3208 | {
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Release of TensorFlow 2.7.0 contains an incompatibility with Keras. See:
- keras-team/keras#15579
This breaks our CI test suite: https://app.circleci.com/pipelines/github/huggingface/datasets/8493/workflows/055c7ae2-43bc-49b4-9f11-8fc71f35a25c/jobs/52363
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https://api.github.com/repos/huggingface/datasets/issues/3206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3206/comments | https://api.github.com/repos/huggingface/datasets/issues/3206/events | https://github.com/huggingface/datasets/pull/3206 | 1,044,216,270 | PR_kwDODunzps4uEZJe | 3,206 | [WIP] Allow user-defined hash functions via a registry | {
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} | Inspired by the discussion on hashing in https://github.com/huggingface/datasets/issues/3178#issuecomment-959016329, @lhoestq suggested that it would be neat to allow users more control over the hashing process. Specifically, it would be great if users can specify specific hashing functions depending on the **class** of the object.
As an example, we found in the linked topic that loaded spaCy models (`Language` objects) have different hashes when `dump`'d, but their byte representation with `Language.to_bytes()` _is_ deterministic. It would therefore be great if we could specify that for `Language` objects, the hasher should hash the objects `to_bytes()` return value instead of the object itself.
This PR adds a new, but tiny, dependency to manage the registry, namely [`catalogue`](https://github.com/explosion/catalogue).
Two files have been changed (apart from the added dependency in `setup.py`) and one file has been added.
**utils.registry** (added)
This file defines our custom Registry and builds a registry called "hashers". A Registry is basically dictionary from names (str) to functions. A function can be added to the registry by a decorator, e.g.
```python
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
```
You'll notice that `spacy.Language` is not a string, even though the registry holds a str->func mapping. To accomplish this with classes in a dynamic way, catalogue.Registry needed to be subclassed and modified as `DatasetsRegistry`. All methods that use a name as an input are now modified so that classes are deterministically converted in strings in such a way that we can later retrieve the actual class from the string (below).
**utils.py_utils** (modified)
Added two functions to deal with classes and their qualified names, that is, their full descriptive name including the module. On the one hand it allows us to retrieve a string from a given class, e.g. given `Module` class, return `torch.nn.Module` str. Conversly, a function is added to convert such a full qualified name into a class. For instance, given the string `torch.nn.Module`, return the `Module` class. These straightforward methods allow us to interchangeably use classes and strings without any needed user interaction - they can just register a class, and behind the scenes `DatasetsRegistry` converts these to deterministic strings.
**fingerprint** (modified)
Updated Hasher.hash so that if the object to hash is an instance of a class in the registry, the registered function is used to hash the object instead of the default behavior. To do so we iterate over the registry `hashers` and convert its keys (strings) into classes, and then we can use `isinstance`.
```python
# Check if the current object is an instance that is
# applicable to the user-defined hashers. If so, hash
# with the user-defined function
for full_module_name, func in hashers.get_all().items():
registered_cls = get_cls_from_qualname(full_module_name)
if isinstance(value, registered_cls):
return func(value)
```
**Putting it all together**
To test this, you can try the following example with spaCy. First install spaCy from source and checkout a specific commit.
```shell
git clone https://github.com/explosion/spaCy.git
cd spaCy/
git checkout cab9209c3dfcd1b75dfe5657f10e52c4d847a3cf
cd ..
git clone https://github.com/BramVanroy/datasets.git
cd datasets
git checkout registry
pip install -e .
pip install ../spaCy
spacy download en_core_web_sm
```
Now you can run the following script. By default it will use the custom hasher function for the Language object. You can enable the default behavior by commenting out `@hashers.register...`.
```python
import spacy
from datasets.fingerprint import Hasher
from datasets.utils.registry import hashers
# Register a function so that when the Hasher encounters a spacy.Language object
# it uses this custom function to hash instead of the default
@hashers.register(spacy.Language)
def hash_spacy_language(nlp):
return Hasher.hash(nlp.to_bytes())
def main():
print(hashers.get_all())
nlp = spacy.load("en_core_web_sm")
dump1 = Hasher.hash(nlp)
nlp = spacy.load("en_core_web_sm")
dump2 = Hasher.hash(nlp)
print(dump1)
# succeeds when using the registered custom function
# fails if using the default
assert dump1 == dump2
if __name__ == '__main__':
main()
```
To do
====
- The above is just a proof-of-concept. I am open to changes/suggestions
- Tests still need to be written
- We should consider whether we can make `DatasetsRegistry` very restrictive and ONLY allowing classes. That would make testing easier - otherwise we also need to test for other sorts of objects.
- Maybe the `hashers` definition is better suited in `fingerprint`?
- Documentation/examples need to be updated
- Not sure why the logger is not working in `hash()`
- `get_cls_from_qualname` might need a fail-safe: is it possible for a full_qualname to not have a module, and if so how do we deal with that?
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] | null | 3 | "2021-11-03T14:56:55Z" | "2021-11-05T15:51:15Z" | "2021-11-05T14:16:05Z" | NONE | null | null | null | Hi,
`dataset = datasets.load_dataset('tuple_ie', 'all')`
returns a FileNotFound error. Is the data not available?
Many thanks.
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Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html).
Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset.
An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance.
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https://api.github.com/repos/huggingface/datasets/issues/3201 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3201/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3201/comments | https://api.github.com/repos/huggingface/datasets/issues/3201/events | https://github.com/huggingface/datasets/issues/3201 | 1,043,209,142 | I_kwDODunzps4-Lhu2 | 3,201 | Add GSM8K dataset | {
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- **Name:** GSM8K (short for Grade School Math 8k)
- **Description:** GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers.
- **Paper:** https://openai.com/blog/grade-school-math/
- **Data:** https://github.com/openai/grade-school-math
- **Motivation:** The dataset is useful to investigate the reasoning abilities of large Transformer models, such as GPT-3.
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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I modified the fixture in the test that uses staging to ignore this error | {
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https://api.github.com/repos/huggingface/datasets/issues/3199 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3199/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3199/comments | https://api.github.com/repos/huggingface/datasets/issues/3199/events | https://github.com/huggingface/datasets/pull/3199 | 1,042,860,935 | PR_kwDODunzps4uAVzQ | 3,199 | Bump huggingface_hub | {
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It was supposed to be part of 1.15.0 but I'm adding it for 1.15.1 | {
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#3124 introduced a regression that made the benchmarks CI fail because of a bad array comparison when checking the first encoded element. This PR fixes this by making sure that encoding is applied on all sequence types except lists.
cc @eladsegal fyi (no big deal) | {
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* automatically calls `flatten_indices` where needed: in `unique` and `save_to_disk` to avoid saving the indices file
* adds descriptions to the map calls
Fix #3040 | {
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} | PyArrow has explicit support for `null` values, so it makes sense to support Nones on our side as well.
[Colab Notebook with examples](https://colab.research.google.com/drive/1zcK8BnZYnRe3Ao2271u1T19ag9zLEiy3?usp=sharing)
Changes:
* allow None for the features types with special encoding (`ClassLabel, TranslationVariableLanguages, Value, _ArrayXD`)
* handle None in `class_encode_column` (also there is an option to stringify Nones and treat them as a class)
* support None sorting in `sort` (use pandas for that)
* handle None in align_labels_with_mapping
* support for None in ArrayXD (converts `None` to `np.nan` to align the behavior with PyArrow)
* support for None in the Audio/Image feature
* allow promotion when concatenating tables (`pa.concat_tables(table_list, promote=True)`) and `null` row/~~column~~ broadcasting similar to pandas
Additional notes:
* use `null` instead of `none` for function arguments for consistency with existing `disable_nullable`
* fixes a bug with the `update_metadata_with_features` call in `Dataset.rename_columns`
* had to update some tests, let me know if that's ok
TODO:
- [x] check how the Audio features behaves with Nones
- [x] Better None handling in `concatenate_datasets`/`add_item`
- [x] Fix formatting with Nones
- [x] Add Colab with examples
- [x] Tests
TODOs for subsequent PRs:
- Mention None handling in the docs
- Add `drop_null`/`fill_null` to `Dataset`/`DatasetDict`
Fix #3181 #3253 | {
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- huggingface/datasets-tagging#22
We should update the link in Datasets. | {
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] | open | false | null | [] | null | 0 | "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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**Link:** https://huggingface.co/datasets/compguesswhat
File not found
Am I the one who added this dataset ? No
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] | closed | false | null | [] | null | 3 | "2021-11-01T13:07:29Z" | "2021-11-02T10:50:49Z" | "2021-11-02T10:50:49Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
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"default": true,
"description": "Something isn't working",
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"name": "bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
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] | closed | false | null | [] | null | 1 | "2021-11-01T11:03:30Z" | "2021-11-09T10:40:58Z" | "2021-11-09T10:40:58Z" | CONTRIBUTOR | null | null | null | In the [conll2003](https://huggingface.co/datasets/conll2003#data-fields) README, the labels are described as follows
> - `id`: a `string` feature.
> - `tokens`: a `list` of `string` features.
> - `pos_tags`: a `list` of classification labels, with possible values including `"` (0), `''` (1), `#` (2), `$` (3), `(` (4).
> - `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
> - `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4) `B-LOC` (5), `I-LOC` (6) `B-MISC` (7), `I-MISC` (8).
First of all, it would be great if we can get a list of ALL possible pos_tags.
Second, the chunk tags labels cannot be correct. The description says the values go from 0 to 4 whereas the data shows values from at least 11 to 21 and 0.
EDIT: not really a bug, sorry for mistagging. | {
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https://api.github.com/repos/huggingface/datasets/issues/3188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3188/comments | https://api.github.com/repos/huggingface/datasets/issues/3188/events | https://github.com/huggingface/datasets/issues/3188 | 1,040,980,712 | I_kwDODunzps4-DBro | 3,188 | conll2002 issues | {
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] | closed | false | null | [] | null | 4 | "2021-11-01T09:49:24Z" | "2021-11-15T13:50:59Z" | "2021-11-12T17:18:11Z" | CONTRIBUTOR | null | null | null | **Link:** https://huggingface.co/datasets/conll2002
The dataset viewer throws a server error when trying to preview the dataset.
```
Message: Extraction protocol 'train' for file at 'https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/esp.train' is not implemented yet
```
In addition, the "point of contact" has encoding issues and does not work when clicked.
Am I the one who added this dataset ? No, @lhoestq did | {
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} | Similar to my [PR for TER](https://github.com/huggingface/datasets/pull/3153), it feels only right to also include ChrF and friends. These are present in Sacrebleu and are therefore very similar to implement as TER and sacrebleu. I tested the implementation with sacrebleu's tests to verify. You can try this below for yourself
```python
import datasets
EPSILON = 1e-4
chrf = datasets.load_metric(r"path\to\datasets\metrics\chrf")
test_cases = [
(["abcdefg"], ["hijklmnop"], 0.0),
(["a"], ["b"], 0.0),
([""], ["b"], 0.0),
([""], ["ref"], 0.0),
([""], ["reference"], 0.0),
(["aa"], ["ab"], 8.3333),
(["a", "b"], ["a", "c"], 8.3333),
(["a"], ["a"], 16.6667),
(["a b c"], ["a b c"], 50.0),
(["a b c"], ["abc"], 50.0),
([" risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."],
["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."], 63.361730),
([" Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich. "],
["Das VerhΓ€ltnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."], 64.1302698),
(["Niemand hat die Absicht, eine Mauer zu errichten"], ["Niemand hat die Absicht, eine Mauer zu errichten"], 100.0),
]
for hyp, ref, score in test_cases:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3, eps_smoothing=True)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
test_cases_effective_order = [
(["a"], ["a"], 100.0),
([""], ["reference"], 0.0),
(["a b c"], ["a b c"], 100.0),
(["a b c"], ["abc"], 100.0),
([""], ["c"], 0.0),
(["a", "b"], ["a", "c"], 50.0),
(["aa"], ["ab"], 25.0),
]
for hyp, ref, score in test_cases_effective_order:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3, eps_smoothing=False)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
test_cases_keep_whitespace = [
(
["Die Beziehung zwischen Obama und Netanjahu ist nicht gerade freundlich."],
["Das VerhΓ€ltnis zwischen Obama und Netanyahu ist nicht gerade freundschaftlich."],
67.3481606,
),
(
["risk assessment must be made of those who are qualified and expertise in the sector - these are the scientists ."],
["risk assessment has to be undertaken by those who are qualified and expert in that area - that is the scientists ."],
65.2414427,
),
]
for hyp, ref, score in test_cases_keep_whitespace:
# Note the reference transformation which is different from scarebleu's input format
results = chrf.compute(predictions=hyp, references=[[r] for r in ref],
char_order=6, word_order=0, beta=3,
whitespace=True)
if abs(score - results["score"]) > EPSILON:
print(f"expected {score}, got {results['score']} for {hyp} - {ref}")
predictions = ["The relationship between Obama and Netanyahu is not exactly friendly."]
references = [["The ties between Obama and Netanyahu are not particularly friendly."]]
print(chrf.compute(predictions=predictions, references=references))
``` | {
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] | null | 6 | "2021-10-31T03:56:33Z" | "2022-09-12T09:15:34Z" | "2022-09-12T08:43:09Z" | CONTRIBUTOR | null | null | null | ## Dataset viewer issue for '*nli_tr*'
**Link:** https://huggingface.co/datasets/nli_tr
Hello,
Thank you for the new dataset preview feature that will help the users to view the datasets online.
We just noticed that the dataset viewer widget in the `nli_tr` dataset shows the error below. The error must be due to a temporary problem that may have blocked access to the dataset through the dataset viewer. But the dataset is currently accessible through the link in the error message. May we kindly ask if it would be possible to rerun the job so that it can access the dataset for the dataset viewer function?
Thank you.
Emrah
------------------------------------------
Server Error
Status code: 404
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip://snli_tr_1.0_train.jsonl::https://tabilab.cmpe.boun.edu.tr/datasets/nli_datasets/snli_tr_1.0.zip
------------------------------------------
Am I the one who added this dataset ? Yes
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**Link:** https://huggingface.co/datasets/samsum
Server Error
Status code: 400
Exception: NotImplementedError
Message: Extraction protocol '7z' for file at 'https://arxiv.org/src/1911.12237v2/anc/corpus.7z' is not implemented yet
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https://api.github.com/repos/huggingface/datasets/issues/3184 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3184/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3184/comments | https://api.github.com/repos/huggingface/datasets/issues/3184/events | https://github.com/huggingface/datasets/pull/3184 | 1,040,114,102 | PR_kwDODunzps4t4J61 | 3,184 | RONEC v2 | {
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} | Hi, as we've recently finished with the new RONEC (Romanian Named Entity Corpus), we'd like to update the dataset here as well. It's actually essential as links to V1 are no longer valid.
In reality we'd like to replace completely v1, as v2 is a full re-annotation of v1 with additional data (up to 2x size vs v1).
I've run the make style and all the dummy and real data test, and they passed.
I hope it's okay to merge the new RONEC v2 in the datasets.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/3183 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3183/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3183/comments | https://api.github.com/repos/huggingface/datasets/issues/3183/events | https://github.com/huggingface/datasets/pull/3183 | 1,039,761,120 | PR_kwDODunzps4t3Dag | 3,183 | Add missing docstring to DownloadConfig | {
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https://api.github.com/repos/huggingface/datasets/issues/3182 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3182/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3182/comments | https://api.github.com/repos/huggingface/datasets/issues/3182/events | https://github.com/huggingface/datasets/pull/3182 | 1,039,739,606 | PR_kwDODunzps4t2-9J | 3,182 | Don't memoize strings when hashing since two identical strings may have different python ids | {
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} | When hashing an object that has several times the same string, the hashing could return a different hash if the identical strings share the same python `id()` or not.
Here is an example code that shows how the issue can affect the caching:
```python
import json
import pyarrow as pa
from datasets.features import Features
from datasets.fingerprint import Hasher
schema = pa.schema([pa.field("some_string", pa.string()), pa.field("another_string", pa.string())])
features_from_schema = Features.from_arrow_schema(schema)
Hasher.hash(features_from_schema) # dffa9dca9a73fd8c
features_dict = json.loads('{"some_string": {"dtype": "string", "id": null, "_type": "Value"}, "another_string": {"dtype": "string", "id": null, "_type": "Value"}}')
features_from_json = Features.from_dict(features_dict)
Hasher.hash(features_from_json) # 3812e76b15e6420e
features_from_schema == features_from_json # True
```
This is because in `features_dict`, some strings like "dtype" are repeated but don't share the same id, contrary to the ones in `features_from_schema`.
I fixed that by disabling memoization for strings.
This could be optimized in the future by implementing a smarter memoization with a special handling for strings. | {
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] | null | 9 | "2021-10-29T15:23:53Z" | "2021-12-11T01:16:40Z" | "2021-12-09T14:26:57Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
When loading a dataset `None` values of the type `NoneType` are converted to `'None'` of the type `str`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
qasper = load_dataset("qasper", split="train", download_mode="reuse_cache_if_exists")
print(qasper[60]["full_text"]["section_name"])
```
When installing version 1.1.40, the output is
`[None, 'Introduction', 'Benchmark Datasets', ...]`
When installing from the master branch, the output is
`['None', 'Introduction', 'Benchmark Datasets', ...]`
Notice how the first element was changed from `NoneType` to `str`.
## Expected results
`None` should stay as is.
## Actual results
`None` is converted to a string.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: master
- Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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} | Fixing label mapping for hlgd.
0 correponds to same event and 1 corresponds to different event
<img width="642" alt="Capture dβeΜcran 2021-10-29 aΜ 10 39 58 AM" src="https://user-images.githubusercontent.com/16107619/139454810-1f225e3d-ad48-44a8-b8b1-9205c9533839.png">
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https://api.github.com/repos/huggingface/datasets/issues/3179 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3179/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3179/comments | https://api.github.com/repos/huggingface/datasets/issues/3179/events | https://github.com/huggingface/datasets/issues/3179 | 1,039,571,928 | I_kwDODunzps499pvY | 3,179 | Cannot load dataset when the config name is "special" | {
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] | closed | false | null | [] | null | 1 | "2021-10-29T13:30:47Z" | "2021-10-29T13:35:21Z" | "2021-10-29T13:35:21Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
After https://github.com/huggingface/datasets/pull/3159, we can get the config name of "Check/region_1", which is "Check___region_1".
But now we cannot load the dataset (not sure it's related to the above PR though). It's the case for all the similar datasets, listed in https://github.com/huggingface/datasets-preview-backend/issues/78
## Steps to reproduce the bug
```python
>>> from datasets import get_dataset_config_names
>>> get_dataset_config_names("Check/region_1")
['Check___region_1']
>>> load_dataset("Check/region_1")
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
Downloading and preparing dataset json/Check___region_1 to /home/slesage/.cache/huggingface/datasets/json/Check___region_1-d2b3bc48f11c9be2/0.0.0/c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426...
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 4443.12it/s]
100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1/1 [00:00<00:00, 1277.19it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/load.py", line 1632, in load_dataset
builder_instance.download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 607, in download_and_prepare
self._download_and_prepare(
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 697, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1159, in _prepare_split
writer.write_table(table)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in write_table
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "/home/slesage/hf/datasets-preview-backend/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 442, in <listcomp>
pa_table = pa.Table.from_arrays([pa_table[name] for name in self._schema.names], schema=self._schema)
File "pyarrow/table.pxi", line 1249, in pyarrow.lib.Table.__getitem__
File "pyarrow/table.pxi", line 1825, in pyarrow.lib.Table.column
File "pyarrow/table.pxi", line 1800, in pyarrow.lib.Table._ensure_integer_index
KeyError: 'Field "builder_name" does not exist in table schema'
```
Loading in streaming mode also returns something strange:
```python
>>> list(load_dataset("Check/region_1", streaming=True, split="train"))
Using custom data configuration Check___region_1-d2b3bc48f11c9be2
[{'builder_name': None, 'citation': '', 'config_name': None, 'dataset_size': None, 'description': '', 'download_checksums': None, 'download_size': None, 'features': {'speech': {'feature': {'dtype': 'float64', 'id': None, '_type': 'Value'}, 'length': -1, 'id': None, '_type': 'Sequence'}, 'sampling_rate': {'dtype': 'int64', 'id': None, '_type': 'Value'}, 'label': {'dtype': 'string', 'id': None, '_type': 'Value'}}, 'homepage': '', 'license': '', 'post_processed': None, 'post_processing_size': None, 'size_in_bytes': None, 'splits': None, 'supervised_keys': None, 'task_templates': None, 'version': None}, {'_data_files': [{'filename': 'dataset.arrow'}], '_fingerprint': 'f1702bb5533c549c', '_format_columns': ['speech', 'sampling_rate', 'label'], '_format_kwargs': {}, '_format_type': None, '_indexes': {}, '_indices_data_files': None, '_output_all_columns': False, '_split': None}]
```
## Expected results
The dataset should be loaded
## Actual results
An error occurs
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Linux-5.11.0-1020-aws-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/3178 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3178/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3178/comments | https://api.github.com/repos/huggingface/datasets/issues/3178/events | https://github.com/huggingface/datasets/issues/3178 | 1,039,539,076 | I_kwDODunzps499huE | 3,178 | "Property couldn't be hashed properly" even though fully picklable | {
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] | closed | false | null | [] | null | 23 | "2021-10-29T12:56:09Z" | "2023-01-04T15:33:16Z" | "2022-11-02T17:18:43Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
I am trying to tokenize a dataset with spaCy. I found that no matter what I do, the spaCy language object (`nlp`) prevents `datasets` from pickling correctly - or so the warning says - even though manually pickling is no issue. It should not be an issue either, since spaCy objects are picklable.
## Steps to reproduce the bug
Here is a [colab](https://colab.research.google.com/drive/1gt75LCBIzsmBMvvipEOvWulvyZseBiA7?usp=sharing) but for some reason I cannot reproduce it there. That may have to do with logging/tqdm on Colab, or with running things in notebooks. I tried below code on Windows and Ubuntu as a Python script and getting the same issue (warning below).
```python
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10%]")
ds = ds.map(self.parse, batched=True, num_proc=6)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled!")
pr.process()
```
---
Here is a small change that includes `Hasher.hash` that shows that the hasher cannot seem to successfully pickle parts form the NLP object.
```python
from datasets.fingerprint import Hasher
import pickle
from datasets import load_dataset
import spacy
class Processor:
def __init__(self):
self.nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser", "ner", "lemmatizer"])
@staticmethod
def collate(batch):
return [d["en"] for d in batch]
def parse(self, batch):
batch = batch["translation"]
return {"translation_tok": [{"en_tok": " ".join([t.text for t in doc])} for doc in self.nlp.pipe(self.collate(batch))]}
def process(self):
ds = load_dataset("wmt16", "de-en", split="train[:10]")
return ds.map(self.parse, batched=True)
if __name__ == '__main__':
pr = Processor()
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr, f)
print("Successfully pickled class instance!")
# succeeds
with open("temp.pkl", "wb") as f:
pickle.dump(pr.nlp, f)
print("Successfully pickled nlp!")
# fails
print(Hasher.hash(pr.nlp))
pr.process()
```
## Expected results
This to be picklable, working (fingerprinted), and no warning.
## Actual results
In the first snippet, I get this warning
Parameter 'function'=<function Processor.parse at 0x7f44982247a0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.
In the second, I get this traceback which directs to the `Hasher.hash` line.
```
Traceback (most recent call last):
File " \Python\Python36\lib\pickle.py", line 918, in save_global
obj2, parent = _getattribute(module, name)
File " \Python\Python36\lib\pickle.py", line 266, in _getattribute
.format(name, obj))
AttributeError: Can't get local attribute 'add_codes.<locals>.ErrorsWithCodes' on <function add_codes at 0x00000296FF606EA0>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File " scratch_4.py", line 40, in <module>
print(Hasher.hash(pr.nlp))
File " \lib\site-packages\datasets\fingerprint.py", line 191, in hash
return cls.hash_default(value)
File " \lib\site-packages\datasets\fingerprint.py", line 184, in hash_default
return cls.hash_bytes(dumps(value))
File " \lib\site-packages\datasets\utils\py_utils.py", line 345, in dumps
dump(obj, file)
File " \lib\site-packages\datasets\utils\py_utils.py", line 320, in dump
Pickler(file, recurse=True).dump(obj)
File " \lib\site-packages\dill\_dill.py", line 498, in dump
StockPickler.dump(self, obj)
File " \Python\Python36\lib\pickle.py", line 409, in dump
self.save(obj)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 781, in save_list
self._batch_appends(obj)
File " \Python\Python36\lib\pickle.py", line 805, in _batch_appends
save(x)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 634, in save_reduce
save(state)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1176, in save_instancemethod0
pickler.save_reduce(MethodType, (obj.__func__, obj.__self__), obj=obj)
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 736, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\datasets\utils\py_utils.py", line 523, in save_function
obj=obj,
File " \Python\Python36\lib\pickle.py", line 610, in save_reduce
save(args)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \Python\Python36\lib\pickle.py", line 751, in save_tuple
save(element)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 990, in save_module_dict
StockPickler.save_dict(pickler, obj)
File " \Python\Python36\lib\pickle.py", line 821, in save_dict
self._batch_setitems(obj.items())
File " \Python\Python36\lib\pickle.py", line 847, in _batch_setitems
save(v)
File " \Python\Python36\lib\pickle.py", line 521, in save
self.save_reduce(obj=obj, *rv)
File " \Python\Python36\lib\pickle.py", line 605, in save_reduce
save(cls)
File " \Python\Python36\lib\pickle.py", line 476, in save
f(self, obj) # Call unbound method with explicit self
File " \lib\site-packages\dill\_dill.py", line 1439, in save_type
StockPickler.save_global(pickler, obj, name=name)
File " \Python\Python36\lib\pickle.py", line 922, in save_global
(obj, module_name, name))
_pickle.PicklingError: Can't pickle <class 'spacy.errors.add_codes.<locals>.ErrorsWithCodes'>: it's not found as spacy.errors.add_codes.<locals>.ErrorsWithCodes
```
## Environment info
Tried on both Linux and Windows
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0 + Python 3.7.9; Linux-5.11.0-38-generic-x86_64-with-Ubuntu-20.04-focal + Python 3.7.12
- PyArrow version: 6.0.0
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] | closed | false | null | [] | null | 2 | "2021-10-29T11:56:16Z" | "2023-02-13T20:16:40Z" | "2023-02-13T20:16:40Z" | CONTRIBUTOR | null | null | null | It would help with the clutter in my terminal if tqdm is only shown for rank 0 when using `num_proces>0` in the map and filter methods of datasets.
```python
dataset.map(lambda examples: tokenize(examples["text"]), batched=True, num_proc=6)
```
The above snippet leads to a lot of TQDM bars and depending on your terminal, these will not overwrite but keep pushing each other down.
```
#0: 0%| | 0/13 [00:00<?, ?ba/s]
#1: 0%| | 0/13 [00:00<?, ?ba/s]
#2: 0%| | 0/13 [00:00<?, ?ba/s]
#3: 0%| | 0/13 [00:00<?, ?ba/s]
#4: 0%| | 0/13 [00:00<?, ?ba/s]
#5: 0%| | 0/13 [00:00<?, ?ba/s]
#0: 8%| | 1/13 [00:00<?, ?ba/s]
#1: 8%| | 1/13 [00:00<?, ?ba/s]
...
```
Instead, it would be welcome if we had the option to only show the progress of rank 0. | {
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} | Fixed #3168.
The SLR38 indices are CSV files and there wasn't any code in openslr.py to process these files properly. The end result was an empty table.
I've added code to properly process these CSV files. | {
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} | This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`:
- Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 π
).
- Add an example for loading dataset from multiple zipped CSV files to the Load section.
- Add an example for removing columns for an `IterableDataset`.
- Add graphic for visualizing streaming. | {
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} | I've replaced two asserts with their proper exceptions following the guidelines described in issue #3171 by following the contributing guidelines.
PS: This is one of my first PRs, hoping I don't break anything! | {
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https://api.github.com/repos/huggingface/datasets/issues/3173 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3173/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3173/comments | https://api.github.com/repos/huggingface/datasets/issues/3173/events | https://github.com/huggingface/datasets/pull/3173 | 1,038,404,300 | PR_kwDODunzps4typcA | 3,173 | Fix issue with filelock filename being too long on encrypted filesystems | {
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} | Infer max filename length in filelock on Unix-like systems. Should fix problems on encrypted filesystems such as eCryptfs.
Fix #2924
cc: @lmmx | {
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https://api.github.com/repos/huggingface/datasets/issues/3172 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3172/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3172/comments | https://api.github.com/repos/huggingface/datasets/issues/3172/events | https://github.com/huggingface/datasets/issues/3172 | 1,038,351,587 | I_kwDODunzps494_zj | 3,172 | `SystemError 15` thrown in `Dataset.__del__` when using `Dataset.map()` with `num_proc>1` | {
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] | closed | false | null | [] | null | 11 | "2021-10-28T10:29:00Z" | "2023-09-04T14:20:49Z" | "2021-11-03T11:26:10Z" | NONE | null | null | null | ## Describe the bug
I use `datasets.map` to preprocess some data in my application. The error `SystemError 15` is thrown at the end of the execution of `Dataset.map()` (only with `num_proc>1`. Traceback included bellow.
The exception is raised only when the code runs within a specific context. Despite ~10h spent investigating this issue, I have failed to isolate the bug, so let me describe my setup.
In my project, `Dataset` is wrapped into a `LightningDataModule` and the data is preprocessed when calling `LightningDataModule.setup()`. Calling `.setup()` in an isolated script works fine (even when wrapped with `hydra.main()`). However, when calling `.setup()` within the experiment script (depends on `pytorch_lightning`), the script crashes and `SystemError 15`.
I could avoid throwing this error by modifying ` Dataset.__del__()` (see bellow), but I believe this only moves the problem somewhere else. I am completely stuck with this issue, any hint would be welcome.
```python
class Dataset()
...
def __del__(self):
if hasattr(self, "_data"):
_ = self._data # <- ugly trick that allows avoiding the issue.
del self._data
if hasattr(self, "_indices"):
del self._indices
```
## Steps to reproduce the bug
```python
# Unfortunately I couldn't isolate the bug.
```
## Expected results
Calling `Dataset.map()` without throwing an exception. Or at least raising a more detailed exception/traceback.
## Actual results
```
Exception ignored in: <function Dataset.__del__ at 0x7f7cec179160>βββββββββββββββββββββββββββββββββββββββββββββββββββ| 5/5 [00:05<00:00, 1.17ba/s]
Traceback (most recent call last):
File ".../python3.8/site-packages/datasets/arrow_dataset.py", line 906, in __del__
del self._data
File ".../python3.8/site-packages/ray/worker.py", line 1033, in sigterm_handler
sys.exit(signum)
SystemExit: 15
```
## Environment info
Tested on 2 environments:
**Environment 1.**
- `datasets` version: 1.14.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 6.0.0
**Environment 2.**
- `datasets` version: 1.14.0
- Platform: Linux-4.18.0-305.19.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.7
- PyArrow version: 6.0.0
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] | closed | false | null | [] | null | 4 | "2021-10-27T18:26:52Z" | "2021-12-23T16:40:37Z" | "2021-12-23T16:40:37Z" | CONTRIBUTOR | null | null | null | Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. | {
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https://api.github.com/repos/huggingface/datasets/issues/3170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3170/comments | https://api.github.com/repos/huggingface/datasets/issues/3170/events | https://github.com/huggingface/datasets/pull/3170 | 1,037,601,926 | PR_kwDODunzps4twDUo | 3,170 | Preserve ordering in `zip_dict` | {
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} | Replace `set` with the `unique_values` generator in `zip_dict`.
This PR fixes the problem with the different ordering of the example keys across different Python sessions caused by the `zip_dict` call in `Features.decode_example`. | {
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https://api.github.com/repos/huggingface/datasets/issues/3169 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3169/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3169/comments | https://api.github.com/repos/huggingface/datasets/issues/3169/events | https://github.com/huggingface/datasets/pull/3169 | 1,036,773,357 | PR_kwDODunzps4ttYmZ | 3,169 | Configurable max filename length in file locks | {
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} | Resolve #2924 (https://github.com/huggingface/datasets/issues/2924#issuecomment-952330956) wherein the assumption of file lock maximum filename length to be 255 raises an OSError on encrypted drives (ecryptFS on Linux uses part of the lower filename, reducing the maximum filename size to 143). Allowing this limit to be set in the config module allows this to be modified by users. Will not affect Windows users, as their class passes 255 on init explicitly.
Reproduced with the following example ([the first few lines of a script from Lightning Flash](https://lightning-flash.readthedocs.io/en/latest/reference/speech_recognition.html), fine-tuning a HF model):
```py
import torch
import flash
from flash.audio import SpeechRecognition, SpeechRecognitionData
from flash.core.data.utils import download_data
# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/timit_data.zip", "./data")
datamodule = SpeechRecognitionData.from_json(
input_fields="file",
target_fields="text",
train_file="data/timit/train.json",
test_file="data/timit/test.json",
)
```
Which gave this traceback:
```py
Traceback (most recent call last):
File "lf_ft.py", line 10, in <module>
datamodule = SpeechRecognitionData.from_json(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json
return cls.from_data_source(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source
train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets
train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset
data = load_data(data, mock_dataset)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data
dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1599, in load_dataset
builder_instance = load_dataset_builder(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/load.py", line 1457, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/builder.py", line 285, in __init__
with FileLock(lock_path):
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/datasets/utils/filelock.py", line 403, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock'
```
Note the filename is 145 chars long:
```
>>> len("_home_louis_.cache_huggingface_datasets_json_default-98e6813a547f72fa_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279000f993c7bfd18937fd7f426.lock")
145
```
After installing datasets as an editable local package and modifying the script I was running to first include:
```py
import datasets
datasets.config.MAX_DATASET_CONFIG_ID_READABLE_LENGTH = 143
```
The error goes away.
If I instead deliberately set the value incorrectly as 144, the OSError returns:
```
Traceback (most recent call last):
File "lf_ft.py", line 14, in <module>
datamodule = SpeechRecognitionData.from_json(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 1005, in from_json
return cls.from_data_source(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_module.py", line 571, in from_data_source
train_dataset, val_dataset, test_dataset, predict_dataset = data_source.to_datasets(
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 307, in to_datasets
train_dataset = self.generate_dataset(train_data, RunningStage.TRAINING)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/core/data/data_source.py", line 344, in generate_dataset
data = load_data(data, mock_dataset)
File "/home/louis/miniconda3/envs/w2vlf/lib/python3.8/site-packages/flash/audio/speech_recognition/data.py", line 103, in load_data
dataset_dict = load_dataset(self.filetype, data_files={stage: str(file)})
File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1605, in load_dataset
builder_instance = load_dataset_builder(
File "/home/louis/dev/hf_datasets/src/datasets/load.py", line 1463, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/louis/dev/hf_datasets/src/datasets/builder.py", line 285, in __init__
with FileLock(lock_path):
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 326, in __enter__
self.acquire()
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 275, in acquire
self._acquire()
File "/home/louis/dev/hf_datasets/src/datasets/utils/filelock.py", line 406, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 36] File name too long: '/home/louis/.cache/huggingface/datasets/_home_louis_.cache_huggingface_datasets_json_default-32c812b5c1272d64_0.0.0_c2d554c3377ea79c7664b93dc65d0803b45e3279...-5794079643713042223.lock'
``` | {
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] | null | 3 | "2021-10-26T19:42:21Z" | "2021-10-29T10:04:09Z" | "2021-10-29T10:04:09Z" | CONTRIBUTOR | null | null | null | ## Describe the bug
As the summary says, openslr / SLR83 / train is empty.
The dataset returned after loading indicates there are **zero** rows. The correct number should be **17877**.
## Steps to reproduce the bug
```python
import datasets
datasets.load_dataset('openslr', 'SLR83')
```
## Expected results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 17877
})
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'sentence'],
num_rows: 0
})
})
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.1.dev0 (master HEAD)
- Platform: Ubuntu 20.04
- Python version: 3.7.10
- PyArrow version: 3.0.0
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