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https://api.github.com/repos/huggingface/datasets/issues/2736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2736/comments | https://api.github.com/repos/huggingface/datasets/issues/2736/events | https://github.com/huggingface/datasets/issues/2736 | 956,895,199 | MDU6SXNzdWU5NTY4OTUxOTk= | 2,736 | Add Microsoft Building Footprints dataset | {
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"color": "bfdadc",... | open | false | null | [] | null | [
"Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it!"
] | 2021-07-30T16:17:08Z | 2021-12-08T12:09:03Z | null | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @sashavor | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2735/comments | https://api.github.com/repos/huggingface/datasets/issues/2735/events | https://github.com/huggingface/datasets/issues/2735 | 956,889,365 | MDU6SXNzdWU5NTY4ODkzNjU= | 2,735 | Add Open Buildings dataset | {
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] | open | false | null | [] | null | [] | 2021-07-30T16:08:39Z | 2021-07-31T05:01:25Z | null | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** Open Buildings
- **Description:** A dataset of building footprints to support social good applications.
Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa.
See: "Mapping Africa's Buildings with Satellite Imagery" https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html
- **Paper:** https://arxiv.org/abs/2107.12283
- **Data:** https://sites.research.google/open-buildings/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @osanseviero | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2730 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2730/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2730/comments | https://api.github.com/repos/huggingface/datasets/issues/2730/events | https://github.com/huggingface/datasets/issues/2730 | 955,987,834 | MDU6SXNzdWU5NTU5ODc4MzQ= | 2,730 | Update CommonVoice with new release | {
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"cc @patrickvonplaten?",
"Does anybody know if there is a bundled link, which would allow direct data download instead of manual? \r\nSomething similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj \r\n",
"Also see... | 2021-07-29T15:59:59Z | 2021-08-07T16:19:19Z | null | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/2728 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2728/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2728/comments | https://api.github.com/repos/huggingface/datasets/issues/2728/events | https://github.com/huggingface/datasets/issues/2728 | 955,892,970 | MDU6SXNzdWU5NTU4OTI5NzA= | 2,728 | Concurrent use of same dataset (already downloaded) | {
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"Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded file.",
"If i have two jobs that use the same dataset. I got :\r\n\r\n\r\n File \"compute_measures.py\", line 181, in <module>\r\n train_loader, val_loade... | 2021-07-29T14:18:38Z | 2021-08-02T07:25:57Z | null | CONTRIBUTOR | null | null | null | null | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
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https://api.github.com/repos/huggingface/datasets/issues/2727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2727/comments | https://api.github.com/repos/huggingface/datasets/issues/2727/events | https://github.com/huggingface/datasets/issues/2727 | 955,812,149 | MDU6SXNzdWU5NTU4MTIxNDk= | 2,727 | Error in loading the Arabic Billion Words Corpus | {
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"I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:\r\nFor the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:\r\n```\r\n<Techreen>\... | 2021-07-29T12:53:09Z | 2021-07-30T13:03:55Z | 2021-07-30T13:03:55Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2724 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2724/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2724/comments | https://api.github.com/repos/huggingface/datasets/issues/2724/events | https://github.com/huggingface/datasets/issues/2724 | 954,919,607 | MDU6SXNzdWU5NTQ5MTk2MDc= | 2,724 | 404 Error when loading remote data files from private repo | {
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"I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/35b5e4bc0cb2ed896e40f3eb2a4aa3de1cb1a6c5/src/datasets/builder.py#L160-L160",
"Yes, I remember having properly implemented that: \r... | 2021-07-28T14:24:23Z | 2021-07-29T04:58:49Z | 2021-07-28T16:38:01Z | MEMBER | null | null | null | null | ## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
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"This could be solved by going to the glue/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset.",
"Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset"
] | 2021-07-28T03:52:07Z | 2022-03-21T08:27:51Z | 2022-03-21T08:27:51Z | NONE | null | null | null | null | Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
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https://api.github.com/repos/huggingface/datasets/issues/2719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2719/comments | https://api.github.com/repos/huggingface/datasets/issues/2719/events | https://github.com/huggingface/datasets/issues/2719 | 953,932,416 | MDU6SXNzdWU5NTM5MzI0MTY= | 2,719 | Use ETag in streaming mode to detect resource updates | {
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"default": fals... | open | false | null | [] | null | [] | 2021-07-27T14:17:09Z | 2021-10-22T09:36:08Z | null | COLLABORATOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
I want to cache data I generate from processing a dataset I've loaded in streaming mode, but I've currently no way to know if the remote data has been updated or not, thus I don't know when to invalidate my cache.
**Describe the solution you'd like**
Take the ETag of the data files into account and provide it (directly or through a hash) to give a signal that I can invalidate my cache.
**Describe alternatives you've considered**
None
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https://api.github.com/repos/huggingface/datasets/issues/2716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2716/comments | https://api.github.com/repos/huggingface/datasets/issues/2716/events | https://github.com/huggingface/datasets/issues/2716 | 952,902,778 | MDU6SXNzdWU5NTI5MDI3Nzg= | 2,716 | Calling shuffle on IterableDataset will disable batching in case any functions were mapped | {
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"Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;)",
"Have raised the PR [here](https://github.com/huggingface/datasets/pull/2717)",
"Fixed by #2717."
] | 2021-07-26T13:24:59Z | 2021-07-26T18:04:43Z | 2021-07-26T18:04:43Z | CONTRIBUTOR | null | null | null | null | When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`
I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
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"We already have this information in the dataset_infos.json files of each dataset.\r\nMaybe we can parse these files in the backend to return their content with the endpoint at huggingface.co/api/datasets\r\n\r\nFor now if you want to access this info you have to load the json for each dataset. For example:\r\n- fo... | 2021-07-26T07:11:03Z | 2021-07-26T09:16:25Z | null | NONE | null | null | null | null | For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2709 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2709/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2709/comments | https://api.github.com/repos/huggingface/datasets/issues/2709/events | https://github.com/huggingface/datasets/issues/2709 | 951,534,757 | MDU6SXNzdWU5NTE1MzQ3NTc= | 2,709 | Missing documentation for wnut_17 (ner_tags) | {
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"Hi @maxpel, thanks for reporting this issue.\r\n\r\nIndeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.\r\n\r\nAs the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and ... | 2021-07-23T12:25:32Z | 2021-07-26T09:30:55Z | 2021-07-26T09:30:55Z | CONTRIBUTOR | null | null | null | null | On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner! | {
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https://api.github.com/repos/huggingface/datasets/issues/2708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2708/comments | https://api.github.com/repos/huggingface/datasets/issues/2708/events | https://github.com/huggingface/datasets/issues/2708 | 951,092,660 | MDU6SXNzdWU5NTEwOTI2NjA= | 2,708 | QASC: incomplete training set | {
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"Hi @danyaljj, thanks for reporting.\r\n\r\nUnfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:\r\n```ipython\r\nIn [10]: ds[\"train\"]\r\nOut[10]:\r\nDataset({\r\n features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_quest... | 2021-07-22T21:59:44Z | 2021-07-23T13:30:07Z | 2021-07-23T13:30:07Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2707 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2707/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2707/comments | https://api.github.com/repos/huggingface/datasets/issues/2707/events | https://github.com/huggingface/datasets/issues/2707 | 950,812,945 | MDU6SXNzdWU5NTA4MTI5NDU= | 2,707 | 404 Not Found Error when loading LAMA dataset | {
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"Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :)",
"Hi @dwil2444, thanks for reporting.\r\n\r\nCould you please confirm which `datasets` version you were using and if the problem persists after you update it to the ... | 2021-07-22T15:52:33Z | 2021-07-26T14:29:07Z | 2021-07-26T14:29:07Z | NONE | null | null | null | null | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | {
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https://api.github.com/repos/huggingface/datasets/issues/2705 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2705/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2705/comments | https://api.github.com/repos/huggingface/datasets/issues/2705/events | https://github.com/huggingface/datasets/issues/2705 | 950,488,583 | MDU6SXNzdWU5NTA0ODg1ODM= | 2,705 | 404 not found error on loading WIKIANN dataset | {
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"Hi @ronbutan, thanks for reporting.\r\n\r\nYou are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.\r\n\r\nWe have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contac... | 2021-07-22T09:55:50Z | 2021-07-23T08:07:32Z | 2021-07-23T08:07:32Z | NONE | null | null | null | null | ## Describe the bug
Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2703 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2703/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2703/comments | https://api.github.com/repos/huggingface/datasets/issues/2703/events | https://github.com/huggingface/datasets/issues/2703 | 950,482,284 | MDU6SXNzdWU5NTA0ODIyODQ= | 2,703 | Bad message when config name is missing | {
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"gists... | null | [] | 2021-07-22T09:47:23Z | 2021-07-22T10:02:40Z | 2021-07-22T10:02:40Z | MEMBER | null | null | null | null | When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name.
However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message:
```python
import datasets
datasets.load_dataset("glue")
```
raises
```python
AttributeError: 'BuilderConfig' object has no attribute 'text_features'
```
instead of
```python
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2700 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2700/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2700/comments | https://api.github.com/repos/huggingface/datasets/issues/2700/events | https://github.com/huggingface/datasets/issues/2700 | 950,276,325 | MDU6SXNzdWU5NTAyNzYzMjU= | 2,700 | from datasets import Dataset is failing | {
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"Hi @kswamy15, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm`"
] | 2021-07-22T03:51:23Z | 2021-07-22T07:23:45Z | 2021-07-22T07:09:07Z | NONE | null | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/2699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2699/comments | https://api.github.com/repos/huggingface/datasets/issues/2699/events | https://github.com/huggingface/datasets/issues/2699 | 950,221,226 | MDU6SXNzdWU5NTAyMjEyMjY= | 2,699 | cannot combine splits merging and streaming? | {
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"Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations",
"is there an update on this? ran into the same issue on 2.17.1.\r\n\r\nOn a similar note, the keyword `split=\"... | 2021-07-22T01:13:25Z | 2024-04-08T13:26:46Z | null | NONE | null | null | null | null | this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2695 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2695/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2695/comments | https://api.github.com/repos/huggingface/datasets/issues/2695/events | https://github.com/huggingface/datasets/issues/2695 | 949,864,823 | MDU6SXNzdWU5NDk4NjQ4MjM= | 2,695 | Cannot import load_dataset on Colab | {
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"I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n 3 \r\n 4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n 6 from datasets import lo... | 2021-07-21T15:52:51Z | 2021-07-22T07:26:25Z | 2021-07-22T07:09:07Z | NONE | null | null | null | null | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2691 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2691/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2691/comments | https://api.github.com/repos/huggingface/datasets/issues/2691/events | https://github.com/huggingface/datasets/issues/2691 | 949,758,379 | MDU6SXNzdWU5NDk3NTgzNzk= | 2,691 | xtreme / pan-x cannot be downloaded | {
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"Hi @severo, thanks for reporting.\r\n\r\nHowever I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?\r\n\r\nMaybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried.",
"Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bj... | 2021-07-21T14:18:05Z | 2021-07-26T09:34:22Z | 2021-07-26T09:34:22Z | COLLABORATOR | null | null | null | null | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2689/comments | https://api.github.com/repos/huggingface/datasets/issues/2689/events | https://github.com/huggingface/datasets/issues/2689 | 949,447,104 | MDU6SXNzdWU5NDk0NDcxMDQ= | 2,689 | cannot save the dataset to disk after rename_column | {
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"Hi ! That's because you are trying to overwrite a file that is already open and being used.\r\nIndeed `foo/dataset.arrow` is open and used by your `dataset` object.\r\n\r\nWhen you do `rename_column`, the resulting dataset reads the data from the same arrow file.\r\nIn other cases like when using `map` on the othe... | 2021-07-21T08:13:40Z | 2025-02-11T23:23:17Z | 2021-07-21T13:11:04Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')
/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
598 raise PermissionError(
--> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
600 )
601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:
PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```
N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2688/comments | https://api.github.com/repos/huggingface/datasets/issues/2688/events | https://github.com/huggingface/datasets/issues/2688 | 949,182,074 | MDU6SXNzdWU5NDkxODIwNzQ= | 2,688 | hebrew language codes he and iw should be treated as aliases | {
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"Hi @eyaler, thanks for reporting.\r\n\r\nWhile you are true with respect the Hebrew language tag (\"iw\" is deprecated and \"he\" is the preferred value), in the \"mc4\" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datas... | 2021-07-20T23:13:52Z | 2021-07-21T16:34:53Z | 2021-07-21T16:34:53Z | NONE | null | null | null | null | https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. | {
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"gists... | null | [] | 2021-07-20T14:37:57Z | 2021-07-20T16:27:15Z | 2021-07-20T16:27:15Z | MEMBER | null | null | null | null | Since #2659 I can see weird cache directory names with hashes in the config id, even though no additional config kwargs are passed. For example:
```python
from datasets import load_dataset_builder
c4_builder = load_dataset_builder("c4", "en")
print(c4_builder.cache_dir)
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en-174d3b7155eb68db/0.0.0/...
# instead of
# /Users/quentinlhoest/.cache/huggingface/datasets/c4/en/0.0.0/...
```
This issue could be annoying since it would simply ignore old cache directories for users, and regenerate datasets
cc @stas00 this is what you experienced a few days ago
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"Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix\r\n\r\nFor context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingfac... | 2021-07-20T14:25:00Z | 2021-07-20T15:44:17Z | 2021-07-20T15:44:17Z | COLLABORATOR | null | null | null | null | ## Describe the bug
In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:
- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
<img width="838" alt="Capture d’écran 2021-07-20 à 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">
- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli
Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.
## Steps to reproduce the bug
Visit the Paperswithcode links, and look at the "Dataset Loaders" section
## Expected results
There should only be one reference to a Hugging Face dataset loader
## Actual results
Two Hugging Face dataset loaders
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https://api.github.com/repos/huggingface/datasets/issues/2679 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2679/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2679/comments | https://api.github.com/repos/huggingface/datasets/issues/2679/events | https://github.com/huggingface/datasets/issues/2679 | 948,506,638 | MDU6SXNzdWU5NDg1MDY2Mzg= | 2,679 | Cannot load the blog_authorship_corpus due to codec errors | {
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"Hi @izaskr, thanks for reporting.\r\n\r\nHowever the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...\r\n\r\nI'm going to have a look at the dataset anyway...",
"Hi @izaskr, thanks... | 2021-07-20T10:13:20Z | 2021-07-21T17:02:21Z | 2021-07-21T13:11:58Z | NONE | null | null | null | null | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
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"This looks like an issue with PyArrow. Did you try reinstalling it ?",
"@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.\r\n\r\nEdit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstal... | 2021-07-20T09:28:38Z | 2021-07-21T13:59:26Z | 2021-07-21T13:03:02Z | NONE | null | null | null | null | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
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"Hi Thanks for reporting !\r\nIt looks like these files are not correctly reported in the list of expected files to download, let me fix that ;)",
"Alright this is fixed now. We'll do a new release soon to make the fix available.\r\n\r\nIn the meantime feel free to simply pass `ignore_verifications=True` to `load... | 2021-07-20T08:37:30Z | 2021-07-20T14:41:31Z | 2021-07-20T14:38:10Z | NONE | null | null | null | null | Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png"> | {
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https://api.github.com/repos/huggingface/datasets/issues/2670 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2670/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2670/comments | https://api.github.com/repos/huggingface/datasets/issues/2670/events | https://github.com/huggingface/datasets/issues/2670 | 947,120,709 | MDU6SXNzdWU5NDcxMjA3MDk= | 2,670 | Using sharding to parallelize indexing | {
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] | open | false | null | [] | null | [] | 2021-07-18T21:26:26Z | 2021-10-07T13:33:25Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Creating an elasticsearch index on large dataset could be quite long and cannot be parallelized on shard (the index creation is colliding)
**Describe the solution you'd like**
When working on dataset shards, if an index already exists, its mapping should be checked and if compatible, the indexing process should continue with the shard data.
Additionally, at the end of the process, the `_indexes` dict should be send back to the original dataset object (from which the shards have been created) to allow to use the index for later filtering on the whole dataset.
**Describe alternatives you've considered**
Each dataset shard could created independent partial indices. then on the whole dataset level, indices should be all referred in `_indexes` dict and be used in querying through `get_nearest_examples()`. The drawback is that the scores will be computed independently on the partial indices leading to inconsistent values for most scoring based on corpus level statistics (tf/idf, BM25).
**Additional context**
The objectives is to parallelize the index creation to speed-up the process (ie surcharging the ES server which is fine to handle large load) while later enabling search on the whole dataset. | null | {
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"Hi @BramVanroy, thanks for reporting.\r\n\r\nFirst, note that `\"min\"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{\"micro\", \"macro\", \"samples\", \"weigh... | 2021-07-18T08:32:31Z | 2021-07-18T18:36:05Z | 2021-07-18T11:19:04Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/2663 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2663/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2663/comments | https://api.github.com/repos/huggingface/datasets/issues/2663/events | https://github.com/huggingface/datasets/issues/2663 | 946,552,273 | MDU6SXNzdWU5NDY1NTIyNzM= | 2,663 | [`to_json`] add multi-proc sharding support | {
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"Hi @stas00, \r\nI want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow ... | 2021-07-16T19:41:50Z | 2021-09-13T13:56:37Z | 2021-09-13T13:56:37Z | CONTRIBUTOR | null | null | null | null | As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.
I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?
I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.
I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs.
The code I was using:
```
from multiprocessing import cpu_count, Process, Queue
[...]
filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())
DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
print(f"Sharding {idx}")
ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
# ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
print(f"Saving {DATASET_NAME}-{idx}.jsonl")
ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)
queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
p.start()
for p in processes:
p.join()
```
Thank you!
@lhoestq | {
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"gists... | null | [] | 2021-07-16T10:05:44Z | 2021-07-16T12:46:06Z | 2021-07-16T12:46:06Z | MEMBER | null | null | null | null | When doing `load_dataset("csv", sep=None)`, the `sep` passed to `pd.read_csv` is still the default `sep=","` instead, which makes it impossible to make the csv loader infer the separator.
Related to https://github.com/huggingface/datasets/pull/2656
cc @SBrandeis | {
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1. Could we have a `desc` arg for the tqdm use and a fallback to just `to_json` so that it'd be clear to the user what's happening? Surely, one can just print the description before calling json, but I thought perhaps it'd help to have it self-identify like you did for other progress bars recently.
2. It took me a while to make sense of the reported numbers:
```
22%|██▏ | 1536/7076 [12:30:57<44:09:42, 28.70s/it]
```
So iteration here happens to be 10K samples, and the total is 70M records. But the user does't know that, so the progress bar is perfect, but the numbers it reports are meaningless until one discovers that 1it=10K samples. And one still has to convert these in the head - so it's not quick. Not exactly sure what's the best way to approach this, perhaps it can be part of `desc`? or report M or K, so it'd be built-in if it were to print, e.g.:
```
22%|██▏ | 15360K/70760K [12:30:57<44:09:42, 28.70s/it]
```
or
```
22%|██▏ | 15.36M/70.76M [12:30:57<44:09:42, 28.70s/it]
```
(while of course remaining friendly to small datasets)
I forget if tqdm lets you add a magnitude identifier to the running count.
Thank you! | null | {
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"Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense. \r\nIs there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like on... | 2021-07-15T13:30:45Z | 2024-01-09T15:11:27Z | 2024-01-09T07:46:28Z | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
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"#self-assign",
"I understand it already raises a `NotImplementedError` exception, eg:\r\n\r\n```\r\n>>> dataset = load_dataset(\"journalists_questions\", name=\"plain_text\", split=\"train\", streaming=True)\r\n\r\n[...]\r\nNotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=d... | 2021-07-15T09:07:27Z | 2021-08-02T11:03:21Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
I would love to know if a `dataset` is with the current implementation streamable or not.
**Describe the solution you'd like**
We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive.
**Describe alternatives you've considered**
Add a new metadata tag for "streaming"
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"Note that this subset requires us to:\r\n\r\n* generate the LibriMix corpus from LibriSpeech\r\n* prepare the corpus for diarization\r\n\r\nAs suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data\r\n... | 2021-07-15T07:51:40Z | 2021-08-04T17:03:52Z | 2021-08-04T17:03:52Z | MEMBER | null | null | null | null | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
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"Hi,\r\n\r\nyou can suppress progress bars by patching logging as follows:\r\n```python\r\nimport datasets\r\nimport logging\r\ndatasets.logging.get_verbosity = lambda: logging.NOTSET\r\n# map call ...\r\n```\r\nEDIT: now you have to use `disable_progress_bar `",
"Thank you, it worked :)",
"See https://github.c... | 2021-07-14T21:06:51Z | 2022-07-08T14:51:57Z | 2021-07-15T03:41:35Z | NONE | null | null | null | null | ## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
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"I need the same feature for distributed training",
"I think @TevenLeScao is exploring adding multiprocessing in `GeneratorBasedBuilder._prepare_split` - feel free to post updates here :)",
"Posted a PR to address the building side, still needs something to load sharded arrow files + tests",
"Closing as this ... | 2021-07-14T18:04:58Z | 2023-11-28T19:11:41Z | 2023-11-28T19:11:40Z | CONTRIBUTOR | null | null | null | null | - Some huge datasets take forever to build the first time. (e.g. oscar/en) as it's done in a single cpu core.
- If the build crashes, everything done up to that point gets lost
Request: Shard the build over multiple arrow files, which would enable:
- much faster build by parallelizing the build process
- if the process crashed, the completed arrow files don't need to be re-built again
Thank you!
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"Is this done now? I see progress bars when using `load_dataset`.",
"There are progress bars when downloading data and when preparing them as Arrow files.\r\n\r\nThe \"total silence\" part mentioned in OP refer to checksums verifications which have had some changes in the latest release 2.10:\r\n- they're disable... | 2021-07-14T17:34:39Z | 2023-03-27T10:32:49Z | null | CONTRIBUTOR | null | null | null | null | Please consider:
```
Downloading and preparing dataset oscar/unshuffled_deduplicated_en (download: 462.40 GiB, generated: 1.18 TiB, post-processed: Unknown size, total: 1.63 TiB) to cache/oscar/unshuffled_deduplicated_en/1.0.0/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2...
HF google storage unreachable. Downloading and preparing it from source
```
and no indication whatsoever of whether things work well or when it'll be done. It's important to have an estimated completion time for when doing slurm jobs since some instances have a cap on run-time.
I think for this particular job it sat for 30min in total silence and then after 30min it started generating:
```
897850 examples [07:24, 10286.71 examples/s]
```
which is already great!
Request:
1. ETA - knowing how many hours to allocate for a slurm job
2. progress bar - helps to know things are working and aren't stuck and where we are at.
Thank you!
@lhoestq
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"#take"
] | 2021-07-14T14:24:36Z | 2021-07-14T14:26:12Z | null | CONTRIBUTOR | null | null | null | null | ## Describe:
For getting simple sentences from complex sentence there are dataset and task like wiki_split that is available in hugging face datasets. This web_split is a very similar dataset. There some research paper which states that by combining these two datasets we if we train the model it will yield better results on both tests data.
This dataset is made from web NLG data.
All the dataset related details are provided in the below repository
Github link: https://github.com/shashiongithub/Split-and-Rephrase
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"Hi ! I just tested and it worked fine today for me.\r\n\r\nI think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996 \r\n\r\nFeel free to try again today, now that the quota wa... | 2021-07-14T12:31:05Z | 2022-08-04T08:28:24Z | 2022-08-04T08:28:24Z | NONE | null | null | null | null | ## Describe the bug
I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset
## Steps to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
# Sample code to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
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https://api.github.com/repos/huggingface/datasets/issues/2645 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2645/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2645/comments | https://api.github.com/repos/huggingface/datasets/issues/2645/events | https://github.com/huggingface/datasets/issues/2645 | 944,374,284 | MDU6SXNzdWU5NDQzNzQyODQ= | 2,645 | load_dataset processing failed with OS error after downloading a dataset | {
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"Hi ! It looks like an issue with pytorch.\r\n\r\nCould you try to run `import torch` and see if it raises an error ?",
"> Hi ! It looks like an issue with pytorch.\r\n> \r\n> Could you try to run `import torch` and see if it raises an error ?\r\n\r\nIt works. Thank you!"
] | 2021-07-14T12:23:53Z | 2021-07-15T09:34:02Z | 2021-07-15T09:34:02Z | NONE | null | null | null | null | ## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2644 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2644/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2644/comments | https://api.github.com/repos/huggingface/datasets/issues/2644/events | https://github.com/huggingface/datasets/issues/2644 | 944,254,748 | MDU6SXNzdWU5NDQyNTQ3NDg= | 2,644 | Batched `map` not allowed to return 0 items | {
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"Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed.",
"Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week.",
"Sure if you're interes... | 2021-07-14T09:58:19Z | 2021-07-26T14:55:15Z | 2021-07-26T14:55:15Z | MEMBER | null | null | null | null | ## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2643 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2643/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2643/comments | https://api.github.com/repos/huggingface/datasets/issues/2643/events | https://github.com/huggingface/datasets/issues/2643 | 944,220,273 | MDU6SXNzdWU5NDQyMjAyNzM= | 2,643 | Enum used in map functions will raise a RecursionError with dill. | {
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"I'm running into this as well. (Thank you so much for reporting @jorgeecardona — was staring at this massive stack trace and unsure what exactly was wrong!)",
"Hi ! Thanks for reporting :)\r\n\r\nUntil this is fixed on `dill`'s side, we could implement a custom saving in our Pickler indefined in utils.py_utils.p... | 2021-07-14T09:16:08Z | 2021-11-02T09:51:11Z | null | NONE | null | null | null | null | ## Describe the bug
Enums used in functions pass to `map` will fail at pickling with a maximum recursion exception as described here: https://github.com/uqfoundation/dill/issues/250#issuecomment-852566284
In my particular case, I use an enum to define an argument with fixed options using the `TraininigArguments` dataclass as base class and the `HfArgumentParser`. In the same file I use a `ds.map` that tries to pickle the content of the module including the definition of the enum that runs into the dill bug described above.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from enum import Enum
class A(Enum):
a = 'a'
def main():
a = A.a
def f(x):
return {} if a == a.a else x
ds = load_dataset('cnn_dailymail', '3.0.0')['test']
ds = ds.map(f, num_proc=15)
if __name__ == "__main__":
main()
```
## Expected results
The known problem with dill could be prevented as explained in the link above (workaround.) Since `HFArgumentParser` nicely uses the enum class for choices it makes sense to also deal with this bug under the hood.
## Actual results
```python
File "/home/xxxx/miniconda3/lib/python3.8/site-packages/dill/_dill.py", line 1373, in save_type
pickler.save_reduce(_create_type, (type(obj), obj.__name__,
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 534, in save
self.framer.commit_frame()
File "/home/xxxx/miniconda3/lib/python3.8/pickle.py", line 220, in commit_frame
if f.tell() >= self._FRAME_SIZE_TARGET or force:
RecursionError: maximum recursion depth exceeded while calling a Python object
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.9.0-4-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 3.0.0
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"Hi ! This is a great idea :)\r\nI think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. \r\n\r\nRegarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a... | 2021-07-14T08:22:58Z | 2024-05-03T10:11:04Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).
**Describe the solution you'd like**
Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`.
**Describe alternatives you've considered**
A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.
* https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset
**Additional context**
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"Hi! It's probably because this dataset is stored on google drive and it has a per day quota limit. It should work if you retry, I was able to initiate the download.\r\n\r\nSimilar issue [here](https://github.com/huggingface/datasets/issues/2646)",
"Hi ! Loading the dataset works on my side as well.\r\nFeel free ... | 2021-07-13T21:21:49Z | 2022-08-04T08:30:08Z | 2022-08-04T08:30:08Z | NONE | null | null | null | null | ## Describe the bug
Attempting to download the financial_phrasebank dataset results in a NonMatchingChecksumError
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("financial_phrasebank", 'sentences_allagree')
```
## Expected results
I expect to see the financial_phrasebank dataset downloaded successfully
## Actual results
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://www.researchgate.net/profile/Pekka_Malo/publication/251231364_FinancialPhraseBank-v10/data/0c96051eee4fb1d56e000000/FinancialPhraseBank-v10.zip']
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-4.14.232-177.418.amzn2.x86_64-x86_64-with-debian-10.6
- Python version: 3.7.10
- PyArrow version: 4.0.1
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"To add my experience when trying to debug this issue:\r\n\r\nSeems like previously the workaround given [here](https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308) worked around this issue. But with the latest version of jupyter/tqdm I still get terminal warnings that IPython tried to send a message fro... | 2021-07-12T14:07:13Z | 2022-02-03T15:55:33Z | 2022-02-03T15:55:33Z | MEMBER | null | null | null | null | ## Describe the bug
The progress bars are not Jupyter widgets; regular progress bars appear (like in a terminal).
## Steps to reproduce the bug
```python
ds.map(tokenize, num_proc=10)
```
## Expected results
Jupyter widgets displaying the progress bars.
## Actual results
Simple plane progress bars.
cc: Reported by @thomwolf | {
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"This is so cool, let us know if we can help with anything on the hub side (@Pierrci @elishowk) 🎉 "
] | 2021-07-12T08:45:17Z | 2021-08-25T14:18:08Z | 2021-08-25T14:18:08Z | MEMBER | null | null | null | null | As a user I would like to be able to upload my csv/json/text/parquet/etc. files in a dataset repository on the Hugging Face Hub and be able to load this dataset with `load_dataset` without having to implement a dataset script.
Moreover I would like to be able to specify which file goes into which split using the `data_files` argument.
This feature should be compatible with private repositories and dataset streaming.
This can be implemented by checking the extension of the files in the dataset repository and then by using the right dataset builder that is already packaged in the library (csv/json/text/parquet/etc.) | {
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"Thanks @thomas-happify for reporting and thanks @mariosasko for the fix."
] | 2021-07-10T20:23:45Z | 2021-07-12T05:54:29Z | 2021-07-12T05:54:29Z | NONE | null | null | null | null | ## Describe the bug
```
[2021-07-10 20:13:11,528][datasets.utils.filelock][INFO] - Lock 139705371374976 acquired on /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.lock
[2021-07-10 20:13:11,529][datasets.arrow_writer][INFO] - Done writing 32 examples in 6100 bytes /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow.
[2021-07-10 20:13:11,531][datasets.arrow_dataset][INFO] - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns.
[2021-07-10 20:13:11,543][/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric.py][INFO] - Removing /root/.cache/huggingface/metrics/seqeval/default/default_experiment-1-0.arrow
```
As you can see, `datasets` logging come from different places.
`filelock`, `arrow_writer` & `arrow_dataset` comes from `datasets.*` which are expected
However, `metric.py` logging comes from `/conda/envs/myenv/lib/python3.8/site-packages/datasets/`
So when setting `datasets.utils.logging.set_verbosity_error()`, it still logs the last message which is annoying during evaluation.
I had to do
```
logging.getLogger("/conda/envs/myenv/lib/python3.8/site-packages/datasets/metric").setLevel(logging.ERROR)
```
to fully mute these messages
## Expected results
it shouldn't log these messages when setting `datasets.utils.logging.set_verbosity_error()`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: tried both 1.8.0 & 1.9.0
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.8.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2622 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2622/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2622/comments | https://api.github.com/repos/huggingface/datasets/issues/2622/events | https://github.com/huggingface/datasets/issues/2622 | 941,127,785 | MDU6SXNzdWU5NDExMjc3ODU= | 2,622 | Integration with AugLy | {
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"Hi,\r\n\r\nyou can define your own custom formatting with `Dataset.set_transform()` and then run the tokenizer with the batches of augmented data as follows:\r\n```python\r\ndset = load_dataset(\"imdb\", split=\"train\") # Let's say we are working with the IMDB dataset\r\ndset.set_transform(lambda ex: {\"text\": ... | 2021-07-10T00:03:09Z | 2023-07-20T13:18:48Z | 2023-07-20T13:18:47Z | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Facebook recently launched a library, [AugLy](https://github.com/facebookresearch/AugLy) , that has a unified API for augmentations for image, video and text.
It would be pretty exciting to have it hooked up to HF libraries so that we can make NLP models robust to misspellings or to punctuation, or emojis etc. Plus, with Transformers supporting more CV use cases, having augmentations support becomes crucial.
**Describe the solution you'd like**
The biggest difference between augmentations and preprocessing is that preprocessing happens only once, but you are running augmentations once per epoch. AugLy operates on text directly, so this breaks the typical workflow where we would run the tokenizer once, set format to pt tensors and be ready for the Dataloader.
**Describe alternatives you've considered**
One possible way of implementing these is to make a custom Dataset class where getitem(i) runs the augmentation and the tokenizer every time, though this would slow training down considerably given we wouldn't even run the tokenizer in batches.
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https://api.github.com/repos/huggingface/datasets/issues/2618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2618/comments | https://api.github.com/repos/huggingface/datasets/issues/2618/events | https://github.com/huggingface/datasets/issues/2618 | 940,852,640 | MDU6SXNzdWU5NDA4NTI2NDA= | 2,618 | `filelock.py` Error | {
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"Hi @liyucheng09, thanks for reporting.\r\n\r\nApparently this issue has to do with your environment setup. One question: is your data in an NFS share? Some people have reported this error when using `fcntl` to write to an NFS share... If this is the case, then it might be that your NFS just may not be set up to pr... | 2021-07-09T15:12:49Z | 2024-06-21T06:14:07Z | 2023-11-23T19:06:19Z | NONE | null | null | null | null | ## Describe the bug
It seems that the `filelock.py` went error.
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
```
According to error log, it is OSError, but there is an `except` in the `_acquire` function.
```
def _acquire(self):
open_mode = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
fd = os.open(self._lock_file, open_mode)
except (IOError, OSError):
pass
else:
self._lock_file_fd = fd
return None
```
I don't know why it stucked rather than `pass` directly.
I am not quite familiar with filelock operation, so any help is highly appriciated.
## Steps to reproduce the bug
```python
ds = load_dataset('xsum')
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
>>> ds=load_dataset('xsum')
^CTraceback (most recent call last):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
OSError: [Errno 37] No locks available
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 818, in load_dataset
use_auth_token=use_auth_token,
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/load.py", line 470, in prepare_module
with FileLock(lock_path):
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 323, in __enter__
self.acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 272, in acquire
self._acquire()
File "/user/HS502/yl02706/.conda/envs/lyc/lib/python3.6/site-packages/datasets/utils/filelock.py", line 402, in _acquire
fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
KeyboardInterrupt
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-135-generic-x86_64-with-debian-buster-sid
- Python version: 3.6.13
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2615/comments | https://api.github.com/repos/huggingface/datasets/issues/2615/events | https://github.com/huggingface/datasets/issues/2615 | 940,794,339 | MDU6SXNzdWU5NDA3OTQzMzk= | 2,615 | Jsonlines export error | {
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"Thanks for reporting @TevenLeScao! I'm having a look...",
"(not sure what just happened on the assignations sorry)",
"For some reason this happens (both `datasets` version are on master) only on Python 3.6 and not Python 3.8.",
"@TevenLeScao we are using `pandas` to serialize the dataset to JSON Lines. So it... | 2021-07-09T14:02:05Z | 2021-07-09T15:29:07Z | 2021-07-09T15:28:33Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
When exporting large datasets in jsonlines (c4 in my case) the created file has an error every 9999 lines: the 9999th and 10000th are concatenated, thus breaking the jsonlines format. This sounds like it is related to batching, which is by 10000 by default
## Steps to reproduce the bug
This what I'm running:
in python:
```
from datasets import load_dataset
ptb = load_dataset("ptb_text_only")
ptb["train"].to_json("ptb.jsonl")
```
then out of python:
```
head -10000 ptb.jsonl
```
## Expected results
Properly separated lines
## Actual results
The last line is a concatenation of two lines
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-1046-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
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https://api.github.com/repos/huggingface/datasets/issues/2607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2607/comments | https://api.github.com/repos/huggingface/datasets/issues/2607/events | https://github.com/huggingface/datasets/issues/2607 | 938,796,902 | MDU6SXNzdWU5Mzg3OTY5MDI= | 2,607 | Streaming local gzip compressed JSON line files is not working | {
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"Updating to pyarrow-4.0.1 didn't fix the issue",
"Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python",
"Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj... | 2021-07-07T11:36:33Z | 2021-07-20T09:50:19Z | 2021-07-08T16:08:41Z | MEMBER | null | null | null | null | ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error
## Steps to reproduce the bug
```python
from datasets import load_dataset
streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)
next(iter(streamed_dataset))
```
## Actual results
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
336
337 def __iter__(self):
--> 338 for key, example in self._iter():
339 if self.features:
340 # we encode the example for ClassLabel feature types for example
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
333 else:
334 ex_iterable = self._ex_iterable
--> 335 yield from ex_iterable
336
337 def __iter__(self):
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
76
77 def __iter__(self):
---> 78 for key, example in self.generate_examples_fn(**self.kwargs):
79 yield key, example
80
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
282 def wrapper(**kwargs):
283 python_formatter = PythonFormatter()
--> 284 for key, table in generate_tables_fn(**kwargs):
285 batch = python_formatter.format_batch(table)
286 for i, example in enumerate(_batch_to_examples(batch)):
~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
85 file,
86 read_options=self.config.pa_read_options,
---> 87 parse_options=self.config.pa_parse_options,
88 )
89 except pa.ArrowInvalid as err:
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```
## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
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"#take"
] | 2021-07-07T10:56:04Z | 2021-07-12T22:34:31Z | 2021-07-12T22:34:31Z | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this
While training we require metrics which can give all the output
Currently, we don't have an exact match for text normalized data
**Describe the solution you'd like**
A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary
For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py)
**Describe alternatives you've considered**
Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script
#self-assign | {
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"Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is ... | 2021-07-07T07:56:16Z | 2021-07-19T09:08:18Z | 2021-07-19T09:08:18Z | MEMBER | null | null | null | null | I'm loading a dataset constituted of 44 GB of compressed JSON files.
When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables
Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.
I can maybe tackle this one in the JSON script unless you want a more general solution. | {
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If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes.
## Steps to reproduce the bug
```python
from datasets import Dataset
data = Dataset.from_dict({'id': [0,1]})
data.filter(lambda x: False, num_proc=2)
```
## Expected results
An empty table should be returned without crashing.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter
return self.map(
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map
result = concatenate_datasets(transformed_shards)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets
table = concat_tables(tables_to_concat, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables
blocks = to_blocks(tables[0])
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2598/comments | https://api.github.com/repos/huggingface/datasets/issues/2598/events | https://github.com/huggingface/datasets/issues/2598 | 937,930,632 | MDU6SXNzdWU5Mzc5MzA2MzI= | 2,598 | Unable to download omp dataset | {
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"Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp datas... | 2021-07-06T14:00:52Z | 2021-07-07T12:56:35Z | 2021-07-07T12:56:35Z | NONE | null | null | null | null | ## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError
## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)
## Expected results
This code should download the omp dataset and print the dictionary
## Actual results
Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...
0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split
writer.write(example, key)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "hf_datasets.py", line 32, in <module>
omp = load_dataset('omp', 'posts_labeled')
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.8.0
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.6.9
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2596/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2596/comments | https://api.github.com/repos/huggingface/datasets/issues/2596/events | https://github.com/huggingface/datasets/issues/2596 | 937,598,914 | MDU6SXNzdWU5Mzc1OTg5MTQ= | 2,596 | Transformer Class on dataset | {
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"Hi ! Do you have an example in mind that shows how this could be useful ?",
"Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n —> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n... | 2021-07-06T07:27:15Z | 2022-11-02T14:26:09Z | 2022-11-02T14:26:09Z | NONE | null | null | null | null | Just wondering if you have intenttion to create
TransformerClass :
dataset --> dataset
and make determnistic transformation (ie not fit).
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https://api.github.com/repos/huggingface/datasets/issues/2595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2595/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2595/comments | https://api.github.com/repos/huggingface/datasets/issues/2595/events | https://github.com/huggingface/datasets/issues/2595 | 937,483,120 | MDU6SXNzdWU5Mzc0ODMxMjA= | 2,595 | ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets | {
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"Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.",
"@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. "
] | 2021-07-06T03:20:55Z | 2021-07-06T05:59:49Z | 2021-07-06T05:59:49Z | NONE | null | null | null | null | Error traceback:
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
1 from datasets import load_dataset, load_metric
2
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")
9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
19
20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
22
23
ModuleNotFoundError: No module named 'datasets.tasks' | {
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"Hi! I'm transferring this issue over to `datasets`",
"I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n",
"Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be store... | 2021-07-05T10:43:19Z | 2021-07-19T09:08:19Z | 2021-07-19T09:08:19Z | CONTRIBUTOR | null | null | null | null | I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).
The cache folder is 500gb (and now my disk space is full).
Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).
This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.
Traceback (most recent call last):
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
writer.finalize()
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
self.pa_writer.close()
File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""
The above exception was the direct cause of the following exception:
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https://api.github.com/repos/huggingface/datasets/issues/2585 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2585/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2585/comments | https://api.github.com/repos/huggingface/datasets/issues/2585/events | https://github.com/huggingface/datasets/issues/2585 | 936,484,419 | MDU6SXNzdWU5MzY0ODQ0MTk= | 2,585 | sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index | {
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"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so ... | 2021-07-04T15:39:49Z | 2021-07-07T13:18:51Z | 2021-07-07T13:18:51Z | NONE | null | null | null | null | ## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].
For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.
## Steps to reproduce the bug
```python
import datasets
def check_context_answer_alignment(example):
for a_idx in range(len(example['answers']['text'])):
# check raw dataset for answer consistency between context and answer
answer_text = example['answers']['text'][a_idx]
a_st_idx = example['answers']['answer_start'][a_idx]
a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
answer_text_from_context = example['context'][a_st_idx:a_end_idx]
if answer_text != answer_text_from_context:
#print(example['id'])
return False
return True
dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)
start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
num_proc=1,
keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```
## Expected results
This code should result in 0 rows being filtered out from the dataset.
## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.
This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."
## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y
# activate the virtual environment
conda activate round8
# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia
pip install jsonpickle transformers datasets matplotlib
```
OS: Ubuntu 20.04
Python 3.8
Result of `conda env export`:
```
name: round8
channels:
- pytorch-lts
- nvidia
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- blas=1.0=mkl
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2021.5.25=h06a4308_1
- certifi=2021.5.30=py38h06a4308_0
- cffi=1.14.5=py38h261ae71_0
- chardet=4.0.0=py38h06a4308_1003
- cryptography=3.4.7=py38hd23ed53_0
- cudatoolkit=11.1.74=h6bb024c_0
- ffmpeg=4.2.2=h20bf706_0
- freetype=2.10.4=h5ab3b9f_0
- gmp=6.2.1=h2531618_2
- gnutls=3.6.15=he1e5248_0
- idna=2.10=pyhd3eb1b0_0
- intel-openmp=2021.2.0=h06a4308_610
- jpeg=9b=h024ee3a_2
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libidn2=2.3.1=h27cfd23_0
- libopus=1.3.1=h7b6447c_0
- libpng=1.6.37=hbc83047_0
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libtasn1=4.16.0=h27cfd23_0
- libtiff=4.2.0=h85742a9_0
- libunistring=0.9.10=h27cfd23_0
- libuv=1.40.0=h7b6447c_0
- libvpx=1.7.0=h439df22_0
- libwebp-base=1.2.0=h27cfd23_0
- lz4-c=1.9.3=h2531618_0
- mkl=2021.2.0=h06a4308_296
- mkl-service=2.3.0=py38h27cfd23_1
- mkl_fft=1.3.0=py38h42c9631_2
- mkl_random=1.2.1=py38ha9443f7_2
- ncurses=6.2=he6710b0_1
- nettle=3.7.3=hbbd107a_1
- ninja=1.10.2=hff7bd54_1
- numpy=1.20.2=py38h2d18471_0
- numpy-base=1.20.2=py38hfae3a4d_0
- olefile=0.46=py_0
- openh264=2.1.0=hd408876_0
- openssl=1.1.1k=h27cfd23_0
- pillow=8.2.0=py38he98fc37_0
- pip=21.1.2=py38h06a4308_0
- pycparser=2.20=py_2
- pyopenssl=20.0.1=pyhd3eb1b0_1
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.10=h12debd9_8
- pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
- readline=8.1=h27cfd23_0
- requests=2.25.1=pyhd3eb1b0_0
- setuptools=52.0.0=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.35.4=hdfb4753_0
- tk=8.6.10=hbc83047_0
- torchtext=0.9.1=py38
- torchvision=0.9.1=py38_cu111
- typing_extensions=3.7.4.3=pyha847dfd_0
- urllib3=1.26.4=pyhd3eb1b0_0
- wheel=0.36.2=pyhd3eb1b0_0
- x264=1!157.20191217=h7b6447c_0
- xz=5.2.5=h7b6447c_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.9=haebb681_0
- pip:
- click==8.0.1
- cycler==0.10.0
- datasets==1.8.0
- dill==0.3.4
- filelock==3.0.12
- fsspec==2021.6.0
- huggingface-hub==0.0.8
- joblib==1.0.1
- jsonpickle==2.0.0
- kiwisolver==1.3.1
- matplotlib==3.4.2
- multiprocess==0.70.12.2
- packaging==20.9
- pandas==1.2.4
- pyarrow==3.0.0
- pyparsing==2.4.7
- python-dateutil==2.8.1
- pytz==2021.1
- regex==2021.4.4
- sacremoses==0.0.45
- tokenizers==0.10.3
- tqdm==4.49.0
- transformers==4.6.1
- xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```
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https://api.github.com/repos/huggingface/datasets/issues/2583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2583/comments | https://api.github.com/repos/huggingface/datasets/issues/2583/events | https://github.com/huggingface/datasets/issues/2583 | 936,034,976 | MDU6SXNzdWU5MzYwMzQ5NzY= | 2,583 | Error iteration over IterableDataset using Torch DataLoader | {
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"Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r... | 2021-07-02T19:55:58Z | 2021-07-20T09:04:45Z | 2021-07-05T23:48:23Z | NONE | null | null | null | null | ## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler.
I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me.
## Steps to reproduce the bug
1. Does not work.
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
>>> dataloader.sampler
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
>>> for batch in dataloader:
... print(batch)
```
2. Works.
```python
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
class CustomIterableDataset(IterableDataset):
'Characterizes a dataset for PyTorch'
def __init__(self, data):
'Initialization'
self.data = data
def __iter__(self):
return iter(self.data)
data = list(range(12))
dataset = CustomIterableDataset(data)
dataloader = DataLoader(dataset, batch_size=4)
print("dataloader: ", dataloader.sampler)
for batch in dataloader:
print(batch)
```
## Expected results
To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.
dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>
tensor([0, 1, 2, 3])
tensor([4, 5, 6, 7])
tensor([ 8, 9, 10, 11])
## Actual results
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data
index = self._next_index() # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
for idx in self.sampler:
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__
return iter(range(len(self.data_source)))
TypeError: object of type 'IterableDataset' has no len()
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: '1.8.1.dev0'
- Platform: Linux
- Python version: Python 3.6.8
- PyArrow version: '3.0.0'
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"This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user"
] | 2021-07-01T08:48:35Z | 2021-07-01T19:10:53Z | null | MEMBER | null | null | null | null | As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets
> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)
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"I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook.\r\n\r\n```\r\n!pip install zstandard\r\nfrom datasets import load_dataset\r\n\r\nlds = load_dataset(\r\n \"j... | 2021-07-01T08:37:04Z | 2023-01-03T15:34:01Z | 2021-07-05T10:50:27Z | MEMBER | null | null | null | null | Add support for Zstandard compressed files: https://facebook.github.io/zstd/ | {
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https://api.github.com/repos/huggingface/datasets/issues/2569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2569/comments | https://api.github.com/repos/huggingface/datasets/issues/2569/events | https://github.com/huggingface/datasets/issues/2569 | 933,015,797 | MDU6SXNzdWU5MzMwMTU3OTc= | 2,569 | Weights of model checkpoint not initialized for RobertaModel for Bertscore | {
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"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei... | 2021-06-29T18:55:23Z | 2021-07-01T07:08:59Z | 2021-06-30T07:35:49Z | NONE | null | null | null | null | When applying bertscore out of the box,
```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```
Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html
```
from datasets import load_metric
metric = load_metric('bertscore')
# Example of typical usage
for batch in dataset:
inputs, references = batch
predictions = model(inputs)
metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```
I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version: 3.9.5
- PyArrow version: 3.0.0
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"It is probably worth noting here that the [documentation](https://huggingface.co/docs/datasets/process#concatenate) is misleading (indicating that it does work for IterableDatasets):\r\n\r\n> You can also mix several datasets together by taking alternating examples from each one to create a new dataset. This is kn... | 2021-06-29T08:59:41Z | 2022-06-28T21:15:04Z | 2022-06-28T21:15:04Z | MEMBER | null | null | null | null | Currently `concatenate_datasets` only works for map-style `Dataset`.
It would be nice to have it work for `IterableDataset` objects as well.
It would simply chain the iterables of the iterable datasets. | {
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"gists... | null | [] | 2021-06-29T08:57:24Z | 2021-07-01T09:33:33Z | 2021-07-01T09:33:33Z | MEMBER | null | null | null | null | Currently the `interleave_datasets` functions only works for `IterableDataset`.
Let's make it work for map-style `Dataset` objects as well.
It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user. | {
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https://api.github.com/repos/huggingface/datasets/issues/2561 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2561/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2561/comments | https://api.github.com/repos/huggingface/datasets/issues/2561/events | https://github.com/huggingface/datasets/issues/2561 | 932,321,725 | MDU6SXNzdWU5MzIzMjE3MjU= | 2,561 | Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` | {
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"Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's c... | 2021-06-29T07:43:03Z | 2022-08-04T11:58:36Z | 2022-08-04T11:58:36Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.
## Steps to reproduce the bug
- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.
## Expected results
With `ignore_verifications=True`, `load_dataset` should pick up existing cache.
## Actual results
Creates new cache.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2559 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2559/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2559/comments | https://api.github.com/repos/huggingface/datasets/issues/2559/events | https://github.com/huggingface/datasets/issues/2559 | 931,849,724 | MDU6SXNzdWU5MzE4NDk3MjQ= | 2,559 | Memory usage consistently increases when processing a dataset with `.map` | {
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"Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened",
"This is the same behavior as in #4883, so I'm closing this issue as a duplicate. "
] | 2021-06-28T18:31:58Z | 2023-07-20T13:34:10Z | 2023-07-20T13:34:10Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.
## Steps to reproduce the bug
Providing code as it is would be hard. I can provide a MVP if that helps.
## Expected results
Memory usage should become consistent after some time following the launch of processing.
## Actual results
Memory usage keeps on increasing.
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
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https://api.github.com/repos/huggingface/datasets/issues/2556 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2556/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2556/comments | https://api.github.com/repos/huggingface/datasets/issues/2556/events | https://github.com/huggingface/datasets/issues/2556 | 931,595,872 | MDU6SXNzdWU5MzE1OTU4NzI= | 2,556 | Better DuplicateKeysError error to help the user debug the issue | {
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"excuse me, my `datasets` version is `2.2.2`, but I also just see the error info like \r\n```\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```",
"Hi ! for which dataset do you have this error ?\r\n\r\nAlso note that this is... | 2021-06-28T13:50:57Z | 2022-06-28T09:26:04Z | 2022-06-28T09:26:04Z | MEMBER | null | null | null | null | As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.
The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
and we could have something that guides the user to debugging the issue:
```python
DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.
Please fix the dataset script at <path/to/the/dataset/script>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2554/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2554/comments | https://api.github.com/repos/huggingface/datasets/issues/2554/events | https://github.com/huggingface/datasets/issues/2554 | 931,453,855 | MDU6SXNzdWU5MzE0NTM4NTU= | 2,554 | Multilabel metrics not supported | {
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"Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.",
"Looks nice, thank you very much! 🚀 ",
"Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enou... | 2021-06-28T11:09:46Z | 2021-10-13T12:29:13Z | 2021-07-08T08:40:15Z | NONE | null | null | null | null | When I try to use a metric like F1 macro I get the following error:
```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274
And looks like this is because here
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88
the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:
```python
class F1(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
}
),
reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
)
def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
),
}
```
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"Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.",
"This is fixed on `master` now :)\r\nWe'll do a new release soon !"
] | 2021-06-28T09:26:46Z | 2021-06-28T17:23:39Z | 2021-06-28T17:23:16Z | NONE | null | null | null | null | Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev")
```
Gives
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: macOS-11.3.1-x86_64-i386-64bit
- Python version: 3.9.4
- PyArrow version: 3.0.0
Also tested on Linux, with python 3.6.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/2552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2552/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2552/comments | https://api.github.com/repos/huggingface/datasets/issues/2552/events | https://github.com/huggingface/datasets/issues/2552 | 931,354,687 | MDU6SXNzdWU5MzEzNTQ2ODc= | 2,552 | Keys should be unique error on code_search_net | {
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"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in... | 2021-06-28T09:15:20Z | 2021-09-06T14:08:30Z | 2021-09-02T08:25:29Z | MEMBER | null | null | null | null | ## Describe the bug
Loading `code_search_net` seems not possible at the moment.
## Steps to reproduce the bug
```python
>>> load_dataset('code_search_net')
Downloading: 8.50kB [00:00, 3.09MB/s]
Downloading: 19.1kB [00:00, 10.1MB/s]
No config specified, defaulting to: code_search_net/all
Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...
Traceback (most recent call last):
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split
writer.write(example, key)
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write
self.check_duplicate_keys()
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
## Environment info
- `datasets` version: 1.8.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 2.0.0
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] | closed | false | null | [] | null | [] | 2021-06-27T15:00:58Z | 2021-09-26T13:42:39Z | 2021-09-26T13:42:39Z | CONTRIBUTOR | null | null | null | null | Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.
If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411
If this change is ok with you, I'll open a pull request.
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"Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi... | 2021-06-25T04:32:23Z | 2021-06-25T21:07:57Z | 2021-06-25T21:07:56Z | NONE | null | null | null | null | Hi!
Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).
For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error:
```
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example
return encode_nested_example(self, example)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example
return schema.encode_example(obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example
if not -1 <= example_data < self.num_classes:
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
```
What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers? | {
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https://api.github.com/repos/huggingface/datasets/issues/2548 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2548/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2548/comments | https://api.github.com/repos/huggingface/datasets/issues/2548/events | https://github.com/huggingface/datasets/issues/2548 | 929,232,831 | MDU6SXNzdWU5MjkyMzI4MzE= | 2,548 | Field order issue in loading json | {
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] | closed | false | null | [] | null | [
"Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists."
] | 2021-06-24T13:29:53Z | 2021-06-24T14:36:43Z | 2021-06-24T14:34:05Z | NONE | null | null | null | null | ## Describe the bug
The `load_dataset` function expects columns in alphabetical order when loading json files.
Similar bug was previously reported for csv in #623 and fixed in #684.
## Steps to reproduce the bug
For a json file `j.json`,
```
{"c":321, "a": 1, "b": 2}
```
Running the following,
```
f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')})
json_data = datasets.load_dataset('json', data_files='j.json', features=f)
```
## Expected results
A successful load.
## Actual results
```
File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast
ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2547 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2547/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2547/comments | https://api.github.com/repos/huggingface/datasets/issues/2547/events | https://github.com/huggingface/datasets/issues/2547 | 929,192,329 | MDU6SXNzdWU5MjkxOTIzMjk= | 2,547 | Dataset load_from_disk is too slow | {
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"Hi ! It looks like an issue with the virtual disk you are using.\r\n\r\nWe load datasets using memory mapping. In general it makes it possible to load very big files instantaneously since it doesn't have to read the file (it just assigns virtual memory to the file on disk).\r\nHowever there happens to be issues wi... | 2021-06-24T12:45:44Z | 2021-06-25T14:56:38Z | null | NONE | null | null | null | null | @lhoestq
## Describe the bug
It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example).
## Steps to reproduce the bug
Just get the oscar in spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.
## Expected results
I expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Ubuntu 18
- Python version: 3.8
I've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model. | null | {
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"Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code."
] | 2021-06-23T19:26:55Z | 2021-06-25T13:40:19Z | 2021-06-25T13:40:19Z | CONTRIBUTOR | null | null | null | null | In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.
The trouble is that now we get a ton of these:
```
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.
06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns.
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
```
May I suggest that these can be `log.debug` as it's no informative to the user.
More examples: these are not informative - too much information:
```
06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports.
06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
```
While these are:
```
06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy.
But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`.
e.g.:
```
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`.
Warnings are typically something that's bordering error or the first thing to check when things don't work as expected.
infrequent info is there to inform of the different stages or important events.
Everything else is debug.
At least the way I understand things.
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https://api.github.com/repos/huggingface/datasets/issues/2542 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2542/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2542/comments | https://api.github.com/repos/huggingface/datasets/issues/2542/events | https://github.com/huggingface/datasets/issues/2542 | 928,540,382 | MDU6SXNzdWU5Mjg1NDAzODI= | 2,542 | `datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA` | {
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"very much related: https://github.com/huggingface/datasets/pull/2333",
"Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue wit... | 2021-06-23T18:41:16Z | 2021-06-25T21:50:05Z | 2021-06-24T14:57:08Z | CONTRIBUTOR | null | null | null | null | ## Describe the bug
Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("drop")
load_dataset("adversarial_qa", "adversarialQA")
```
## Expected results
The examples keys should be unique.
## Actual results
```bash
>>> load_dataset("drop")
Using custom data configuration default
Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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"Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[... | 2021-06-23T07:19:52Z | 2023-04-19T11:05:38Z | null | NONE | null | null | null | null | I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits).
Every time I execute load_dataset for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.
Is there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.
Something like a debug mode would really help. Thanks! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2536 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2536/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2536/comments | https://api.github.com/repos/huggingface/datasets/issues/2536/events | https://github.com/huggingface/datasets/issues/2536 | 927,338,639 | MDU6SXNzdWU5MjczMzg2Mzk= | 2,536 | Use `Audio` features for `AutomaticSpeechRecognition` task template | {
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"I'm just retaking and working on #2324. 😉 ",
"Resolved via https://github.com/huggingface/datasets/pull/4006."
] | 2021-06-22T15:07:21Z | 2022-06-01T17:18:16Z | 2022-06-01T17:18:16Z | MEMBER | null | null | null | null | In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'.
The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in `datasets`, but should be included in the `AutomaticSpeechRecognition` template once they are. | {
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"Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?",
"> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers"
] | 2021-06-22T10:08:18Z | 2021-06-23T05:17:25Z | 2021-06-23T05:17:25Z | CONTRIBUTOR | null | null | null | null | [This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner).
The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`:
Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)
It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ヿ')` return 'コト'.
One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.
I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.
p.s.
**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)**
`get_dataset `is just a simple wrapping for `load_dataset`
and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")` | {
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"Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`."
] | 2021-06-21T15:04:54Z | 2021-06-24T14:42:47Z | 2021-06-24T14:42:47Z | NONE | null | null | null | null | ## Describe the bug
In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449)
`disable=bool(logging.get_verbosity() == logging.NOTSET)`
and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493)
`not_verbose = bool(logger.getEffectiveLevel() > WARNING)`
## Steps to reproduce the bug
```python
import datasets
import logging
datasets.logging.get_verbosity = lambda : logging.NOTSET
datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
```
## Expected results
The code should download and load the dataset as normal without displaying progress bars
## Actual results
```ImportError Traceback (most recent call last)
<ipython-input-4-aec65c0509c6> in <module>
----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
~/venv/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)
713 dataset=True,
714 return_resolved_file_path=True,
--> 715 use_auth_token=use_auth_token,
716 )
717 # Set the base path for downloads as the parent of the script location
~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
350 file_path = hf_bucket_url(path, filename=name, dataset=False)
351 try:
--> 352 local_path = cached_path(file_path, download_config=download_config)
353 except FileNotFoundError:
354 raise FileNotFoundError(
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
289 use_etag=download_config.use_etag,
290 max_retries=download_config.max_retries,
--> 291 use_auth_token=download_config.use_auth_token,
292 )
293 elif os.path.exists(url_or_filename):
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
668 headers=headers,
669 cookies=cookies,
--> 670 max_retries=max_retries,
671 )
672
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)
493 initial=resume_size,
494 desc="Downloading",
--> 495 disable=not_verbose,
496 )
497 for chunk in response.iter_content(chunk_size=1024):
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs)
217 total = self.total * unit_scale if self.total else self.total
218 self.container = self.status_printer(
--> 219 self.fp, total, self.desc, self.ncols)
220 self.sp = self.display
221
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols)
95 if IProgress is None: # #187 #451 #558 #872
96 raise ImportError(
---> 97 "IProgress not found. Please update jupyter and ipywidgets."
98 " See https://ipywidgets.readthedocs.io/en/stable"
99 "/user_install.html")
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8
- Python version: 3.7.10
- PyArrow version: 3.0.0
I am running this code on Deepnote and which important to this issue **does not** support IPywidgets
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"I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco",
"I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.... | 2021-06-21T07:48:32Z | 2023-06-22T14:12:18Z | null | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** COCO
- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.
- **Paper + website:** https://cocodataset.org/#home
- **Data:** https://cocodataset.org/#download
- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.
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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"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.",
"The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are ... | 2021-06-19T07:08:57Z | 2023-01-02T12:04:58Z | 2023-01-02T12:04:58Z | NONE | null | null | null | null | As per documentation,
Dataset: emotion
Homepage: https://github.com/dair-ai/emotion_dataset
Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py
Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion
Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.
But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust. | {
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"The `task_templates` API is deprecated in favor of the `train-eval-index` YAML field, so I'm closing this issue."
] | 2021-06-18T15:33:57Z | 2023-07-20T13:20:32Z | 2023-07-20T13:20:32Z | MEMBER | null | null | null | null | I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for.
## Text classification
* [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` format and each sample appears to be tokenized.
* [muchocine](https://huggingface.co/datasets/muchocine): contains two candidate text columns (long-form and summary) which in principle requires two `TextClassification` templates which is not currently supported | {
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https://api.github.com/repos/huggingface/datasets/issues/2516 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2516/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2516/comments | https://api.github.com/repos/huggingface/datasets/issues/2516/events | https://github.com/huggingface/datasets/issues/2516 | 924,597,470 | MDU6SXNzdWU5MjQ1OTc0NzA= | 2,516 | datasets.map pickle issue resulting in invalid mapping function | {
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"Hi ! `map` calls `__getstate__` using `dill` to hash your map function. This is used by the caching mechanism to recover previously computed results. That's why you don't see any `__setstate__` call.\r\n\r\nWhy do you change an attribute of your tokenizer when `__getstate__` is called ?",
"@lhoestq because if I ... | 2021-06-18T06:47:26Z | 2021-06-23T13:47:49Z | null | NONE | null | null | null | null | I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` / `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.
The following reproduces the issue - most likely I'm missing something
A simulated tokeniser which can be pickled
```
class CustomTokenizer:
def __init__(self):
self.state = "init"
def __getstate__(self):
print("__getstate__ called")
out = self.__dict__.copy()
self.state = "pickled"
return out
def __setstate__(self, d):
print("__setstate__ called")
self.__dict__ = d
self.state = "restored"
tokenizer = CustomTokenizer()
```
Test that it actually works - prints "__getstate__ called" and "__setstate__ called"
```
import pickle
serialized = pickle.dumps(tokenizer)
restored = pickle.loads(serialized)
assert restored.state == "restored"
```
Simulate a function that tokenises examples, when dataset.map is called, this function
```
def tokenize_function(examples):
assert tokenizer.state == "restored" # this shouldn't fail but it does
output = tokenizer(examples) # this will fail as tokenizer isn't really a tokenizer
return output
```
Use map to simulate tokenization
```
import glob
from datasets import load_dataset
assert tokenizer.state == "restored"
train_files = glob.glob('train*.csv')
validation_files = glob.glob('validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
)
```
What's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ / __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-22-a2aef4f74aaa> in <module>
8 tokenized_datasets = datasets.map(
9 tokenize_function,
---> 10 batched=True,
11 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)
487 desc=desc,
488 )
--> 489 for k, dataset in self.items()
490 }
491 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
487 desc=desc,
488 )
--> 489 for k, dataset in self.items()
490 }
491 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1633 fn_kwargs=fn_kwargs,
1634 new_fingerprint=new_fingerprint,
-> 1635 desc=desc,
1636 )
1637 else:
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
184 }
185 # apply actual function
--> 186 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
187 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
188 # re-apply format to the output
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)
1961 indices,
1962 check_same_num_examples=len(input_dataset.list_indexes()) > 0,
-> 1963 offset=offset,
1964 )
1965 except NumExamplesMismatch:
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1853 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1854 processed_inputs = (
-> 1855 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1856 )
1857 if update_data is None:
<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)
1 def tokenize_function(examples):
----> 2 assert tokenizer.state == "restored"
3 tokenizer(examples)
4 return examples
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https://api.github.com/repos/huggingface/datasets/issues/2514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2514/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2514/comments | https://api.github.com/repos/huggingface/datasets/issues/2514/events | https://github.com/huggingface/datasets/issues/2514 | 924,417,172 | MDU6SXNzdWU5MjQ0MTcxNzI= | 2,514 | Can datasets remove duplicated rows? | {
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"Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to b... | 2021-06-17T23:35:38Z | 2024-07-19T13:23:01Z | null | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..
**Describe the solution you'd like**
have a functionality of " remove duplicated rows"
**Describe alternatives you've considered**
convert dataset to pandas, remove duplicate, and convert back...
**Additional context**
no | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2513 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2513/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2513/comments | https://api.github.com/repos/huggingface/datasets/issues/2513/events | https://github.com/huggingface/datasets/issues/2513 | 924,174,413 | MDU6SXNzdWU5MjQxNzQ0MTM= | 2,513 | Corelation should be Correlation | {
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"Hi @colbym-MM, thanks for reporting. We are fixing it."
] | 2021-06-17T17:28:48Z | 2021-06-18T08:43:55Z | 2021-06-18T08:43:55Z | NONE | null | null | null | null | https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66 | {
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https://api.github.com/repos/huggingface/datasets/issues/2512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2512/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2512/comments | https://api.github.com/repos/huggingface/datasets/issues/2512/events | https://github.com/huggingface/datasets/issues/2512 | 924,069,353 | MDU6SXNzdWU5MjQwNjkzNTM= | 2,512 | seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict' | {
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"Sorry, I was using an old version of sequeval"
] | 2021-06-17T15:36:02Z | 2021-06-17T15:46:07Z | 2021-06-17T15:46:07Z | NONE | null | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
seqeval = load_metric("seqeval")
seqeval.compute(predictions=[['A']], references=[['A']])
```
## Expected results
The function computes a dict with metrics
## Actual results
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-69a57f5cf06f> in <module>
1 from datasets import load_dataset, load_metric
2 seqeval = load_metric("seqeval")
----> 3 seqeval.compute(predictions=[['A']], references=[['A']])
~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix)
95
96 def _compute(self, predictions, references, suffix=False):
---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True)
98 report.pop("macro avg")
99 report.pop("weighted avg")
TypeError: classification_report() got an unexpected keyword argument 'output_dict'
```
## Environment info
sklearn=0.24
datasets=1.1.3
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"Update on this: I'm computing the checksums of the data files. It will be available soon",
"Added in #2575 :)"
] | 2021-06-17T10:31:04Z | 2021-07-05T12:36:58Z | 2021-07-05T12:36:57Z | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** *C4*
- **Description:** *https://github.com/allenai/allennlp/discussions/5056*
- **Paper:** *https://arxiv.org/abs/1910.10683*
- **Data:** *https://huggingface.co/datasets/allenai/c4*
- **Motivation:** *Used a lot for pretraining*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Should fix https://github.com/huggingface/datasets/issues/1710 | {
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https://api.github.com/repos/huggingface/datasets/issues/2508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2508/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2508/comments | https://api.github.com/repos/huggingface/datasets/issues/2508/events | https://github.com/huggingface/datasets/issues/2508 | 921,863,173 | MDU6SXNzdWU5MjE4NjMxNzM= | 2,508 | Load Image Classification Dataset from Local | {
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"Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path... | 2021-06-15T22:43:33Z | 2022-03-01T16:29:44Z | 2022-03-01T16:29:44Z | NONE | null | null | null | null | **Is your feature request related to a problem? Please describe.**
Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader.
**Describe the solution you'd like**
Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10".
**Describe alternatives you've considered**
Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)
Write custom data loader logic
**Additional context**
We're training ViT on custom dataset
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https://api.github.com/repos/huggingface/datasets/issues/2503 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2503/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2503/comments | https://api.github.com/repos/huggingface/datasets/issues/2503/events | https://github.com/huggingface/datasets/issues/2503 | 920,636,186 | MDU6SXNzdWU5MjA2MzYxODY= | 2,503 | SubjQA wrong boolean values in entries | {
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"Hi @arnaudstiegler, thanks for reporting. I'm investigating it.",
"@arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https://github.com/megagonlabs/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.",
"I have:\r\n- opened an issue in th... | 2021-06-14T17:42:46Z | 2021-08-25T03:52:06Z | null | NONE | null | null | null | null | ## Describe the bug
SubjQA seems to have a boolean that's consistently wrong.
It defines:
- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).
- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)
However, `is_ques_subjective` seems to have wrong values in the entire dataset.
For instance, in the example in the dataset card, we have:
- "question_subj_level": 2
- "is_ques_subjective": false
However, according to the description, the question should be subjective since the `question_subj_level` is below 4
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"👀 @TalSchuster",
"Thanks @VictorSanh!\r\nThere's also a [notebook](https://aka.ms/python_puzzles) and [demo](https://aka.ms/python_puzzles_study) available now to try out some of the puzzles"
] | 2021-06-14T13:27:18Z | 2021-06-15T18:14:14Z | null | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** Python Programming Puzzles
- **Description:** Programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis
- **Paper:** https://arxiv.org/pdf/2106.05784.pdf
- **Data:** https://github.com/microsoft/PythonProgrammingPuzzles ([Scrolling through the data](https://github.com/microsoft/PythonProgrammingPuzzles/blob/main/problems/README.md))
- **Motivation:** Spans a large range of difficulty, problems, and domains. A useful resource for evaluation as we don't have a clear understanding of the abilities and skills of extremely large LMs.
Note: it's a growing dataset (contributions are welcome), so we'll need careful versioning for 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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"That’s interesting thanks, let’s see what we can do. Can you detail your last sentence? I’m not sure I understand it well.",
"Hi ! I just re-ran a quick benchmark and using `to_numpy()` seems to be faster now:\r\n\r\n```python\r\nimport pyarrow as pa # I used pyarrow 3.0.0\r\nimport numpy as np\r\n\r\nn, max_le... | 2021-06-14T13:25:24Z | 2022-07-15T17:12:04Z | null | CONTRIBUTOR | null | null | null | null | **Is your feature request related to a problem? Please describe.**
It would be great, if possible, to further improve read performance of raw encoded datasets and their subsequent conversion to torch tensors.
A bit more background. I am working on LM pre-training using HF ecosystem. We use encoded HF Wikipedia and BookCorpus datasets. The training machines are similar to DGX-1 workstations. We use HF trainer torch.distributed training approach on a single machine with 8 GPUs.
The current performance is about 30% slower than NVidia optimized BERT [examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling) baseline. Quite a bit of customized code and training loop tricks were used to achieve the baseline performance. It would be great to achieve the same performance while using nothing more than off the shelf HF ecosystem. Perhaps, in the future, with @stas00 work on deepspeed integration, it could even be exceeded.
**Describe the solution you'd like**
Using profiling tools we've observed that appx. 25% of cumulative run time is spent on data loader next call.
As you can observe most of the data loader next call is spent in HF datasets torch_formatter.py format_batch call.
Digging a bit deeper into format_batch we can see the following profiler data:
Once again, a lot of time is spent in pyarrow table conversion to pandas which seems like an intermediary step. Offline @lhoestq told me that this approach was, for some unknown reason, faster than direct to numpy conversion.
**Describe alternatives you've considered**
I am not familiar with pyarrow and have not yet considered the alternatives to the current approach.
Most of the online advice around data loader performance improvements revolve around increasing number of workers, using pin memory for copying tensors from host device to gpus but we've already tried these avenues without much performance improvement. Weights & Biases dashboard for the pre-training task reports CPU utilization of ~ 10%, GPUs are completely saturated (GPU utilization is above 95% on all GPUs), while disk utilization is above 90%.
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"gists... | null | [] | 2021-06-14T09:20:26Z | 2021-06-21T15:05:03Z | 2021-06-21T15:05:03Z | MEMBER | null | null | null | null | `Dataset.map` uses the dataset fingerprint (a hash) for caching.
However the fingerprint seems to change when someone moves the cache directory of the dataset.
This is because it uses the default fingerprint generation:
1. the dataset path is used to get the fingerprint
2. the modification times of the arrow file is also used to get the fingerprint
To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory. | {
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"Hi @albertvillanova, I hope you are doing well.\r\n\r\nI am interested in this issue, is this still unresolved and open ?\r\n\r\nThe link you have provided in the above message directs to a webpage that does not exist.\r\n\r\nThanks and Regards",
"#self-assign"
] | 2021-06-14T08:11:48Z | 2025-06-28T18:55:38Z | null | MEMBER | null | null | null | null | In the ["Enhancing performance"](https://huggingface.co/docs/datasets/loading_datasets.html#enhancing-performance) section of docs, add specific use cases:
- How to make datasets the fastest
- How to make datasets take the less RAM
- How to make datasets take the less hard drive mem
cc: @thomwolf
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- it was fixed on 3 May 2021
- version 4.0.1 was released on 19 May 2021 with the bug fix | {
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https://api.github.com/repos/huggingface/datasets/issues/2485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2485/comments | https://api.github.com/repos/huggingface/datasets/issues/2485/events | https://github.com/huggingface/datasets/issues/2485 | 919,099,218 | MDU6SXNzdWU5MTkwOTkyMTg= | 2,485 | Implement layered building | {
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] | open | false | null | [] | null | [] | 2021-06-11T18:54:25Z | 2021-06-11T18:54:25Z | null | MEMBER | null | null | null | null | As discussed with @stas00 and @lhoestq (see also here https://github.com/huggingface/datasets/issues/2481#issuecomment-859712190):
> My suggestion for this would be to have this enabled by default.
>
> Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:
>
> 1. uncompress a handful of files via a generator enough to generate one arrow file
> 2. process arrow file 1
> 3. delete all the files that went in and aren't needed anymore.
>
> rinse and repeat.
>
> 1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project
> 2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing
> 3. It would already include deleting temp files this issue is talking about
>
> I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders. | null | {
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