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https://api.github.com/repos/huggingface/datasets/issues/3983 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3983/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3983/comments | https://api.github.com/repos/huggingface/datasets/issues/3983/events | https://github.com/huggingface/datasets/issues/3983 | 1,175,759,412 | I_kwDODunzps5GFKo0 | 3,983 | Infinitely attempting lock | [] | closed | false | null | 1 | 2022-03-21T18:11:57Z | 2022-05-06T16:12:18Z | 2022-05-06T16:12:18Z | null | I am trying to run one of the examples of the `transformers` repo, which makes use of `datasets`.
Important to note is that I am trying to run this via a Databricks notebook, and all the files reside in the Databricks Filesystem (DBFS).
```
%sh
python /dbfs/transformers/examples/pytorch/summarization/run_summarization.py \
--model_name_or_path t5-small \
--do_train \
--do_eval \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--source_prefix "summarize: " \
--output_dir /dbfs/transformers/tmp/tst-summarization \
--per_device_train_batch_size=4 \
--per_device_eval_batch_size=4 \
--overwrite_output_dir \
--predict_with_generate \
--log_level debug \
--cache_dir /dbfs/transformers/cache
```
All goes well until acquiring a lock --
```
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:19 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Attempting to acquire lock 140386484514192 on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock
03/21/2022 17:53:20 - DEBUG - datasets.utils.filelock - Lock 140386484514192 not acquired on /dbfs/transformers/cache/_dbfs_transformers_cache_cnn_dailymail_3.0.0_3.0.0_3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234.lock, waiting 0.05 seconds ...
```
and so on.
I imagine this has to do with DBFS -- is there a way to tackle this? | {
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} | https://api.github.com/repos/huggingface/datasets/issues/3983/timeline | null | completed | null | null | false | [
"Hi ! Thanks for reporting. We're using `py-filelock` as our locking mechanism.\r\n\r\nCan you try deleting the .lock file mentioned in the logs and try again ? Make sure that no other process is generating the `cnn_dailymail` dataset.\r\n\r\nIf it doesn't work, could you try to set up a lock using the latest vers... |
https://api.github.com/repos/huggingface/datasets/issues/1386 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1386/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1386/comments | https://api.github.com/repos/huggingface/datasets/issues/1386/events | https://github.com/huggingface/datasets/pull/1386 | 760,365,505 | MDExOlB1bGxSZXF1ZXN0NTM1MjA5NDUx | 1,386 | Add RecipeNLG Dataset (manual download) | [] | closed | false | null | 1 | 2020-12-09T14:13:19Z | 2020-12-10T16:58:22Z | 2020-12-10T16:58:21Z | null | {
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"@lhoestq yes. I asked the authors for direct link but unfortunately we need to fill a form (captcha)"
] | |
https://api.github.com/repos/huggingface/datasets/issues/5244 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5244/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5244/comments | https://api.github.com/repos/huggingface/datasets/issues/5244/events | https://github.com/huggingface/datasets/issues/5244 | 1,450,019,225 | I_kwDODunzps5WbYmZ | 5,244 | Allow dataset streaming from private a private source when loading a dataset with a dataset loading script | [
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] | open | false | null | 5 | 2022-11-15T16:02:10Z | 2022-11-23T14:02:30Z | null | null | ### Feature request
Add arguments to the function _get_authentication_headers_for_url_ like custom_endpoint and custom_token in order to add flexibility when downloading files from a private source.
It should also be possible to provide these arguments from the dataset loading script, maybe giving them to the dl_manager
### Motivation
It is possible to share a dataset hosted on another platform by writing a dataset loading script. It works perfectly for publicly available resources.
For resources that require authentication, you can provide a [download_custom](https://huggingface.co/docs/datasets/package_reference/builder_classes#datasets.DownloadManager) method to the download_manager.
Unfortunately, this function doesn't work with **dataset streaming**.
A solution so as to allow dataset streaming from private sources would be a more flexible _get_authentication_headers_for_url_ function.
### Your contribution
Would you be interested in this improvement ?
If so I could provide a PR. I've got something working locally, but it's not very clean, I'd need some guidance regarding integration. | {
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"Hi ! What kind of private source ? We're exploring adding support for cloud storage and URIs like s3://, gs:// etc. with authentication in the download manager",
"Hello! It's a google cloud storage, so gs://, but I'm using it with https.\r\nBeing able to provide a file system like [here](https://huggingface.co/d... |
https://api.github.com/repos/huggingface/datasets/issues/5818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5818/comments | https://api.github.com/repos/huggingface/datasets/issues/5818/events | https://github.com/huggingface/datasets/issues/5818 | 1,695,052,555 | I_kwDODunzps5lCHML | 5,818 | Ability to update a dataset | [
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] | open | false | null | 3 | 2023-05-04T01:08:13Z | 2023-05-04T20:43:39Z | null | null | ### Feature request
The ability to load a dataset, add or change something, and save it back to disk.
Maybe it's possible, but I can't work out how to do it, e.g. this fails:
```py
import datasets
dataset = datasets.load_from_disk("data/test1")
dataset = dataset.add_item({"text": "A new item"})
dataset.save_to_disk("data/test1")
```
With the error:
```
PermissionError: Tried to overwrite /mnt/c/Users/david/py/learning/mini_projects/data_sorting_and_filtering/data/test1 but a dataset can't overwrite itself.
```
### Motivation
My use case is that I want to process a dataset in a particular way but it doesn't fit in memory if I do it in one go. So I want to perform a loop and at each step in the loop, process one shard and append it to an ever-growing dataset. The code in the loop will load a dataset, add some rows, then save it again.
Maybe I'm just thinking about things incorrectly and there's a better approach. FWIW I can't use `dataset.map()` to do the task because that doesn't work with `num_proc` when adding rows, so is confined to a single process which is too slow.
The only other way I can think of is to create a new file each time, but surely that's not how people do this sort of thing.
### Your contribution
na | {
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"This [reply](https://discuss.huggingface.co/t/how-do-i-add-things-rows-to-an-already-saved-dataset/27423) from @mariosasko on the forums may be useful :)",
"In this case, I think we can avoid the `PermissionError` by unpacking the underlying `ConcatenationTable` and saving only the newly added data blocks (in ne... |
https://api.github.com/repos/huggingface/datasets/issues/63 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/63/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/63/comments | https://api.github.com/repos/huggingface/datasets/issues/63/events | https://github.com/huggingface/datasets/pull/63 | 614,666,365 | MDExOlB1bGxSZXF1ZXN0NDE1MTczODU5 | 63 | [Dataset scripts] add all datasets scripts | [] | closed | false | null | 0 | 2020-05-08T10:50:15Z | 2020-05-08T17:39:22Z | 2020-05-08T11:34:00Z | null | As mentioned, we can have the canonical datasets in the master. For now I also want to include all the data as present on S3 to make the synchronization easier when uploading new datastes.
@mariamabarham @lhoestq @thomwolf - what do you think?
If this is ok for you, I can sync up the master with the `add_dataset` branch: https://github.com/huggingface/nlp/pull/37 so that master is up to date. | {
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https://api.github.com/repos/huggingface/datasets/issues/1573 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1573/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1573/comments | https://api.github.com/repos/huggingface/datasets/issues/1573/events | https://github.com/huggingface/datasets/pull/1573 | 767,011,938 | MDExOlB1bGxSZXF1ZXN0NTM5ODYyNjcx | 1,573 | adding dataset for diplomacy detection-2 | [] | closed | false | null | 0 | 2020-12-14T23:21:37Z | 2020-12-14T23:36:57Z | 2020-12-14T23:36:57Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2117/comments | https://api.github.com/repos/huggingface/datasets/issues/2117/events | https://github.com/huggingface/datasets/issues/2117 | 841,535,283 | MDU6SXNzdWU4NDE1MzUyODM= | 2,117 | load_metric from local "glue.py" meet error 'NoneType' object is not callable | [] | closed | false | null | 3 | 2021-03-26T02:35:22Z | 2021-08-25T21:44:05Z | 2021-03-26T02:40:26Z | null | actual_task = "mnli" if task == "mnli-mm" else task
dataset = load_dataset(path='/home/glue.py', name=actual_task)
metric = load_metric(path='/home/glue.py', name=actual_task)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-7ab77a465d81> in <module>
1 actual_task = "mnli" if task == "mnli-mm" else task
2 dataset = load_dataset(path='/home/jcli/glue.py', name=actual_task)
----> 3 metric = load_metric(path='/home/jcli/glue.py', name=actual_task)
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
508 keep_in_memory=keep_in_memory,
509 experiment_id=experiment_id,
--> 510 **metric_init_kwargs,
511 )
512
TypeError: 'NoneType' object is not callable
Please help | {
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"@Frankie123421 what was the resolution to this?",
"> @Frankie123421 what was the resolution to this?\r\n\r\nuse glue_metric.py instead of glue.py in load_metric",
"thank you!"
] |
https://api.github.com/repos/huggingface/datasets/issues/5689 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5689/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5689/comments | https://api.github.com/repos/huggingface/datasets/issues/5689/events | https://github.com/huggingface/datasets/pull/5689 | 1,648,956,349 | PR_kwDODunzps5NVMuI | 5,689 | Support streaming Beam datasets from HF GCS preprocessed data | [] | closed | false | null | 4 | 2023-03-31T08:44:24Z | 2023-04-12T05:57:55Z | 2023-04-12T05:50:31Z | null | This PR implements streaming Apache Beam datasets that are already preprocessed by us and stored in the HF Google Cloud Storage:
- natural_questions
- wiki40b
- wikipedia
This is done by streaming from the prepared Arrow files in HF Google Cloud Storage.
This will fix their corresponding dataset viewers. Related to:
- https://github.com/huggingface/datasets-server/pull/988#discussion_r1150767138
Related to:
- https://huggingface.co/datasets/natural_questions/discussions/4
- https://huggingface.co/datasets/wiki40b/discussions/2
- https://huggingface.co/datasets/wikipedia/discussions/9
CC: @severo | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"```python\r\nIn [1]: from datasets import load_dataset\r\n\r\nIn [2]: ds = load_dataset(\"wikipedia\", \"20220301.en\", split=\"train\", streaming=True); item = next(iter(ds)); item\r\nOut[2]: \r\n{'id': '12',\r\n 'url': 'https://en.... |
https://api.github.com/repos/huggingface/datasets/issues/3122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3122/comments | https://api.github.com/repos/huggingface/datasets/issues/3122/events | https://github.com/huggingface/datasets/issues/3122 | 1,031,787,509 | I_kwDODunzps49f9P1 | 3,122 | OSError with a custom dataset loading script | [
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] | closed | false | null | 8 | 2021-10-20T20:08:39Z | 2021-11-23T09:55:38Z | 2021-11-23T09:55:38Z | null | ## Describe the bug
I am getting an OS error when trying to load the newly uploaded dataset classla/janes_tag. What puzzles me is that I have already uploaded a very similar dataset - classla/reldi_hr - with no issues. The loading scripts for the two datasets are almost identical and they have the same directory structure, yet I am only getting an error with janes_tag.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('classla/janes_tag', split='validation')
```
## Expected results
Dataset correctly loaded.
## Actual results
Traceback (most recent call last):
File "C:/mypath/test.py", line 91, in <module>
load_and_print('janes_tag')
File "C:/mypath/test.py", line 32, in load_and_print
dataset = datasets.load_dataset('classla/{}'.format(ds_name), split='validation')
File "C:\mypath\venv\lib\site-packages\datasets\load.py", line 1632, in load_dataset
use_auth_token=use_auth_token,
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 608, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "C:\mypath\venv\lib\site-packages\datasets\builder.py", line 704, in _download_and_prepare
) from None
OSError: Cannot find data file.
Original error:
[Errno 2] No such file or directory: 'C:\\mypath\\.cache\\huggingface\\datasets\\downloads\\2c9996e44bdc5af9c89bffb9e6d7a3e42fdb2f56bacab45de13b20f3032ea7ca\\data\\train_all.conllup'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.14.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.5
- PyArrow version: 3.0.0
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"Hi,\r\n\r\nthere is a difference in how the `data_dir` is zipped between the `classla/janes_tag` and the `classla/reldi_hr` dataset. After unzipping, for the former, the data files (`*.conllup`) are in the root directory (root -> data files), and for the latter, they are inside the `data` directory (root -> `data`... |
https://api.github.com/repos/huggingface/datasets/issues/3491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3491/comments | https://api.github.com/repos/huggingface/datasets/issues/3491/events | https://github.com/huggingface/datasets/issues/3491 | 1,089,918,018 | I_kwDODunzps5A9tRC | 3,491 | Update version of pib dataset | [
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"id": 2067376369,
"name": "dataset request",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
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] | closed | false | null | 0 | 2021-12-28T14:03:58Z | 2021-12-29T08:42:57Z | 2021-12-29T08:42:57Z | null | On the Hub we have v0, while there exists v1.3.
Related to bigscience-workshop/data_tooling#130
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https://api.github.com/repos/huggingface/datasets/issues/861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/861/comments | https://api.github.com/repos/huggingface/datasets/issues/861/events | https://github.com/huggingface/datasets/issues/861 | 744,753,458 | MDU6SXNzdWU3NDQ3NTM0NTg= | 861 | Possible Bug: Small training/dataset file creates gigantic output | [
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"default": true... | closed | false | null | 7 | 2020-11-17T13:48:59Z | 2021-03-30T14:04:04Z | 2021-03-22T12:04:55Z | null | Hey guys,
I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.
I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?
I've used the following CMD:
`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`
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"The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is w... |
https://api.github.com/repos/huggingface/datasets/issues/1143 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1143/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1143/comments | https://api.github.com/repos/huggingface/datasets/issues/1143/events | https://github.com/huggingface/datasets/pull/1143 | 757,448,920 | MDExOlB1bGxSZXF1ZXN0NTMyODI0NzMx | 1,143 | Add the Winograd Schema Challenge | [] | closed | false | null | 0 | 2020-12-04T22:26:59Z | 2020-12-09T15:11:31Z | 2020-12-09T09:32:34Z | null | Adds the Winograd Schema Challenge, including configs for the more canonical wsc273 as well as wsc285 with 12 new examples.
- https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
The data format was a bit of a nightmare but I think I got it to a workable format. | {
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https://api.github.com/repos/huggingface/datasets/issues/5156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5156/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5156/comments | https://api.github.com/repos/huggingface/datasets/issues/5156/events | https://github.com/huggingface/datasets/issues/5156 | 1,421,667,125 | I_kwDODunzps5UvOs1 | 5,156 | Unable to download dataset using Azure Data Lake Gen 2 | [] | closed | false | null | 2 | 2022-10-25T00:43:18Z | 2022-11-17T23:37:09Z | 2022-11-17T23:37:08Z | null | ### Describe the bug
When using the DatasetBuilder method with the credentials for the cloud storage Azure Data Lake (adl) Gen2, the following error is showed:
```
Traceback (most recent call last):
File "download_hf_dataset.py", line 143, in <module>
main()
File "download_hf_dataset.py", line 102, in main
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/datasets/builder.py", line 671, in download_and_prepare
fs_token_paths = fsspec.get_fs_token_paths(output_dir, storage_options=storage_options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/core.py", line 639, in get_fs_token_paths
fs = cls(**options)
File "/home/clarisses/miniconda3/envs/hf_datasets_env/lib/python3.8/site-packages/fsspec/spec.py", line 76, in __call__
obj = super().__call__(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'account_name'
```
If I don't pass the storage_options argument (leave it as None), it requires the credentials used in ADL Gen 1:
`TypeError: __init__() missing 3 required positional arguments: 'tenant_id', 'client_id', and 'client_secret'`
Thus, it is not possible to download a dataset from the cloud using Azure Data Lake (adl) Gen2.
### Steps to reproduce the bug
Assuming that you have an account on Azure and at Storage Account that can be used for reproduce:
1. Create a dict with the format to connect to Azure Data Lake Gen 2
```
storage_options = {"account_name": ACCOUNT_NAME, "account_key": ACCOUNT_KEY) # gen 2 filesystem
```
2. Create a dataset builder for any HF hosted dataset
```
builder = load_dataset_builder(dataset_name)
```
3. Try to download the dataset passing the storage_options as an argument
```
save_dir = 'adl://my_save_dir'
builder.download_and_prepare(save_dir, storage_options=storage_options, max_shard_size="250MB", file_format="parquet")
```
### Expected behavior
Not seeing the error mentioned above and being able to download the dataset to the provided path on ADL
### Environment info
- `datasets` version: 2.6.1
- Platform: Linux-5.15.0-46-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.1 | {
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"Hi ! From the `adlfs` docs, there are two filesystems you can use:\r\n> To use the Gen1 filesystem:\r\n> - known_implementations[‘adl’] = {‘class’: ‘adlfs.AzureDatalakeFileSystem’}\r\n> \r\n> To use the Gen2 filesystem:\r\n> - known_implementations[‘abfs’] = {‘class’: ‘adlfs.AzureBlobFileSystem’}\r\n\r\nIf I'm no... |
https://api.github.com/repos/huggingface/datasets/issues/2673 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2673/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2673/comments | https://api.github.com/repos/huggingface/datasets/issues/2673/events | https://github.com/huggingface/datasets/pull/2673 | 947,300,008 | MDExOlB1bGxSZXF1ZXN0NjkyMzAxMTgw | 2,673 | Fix potential DuplicatedKeysError in SQuAD | [] | closed | false | null | 0 | 2021-07-19T06:08:00Z | 2021-07-19T07:08:03Z | 2021-07-19T07:08:03Z | null | DONE:
- Fix potential DiplicatedKeysError by ensuring keys are unique.
- Align examples in the docs with SQuAD code.
We should promote as a good practice, that the keys should be programmatically generated as unique, instead of read from data (which might be not unique). | {
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https://api.github.com/repos/huggingface/datasets/issues/2160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2160/comments | https://api.github.com/repos/huggingface/datasets/issues/2160/events | https://github.com/huggingface/datasets/issues/2160 | 849,052,921 | MDU6SXNzdWU4NDkwNTI5MjE= | 2,160 | data_args.preprocessing_num_workers almost freezes | [] | closed | false | null | 2 | 2021-04-02T07:56:13Z | 2021-04-02T10:14:32Z | 2021-04-02T10:14:31Z | null | Hi @lhoestq
I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py
to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.
thanks | {
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"Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3: 11%|███████████████▊ ... |
https://api.github.com/repos/huggingface/datasets/issues/4405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4405/comments | https://api.github.com/repos/huggingface/datasets/issues/4405/events | https://github.com/huggingface/datasets/issues/4405 | 1,248,574,087 | I_kwDODunzps5Ka7qH | 4,405 | [TypeError: Couldn't cast array of type] Cannot process dataset in v2.2.2 | [
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] | closed | false | null | 1 | 2022-05-25T18:56:43Z | 2022-06-07T14:27:20Z | 2022-06-07T14:27:20Z | null | ## Describe the bug
I am trying to process the [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5) dataset in `datasets` v2.2.2 and am running into a type error when casting the features.
## Steps to reproduce the bug
```python
import os
from typing import (
List,
Dict,
)
from collections import (
defaultdict,
)
from dataclasses import (
dataclass,
)
from datasets import (
load_dataset,
)
@dataclass
class ConllConverter:
path: str
name: str
cache_dir: str
def __post_init__(
self,
):
self.dataset = load_dataset(
path=self.path,
name=self.name,
cache_dir=self.cache_dir,
)
def convert(
self,
):
class_label = self.dataset["train"].features["sentences"][0]["named_entities"].feature
# label_set = list(set([
# label.split("-")[1] if label != "O" else label for label in class_label.names
# ]))
def prepare_chunk(token, entity):
assert len(token) == len(entity)
# Sequence length
length = len(token)
# Variable used
entity_chunk = defaultdict(list)
idx = flag = 0
# While loop
while idx < length:
if entity[idx] == "O":
flag += 1
idx += 1
else:
iob_tp, lab_tp = entity[idx].split("-")
assert iob_tp == "B"
idx += 1
while idx < length and entity[idx].startswith("I-"):
idx += 1
entity_chunk[lab_tp].append(token[flag: idx])
flag = idx
entity_chunk = dict(entity_chunk)
# for label in label_set:
# if label != "O" and label not in entity_chunk.keys():
# entity_chunk[label] = None
return entity_chunk
def prepare_features(
batch: Dict[str, List],
) -> Dict[str, List]:
sentence = [
sent for doc_sent in batch["sentences"] for sent in doc_sent
]
feature = {
"sentence": list(),
}
for sent in sentence:
token = sent["words"]
entity = class_label.int2str(sent["named_entities"])
entity_chunk = prepare_chunk(token, entity)
sent_feat = {
"token": token,
"entity": entity,
"entity_chunk": entity_chunk,
}
feature["sentence"].append(sent_feat)
return feature
column_names = self.dataset.column_names["train"]
dataset = self.dataset.map(
function=prepare_features,
with_indices=False,
batched=True,
batch_size=3,
remove_columns=column_names,
num_proc=1,
)
dataset.save_to_disk(
dataset_dict_path=os.path.join("data", self.path, self.name)
)
if __name__ == "__main__":
converter = ConllConverter(
path="conll2012_ontonotesv5",
name="english_v4",
cache_dir="cache",
)
converter.convert()
```
## Expected results
I want to use the dataset to perform NER task and to change the label list into a {Entity Type: list of spans} format.
## Actual results
<details>
<summary>Traceback</summary>
```python
Traceback (most recent call last): | 0/81 [00:00<?, ?ba/s]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 532, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 499, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/fingerprint.py", line 458, in wrapper
out = func(self, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2751, in _map_single
writer.write_batch(batch)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 503, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 230, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_writer.py", line 198, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1793, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1675, in wrapper
return func(array, *args, **kwargs)
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/table.py", line 1844, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 110, in <module>
converter.convert()
File "/home2/jiangwangyi/workspace/work/Entity/dataconverter.py", line 91, in convert
dataset = self.dataset.map(
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 770, in map
{
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/dataset_dict.py", line 771, in <dictcomp>
k: dataset.map(
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2459, in map
transformed_shards[index] = async_result.get()
File "/home2/jiangwangyi/miniconda3/lib/python3.9/site-packages/multiprocess/pool.py", line 771, in get
raise self._value
TypeError: Couldn't cast array of type
struct<CARDINAL: list<item: list<item: string>>, DATE: list<item: list<item: string>>, EVENT: list<item: list<item: string>>, FAC: list<item: list<item: string>>, GPE: list<item: list<item: string>>, LANGUAGE: list<item: list<item: string>>, LAW: list<item: list<item: string>>, LOC: list<item: list<item: string>>, MONEY: list<item: list<item: string>>, NORP: list<item: list<item: string>>, ORDINAL: list<item: list<item: string>>, ORG: list<item: list<item: string>>, PERCENT: list<item: list<item: string>>, PERSON: list<item: list<item: string>>, QUANTITY: list<item: list<item: string>>, TIME: list<item: list<item: string>>, WORK_OF_ART: list<item: list<item: string>>>
to
{'CARDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'DATE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'EVENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'FAC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'GPE': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LAW': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'LOC': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'MONEY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'NORP': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORDINAL': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'ORG': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERCENT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PERSON': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'PRODUCT': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'QUANTITY': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'TIME': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None), 'WORK_OF_ART': Sequence(feature=Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), length=-1, id=None)}
```
</details>
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Ubuntu 18.04
- Python version: 3.9.7
- PyArrow version: 7.0.0
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} | https://api.github.com/repos/huggingface/datasets/issues/4405/timeline | null | completed | null | null | false | [
"And if the problem is that the way I am to construct the {Entity Type: list of spans} makes entity types without any spans hard to handle, is there a better way to meet the demand? Although I have verified that to make entity types without any spans to behave like `entity_chunk[label] = [[\"\"]]` can perform norma... |
https://api.github.com/repos/huggingface/datasets/issues/4359 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4359/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4359/comments | https://api.github.com/repos/huggingface/datasets/issues/4359/events | https://github.com/huggingface/datasets/pull/4359 | 1,237,149,578 | PR_kwDODunzps434Pb6 | 4,359 | Fix Version equality | [] | closed | false | null | 1 | 2022-05-16T13:19:26Z | 2022-05-24T16:25:37Z | 2022-05-24T16:17:14Z | null | I think `Version` equality should align with other similar cases in Python, like:
```python
In [1]: "a" == 5, "a" == None
Out[1]: (False, False)
In [2]: "a" != 5, "a" != None
Out[2]: (True, True)
```
With this PR, we will get:
```python
In [3]: Version("1.0.0") == 5, Version("1.0.0") == None
Out[3]: (False, False)
In [4]: Version("1.0.0") != 5, Version("1.0.0") != None
Out[4]: (True, True)
```
Note I found this issue when `doc-builder` tried to compare:
```python
if param.default != inspect._empty
```
where `param.default` is an instance of `Version`. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/505/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/505/comments | https://api.github.com/repos/huggingface/datasets/issues/505/events | https://github.com/huggingface/datasets/pull/505 | 678,791,400 | MDExOlB1bGxSZXF1ZXN0NDY3NjgxMjY4 | 505 | tmp_file referenced before assignment | [] | closed | false | null | 2 | 2020-08-13T23:27:33Z | 2020-08-14T13:42:46Z | 2020-08-14T13:42:46Z | null | Just learning about this library - so might've not set up all the flags correctly, but was getting this error about "tmp_file". | {
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} | true | [
"Thanks for reporting the issue ! I'm creating a new PR to fix it and add tests.\r\n(I'm doing a new PR because I know there's some other place where it needs to be fixed)",
"I'm closing this one as I created the other PR."
] |
https://api.github.com/repos/huggingface/datasets/issues/3190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3190/comments | https://api.github.com/repos/huggingface/datasets/issues/3190/events | https://github.com/huggingface/datasets/issues/3190 | 1,041,153,631 | I_kwDODunzps4-Dr5f | 3,190 | combination of shuffle and filter results in a bug | [
{
"color": "d73a4a",
"default": true,
"description": "Something isn't working",
"id": 1935892857,
"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
}
] | closed | false | null | 3 | 2021-11-01T13:07:29Z | 2021-11-02T10:50:49Z | 2021-11-02T10:50:49Z | null | ## Describe the bug
Hi,
I would like to shuffle a dataset, then filter it based on each existing label. however, the combination of `filter`, `shuffle` seems to results in a bug. In the minimal example below, as you see in the filtered results, the filtered labels are not unique, meaning filter has not worked. Any suggestions as a temporary fix is appreciated @lhoestq.
Thanks.
Best regards
Rabeeh
## Steps to reproduce the bug
```python
import numpy as np
import datasets
datasets = datasets.load_dataset('super_glue', 'rte', script_version="master")
shuffled_data = datasets["train"].shuffle(seed=42)
for label in range(2):
print("label ", label)
data = shuffled_data.filter(lambda example: int(example['label']) == label)
print("length ", len(data), np.unique(data['label']))
```
## Expected results
Filtering per label, should only return the data with that specific label.
## Actual results
As you can see, filtered data per label, has still two labels of [0, 1]
```
label 0
length 1249 [0 1]
label 1
length 1241 [0 1]
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.12.1
- Platform: linux
- Python version: 3.7.11
- PyArrow version: 5.0.0
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"I cannot reproduce this on master and pyarrow==4.0.1.\r\n",
"Hi ! There was a regression in `datasets` 1.12 that introduced this bug. It has been fixed in #3019 in 1.13\r\n\r\nCan you try to update `datasets` and try again ?",
"Thanks a lot, fixes with 1.13"
] |
https://api.github.com/repos/huggingface/datasets/issues/2087 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2087/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2087/comments | https://api.github.com/repos/huggingface/datasets/issues/2087/events | https://github.com/huggingface/datasets/pull/2087 | 836,587,392 | MDExOlB1bGxSZXF1ZXN0NTk3MDg4NTk2 | 2,087 | Update metadata if dataset features are modified | [] | closed | false | null | 4 | 2021-03-20T02:05:23Z | 2021-04-09T09:25:33Z | 2021-04-09T09:25:33Z | null | This PR adds a decorator that updates the dataset metadata if a previously executed transform modifies its features.
Fixes #2083
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"@lhoestq I'll try to add a test later if you think this approach with the wrapper is good.",
"Awesome thank you !\r\nYes this approach with a wrapper is good :)",
"@lhoestq Added a test. To verify that this change fixes the problem, replace:\r\n```\r\n!pip install datasets==1.5\r\n```\r\nwith:\r\n```\r\n!pip i... |
https://api.github.com/repos/huggingface/datasets/issues/329 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/329/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/329/comments | https://api.github.com/repos/huggingface/datasets/issues/329/events | https://github.com/huggingface/datasets/issues/329 | 648,446,979 | MDU6SXNzdWU2NDg0NDY5Nzk= | 329 | [Bug] FileLock dependency incompatible with filesystem | [] | closed | false | null | 9 | 2020-06-30T19:45:31Z | 2022-09-08T20:58:37Z | 2020-06-30T21:33:06Z | null | I'm downloading a dataset successfully with
`load_dataset("wikitext", "wikitext-2-raw-v1")`
But when I attempt to cache it on an external volume, it hangs indefinitely:
`load_dataset("wikitext", "wikitext-2-raw-v1", cache_dir="/fsx") # /fsx is an external volume mount`
The filesystem when hanging looks like this:
```bash
/fsx
----downloads
----94be...73.lock
----wikitext
----wikitext-2-raw
----wikitext-2-raw-1.0.0.incomplete
```
It appears that on this filesystem, the FileLock object is forever stuck in its "acquire" stage. I have verified that the issue lies specifically with the `filelock` dependency:
```python
open("/fsx/hello.txt").write("hello") # succeeds
from filelock import FileLock
with FileLock("/fsx/hello.lock"):
open("/fsx/hello.txt").write("hello") # hangs indefinitely
```
Has anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that. | {
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"Hi, can you give details on your environment/os/packages versions/etc?",
"Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile th... |
https://api.github.com/repos/huggingface/datasets/issues/2638 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2638/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2638/comments | https://api.github.com/repos/huggingface/datasets/issues/2638/events | https://github.com/huggingface/datasets/pull/2638 | 943,484,913 | MDExOlB1bGxSZXF1ZXN0Njg5MTA5NTg1 | 2,638 | Streaming for the Json loader | [] | closed | false | null | 2 | 2021-07-13T14:37:06Z | 2021-07-16T15:59:32Z | 2021-07-16T15:59:31Z | null | It was not using `open` in the builder. Therefore `pyarrow.json.read_json` was downloading the full file to start yielding rows.
Moreover, it appeared that `pyarrow.json.read_json` was not really suited for streaming as it was downloading too much data and failing if `block_size` was not properly configured (related to #2573).
So I switched to using `open` which is extended to support reading from remote file progressively, and I removed the pyarrow json reader which was not practical.
Instead, I'm using the classical `json.loads` from the standard library. | {
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"A note is that I think we should add a few indicator of status (as mentioned by @stas00 in #2649), probably at the (1) downloading, (2) extracting and (3) reading steps. In particular when loading many very large files it's interesting to know a bit where we are in the process.",
"I tested locally, and the built... |
https://api.github.com/repos/huggingface/datasets/issues/4026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4026/comments | https://api.github.com/repos/huggingface/datasets/issues/4026/events | https://github.com/huggingface/datasets/pull/4026 | 1,180,968,774 | PR_kwDODunzps41Btcm | 4,026 | Support streaming xtreme dataset for bucc18 config | [] | closed | false | null | 1 | 2022-03-25T16:00:40Z | 2022-03-25T16:26:50Z | 2022-03-25T16:21:52Z | null | Support streaming xtreme dataset for bucc18 config. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/1941 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1941/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1941/comments | https://api.github.com/repos/huggingface/datasets/issues/1941/events | https://github.com/huggingface/datasets/issues/1941 | 815,985,167 | MDU6SXNzdWU4MTU5ODUxNjc= | 1,941 | Loading of FAISS index fails for index_name = 'exact' | [] | closed | false | null | 3 | 2021-02-25T01:30:54Z | 2021-02-25T14:28:46Z | 2021-02-25T14:28:46Z | null | Hi,
It looks like loading of FAISS index now fails when using index_name = 'exact'.
For example, from the RAG [model card](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage).
Running `transformers==4.3.2` and datasets installed from source on latest `master` branch.
```bash
(venv) sergey_mkrtchyan datasets (master) $ python
Python 3.8.6 (v3.8.6:db455296be, Sep 23 2020, 13:31:39)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
>>> retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
Using custom data configuration dummy.psgs_w100.nq.no_index-dummy=True,with_index=False
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.no_index-dummy=True,with_index=False/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
Using custom data configuration dummy.psgs_w100.nq.exact-50b6cda57ff32ab4
Reusing dataset wiki_dpr (/Users/sergey_mkrtchyan/.cache/huggingface/datasets/wiki_dpr/dummy.psgs_w100.nq.exact-50b6cda57ff32ab4/0.0.0/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb)
0%| | 0/10 [00:00<?, ?it/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 425, in from_pretrained
return cls(
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 387, in __init__
self.init_retrieval()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 458, in init_retrieval
self.index.init_index()
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/transformers/models/rag/retrieval_rag.py", line 284, in init_index
self.dataset = load_dataset(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/load.py", line 750, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 734, in as_dataset
datasets = utils.map_nested(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/builder.py", line 769, in _build_single_dataset
post_processed = self._post_process(ds, resources_paths)
File "/Users/sergey_mkrtchyan/.cache/huggingface/modules/datasets_modules/datasets/wiki_dpr/8a97e0f4fa5bc46e179474db6a61b09d5d2419d2911835bd3f91d110c936d8bb/wiki_dpr.py", line 205, in _post_process
dataset.add_faiss_index("embeddings", custom_index=index)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/arrow_dataset.py", line 2516, in add_faiss_index
super().add_faiss_index(
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 416, in add_faiss_index
faiss_index.add_vectors(self, column=column, train_size=train_size, faiss_verbose=faiss_verbose)
File "/Users/sergey_mkrtchyan/workspace/huggingface/datasets/src/datasets/search.py", line 281, in add_vectors
self.faiss_index.add(vecs)
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/__init__.py", line 104, in replacement_add
self.add_c(n, swig_ptr(x))
File "/Users/sergey_mkrtchyan/workspace/cformers/venv/lib/python3.8/site-packages/faiss/swigfaiss.py", line 3263, in add
return _swigfaiss.IndexHNSW_add(self, n, x)
RuntimeError: Error in virtual void faiss::IndexHNSW::add(faiss::Index::idx_t, const float *) at /Users/runner/work/faiss-wheels/faiss-wheels/faiss/faiss/IndexHNSW.cpp:356: Error: 'is_trained' failed
>>>
```
The issue seems to be related to the scalar quantization in faiss added in this commit: 8c5220307c33f00e01c3bf7b8. Reverting it fixes the issue.
| {
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"Thanks for reporting ! I'm taking a look",
"Index training was missing, I fixed it here: https://github.com/huggingface/datasets/commit/f5986c46323583989f6ed1dabaf267854424a521\r\n\r\nCan you try again please ?",
"Works great 👍 I just put a minor comment on the commit, I think you meant to pass the `train_siz... |
https://api.github.com/repos/huggingface/datasets/issues/3389 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3389/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3389/comments | https://api.github.com/repos/huggingface/datasets/issues/3389/events | https://github.com/huggingface/datasets/issues/3389 | 1,072,191,865 | I_kwDODunzps4_6Fl5 | 3,389 | Add EDGAR | [
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] | open | false | null | 2 | 2021-12-06T14:06:11Z | 2022-10-05T10:40:22Z | null | null | ## Adding a Dataset
- **Name:** EDGAR Database
- **Description:** https://www.sec.gov/edgar/about EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system, is the primary system for companies and others submitting documents under the Securities Act of 1933, the Securities Exchange Act of 1934, the Trust Indenture Act of 1939, and the Investment Company Act of 1940. Containing millions of company and individual filings, EDGAR benefits investors, corporations, and the U.S. economy overall by increasing the efficiency, transparency, and fairness of the securities markets. The system processes about 3,000 filings per day, serves up 3,000 terabytes of data to the public annually, and accommodates 40,000 new filers per year on average. EDGAR® and EDGARLink® are registered trademarks of the SEC.
- **Data:** https://www.sec.gov/os/accessing-edgar-data
- **Motivation:** Enabling and improving FSI (Financial Services Industry) datasets to increase ease of use
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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"cc @juliensimon ",
"Datasets are not tracked in this repository anymore. But you can make your own dataset in the huggingface hub"
] |
https://api.github.com/repos/huggingface/datasets/issues/4224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4224/comments | https://api.github.com/repos/huggingface/datasets/issues/4224/events | https://github.com/huggingface/datasets/pull/4224 | 1,216,209,667 | PR_kwDODunzps420KX2 | 4,224 | autoeval config | [] | closed | false | null | 0 | 2022-04-26T16:35:19Z | 2022-04-26T16:36:45Z | 2022-04-26T16:36:45Z | null | add train eval index for autoeval | {
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https://api.github.com/repos/huggingface/datasets/issues/486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/486/comments | https://api.github.com/repos/huggingface/datasets/issues/486/events | https://github.com/huggingface/datasets/issues/486 | 675,649,034 | MDU6SXNzdWU2NzU2NDkwMzQ= | 486 | Bookcorpus data contains pretokenized text | [] | closed | false | null | 8 | 2020-08-09T06:53:24Z | 2022-10-04T17:44:33Z | 2022-10-04T17:44:33Z | null | It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end quotes, respectively.
On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/486/timeline | null | completed | null | null | false | [
"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do... |
https://api.github.com/repos/huggingface/datasets/issues/6077 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6077/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6077/comments | https://api.github.com/repos/huggingface/datasets/issues/6077/events | https://github.com/huggingface/datasets/issues/6077 | 1,822,486,810 | I_kwDODunzps5soPEa | 6,077 | Mapping gets stuck at 99% | [] | open | false | null | 3 | 2023-07-26T14:00:40Z | 2023-07-27T12:19:23Z | null | null | ### Describe the bug
Hi !
I'm currently working with a large (~150GB) unnormalized dataset at work.
The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it.
I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset.
The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why.
Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me.
### Steps to reproduce the bug
I'm able to reproduce the problem using the following scripts:
```python
# random_data.py
import datasets
import torch
_VERSION = "1.0.0"
class RandomDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
version=_VERSION,
supervised_keys=None,
features=datasets.Features(
{
"positions": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"normals": datasets.Array2D(
shape=(30000, 3),
dtype="float32",
),
"features": datasets.Array2D(
shape=(30000, 6),
dtype="float32",
),
"scalars": datasets.Sequence(
feature=datasets.Value("float32"),
length=20,
),
},
),
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, # type: ignore
gen_kwargs={"nb_samples": 1000},
),
datasets.SplitGenerator(
name=datasets.Split.TEST, # type: ignore
gen_kwargs={"nb_samples": 100},
),
]
def _generate_examples(self, nb_samples: int):
for idx in range(nb_samples):
yield idx, {
"positions": torch.randn(30000, 3),
"normals": torch.randn(30000, 3),
"features": torch.randn(30000, 6),
"scalars": torch.randn(20),
}
```
```python
# main.py
import datasets
import torch
def apply_mean_std(
dataset: datasets.Dataset,
means: dict[str, torch.Tensor],
stds: dict[str, torch.Tensor],
) -> dict[str, torch.Tensor]:
"""Normalize the dataset using the mean and standard deviation of each feature.
Args:
dataset (`Dataset`): A huggingface dataset.
mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature.
std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature.
Returns:
dict: A dictionary containing the normalized dataset.
"""
result = {}
for key in means.keys():
# extract data from dataset
data: torch.Tensor = dataset[key] # type: ignore
# extract mean and std from dict
mean = means[key] # type: ignore
std = stds[key] # type: ignore
# normalize data
normalized_data = (data - mean) / std
result[key] = normalized_data
return result
# get dataset
ds = datasets.load_dataset(
path="random_data.py",
split="train",
).with_format("torch")
# compute mean (along last axis)
means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names}
means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names}
for batch in ds.iter(batch_size=8):
for key in ds.column_names:
data = batch[key]
batch_size = data.shape[0]
data = data.reshape(-1, data.shape[-1])
means[key] += data.mean(dim=0) / len(ds) * batch_size
means_sq[key] += (data**2).mean(dim=0) / len(ds) * batch_size
# compute std (along last axis)
stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names}
# normalize each feature of the dataset
ds_normalized = ds.map(
desc="Applying mean/std", # type: ignore
function=apply_mean_std,
batched=False,
fn_kwargs={
"means": means,
"stds": stds,
},
)
```
### Expected behavior
Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster.
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.2 | {
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"The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so i... |
https://api.github.com/repos/huggingface/datasets/issues/5255 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5255/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5255/comments | https://api.github.com/repos/huggingface/datasets/issues/5255/events | https://github.com/huggingface/datasets/issues/5255 | 1,452,631,517 | I_kwDODunzps5WlWXd | 5,255 | Add a Depth Estimation dataset - DIODE / NYUDepth / KITTI | [
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] | closed | false | null | 21 | 2022-11-17T03:22:22Z | 2022-12-17T12:20:38Z | 2022-12-17T12:20:37Z | null | ### Name
NYUDepth
### Paper
http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf
### Data
https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
### Motivation
Depth estimation is an important problem in computer vision. We have a couple of Depth Estimation models on Hub as well:
* [GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)
* [DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
Would be nice to have a dataset for depth estimation. These datasets usually have three things: input image, depth map image, and depth mask (validity mask to indicate if a reading for a pixel is valid or not). Since we already have [semantic segmentation datasets on the Hub](https://huggingface.co/datasets?task_categories=task_categories:image-segmentation&sort=downloads), I don't think we need any extended utilities to support this addition.
Having this dataset would also allow us to author data preprocessing guides for depth estimation, particularly like the ones we have for other tasks ([example](https://huggingface.co/docs/datasets/image_classification)).
Ccing @osanseviero @nateraw @NielsRogge
Happy to work on adding it. | {
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"Also cc @mariosasko and @lhoestq ",
"Cool ! Let us know if you have questions or if we can help :)\r\n\r\nI guess we'll also have to create the NYU CS Department on the Hub ?",
"> I guess we'll also have to create the NYU CS Department on the Hub ?\r\n\r\nYes, you're right! Let me add it to my profile first, a... |
https://api.github.com/repos/huggingface/datasets/issues/3800 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3800/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3800/comments | https://api.github.com/repos/huggingface/datasets/issues/3800/events | https://github.com/huggingface/datasets/pull/3800 | 1,155,620,761 | PR_kwDODunzps4zvkjA | 3,800 | Added computer vision tasks | [] | closed | false | null | 0 | 2022-03-01T17:37:46Z | 2022-03-04T07:15:55Z | 2022-03-04T07:15:55Z | null | Previous PR was in my fork so thought it'd be easier if I do it from a branch. Added computer vision task datasets according to HF tasks. | {
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https://api.github.com/repos/huggingface/datasets/issues/4025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4025/comments | https://api.github.com/repos/huggingface/datasets/issues/4025/events | https://github.com/huggingface/datasets/issues/4025 | 1,180,963,105 | I_kwDODunzps5GZBEh | 4,025 | Missing argument in precision/recall | [
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] | closed | false | null | 1 | 2022-03-25T15:55:52Z | 2022-03-28T09:53:06Z | 2022-03-28T09:53:06Z | null | **Is your feature request related to a problem? Please describe.**
[`sklearn.metrics.precision_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) accepts an argument `zero_division`, but it is not available in [precision Metric](https://github.com/huggingface/datasets/blob/master/metrics/precision/precision.py#L117)
Same issue is present for Recall.
**Describe the solution you'd like**
Support for **kwargs or adding a new field for `zero_division`.
**Describe alternatives you've considered**
I could filter the warnings myself, but that is not ideal.
**Additional context**
I can make the requested changes if this is approved. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4025/timeline | null | completed | null | null | false | [
"Thanks for the suggestion, @Dref360.\r\n\r\nWe are adding that argument. "
] |
https://api.github.com/repos/huggingface/datasets/issues/1343 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1343/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1343/comments | https://api.github.com/repos/huggingface/datasets/issues/1343/events | https://github.com/huggingface/datasets/pull/1343 | 759,809,999 | MDExOlB1bGxSZXF1ZXN0NTM0NzQ4NTE4 | 1,343 | Add LiveQA | [] | closed | false | null | 0 | 2020-12-08T21:52:36Z | 2020-12-14T09:40:28Z | 2020-12-14T09:40:28Z | null | This PR adds LiveQA, the Chinese real-time/timeline-based QA task by [Liu et al., 2020](https://arxiv.org/pdf/2010.00526.pdf). | {
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https://api.github.com/repos/huggingface/datasets/issues/4041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4041/comments | https://api.github.com/repos/huggingface/datasets/issues/4041/events | https://github.com/huggingface/datasets/issues/4041 | 1,183,599,461 | I_kwDODunzps5GjEtl | 4,041 | Add support for IIIF in datasets | [
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] | open | false | null | 1 | 2022-03-28T15:19:25Z | 2022-04-05T18:20:53Z | null | null | This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred.
## What is [IIIF](https://iiif.io/)?
IIIF (International Image Interoperability Framework)
> is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions.
The tl;dr is that IIIF provides various specifications for implementing useful functionality for:
- Institutions to make available images for various use cases
- Users to have a consistent way of interacting/requesting these images
- For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF).
Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/
## IIIF APIs
IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/)
### IIIF Image API
The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL:
```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}```
A concrete example of this:
```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg```
As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:
We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`
We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`
A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size
## Why would/could this be useful for datasets?
There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows:
- images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller
- can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use.
- options for quality, rotation, the format can all be encoded in the URL request.
These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact.
## What could this look like in datasets?
I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach.
### Use through datasets scripts
Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script:
```python
ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg")
```
This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script.
### Support through dataset scripts (with some datasets support)
This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like:
```python
features = {"label": ClassLabel(names=['dog','cat']),
"url": datasets.IIIFURL()}
```
inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset.
### Other possible integrations
Some other possible pseudocode ways that a user could interact with IIIF URLs:
The ability to cast to an `IIIFImage` feature type:
```
ds.cast_column('url', IIIFImage, download=False)
```
The ability to specify some options associated with IIIF urls.
```
ds = ds.set_iiif_options(column='url', size="250,250")
```
I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways.
## prerequisite requirements
There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support:
### support for handling failed images loaded via a URL (or a specific IIIFImage feature).
Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs.
```python
from datasets import Dataset
import datasets
urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3
urls.append("badurl.com/image.jpg")
data = {"url":urls}
ds = Dataset.from_dict(data)
ds = ds.cast_column('url', datasets.Image())
ds[3]['url']
```
returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this.
### Caching support
Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs.
### Support for Parsing IIIF URLs
This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share.
## Why it might not be worthwhile/suitable for datasets
There are some reasons that this might not be worth implementing:
- currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models.
- It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble.
- The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from.
Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets.
## Suggested next steps:
I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.
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"Hi! Thanks for the detailed analysis of adding IIIF support. I like the idea of \"using IIIF through datasets scripts\" due to its ease of use. Another approach that I like is yielding image ids and using the `piffle` library (which offers a bit more flexibility) + `map` to download + cache images. We can handle b... |
https://api.github.com/repos/huggingface/datasets/issues/6008 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6008/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6008/comments | https://api.github.com/repos/huggingface/datasets/issues/6008/events | https://github.com/huggingface/datasets/issues/6008 | 1,789,869,344 | I_kwDODunzps5qrz0g | 6,008 | Dataset.from_generator consistently freezes at ~1000 rows | [] | closed | false | null | 3 | 2023-07-05T16:06:48Z | 2023-07-10T13:46:39Z | 2023-07-10T13:46:39Z | null | ### Describe the bug
Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I
Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset.
I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset.
Let me know if you have ideas how to resolve it!
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
def gen():
for row in range(10000):
yield {"i": np.random.rand(512, 512, 3)}
Dataset.from_generator(gen)
# -> 90% of the time gets stuck around 1000 rows
```
### Expected behavior
Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 12.0.1
- Pandas version: 1.5.1
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"By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Arra... |
https://api.github.com/repos/huggingface/datasets/issues/1314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1314/comments | https://api.github.com/repos/huggingface/datasets/issues/1314/events | https://github.com/huggingface/datasets/pull/1314 | 759,541,937 | MDExOlB1bGxSZXF1ZXN0NTM0NTMwMDE5 | 1,314 | Add snips built in intents 2016 12 | [] | closed | false | null | 3 | 2020-12-08T15:30:19Z | 2020-12-14T09:59:07Z | 2020-12-14T09:59:07Z | null | This PR proposes to add the Snips.ai built in intents dataset. The first configuration added is for the intent labels only, but the dataset includes entity slots that may in future be added as alternate configurations. | {
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"It is not clear how to automatically add the dummy data if the source data is a more complex json format. Should I manually take a fraction of the source data and include it as dummy data?\r\n",
"Added a fraction of the real data as dummy data.",
"merging since the CI is fixed on master"
] |
https://api.github.com/repos/huggingface/datasets/issues/5754 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5754/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5754/comments | https://api.github.com/repos/huggingface/datasets/issues/5754/events | https://github.com/huggingface/datasets/pull/5754 | 1,668,755,035 | PR_kwDODunzps5OWozh | 5,754 | Minor tqdm fixes | [] | closed | false | null | 2 | 2023-04-14T18:15:14Z | 2023-04-20T15:27:58Z | 2023-04-20T15:21:00Z | null | `GeneratorBasedBuilder`'s TQDM bars were not used as context managers. This PR fixes that (missed these bars in https://github.com/huggingface/datasets/pull/5560).
Also, this PR modifies the single-proc `save_to_disk` to fix the issue with the TQDM bar not accumulating the progress in the multi-shard setting (again, this bug was introduced by me in the linked PR 😎) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/1397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1397/comments | https://api.github.com/repos/huggingface/datasets/issues/1397/events | https://github.com/huggingface/datasets/pull/1397 | 760,467,501 | MDExOlB1bGxSZXF1ZXN0NTM1Mjk0MDgz | 1,397 | datasets card-creator link added | [] | closed | false | null | 0 | 2020-12-09T16:15:18Z | 2020-12-09T16:47:48Z | 2020-12-09T16:47:48Z | null | dataset card creator link has been added
link: https://huggingface.co/datasets/card-creator/ | {
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https://api.github.com/repos/huggingface/datasets/issues/1621 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1621/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1621/comments | https://api.github.com/repos/huggingface/datasets/issues/1621/events | https://github.com/huggingface/datasets/pull/1621 | 772,940,417 | MDExOlB1bGxSZXF1ZXN0NTQ0MTE4MTAz | 1,621 | updated dutch_social.py for loading jsonl (lines instead of list) files | [] | closed | false | null | 0 | 2020-12-22T13:18:11Z | 2020-12-23T11:51:51Z | 2020-12-23T11:51:51Z | null | the data_loader is modified to load files on the fly. Earlier it was reading the entire file and then processing the records
Pls refer to previous PR #1321 | {
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https://api.github.com/repos/huggingface/datasets/issues/1733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1733/comments | https://api.github.com/repos/huggingface/datasets/issues/1733/events | https://github.com/huggingface/datasets/issues/1733 | 784,903,002 | MDU6SXNzdWU3ODQ5MDMwMDI= | 1,733 | connection issue with glue, what is the data url for glue? | [] | closed | false | null | 1 | 2021-01-13T08:37:40Z | 2021-08-04T18:13:55Z | 2021-08-04T18:13:55Z | null | Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks | {
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"Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py"
] |
https://api.github.com/repos/huggingface/datasets/issues/1599 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1599/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1599/comments | https://api.github.com/repos/huggingface/datasets/issues/1599/events | https://github.com/huggingface/datasets/pull/1599 | 770,431,389 | MDExOlB1bGxSZXF1ZXN0NTQyMTgwMzI4 | 1,599 | add Korean Sarcasm Dataset | [] | closed | false | null | 0 | 2020-12-17T22:49:56Z | 2021-09-17T16:54:32Z | 2020-12-23T17:25:59Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/5381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5381/comments | https://api.github.com/repos/huggingface/datasets/issues/5381/events | https://github.com/huggingface/datasets/issues/5381 | 1,504,498,387 | I_kwDODunzps5ZrNLT | 5,381 | Wrong URL for the_pile dataset | [] | closed | false | null | 1 | 2022-12-20T12:40:14Z | 2023-02-15T16:24:57Z | 2023-02-15T16:24:57Z | null | ### Describe the bug
When trying to load `the_pile` dataset from the library, I get a `FileNotFound` error.
### Steps to reproduce the bug
Steps to reproduce:
Run:
```
from datasets import load_dataset
dataset = load_dataset("the_pile")
```
I get the output:
"name": "FileNotFoundError",
"message": "Unable to resolve any data file that matches '['**']' at /storage/store/work/lgrinszt/memorization/the_pile with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'BLP', 'BMP', 'DIB', 'BUFR', 'CUR', 'PCX', 'DCX', 'DDS', 'PS', 'EPS', 'FIT', 'FITS', 'FLI', 'FLC', 'FTC', 'FTU', 'GBR', 'GIF', 'GRIB', 'H5', 'HDF', 'PNG', 'APNG', 'JP2', 'J2K', 'JPC', 'JPF', 'JPX', 'J2C', 'ICNS', 'ICO', 'IM', 'IIM', 'TIF', 'TIFF', 'JFIF', 'JPE', 'JPG', 'JPEG', 'MPG', 'MPEG', 'MSP', 'PCD', 'PXR', 'PBM', 'PGM', 'PPM', 'PNM', 'PSD', 'BW', 'RGB', 'RGBA', 'SGI', 'RAS', 'TGA', 'ICB', 'VDA', 'VST', 'WEBP', 'WMF', 'EMF', 'XBM', 'XPM', 'aiff', 'au', 'avr', 'caf', 'flac', 'htk', 'svx', 'mat4', 'mat5', 'mpc2k', 'ogg', 'paf', 'pvf', 'raw', 'rf64', 'sd2', 'sds', 'ircam', 'voc', 'w64', 'wav', 'nist', 'wavex', 'wve', 'xi', 'mp3', 'opus', 'AIFF', 'AU', 'AVR', 'CAF', 'FLAC', 'HTK', 'SVX', 'MAT4', 'MAT5', 'MPC2K', 'OGG', 'PAF', 'PVF', 'RAW', 'RF64', 'SD2', 'SDS', 'IRCAM', 'VOC', 'W64', 'WAV', 'NIST', 'WAVEX', 'WVE', 'XI', 'MP3', 'OPUS', 'zip']"
### Expected behavior
`the_pile` dataset should be dowloaded.
### Environment info
- `datasets` version: 2.7.1
- Platform: Linux-4.15.0-112-generic-x86_64-with-glibc2.27
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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"Hi! This error can happen if there is a local file/folder with the same name as the requested dataset. And to avoid it, rename the local file/folder.\r\n\r\nSoon, it will be possible to explicitly request a Hub dataset as follows:https://github.com/huggingface/datasets/issues/5228#issuecomment-1313494020"
] |
https://api.github.com/repos/huggingface/datasets/issues/5753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5753/comments | https://api.github.com/repos/huggingface/datasets/issues/5753/events | https://github.com/huggingface/datasets/issues/5753 | 1,668,659,536 | I_kwDODunzps5jdblQ | 5,753 | [IterableDatasets] Add column followed by interleave datasets gives bogus outputs | [] | closed | false | null | 1 | 2023-04-14T17:32:31Z | 2023-04-14T17:45:52Z | 2023-04-14T17:36:37Z | null | ### Describe the bug
If we add a new column to our iterable dataset using the hack described in #5752, when we then interleave datasets the new column is pinned to one value.
### Steps to reproduce the bug
What we're going to do here is:
1. Load an iterable dataset in streaming mode (`original_dataset`)
2. Add a new column to this dataset using the hack in #5752 (`modified_dataset_1`)
3. Create another new dataset by adding a column with the same key but different values (`modified_dataset_2`)
4. Interleave our new datasets (`modified_dataset_1` + `modified_dataset_2`)
5. Check the value of our newly added column (`new_column`)
```python
from datasets import load_dataset
# load an iterable dataset
original_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
# now add a new column to our streaming dataset using our hack from 5752
name = "new_column"
column = [f"new dataset 1, row {i}" for i in range(50)]
new_features = original_dataset.features.copy()
new_features[name] = new_features["file"] # I know that "file" has the right column type to match our new feature
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_1 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# now create a second modified dataset using the same trick
column = [f"new dataset 2, row {i}" for i in range(50)]
def add_column_fn(example, idx):
if name in example:
raise ValueError(f"Error when adding {name}: column {name} is already in the dataset.")
return {name: column[idx]}
modified_dataset_2 = original_dataset.map(add_column_fn, with_indices=True, features=new_features)
# interleave these datasets
interleaved_dataset = interleave_datasets([modified_dataset_1, modified_dataset_2])
# now check what the value of the added column is
for i, sample in enumerate(interleaved_dataset):
print(sample["new_column"])
if i == 10:
break
```
**Print Output:**
```
new dataset 2, row 0
new dataset 2, row 0
new dataset 2, row 1
new dataset 2, row 1
new dataset 2, row 2
new dataset 2, row 2
new dataset 2, row 3
new dataset 2, row 3
new dataset 2, row 4
new dataset 2, row 4
new dataset 2, row 5
```
We see that we only get outputs from our second dataset.
### Expected behavior
We should interleave between dataset 1 and 2 and increase in row value:
```
new dataset 1, row 0
new dataset 2, row 0
new dataset 1, row 1
new dataset 2, row 1
new dataset 1, row 2
new dataset 2, row 2
...
```
### Environment info
- datasets version: 2.10.2.dev0
- Platform: Linux-4.19.0-23-cloud-amd64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.13.3
- PyArrow version: 10.0.1
- Pandas version: 1.5.2 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5753/timeline | null | completed | null | null | false | [
"Problem with the code snippet! Using global vars and functions was not a good idea with iterable datasets!\r\n\r\nIf we update to:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\noriginal_dataset = load_dataset(\"librispeech_asr\", \"clean\", split=\"validation\", streaming=True)\r\n\r\n# now add a new co... |
https://api.github.com/repos/huggingface/datasets/issues/5749 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5749/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5749/comments | https://api.github.com/repos/huggingface/datasets/issues/5749/events | https://github.com/huggingface/datasets/issues/5749 | 1,668,016,321 | I_kwDODunzps5ja-jB | 5,749 | AttributeError: 'Version' object has no attribute 'match' | [] | closed | false | null | 8 | 2023-04-14T10:48:06Z | 2023-06-30T11:31:17Z | 2023-04-18T12:57:08Z | null | ### Describe the bug
When I run
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
AttributeError: 'Version' object has no attribute 'match'
### Steps to reproduce the bug
from datasets import load_dataset
data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
### Expected behavior
This is error trace:
Downloading and preparing dataset visual_genome/region_descriptions_v1.2.0 to C:/Users/Acer/.cache/huggingface/datasets/visual_genome/region_descriptions_v1.2.0/1.2.0/136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 data = load_dataset("visual_genome", 'region_descriptions_v1.2.0')
File ~\.conda\envs\aai\Lib\site-packages\datasets\load.py:1791, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
1788 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
1790 # Download and prepare data
-> 1791 builder_instance.download_and_prepare(
1792 download_config=download_config,
1793 download_mode=download_mode,
1794 verification_mode=verification_mode,
1795 try_from_hf_gcs=try_from_hf_gcs,
1796 num_proc=num_proc,
1797 storage_options=storage_options,
1798 )
1800 # Build dataset for splits
1801 keep_in_memory = (
1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
1803 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:891, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
889 if num_proc is not None:
890 prepare_split_kwargs["num_proc"] = num_proc
--> 891 self._download_and_prepare(
892 dl_manager=dl_manager,
893 verification_mode=verification_mode,
894 **prepare_split_kwargs,
895 **download_and_prepare_kwargs,
896 )
897 # Sync info
898 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:1651, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs)
1650 def _download_and_prepare(self, dl_manager, verification_mode, **prepare_splits_kwargs):
-> 1651 super()._download_and_prepare(
1652 dl_manager,
1653 verification_mode,
1654 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS
1655 or verification_mode == VerificationMode.ALL_CHECKS,
1656 **prepare_splits_kwargs,
1657 )
File ~\.conda\envs\aai\Lib\site-packages\datasets\builder.py:964, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
962 split_dict = SplitDict(dataset_name=self.name)
963 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 964 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
966 # Checksums verification
967 if verification_mode == VerificationMode.ALL_CHECKS and dl_manager.record_checksums:
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:377, in VisualGenome._split_generators(self, dl_manager)
375 def _split_generators(self, dl_manager):
376 # Download image meta datas.
--> 377 image_metadatas_dir = dl_manager.download_and_extract(self.config.image_metadata_url)
378 image_metadatas_file = os.path.join(
379 image_metadatas_dir, _get_decompressed_filename_from_url(self.config.image_metadata_url)
380 )
382 # Download annotations
File ~\.cache\huggingface\modules\datasets_modules\datasets\visual_genome\136fe5b83f6691884566c5530313288171e053a3b33bfe3ea2e4c8b39abaf7f3\visual_genome.py:328, in VisualGenomeConfig.image_metadata_url(self)
326 @property
327 def image_metadata_url(self):
--> 328 if not self.version.match(_LATEST_VERSIONS["image_metadata"]):
329 logger.warning(
330 f"Latest image metadata version is {_LATEST_VERSIONS['image_metadata']}. Trying to generate a dataset of version: {self.version}. Please double check that image data are unchanged between the two versions."
331 )
332 return f"{_BASE_ANNOTATION_URL}/image_data.json.zip"
### Environment info
datasets 2.11.0
python 3.11.3 | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5749/timeline | null | completed | null | null | false | [
"I got the same error, and the official website for visual genome is down. Did you solve this problem? ",
"I am in the same situation now :( ",
"Thanks for reporting, @gulnaz-zh.\r\n\r\nI am investigating it.",
"The host server is down: https://visualgenome.org/\r\n\r\nWe are contacting the dataset authors.",... |
https://api.github.com/repos/huggingface/datasets/issues/5690 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5690/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5690/comments | https://api.github.com/repos/huggingface/datasets/issues/5690/events | https://github.com/huggingface/datasets/issues/5690 | 1,649,289,883 | I_kwDODunzps5iTiqb | 5,690 | raise AttributeError(f"No {package_name} attribute {name}") AttributeError: No huggingface_hub attribute hf_api | [
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}
] | closed | false | null | 5 | 2023-03-31T08:22:22Z | 2023-07-21T14:21:57Z | 2023-07-21T14:21:57Z | null | ### Describe the bug
rta.sh
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
### Reproduction
_No response_
### Logs
```shell
Traceback (most recent call last):
File "run.py", line 7, in <module>
import datasets
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/__init__.py", line 37, in <module>
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/builder.py", line 44, in <module>
from .data_files import DataFilesDict, _sanitize_patterns
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/datasets/data_files.py", line 120, in <module>
dataset_info: huggingface_hub.hf_api.DatasetInfo,
File "/home/appuser/miniconda3/envs/pt2/lib/python3.8/site-packages/huggingface_hub/__init__.py", line 290, in __getattr__
raise AttributeError(f"No {package_name} attribute {name}")
AttributeError: No huggingface_hub attribute hf_api
```
### System info
```shell
- huggingface_hub version: 0.13.2
- Platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Token path ?: /home/appuser/.cache/huggingface/token
- Has saved token ?: False
- Configured git credential helpers:
- FastAI: N/A
- Tensorflow: N/A
- Torch: 1.7.1
- Jinja2: N/A
- Graphviz: N/A
- Pydot: N/A
- Pillow: 9.3.0
- hf_transfer: N/A
- ENDPOINT: https://huggingface.co
- HUGGINGFACE_HUB_CACHE: /home/appuser/.cache/huggingface/hub
- HUGGINGFACE_ASSETS_CACHE: /home/appuser/.cache/huggingface/assets
- HF_TOKEN_PATH: /home/appuser/.cache/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
```
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} | https://api.github.com/repos/huggingface/datasets/issues/5690/timeline | null | completed | null | null | false | [
"Hi @wccccp, thanks for reporting. \r\nThat's weird since `huggingface_hub` _has_ a module called `hf_api` and you are using a recent version of it. \r\n\r\nWhich version of `datasets` are you using? And is it a bug that you experienced only recently? (cc @lhoestq can it be somehow related to the recent release of ... |
https://api.github.com/repos/huggingface/datasets/issues/3778 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3778/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3778/comments | https://api.github.com/repos/huggingface/datasets/issues/3778/events | https://github.com/huggingface/datasets/issues/3778 | 1,147,898,946 | I_kwDODunzps5Ea4xC | 3,778 | Not be able to download dataset - "Newsroom" | [
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}
] | closed | false | null | 2 | 2022-02-23T10:15:50Z | 2022-02-23T17:05:04Z | 2022-02-23T13:26:40Z | null | Hello,
I tried to download the **newsroom** dataset but it didn't work out for me. it said me to **download it manually**!
For manually, Link is also didn't work! It is sawing some ad or something!
If anybody has solved this issue please help me out or if somebody has this dataset please share your google drive link, it would be a great help!
Thanks
Darshan Tank | {
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} | https://api.github.com/repos/huggingface/datasets/issues/3778/timeline | null | completed | null | null | false | [
"Hi @Darshan2104, thanks for reporting.\r\n\r\nPlease note that at Hugging Face we do not host the data of this dataset, but just a loading script pointing to the host of the data owners.\r\n\r\nApparently the data owners changed their data host server. After googling it, I found their new website at: https://lil.n... |
https://api.github.com/repos/huggingface/datasets/issues/4271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4271/comments | https://api.github.com/repos/huggingface/datasets/issues/4271/events | https://github.com/huggingface/datasets/issues/4271 | 1,224,404,403 | I_kwDODunzps5I-u2z | 4,271 | A typo in docs of datasets.disable_progress_bar | [
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] | closed | false | null | 1 | 2022-05-03T17:44:56Z | 2022-05-04T06:58:35Z | 2022-05-04T06:58:35Z | null | ## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable". | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4271/timeline | null | completed | null | null | false | [
"Hi! Thanks for catching and reporting the typo, a PR has been opened to fix it :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/2706 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2706/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2706/comments | https://api.github.com/repos/huggingface/datasets/issues/2706/events | https://github.com/huggingface/datasets/pull/2706 | 950,606,561 | MDExOlB1bGxSZXF1ZXN0Njk1MTI3ODgz | 2,706 | Update BibTeX entry | [] | closed | false | null | 0 | 2021-07-22T12:29:29Z | 2021-07-22T12:43:00Z | 2021-07-22T12:43:00Z | null | Update BibTeX entry. | {
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https://api.github.com/repos/huggingface/datasets/issues/2361 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2361/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2361/comments | https://api.github.com/repos/huggingface/datasets/issues/2361/events | https://github.com/huggingface/datasets/pull/2361 | 891,982,808 | MDExOlB1bGxSZXF1ZXN0NjQ0NzYzNTU4 | 2,361 | Preserve dtype for numpy/torch/tf/jax arrays | [] | closed | false | null | 6 | 2021-05-14T14:45:23Z | 2021-08-17T08:30:04Z | 2021-08-17T08:30:04Z | null | Fixes #625. This lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array. | {
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"Hi @lhoestq, \r\nIt turns out that pyarrow `ListArray` are not recognized as list-like when we get output from `numpy_to_pyarrow_listarray`. This might cause tests to fail. If possible can we convert that `ListArray` output to list inorder for tests to pass? Under the hood it'll maintain the dtype as that of numpy... |
https://api.github.com/repos/huggingface/datasets/issues/5383 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5383/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5383/comments | https://api.github.com/repos/huggingface/datasets/issues/5383/events | https://github.com/huggingface/datasets/issues/5383 | 1,507,293,968 | I_kwDODunzps5Z13sQ | 5,383 | IterableDataset missing column_names, differs from Dataset interface | [
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"default": true... | closed | false | null | 6 | 2022-12-22T05:27:02Z | 2023-03-13T19:03:33Z | 2023-03-13T19:03:33Z | null | ### Describe the bug
The documentation on [Stream](https://huggingface.co/docs/datasets/v1.18.2/stream.html) seems to imply that IterableDataset behaves just like a Dataset. However, examples like
```
dataset.map(augment_data, batched=True, remove_columns=dataset.column_names, ...)
```
will not work because `.column_names` does not exist on IterableDataset. I cannot find any clear explanation on why this is not available, is it an oversight? We do have `iterable_ds.features` available.
### Steps to reproduce the bug
See above
### Expected behavior
Dataset and IterableDataset would be expected to have the same interface, with any differences noted in the documentation.
### Environment info
n/a | {
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"Another example is that `IterableDataset.map` does not have `fn_kwargs`, among other arguments. It makes it harder to convert code from Dataset to IterableDataset.",
"Hi! `fn_kwargs` was added to `IterableDataset.map` in `datasets 2.5.0`, so please update your installation (`pip install -U datasets`) to use it.\... |
https://api.github.com/repos/huggingface/datasets/issues/6070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6070/comments | https://api.github.com/repos/huggingface/datasets/issues/6070/events | https://github.com/huggingface/datasets/pull/6070 | 1,820,836,330 | PR_kwDODunzps5WXDLc | 6,070 | Fix Quickstart notebook link | [] | closed | false | null | 3 | 2023-07-25T17:48:37Z | 2023-07-25T18:19:01Z | 2023-07-25T18:10:16Z | null | Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/3898 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3898/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3898/comments | https://api.github.com/repos/huggingface/datasets/issues/3898/events | https://github.com/huggingface/datasets/pull/3898 | 1,166,778,250 | PR_kwDODunzps40UWG4 | 3,898 | Create README.md for WER metric | [] | closed | false | null | 4 | 2022-03-11T19:29:09Z | 2022-03-15T17:05:00Z | 2022-03-15T17:04:59Z | null | Proposing a draft WER metric card, @lhoestq I'm not very certain about "Values from popular papers" -- I don't know ASR very well, what do you think of the examples I found? | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3898). All of your documentation changes will be reflected on that endpoint.",
"For ASR you can probably ping @patrickvonplaten ",
"Ah only noticed now that ` # Values from popular papers` is from a template. @lhoestq @sashav... |
https://api.github.com/repos/huggingface/datasets/issues/2972 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2972/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2972/comments | https://api.github.com/repos/huggingface/datasets/issues/2972/events | https://github.com/huggingface/datasets/issues/2972 | 1,007,808,714 | I_kwDODunzps48EfDK | 2,972 | OSError: Not enough disk space. | [
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] | closed | false | null | 5 | 2021-09-27T07:41:22Z | 2022-08-29T23:21:36Z | 2021-09-28T06:43:15Z | null | ## Describe the bug
I'm trying to download `natural_questions` dataset from the Internet, and I've specified the cache_dir which locates in a mounted disk and has enough disk space. However, even though the space is enough, the disk space checking function still reports the space of root `/` disk having no enough space.
The file system structure is like below. The root `/` has `115G` disk space available, and the `sda1` is mounted to `/mnt`, which has `1.2T` disk space available:
```
/
/mnt/sda1/path/to/args.dataset_cache_dir
```
## Steps to reproduce the bug
```python
dataset_config = DownloadConfig(
cache_dir=os.path.abspath(args.dataset_cache_dir),
resume_download=True,
)
dataset = load_dataset("natural_questions", download_config=dataset_config)
```
## Expected results
Can download the dataset without an error.
## Actual results
The following error raised:
```
OSError: Not enough disk space. Needed: 134.92 GiB (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Ubuntu 18.04
- Python version: 3.8.10
- PyArrow version:
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"Maybe we can change the disk space calculating API from `shutil.disk_usage` to `os.statvfs` in UNIX-like system, which can provide correct results.\r\n```\r\nstatvfs = os.statvfs('path')\r\navail_space_bytes = statvfs.f_frsize * statvfs.f_bavail\r\n```",
"Hi @qqaatw, thanks for reporting.\r\n\r\nCould you pleas... |
https://api.github.com/repos/huggingface/datasets/issues/2731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2731/comments | https://api.github.com/repos/huggingface/datasets/issues/2731/events | https://github.com/huggingface/datasets/pull/2731 | 956,087,452 | MDExOlB1bGxSZXF1ZXN0Njk5NzQwMjg5 | 2,731 | Adding to_tf_dataset method | [] | closed | false | null | 7 | 2021-07-29T18:10:25Z | 2021-09-16T13:50:54Z | 2021-09-16T13:50:54Z | null | Oh my **god** do not merge this yet, it's just a draft.
I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work.
A number of issues need to be resolved before it's ready to merge, though:
1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too?
2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon.
3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer?
4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is. | {
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"This seems to be working reasonably well in testing, and performance is way better. `tf.py_function` has been dropped for an input generator, but I moved as much of the code as possible outside the generator to allow TF to compile it correctly. I also avoid `tf.RaggedTensor` at all costs, and do the shuffle in the... |
https://api.github.com/repos/huggingface/datasets/issues/3280 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3280/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3280/comments | https://api.github.com/repos/huggingface/datasets/issues/3280/events | https://github.com/huggingface/datasets/pull/3280 | 1,054,766,828 | PR_kwDODunzps4ulgye | 3,280 | Fix bookcorpusopen RAM usage | [] | closed | false | null | 0 | 2021-11-16T11:27:52Z | 2021-11-17T15:53:28Z | 2021-11-16T13:34:30Z | null | Each document is a full book, so the default arrow writer batch size of 10,000 is too big, and it can fill up RAM quickly before flushing the first batch on disk. I changed its batch size to 256 to use maximum 100MB of memory
Fix #3167. | {
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https://api.github.com/repos/huggingface/datasets/issues/3845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3845/comments | https://api.github.com/repos/huggingface/datasets/issues/3845/events | https://github.com/huggingface/datasets/pull/3845 | 1,161,739,483 | PR_kwDODunzps40DvqX | 3,845 | add RMSE and MAE metrics. | [] | closed | false | null | 6 | 2022-03-07T17:53:24Z | 2022-03-09T16:50:03Z | 2022-03-09T16:50:03Z | null | This PR adds RMSE - Root Mean Squared Error and MAE - Mean Absolute Error to the metrics API.
Both implementations are based on usage of sciket-learn.
Feature request here : Add support for continuous metrics (RMSE, MAE) [#3608](https://github.com/huggingface/datasets/issues/3608)
Please suggest any changes if required. Thank you. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3845). All of your documentation changes will be reflected on that endpoint.",
"@mariosasko I've reopened it here. Please suggest any changes if required. Thank you.",
"Thanks for suggestions. :) I have added update the KWARG... |
https://api.github.com/repos/huggingface/datasets/issues/3393 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3393/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3393/comments | https://api.github.com/repos/huggingface/datasets/issues/3393/events | https://github.com/huggingface/datasets/issues/3393 | 1,073,189,777 | I_kwDODunzps4_95OR | 3,393 | Common Voice Belarusian Dataset | [
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"color": "d93f0b",... | open | false | null | 0 | 2021-12-07T10:37:02Z | 2021-12-09T15:56:03Z | null | null | ## Adding a Dataset
- **Name:** *Common Voice Belarusian Dataset*
- **Description:** *[commonvoice.mozilla.org/be](https://commonvoice.mozilla.org/be)*
- **Data:** *[commonvoice.mozilla.org/be/datasets](https://commonvoice.mozilla.org/be/datasets)*
- **Motivation:** *It has more than 7GB of data, so it will be great to have it in this package so anyone can try to train something for Belarusian language.*
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/5344 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5344/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5344/comments | https://api.github.com/repos/huggingface/datasets/issues/5344/events | https://github.com/huggingface/datasets/pull/5344 | 1,485,628,319 | PR_kwDODunzps5E2BPN | 5,344 | Clean up Dataset and DatasetDict | [] | closed | false | null | 1 | 2022-12-09T00:02:08Z | 2022-12-13T00:56:07Z | 2022-12-13T00:53:02Z | null | This PR cleans up the docstrings for the other half of the methods in `Dataset` and finishes `DatasetDict`. | {
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https://api.github.com/repos/huggingface/datasets/issues/5880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5880/comments | https://api.github.com/repos/huggingface/datasets/issues/5880/events | https://github.com/huggingface/datasets/issues/5880 | 1,719,090,101 | I_kwDODunzps5mdzu1 | 5,880 | load_dataset from s3 file system through streaming can't not iterate data | [] | open | false | null | 4 | 2023-05-22T07:40:27Z | 2023-05-26T12:52:08Z | null | null | ### Describe the bug
I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it
<img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0">
<img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1">
we can change 4 lines to fix this bug, you can check whether it is ok for us.
<img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3">
### Steps to reproduce the bug
1. storage a file in you s3 file system
2. use load_dataset to read it through streaming
3. iterate it
### Expected behavior
can iterate it successfully
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
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"This sounds related to #5281.\r\n\r\nCan you try passing `storage_options=s3_client.storage_options` instead passing it to `use_auth_token=` ?",
"I tried `storage_options` before, but it doesn't work, I checked our source code and I found that we even didn't pass this parameter to the following process. if I use... |
https://api.github.com/repos/huggingface/datasets/issues/787 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/787/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/787/comments | https://api.github.com/repos/huggingface/datasets/issues/787/events | https://github.com/huggingface/datasets/pull/787 | 734,070,162 | MDExOlB1bGxSZXF1ZXN0NTEzNjk5MTQz | 787 | Adding nli_tr dataset | [] | closed | false | null | 1 | 2020-11-01T21:49:44Z | 2020-11-12T19:06:02Z | 2020-11-12T19:06:02Z | null | Hello,
In this pull request, we have implemented the necessary interface to add our recent dataset [NLI-TR](https://github.com/boun-tabi/NLI-TR). The datasets will be presented on a full paper at EMNLP 2020 this month. [[arXiv link] ](https://arxiv.org/pdf/2004.14963.pdf)
The dataset is the neural machine translation of SNLI and MultiNLI datasets into Turkish. So, we followed a similar format with the original datasets hosted in the HuggingFace datasets hub.
Our dataset is designed to be accessed as follows by following the interface of the GLUE dataset that provides multiple datasets in a single interface over the HuggingFace datasets hub.
```
from datasets import load_dataset
multinli_tr = load_dataset("nli_tr", "multinli_tr")
snli_tr = load_dataset("nli_tr", "snli_tr")
```
Thanks for your help in reviewing our pull request. | {
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"Thank you @lhoestq for the time you take to review our pull request. We appreciate your help.\r\n\r\nWe've made the changes you described. Hope that it is ready for being merged. Please let me know if you have any additional requests for revisions. "
] |
https://api.github.com/repos/huggingface/datasets/issues/4214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4214/comments | https://api.github.com/repos/huggingface/datasets/issues/4214/events | https://github.com/huggingface/datasets/pull/4214 | 1,214,572,430 | PR_kwDODunzps42utC5 | 4,214 | Skip checksum computation in Imagefolder by default | [] | closed | false | null | 1 | 2022-04-25T14:10:41Z | 2022-05-03T15:28:32Z | 2022-05-03T15:21:29Z | null | Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading.
The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part. | {
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"merged_at": "2022-05-03T15:21:29Z",
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/4916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4916/comments | https://api.github.com/repos/huggingface/datasets/issues/4916/events | https://github.com/huggingface/datasets/issues/4916 | 1,357,076,940 | I_kwDODunzps5Q41nM | 4,916 | Apache Beam unable to write the downloaded wikipedia dataset | [
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] | closed | false | null | 1 | 2022-08-31T09:39:25Z | 2022-08-31T10:53:19Z | 2022-08-31T10:53:19Z | null | ## Describe the bug
Hi, I am currently trying to download wikipedia dataset using
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner'). However, I end up in getting filenotfound error. I get this error for any language I try to download. It downloads the file but while saving it in hugging face cache it fails to write. This happens for any available date of any language in wikipedia dump. I had raised another issue earlier #4915 but probably was not that clear and the solution provider misunderstood my problem. Hence raising one more issue. Any help is appreciated.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikipedia", language="aa", date="20220401", split="train",beam_runner='DirectRunner')
```
## Expected results
to load the dataset
## Actual results
I am pasting the error trace here:
Downloading builder script: 35.9kB [00:00, ?B/s]
Downloading metadata: 30.4kB [00:00, 1.94MB/s]
Using custom data configuration 20220401.aa-date=20220401,language=aa
Downloading and preparing dataset wikipedia/20220401.aa to C:\Users\Shilpa.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559...
Downloading data: 100%|████████████████████████████████████████████████████████████| 11.1k/11.1k [00:00<00:00, 712kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.82s/it]
Extracting data files: 100%|█████████████████████████████████████████████████████████████████████| 1/1 [00:00<?, ?it/s]
Downloading data: 100%|███████████████████████████████████████████████████████████| 35.6k/35.6k [00:00<00:00, 84.3kB/s]
Downloading data files: 100%|████████████████████████████████████████████████████████████| 1/1 [00:02<00:00, 2.93s/it]
Traceback (most recent call last):
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "G:/abc/temp.py", line 32, in
beam_runner='DirectRunner')
File "G:\Python3.7\lib\site-packages\datasets\load.py", line 1751, in load_dataset
use_auth_token=use_auth_token,
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 705, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "G:\Python3.7\lib\site-packages\datasets\builder.py", line 1394, in _download_and_prepare
pipeline_results = pipeline.run()
File "G:\Python3.7\lib\site-packages\apache_beam\pipeline.py", line 574, in run
return self.runner.run_pipeline(self, self._options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\direct\direct_runner.py", line 131, in run_pipeline
return runner.run_pipeline(pipeline, options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 201, in run_pipeline
options)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 212, in run_via_runner_api
return self.run_stages(stage_context, stages)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 443, in run_stages
runner_execution_context, bundle_context_manager, bundle_input)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 776, in _execute_bundle
bundle_manager))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1000, in _run_bundle
data_input, data_output, input_timers, expected_timer_output)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\fn_runner.py", line 1309, in process_bundle
result_future = self._worker_handler.control_conn.push(process_bundle_req)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\portability\fn_api_runner\worker_handlers.py", line 380, in push
response = self.worker.do_instruction(request)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 598, in do_instruction
getattr(request, request_type), request.instruction_id)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\sdk_worker.py", line 635, in process_bundle
bundle_processor.process_bundle(instruction_id))
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 1004, in process_bundle
element.data)
File "G:\Python3.7\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 227, in process_encoded
self.output(decoded_value)
File "apache_beam\runners\worker\operations.py", line 526, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 528, in apache_beam.runners.worker.operations.Operation.output
File "apache_beam\runners\worker\operations.py", line 237, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 324, in apache_beam.runners.worker.operations.GeneralPurposeConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 905, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 623, in apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1491, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1581, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "apache_beam\runners\common.py", line 1694, in apache_beam.runners.common._OutputHandler._write_value_to_tag
File "apache_beam\runners\worker\operations.py", line 240, in apache_beam.runners.worker.operations.SingletonElementConsumerSet.receive
File "apache_beam\runners\worker\operations.py", line 907, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\worker\operations.py", line 908, in apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam\runners\common.py", line 1419, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 1507, in apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam\runners\common.py", line 1417, in apache_beam.runners.common.DoFnRunner.process
File "apache_beam\runners\common.py", line 837, in apache_beam.runners.common.PerWindowInvoker.invoke_process
File "apache_beam\runners\common.py", line 981, in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window
File "apache_beam\runners\common.py", line 1571, in apache_beam.runners.common._OutputHandler.handle_process_outputs
File "G:\Python3.7\lib\site-packages\apache_beam\io\iobase.py", line 1193, in process
self.writer = self.sink.open_writer(init_result, str(uuid.uuid4()))
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 202, in open_writer
return FileBasedSinkWriter(self, writer_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 419, in init
self.temp_handle = self.sink.open(temp_shard_path)
File "G:\Python3.7\lib\site-packages\apache_beam\io\parquetio.py", line 553, in open
self._file_handle = super().open(temp_path)
File "G:\Python3.7\lib\site-packages\apache_beam\options\value_provider.py", line 193, in _f
return fnc(self, *args, **kwargs)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filebasedsink.py", line 139, in open
temp_path, self.mime_type, self.compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\filesystems.py", line 224, in create
return filesystem.create(path, mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 163, in create
return self._path_open(path, 'wb', mime_type, compression_type)
File "G:\Python3.7\lib\site-packages\apache_beam\io\localfilesystem.py", line 140, in _path_open
raw_file = io.open(path, mode)
RuntimeError: FileNotFoundError: [Errno 2] No such file or directory: 'C:\Users\Shilpa\.cache\huggingface\datasets\wikipedia\20220401.aa-date=20220401,language=aa\2.0.0\aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559.incomplete\beam-temp-wikipedia-train-880233e8287e11edaf9d3ca067f2714e\20a05238-6106-4420-a713-4eca6dd5959a.wikipedia-train' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
## Environment info
Python: 3.7.6
Windows 10 Pro
datasets :2.4.0
apache_beam: 2.41.0
mwparserfromhell: 0.6.4 | {
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"See:\r\n- #4915"
] |
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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] | closed | false | null | 2 | 2021-07-22T21:59:44Z | 2021-07-23T13:30:07Z | 2021-07-23T13:30:07Z | 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/2708/timeline | null | completed | null | null | false | [
"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... |
https://api.github.com/repos/huggingface/datasets/issues/5731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5731/comments | https://api.github.com/repos/huggingface/datasets/issues/5731/events | https://github.com/huggingface/datasets/pull/5731 | 1,662,012,913 | PR_kwDODunzps5N_7Un | 5,731 | Temporarily pin fsspec | [] | closed | false | null | 2 | 2023-04-11T08:33:15Z | 2023-04-11T08:57:45Z | 2023-04-11T08:47:55Z | null | Fix #5730. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/3736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3736/comments | https://api.github.com/repos/huggingface/datasets/issues/3736/events | https://github.com/huggingface/datasets/pull/3736 | 1,140,134,483 | PR_kwDODunzps4y7rMR | 3,736 | Local paths in common voice | [] | closed | false | null | 2 | 2022-02-16T15:01:29Z | 2022-09-21T14:58:38Z | 2022-02-22T09:13:43Z | null | Continuation of https://github.com/huggingface/datasets/pull/3664:
- pass the `streaming` parameter to _split_generator
- update @anton-l's code to use this parameter for `common_voice`
- add a comment to explain why we use `download_and_extract` in non-streaming and `iter_archive` in streaming
Now the `common_voice` dataset has a local path back in `ds["path"]`, and this field is `None` in streaming mode.
cc @patrickvonplaten @anton-l @albertvillanova
Fix #3663. | {
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"I just changed to `dl_manager.is_streaming` rather than an additional parameter `streaming` that has to be handled by the DatasetBuilder class - this way the streaming logic doesn't interfere with the base builder's code.\r\n\r\nI think it's better this way, but let me know if you preferred the previous way and I ... |
https://api.github.com/repos/huggingface/datasets/issues/5736 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5736/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5736/comments | https://api.github.com/repos/huggingface/datasets/issues/5736/events | https://github.com/huggingface/datasets/issues/5736 | 1,662,286,061 | I_kwDODunzps5jFHjt | 5,736 | FORCE_REDOWNLOAD raises "Directory not empty" exception on second run | [] | open | false | null | 1 | 2023-04-11T11:29:15Z | 2023-04-21T15:27:40Z | null | null | ### Describe the bug
Running `load_dataset(..., download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` twice raises a `Directory not empty` exception on the second run.
### Steps to reproduce the bug
I cannot test this on datasets v2.11.0 due to #5711, but this happens in v2.10.1.
1. Set up a script `my_dataset.py` to generate and load an offline dataset.
2. Load it with
```python
ds = datasets.load_dataset(path=/path/to/my_dataset.py,
name='toy',
data_dir=/path/to/my_dataset.py,
cache_dir=cache_dir,
download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
)
```
It loads fine
```
Dataset my_dataset downloaded and prepared to /path/to/cache/toy-..e05e/1.0.0/...5b4c. Subsequent calls will reuse this data.
```
3. Try to load it again with the same snippet and the splits are generated, but at the end of the loading process it raises the error
```
2023-04-11 12:10:19,965: DEBUG: open file: /path/to/cache/toy-..e05e/1.0.0/...5b4c.incomplete/dataset_info.json
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/load.py", line 1782, in load_dataset
builder_instance.download_and_prepare(
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 852, in download_and_prepare
with incomplete_dir(self._output_dir) as tmp_output_dir:
File "/path/to/conda/environment/lib/python3.10/contextlib.py", line 142, in __exit__
next(self.gen)
File "/path/to/conda/environment/lib/python3.10/site-packages/datasets/builder.py", line 826, in incomplete_dir
shutil.rmtree(dirname)
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 730, in rmtree
onerror(os.rmdir, path, sys.exc_info())
File "/path/to/conda/environment/lib/python3.10/shutil.py", line 728, in rmtree
os.rmdir(path)
OSError: [Errno 39] Directory not empty: '/path/to/cache/toy-..e05e/1.0.0/...5b4c'
```
### Expected behavior
Regenerate the dataset from scratch and reload it.
### Environment info
- `datasets` version: 2.10.1
- Platform: Linux-4.18.0-483.el8.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.8
- PyArrow version: 11.0.0
- Pandas version: 1.5.2
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} | https://api.github.com/repos/huggingface/datasets/issues/5736/timeline | null | null | null | null | false | [
"Hi ! I couldn't reproduce your issue :/\r\n\r\nIt seems that `shutil.rmtree` failed. It is supposed to work even if the directory is not empty, but you still end up with `OSError: [Errno 39] Directory not empty:`. Can you make sure another process is not using this directory at the same time ?"
] |
https://api.github.com/repos/huggingface/datasets/issues/5404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5404/comments | https://api.github.com/repos/huggingface/datasets/issues/5404/events | https://github.com/huggingface/datasets/issues/5404 | 1,517,566,331 | I_kwDODunzps5adDl7 | 5,404 | Better integration of BIG-bench | [
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] | open | false | null | 1 | 2023-01-03T15:37:57Z | 2023-02-09T20:30:26Z | null | null | ### Feature request
Ideally, it would be nice to have a maintained PyPI package for `bigbench`.
### Motivation
We'd like to allow anyone to access, explore and use any task.
### Your contribution
@lhoestq has opened an issue in their repo:
- https://github.com/google/BIG-bench/issues/906 | {
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"Hi, I made my version : https://huggingface.co/datasets/tasksource/bigbench"
] |
https://api.github.com/repos/huggingface/datasets/issues/597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/597/comments | https://api.github.com/repos/huggingface/datasets/issues/597/events | https://github.com/huggingface/datasets/issues/597 | 697,112,029 | MDU6SXNzdWU2OTcxMTIwMjk= | 597 | Indices incorrect with multiprocessing | [] | closed | false | null | 2 | 2020-09-09T19:50:56Z | 2020-09-10T11:03:37Z | 2020-09-10T11:03:37Z | null | When `num_proc` > 1, the indices argument passed to the map function is incorrect:
```python
d = load_dataset('imdb', split='test[:1%]')
def fn(x, inds):
print(inds)
return x
d.select(range(10)).map(fn, with_indices=True, batched=True)
# [0, 1]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2)
# [0, 1]
# [0, 1]
# [0, 1, 2, 3, 4]
# [0, 1, 2, 3, 4]
```
As you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`. | {
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} | https://api.github.com/repos/huggingface/datasets/issues/597/timeline | null | completed | null | null | false | [
"I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?",
"Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we ar... |
https://api.github.com/repos/huggingface/datasets/issues/6078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6078/comments | https://api.github.com/repos/huggingface/datasets/issues/6078/events | https://github.com/huggingface/datasets/issues/6078 | 1,822,501,472 | I_kwDODunzps5soSpg | 6,078 | resume_download with streaming=True | [] | open | false | null | 2 | 2023-07-26T14:08:22Z | 2023-07-26T21:10:40Z | null | null | ### Describe the bug
I used:
```
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True,
split="train"
)
```
Unfortunately, the server had a problem during the training process. I saved the step my training stopped at.
But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset?
`download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True.
### Steps to reproduce the bug
```
from datasets import load_dataset, DownloadConfig
dataset = load_dataset(
"oscar-corpus/OSCAR-2201",
token=True,
language="fr",
streaming=True, # optional
split="train",
download_config=DownloadConfig(resume_download=True)
)
# interupt the run and try to relaunch it => this restart from scratch
```
### Expected behavior
I would expect a parameter to start streaming from a given index in the dataset.
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.0 | {
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"Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ",
"Ok thank you for your answer",
"I'm closing this as a duplicate of #5380"
] |
https://api.github.com/repos/huggingface/datasets/issues/4157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4157/comments | https://api.github.com/repos/huggingface/datasets/issues/4157/events | https://github.com/huggingface/datasets/pull/4157 | 1,202,239,622 | PR_kwDODunzps42H2Wf | 4,157 | Fix formatting in BLEU metric card | [] | closed | false | null | 1 | 2022-04-12T18:29:51Z | 2022-04-13T14:30:25Z | 2022-04-13T14:16:34Z | null | Fix #4148 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/58 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/58/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/58/comments | https://api.github.com/repos/huggingface/datasets/issues/58/events | https://github.com/huggingface/datasets/pull/58 | 614,362,308 | MDExOlB1bGxSZXF1ZXN0NDE0OTM0NTY4 | 58 | Aborted PR - Fix tests | [] | closed | false | null | 1 | 2020-05-07T21:40:19Z | 2020-05-07T21:48:01Z | 2020-05-07T21:41:27Z | null | @patrickvonplaten I've broken a bit the tests with #25 while simplifying and re-organizing the `load.py` and `download_manager.py` scripts.
I'm trying to fix them here but I have a weird error, do you think you can have a look?
```bash
(datasets) MacBook-Pro-de-Thomas:datasets thomwolf$ python -m pytest -sv ./tests/test_dataset_common.py::DatasetTest::test_builder_class_snli
============================================================================= test session starts =============================================================================
platform darwin -- Python 3.7.7, pytest-5.4.1, py-1.8.1, pluggy-0.13.1 -- /Users/thomwolf/miniconda2/envs/datasets/bin/python
cachedir: .pytest_cache
rootdir: /Users/thomwolf/Documents/GitHub/datasets
plugins: xdist-1.31.0, forked-1.1.3
collected 1 item
tests/test_dataset_common.py::DatasetTest::test_builder_class_snli ERROR
=================================================================================== ERRORS ====================================================================================
____________________________________________________________ ERROR at setup of DatasetTest.test_builder_class_snli ____________________________________________________________
file_path = <module 'tests.test_dataset_common' from '/Users/thomwolf/Documents/GitHub/datasets/tests/test_dataset_common.py'>
download_config = DownloadConfig(cache_dir=None, force_download=False, resume_download=False, local_files_only=False, proxies=None, user_agent=None, extract_compressed_file=True, force_extract=True)
download_kwargs = {}
def setup_module(file_path: str, download_config: Optional[DownloadConfig] = None, **download_kwargs,) -> DatasetBuilder:
r"""
Download/extract/cache a dataset to add to the lib from a path or url which can be:
- a path to a local directory containing the dataset processing python script
- an url to a S3 directory with a dataset processing python script
Dataset codes are cached inside the lib to allow easy import (avoid ugly sys.path tweaks)
and using cloudpickle (among other things).
Return: tuple of
the unique id associated to the dataset
the local path to the dataset
"""
if download_config is None:
download_config = DownloadConfig(**download_kwargs)
download_config.extract_compressed_file = True
download_config.force_extract = True
> name = list(filter(lambda x: x, file_path.split("/")))[-1] + ".py"
E AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
src/nlp/load.py:169: AttributeError
============================================================================== warnings summary ===============================================================================
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15
/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/tensorflow_core/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
import imp
-- Docs: https://docs.pytest.org/en/latest/warnings.html
=========================================================================== short test summary info ===========================================================================
ERROR tests/test_dataset_common.py::DatasetTest::test_builder_class_snli - AttributeError: module 'tests.test_dataset_common' has no attribute 'split'
========================================================================= 1 warning, 1 error in 3.63s =========================================================================
```
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"Wait I messed up my branch, let me clean this."
] |
https://api.github.com/repos/huggingface/datasets/issues/3171 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3171/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3171/comments | https://api.github.com/repos/huggingface/datasets/issues/3171/events | https://github.com/huggingface/datasets/issues/3171 | 1,037,728,059 | I_kwDODunzps492nk7 | 3,171 | Raise exceptions instead of using assertions for control flow | [
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] | closed | false | null | 4 | 2021-10-27T18:26:52Z | 2021-12-23T16:40:37Z | 2021-12-23T16:40:37Z | null | Motivated by https://github.com/huggingface/transformers/issues/12789 in Transformers, one welcoming change would be replacing assertions with proper exceptions. The only type of assertions we should keep are those used as sanity checks.
Currently, there is a total of 87 files with the `assert` statements (located under `datasets` and `src/datasets`), so when working on this, to manage the PR size, only modify 4-5 files at most before submitting a PR. | {
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"Adding the remaining tasks for this issue to help new code contributors. \r\n$ cd src/datasets && ack assert -lc \r\n- [x] commands/convert.py:1\r\n- [x] arrow_reader.py:3\r\n- [x] load.py:7\r\n- [x] utils/py_utils.py:2\r\n- [x] features/features.py:9\r\n- [x] arrow_writer.py:7\r\n- [x] search.py:6\r\n- [x] table... |
https://api.github.com/repos/huggingface/datasets/issues/4918 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4918/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4918/comments | https://api.github.com/repos/huggingface/datasets/issues/4918/events | https://github.com/huggingface/datasets/issues/4918 | 1,357,242,757 | I_kwDODunzps5Q5eGF | 4,918 | Dataset Viewer issue for pysentimiento/spanish-targeted-sentiment-headlines | [
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] | closed | false | null | 2 | 2022-08-31T12:09:07Z | 2022-09-05T21:36:34Z | 2022-09-05T16:32:44Z | null | ### Link
https://huggingface.co/datasets/pysentimiento/spanish-targeted-sentiment-headlines
### Description
After moving the dataset from my user (`finiteautomata`) to the `pysentimiento` organization, the dataset viewer says that it doesn't exist.
### Owner
_No response_ | {
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} | https://api.github.com/repos/huggingface/datasets/issues/4918/timeline | null | completed | null | null | false | [
"Thanks for reporting, it's fixed now (I refreshed it manually). It's a known issue; we hope it will be fixed permanently in a few days.\r\n\r\n<img width=\"1508\" alt=\"Capture d’écran 2022-09-05 à 18 31 22\" src=\"https://user-images.githubusercontent.com/1676121/188489762-0ed86a7e-dfb3-46e8-a125-43b815a2c6f4.p... |
https://api.github.com/repos/huggingface/datasets/issues/5842 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5842/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5842/comments | https://api.github.com/repos/huggingface/datasets/issues/5842/events | https://github.com/huggingface/datasets/issues/5842 | 1,705,510,602 | I_kwDODunzps5lqAbK | 5,842 | Remove columns in interable dataset | [] | closed | false | null | 3 | 2023-05-11T03:48:46Z | 2023-06-21T16:36:42Z | 2023-06-21T16:36:41Z | null | ### Feature request
Right now, remove_columns() produces a NotImplementedError for iterable style datasets
### Motivation
It would be great to have the same functionality irrespective of whether one is using an iterable or a map-style dataset
### Your contribution
hope and courage. | {
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"Transferring this issue as it's related to the 🤗 Datasets library ",
"Hi @surya-narayanan! Could you provide some code snippet?",
"This method has been recently added to the `IterableDataset`, so you need to update the `datasets`' installation (`pip install -U datasets`) to use it."
] |
https://api.github.com/repos/huggingface/datasets/issues/2066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2066/comments | https://api.github.com/repos/huggingface/datasets/issues/2066/events | https://github.com/huggingface/datasets/pull/2066 | 833,480,551 | MDExOlB1bGxSZXF1ZXN0NTk0NDcwMjEz | 2,066 | Fix docstring rendering of Dataset/DatasetDict.from_csv args | [] | closed | false | null | 0 | 2021-03-17T07:23:10Z | 2021-03-17T09:21:21Z | 2021-03-17T09:21:21Z | null | Fix the docstring rendering of Dataset/DatasetDict.from_csv args. | {
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https://api.github.com/repos/huggingface/datasets/issues/1582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1582/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1582/comments | https://api.github.com/repos/huggingface/datasets/issues/1582/events | https://github.com/huggingface/datasets/pull/1582 | 768,776,617 | MDExOlB1bGxSZXF1ZXN0NTQxMTEwODU1 | 1,582 | Adding wiki lingua dataset as new branch | [] | closed | false | null | 0 | 2020-12-16T11:53:07Z | 2020-12-17T18:06:46Z | 2020-12-17T18:06:45Z | null | Adding the dataset as new branch as advised here: #1470
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https://api.github.com/repos/huggingface/datasets/issues/1716 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1716/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1716/comments | https://api.github.com/repos/huggingface/datasets/issues/1716/events | https://github.com/huggingface/datasets/pull/1716 | 782,819,006 | MDExOlB1bGxSZXF1ZXN0NTUyMjgzNzE5 | 1,716 | Add Hatexplain Dataset | [] | closed | false | null | 0 | 2021-01-10T13:30:01Z | 2021-01-18T14:21:42Z | 2021-01-18T14:21:42Z | null | Adding Hatexplain - the first benchmark hate speech dataset covering multiple aspects of the issue | {
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https://api.github.com/repos/huggingface/datasets/issues/4882 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4882/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4882/comments | https://api.github.com/repos/huggingface/datasets/issues/4882/events | https://github.com/huggingface/datasets/pull/4882 | 1,348,913,665 | PR_kwDODunzps49sRtv | 4,882 | Fix language tags resource file | [] | closed | false | null | 1 | 2022-08-24T06:06:01Z | 2022-08-24T13:58:33Z | 2022-08-24T13:58:30Z | null | This PR fixes/updates/adds ALL language tags from IANA (as of 2022-08-08).
This PR also removes all BCP47 suffixes (the languages file only contains language subtags, i.e. ISO 639 1 or 2 codes; no script/region/variant suffixes). See:
- #4753 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4882). All of your documentation changes will be reflected on that endpoint."
] |
https://api.github.com/repos/huggingface/datasets/issues/243 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/243/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/243/comments | https://api.github.com/repos/huggingface/datasets/issues/243/events | https://github.com/huggingface/datasets/pull/243 | 631,735,848 | MDExOlB1bGxSZXF1ZXN0NDI4NTY2MTEy | 243 | Specify utf-8 encoding for GLUE | [] | closed | false | null | 1 | 2020-06-05T16:33:00Z | 2020-06-17T21:16:06Z | 2020-06-08T08:42:01Z | null | #242
This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug. | {
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"Thanks for fixing the encoding :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/4762 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4762/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4762/comments | https://api.github.com/repos/huggingface/datasets/issues/4762/events | https://github.com/huggingface/datasets/pull/4762 | 1,321,261,733 | PR_kwDODunzps48RE56 | 4,762 | Improve features resolution in streaming | [] | closed | false | null | 2 | 2022-07-28T17:28:11Z | 2022-09-09T17:17:39Z | 2022-09-09T17:15:30Z | null | `IterableDataset._resolve_features` was returning the features sorted alphabetically by column name, which is not consistent with non-streaming. I changed this and used the order of columns from the data themselves. It was causing some inconsistencies in the dataset viewer as well.
I also fixed `interleave_datasets` that was not filling missing columns with None, because it was not using the columns from `IterableDataset._resolve_features`
cc @severo | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Just took your comment into account @mariosasko , let me know if it's good for you now :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/2235 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2235/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2235/comments | https://api.github.com/repos/huggingface/datasets/issues/2235/events | https://github.com/huggingface/datasets/pull/2235 | 861,040,716 | MDExOlB1bGxSZXF1ZXN0NjE3Nzc0NDUw | 2,235 | Update README.md | [] | closed | false | null | 0 | 2021-04-19T08:21:02Z | 2021-04-19T12:49:19Z | 2021-04-19T12:49:19Z | null | Adding relevant citations (paper accepted at AAAI 2020 & EMNLP 2020) to the benchmark | {
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https://api.github.com/repos/huggingface/datasets/issues/2813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2813/comments | https://api.github.com/repos/huggingface/datasets/issues/2813/events | https://github.com/huggingface/datasets/issues/2813 | 973,470,580 | MDU6SXNzdWU5NzM0NzA1ODA= | 2,813 | Remove compression from xopen | [
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] | closed | false | null | 1 | 2021-08-18T09:35:59Z | 2021-08-23T15:59:14Z | 2021-08-23T15:59:14Z | null | We implemented support for streaming with 2 requirements:
- transparent use for the end user: just needs to pass the parameter `streaming=True`
- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming
In order to fulfill these requirements, streaming implementation patched some Python functions:
- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`
- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip://`), which are required by `fsspec.open`
Recently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...
Under the hood, the implementation:
- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`
Some concerns have been raised about passing the parameter `compression` to `fsspec.open`:
- https://github.com/huggingface/datasets/pull/2786#discussion_r689550254
- #2811
The main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:
```python
gzip.open(open(urlpath
```
While this is true:
- it is not natural/usual to call `open` inside `gzip.open` (never seen this before)
- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)
In this particular case, there is a natural fix solution: #2811:
- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`
- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression="gzip"`
Are there other issues apart from this?
Note that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just
- `gzip.open`
- `open` (after having called dl_manager.download_and_extract)
TODO:
- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.
- For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:
- oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)
- In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming
- [ ] If this is indeed an issue, which are the possible alternatives? Pros/cons? | {
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"After discussing with @lhoestq, a reasonable alternative:\r\n- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats: \r\n `bz2::http://domain.org/filename.bz2`\r\n- `xopen` parses the `urlpath` a... |
https://api.github.com/repos/huggingface/datasets/issues/1698 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1698/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1698/comments | https://api.github.com/repos/huggingface/datasets/issues/1698/events | https://github.com/huggingface/datasets/pull/1698 | 781,152,561 | MDExOlB1bGxSZXF1ZXN0NTUwOTI0ODQ3 | 1,698 | Update Coached Conv Pref DatasetCard | [] | closed | false | null | 1 | 2021-01-07T09:07:16Z | 2021-01-08T17:04:33Z | 2021-01-08T17:04:32Z | null | Update Coached Conversation Preferance DatasetCard | {
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"Really cool!\r\n\r\nCan you add some task tags for `dialogue-modeling` (under `sequence-modeling`) and `parsing` (under `structured-prediction`)?"
] |
https://api.github.com/repos/huggingface/datasets/issues/3423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3423/comments | https://api.github.com/repos/huggingface/datasets/issues/3423/events | https://github.com/huggingface/datasets/issues/3423 | 1,078,049,638 | I_kwDODunzps5AQbtm | 3,423 | data duplicate when setting num_works > 1 with streaming data | [
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"descrip... | closed | false | null | 14 | 2021-12-13T03:43:17Z | 2022-12-14T16:04:22Z | 2022-12-14T16:04:22Z | null | ## Describe the bug
The data is repeated num_works times when we load_dataset with streaming and set num_works > 1 when construct dataloader
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
import pandas as pd
import numpy as np
import os
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
import shutil
NUM_OF_USER = 1000000
NUM_OF_ACTION = 50000
NUM_OF_SEQUENCE = 10000
NUM_OF_FILES = 32
NUM_OF_WORKERS = 16
if __name__ == "__main__":
shutil.rmtree("./dataset")
for i in range(NUM_OF_FILES):
sequence_data = pd.DataFrame(
{
"imei": np.random.randint(1, NUM_OF_USER, size=NUM_OF_SEQUENCE),
"sequence": np.random.randint(1, NUM_OF_ACTION, size=NUM_OF_SEQUENCE)
}
)
if not os.path.exists("./dataset"):
os.makedirs("./dataset")
sequence_data.to_csv(f"./dataset/sequence_data_{i}.csv",
index=False)
dataset = load_dataset("csv",
data_files=[os.path.join("./dataset",file) for file in os.listdir("./dataset") if file.endswith(".csv")],
split="train",
streaming=True).with_format("torch")
data_loader = DataLoader(dataset,
batch_size=1024,
num_workers=NUM_OF_WORKERS)
result = pd.DataFrame()
for i, batch in tqdm(enumerate(data_loader)):
result = pd.concat([result,
pd.DataFrame(batch)],
axis=0)
result.to_csv(f"num_work_{NUM_OF_WORKERS}.csv", index=False)
```
## Expected results
data do not duplicate
## Actual results
data duplicate NUM_OF_WORKERS = 16
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:datasets==1.14.0
- Platform:transformers==4.11.3
- Python version:3.8
- PyArrow version:
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"Hi ! Thanks for reporting :)\r\n\r\nWhen using a PyTorch's data loader with `num_workers>1` and an iterable dataset, each worker streams the exact same data by default, resulting in duplicate data when iterating using the data loader.\r\n\r\nWe can probably fix this in `datasets` by checking `torch.utils.data.get_... |
https://api.github.com/repos/huggingface/datasets/issues/2894 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2894/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2894/comments | https://api.github.com/repos/huggingface/datasets/issues/2894/events | https://github.com/huggingface/datasets/pull/2894 | 993,375,654 | MDExOlB1bGxSZXF1ZXN0NzMxNTcxODc5 | 2,894 | Fix COUNTER dataset | [] | closed | false | null | 0 | 2021-09-10T16:07:29Z | 2021-09-10T16:27:45Z | 2021-09-10T16:27:44Z | null | Fix filename generating `FileNotFoundError`.
Related to #2866.
CC: @severo. | {
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https://api.github.com/repos/huggingface/datasets/issues/5773 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5773/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5773/comments | https://api.github.com/repos/huggingface/datasets/issues/5773/events | https://github.com/huggingface/datasets/issues/5773 | 1,675,984,633 | I_kwDODunzps5j5X75 | 5,773 | train_dataset does not implement __len__ | [] | open | false | null | 9 | 2023-04-20T04:37:05Z | 2023-07-19T20:33:13Z | null | null | when train using data precessored by the datasets, I get follow warning and it leads to that I can not set epoch numbers:
`ValueError: The train_dataset does not implement __len__, max_steps has to be specified. The number of steps needs to be known in advance for the learning rate scheduler.` | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5773/timeline | null | null | null | null | false | [
"Thanks for reporting, @v-yunbin.\r\n\r\nCould you please give more details, the steps to reproduce the bug, the complete error back trace and the environment information (`datasets-cli env`)?",
"this is a detail error info from transformers：\r\n```\r\nTraceback (most recent call last):\r\n File \"finetune.py\",... |
https://api.github.com/repos/huggingface/datasets/issues/3202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3202/comments | https://api.github.com/repos/huggingface/datasets/issues/3202/events | https://github.com/huggingface/datasets/issues/3202 | 1,043,213,660 | I_kwDODunzps4-Li1c | 3,202 | Add mIoU metric | [
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] | closed | false | null | 1 | 2021-11-03T08:42:32Z | 2022-06-01T17:39:05Z | 2022-06-01T17:39:04Z | null | **Is your feature request related to a problem? Please describe.**
Recently, some semantic segmentation models were added to HuggingFace Transformers, including [SegFormer](https://huggingface.co/transformers/model_doc/segformer.html) and [BEiT](https://huggingface.co/transformers/model_doc/beit.html).
Semantic segmentation (which is the task of labeling every pixel of an image with a corresponding class) is typically evaluated using the Mean Intersection and Union (mIoU). Together with the upcoming Image Feature, adding this metric could be very handy when creating example scripts to fine-tune any Transformer-based model on a semantic segmentation dataset.
An implementation can be found [here](https://github.com/open-mmlab/mmsegmentation/blob/504965184c3e6bc9ec43af54237129ef21981a5f/mmseg/core/evaluation/metrics.py#L132) for instance.
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"Resolved via https://github.com/huggingface/datasets/pull/3745."
] |
https://api.github.com/repos/huggingface/datasets/issues/1028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1028/comments | https://api.github.com/repos/huggingface/datasets/issues/1028/events | https://github.com/huggingface/datasets/pull/1028 | 755,712,854 | MDExOlB1bGxSZXF1ZXN0NTMxMzc0MTYw | 1,028 | Add ASSET dataset for text simplification evaluation | [] | closed | false | null | 1 | 2020-12-03T00:28:29Z | 2020-12-17T10:03:06Z | 2020-12-03T16:34:37Z | null | Adding the ASSET dataset from https://github.com/facebookresearch/asset
One config for the simplification data, one for the human ratings of quality.
The README.md borrows from that written by @juand-r | {
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"Nice, thanks @yjernite !!"
] |
https://api.github.com/repos/huggingface/datasets/issues/3950 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3950/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3950/comments | https://api.github.com/repos/huggingface/datasets/issues/3950/events | https://github.com/huggingface/datasets/issues/3950 | 1,171,560,585 | I_kwDODunzps5F1JiJ | 3,950 | Streaming Datasets don't work with Transformers Trainer when dataloader_num_workers>1 | [
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"descript... | closed | false | null | 1 | 2022-03-16T21:14:11Z | 2022-06-10T20:47:26Z | 2022-06-10T20:47:26Z | null | ## Describe the bug
Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.
## Steps to reproduce the bug
```python
import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("torch")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```
## Expected results
For this code I'd expect a crash related to not having preprocessed the data, but instead we get a pickling error.
## Actual results
```
0%| | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 7, in <module>
Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
for step, inputs in enumerate(epoch_iterator):
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 359, in __iter__
return self._get_iterator()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 918, in __init__
w.start()
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/process.py", line 121, in start
self._popen = self._Popen(self)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/context.py", line 284, in _Popen
return Popen(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_spawn_posix.py", line 47, in _launch
reduction.dump(process_obj, fp)
File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'iterable_dataset.<locals>.TorchIterableDataset'
0%| | 0/1000 [00:00<?, ?it/s]
```
This immediate crash can be fixed by not using a local class to make the `TorchIterableDataset` (Note that you have to do with_format("torch") or you get an exception because the dataset has no len) However, any lambdas etc used as maps will also trigger this crash. A more permanent fix would be to move away from multiprocessing and instead use something like pathos or multiprocessing_on_dill (https://stackoverflow.com/questions/19984152/what-can-multiprocessing-and-dill-do-together)
Note that if you bypass this crash you get another crash. (I'll file a separate bug).
## Environment info
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
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"Hi, thanks for reporting. This could be related to https://github.com/huggingface/datasets/issues/3148 too\r\n\r\nWe should definitely make `TorchIterableDataset` picklable by moving it in the main code instead of inside a function. If you'd like to contribute, feel free to open a Pull Request :)\r\n\r\nI'm also t... |
https://api.github.com/repos/huggingface/datasets/issues/3650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3650/comments | https://api.github.com/repos/huggingface/datasets/issues/3650/events | https://github.com/huggingface/datasets/pull/3650 | 1,118,537,429 | PR_kwDODunzps4xyr2o | 3,650 | Allow 'to_json' to run in unordered fashion in order to lower memory footprint | [] | open | false | null | 4 | 2022-01-30T13:23:19Z | 2022-07-06T15:19:50Z | null | null | I'm using `to_json(..., num_proc=num_proc, compressiong='gzip')` with `num_proc>1`. I'm having an issue where things seem to deadlock at some point. Eventually I see OOM. I'm guessing it's an issue where one process starts to take a long time for a specific batch, and so other process keep accumulating their results in memory.
In order to flush memory, I propose we use optional `imap_unordered`. This will prevent one process to block the other ones. The logical thinking is that index are rarily relevant, and in one wants to keep an index, one can still create another column and reconstruct from there. | {
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"Hi @thomasw21, I remember suggesting `imap_unordered` to @lhoestq at that time to speed up `to_json` further but after trying `pool_imap` on multiple datasets (>9GB) , memory utilisation was almost constant and we decided to go ahead with that only. \r\n\r\n1. Did you try this without `gzip`? Because `gzip` featu... |
https://api.github.com/repos/huggingface/datasets/issues/3912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3912/comments | https://api.github.com/repos/huggingface/datasets/issues/3912/events | https://github.com/huggingface/datasets/pull/3912 | 1,168,720,098 | PR_kwDODunzps40aekr | 3,912 | add draft of registering function for pandas | [] | closed | false | null | 3 | 2022-03-14T17:54:29Z | 2023-01-24T12:57:35Z | 2023-01-24T12:57:10Z | null | This PR adds a register function for `pandas`. It allows to directly push `DataFrame` objects to the hub and in return loading datasets on the hub from `DataFrame`. The motivation for this integration is to enable the vast number of `pandas` users to be able to easily push `DataFrames` to the hub.
Here is an example:
```python
import pandas as pd
from datasets import register_pandas
register_pandas()
# push to hub
df = pd.DataFrame.from_dict({"test": [1,2,3]})
df.push_to_hub("my_test")
# load from hub
df_retrieved = pd.DataFrame.load_from_hub("lvwerra/my_test")
```
It follows a similar philosophy as the `tqdm` [integration](https://github.com/tqdm/tqdm#pandas-integration). Also see [this issue](https://github.com/pandas-dev/pandas/issues/46000) on the `pandas` repository.
This is just a rough draft of what such integration could look like but I would like appreciate some feedback on this: is this something you would like to add the library and is this the way to go? cc @lhoestq @albertvillanova @julien-c | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3912). All of your documentation changes will be reflected on that endpoint.",
"That's cool ! Though I would expect such an integration to only require `huggingface_hub`, not the full `datasets` library. \r\n Indeed if users wa... |
https://api.github.com/repos/huggingface/datasets/issues/797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/797/comments | https://api.github.com/repos/huggingface/datasets/issues/797/events | https://github.com/huggingface/datasets/issues/797 | 735,420,332 | MDU6SXNzdWU3MzU0MjAzMzI= | 797 | Token classification labels are strings and we don't have the list of labels | [
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"default": fals... | closed | false | null | 4 | 2020-11-03T15:33:30Z | 2022-02-14T15:41:54Z | 2022-02-14T15:41:53Z | null | Not sure if this is an issue we want to fix or not, putting it here so it's not forgotten. Right now, in token classification datasets, the labels for NER, POS and the likes are typed as `Sequence` of `strings`, which is wrong in my opinion. These should be `Sequence` of `ClassLabel` or some types that gives easy access to the underlying labels.
The main problem for preprocessing those datasets is that the list of possible labels is not stored inside the `Dataset` object which makes converting the labels to IDs quite difficult (you either have to know the list of labels in advance or run a full pass through the dataset to get the list of labels, the `unique` method being useless with the type `Sequence[str]`). | {
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"Indeed. Pinging @stefan-it here if he want to give an expert opinion :)",
"Related is https://github.com/huggingface/datasets/pull/636",
"Should definitely be a ClassLabel 👍 ",
"Already done."
] |
https://api.github.com/repos/huggingface/datasets/issues/482 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/482/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/482/comments | https://api.github.com/repos/huggingface/datasets/issues/482/events | https://github.com/huggingface/datasets/issues/482 | 674,851,147 | MDU6SXNzdWU2NzQ4NTExNDc= | 482 | Bugs : dataset.map() is frozen on ELI5 | [] | closed | false | null | 8 | 2020-08-07T08:23:35Z | 2023-04-06T09:39:59Z | 2020-08-11T23:55:15Z | null | Hi Huggingface Team!
Thank you guys once again for this amazing repo.
I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)
However, when I run `dataset.map()` on ELI5 to prepare `input_text, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process.
Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow.
----------------------------------------
**More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object)
I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ? | {
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"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look",
"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip in... |
https://api.github.com/repos/huggingface/datasets/issues/3654 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3654/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3654/comments | https://api.github.com/repos/huggingface/datasets/issues/3654/events | https://github.com/huggingface/datasets/pull/3654 | 1,119,717,475 | PR_kwDODunzps4x2kiX | 3,654 | Better TQDM output | [] | closed | false | null | 1 | 2022-01-31T17:22:43Z | 2022-02-03T15:55:34Z | 2022-02-03T15:55:33Z | null | This PR does the following:
* if `dataset_infos.json` exists for a dataset, uses `num_examples` to print the total number of examples that needs to be generated (in `builder.py`)
* fixes `tqdm` + multiprocessing in Jupyter Notebook/Colab (the issue stems from this commit in the `tqdm` repo: https://github.com/tqdm/tqdm/commit/f7722edecc3010cb35cc1c923ac4850a76336f82)
* adds the missing `drop_last_batch` and `with_ranks` params to `DatasetDict.map`
* correctly computes the number of iterations in `map` and the CSV/JSON loader when `batched=True` to fix `tqdm` progress bars
* removes the `bool(logging.get_verbosity() == logging.NOTSET)` (or simplifies `bool(logging.get_verbosity() == logging.NOTSET) or not utils.is_progress_bar_enabled()` to `not utils.is_progress_bar_enabled()`) condition and uses `utils.is_progress_bar_enabled` to check if `tqdm` output is enabled (this comment from @stas00 explains why the `bool(logging.get_verbosity() == logging.NOTSET)` check is problematic: https://github.com/huggingface/transformers/issues/14889#issue-1087318463)
Fix #2630 | {
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"@lhoestq I've created a notebook for you to see the difference: https://colab.research.google.com/drive/1by3EqnoKvC2p-yKW4lPDGOFOZHyGVyeQ?usp=sharing.\r\n\r\nFeel free to suggest better descriptions for the progress bars. \r\n\r\nIf everything looks good, think we can merge."
] |
https://api.github.com/repos/huggingface/datasets/issues/5132 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5132/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5132/comments | https://api.github.com/repos/huggingface/datasets/issues/5132/events | https://github.com/huggingface/datasets/issues/5132 | 1,413,607,306 | I_kwDODunzps5UQe-K | 5,132 | Depracate `num_proc` parameter in `DownloadManager.extract` | [
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"default": true... | closed | false | null | 5 | 2022-10-18T17:41:05Z | 2022-10-25T15:56:46Z | 2022-10-25T15:56:46Z | null | The `num_proc` parameter is only present in `DownloadManager.extract` but not in `StreamingDownloadManager.extract`, making it impossible to support streaming in the dataset scripts that use it (`openwebtext` and `the_pile_stack_exchange`). We can avoid this situation by deprecating this parameter and passing `DownloadConfig`'s `num_proc` to `map_nested` instead, as it's done in `DownloadManager.download`. | {
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"I can take this! #self-assign",
"#self-assign",
"@lazarust i'm already working on this issue :smile: ",
"#self-assign",
"hey @mariosasko , i made a pr for this issue. Could you please review it."
] |
https://api.github.com/repos/huggingface/datasets/issues/6037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6037/comments | https://api.github.com/repos/huggingface/datasets/issues/6037/events | https://github.com/huggingface/datasets/issues/6037 | 1,805,887,184 | I_kwDODunzps5ro6bQ | 6,037 | Documentation links to examples are broken | [] | closed | false | null | 2 | 2023-07-15T04:54:50Z | 2023-07-17T22:35:14Z | 2023-07-17T15:10:32Z | null | ### Describe the bug
The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example
- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)
### Steps to reproduce the bug
Click on links to examples from latest documentation
### Expected behavior
Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py
### Environment info
dataset v1.2.1 | {
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"These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.\r\n\r\nInitially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://hug... |
https://api.github.com/repos/huggingface/datasets/issues/3351 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3351/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3351/comments | https://api.github.com/repos/huggingface/datasets/issues/3351/events | https://github.com/huggingface/datasets/pull/3351 | 1,068,094,873 | PR_kwDODunzps4vO5AS | 3,351 | Add VCTK dataset | [] | closed | false | null | 9 | 2021-12-01T08:13:17Z | 2022-02-28T09:22:03Z | 2021-12-28T15:05:08Z | null | Fixes #1837. | {
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"Hello @patrickvonplaten, I hope it's okay to ping you with a (dumb) question!\r\n\r\nI've been trying to get `dl_manager.download_and_extract(_DL_URL)` to work with no avail. I verified that this is a problem on two different machines (lab server, GCP), so I doubt it's an issue with network connectivity. Here is t... |
https://api.github.com/repos/huggingface/datasets/issues/5080 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5080/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5080/comments | https://api.github.com/repos/huggingface/datasets/issues/5080/events | https://github.com/huggingface/datasets/issues/5080 | 1,398,849,565 | I_kwDODunzps5TYMAd | 5,080 | Use hfh for caching | [
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] | open | false | null | 1 | 2022-10-06T05:51:58Z | 2022-10-06T14:26:05Z | null | null | ## Is your feature request related to a problem?
As previously discussed in our meeting with @Wauplin and agreed on our last datasets team sync meeting, I'm investigating how `datasets` can use `hfh` for caching.
## Describe the solution you'd like
Due to the peculiarities of the `datasets` cache, I would propose adopting `hfh` caching system in stages.
First, we could easily start using `hfh` caching for:
- dataset Python scripts
- dataset READMEs
- dataset infos JSON files (now deprecated)
Second, we could also use `hfh` caching for data files downloaded from the Hub.
Further investigation is needed for:
- files downloaded from non-Hub hosts
- extracted files from downloaded archive/compressed files
- generated Arrow files
## Additional context
Docs about the `hfh` caching system:
- [Manage huggingface_hub cache-system](https://huggingface.co/docs/huggingface_hub/main/en/how-to-cache)
- [Cache-system reference](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/cache)
The `transformers` library has already adopted `hfh` for caching. See:
- huggingface/transformers#18438
- huggingface/transformers#18857
- huggingface/transformers#18966
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"There is some discussion in https://github.com/huggingface/huggingface_hub/pull/1088 if it can help :)"
] |
https://api.github.com/repos/huggingface/datasets/issues/5860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5860/comments | https://api.github.com/repos/huggingface/datasets/issues/5860/events | https://github.com/huggingface/datasets/pull/5860 | 1,709,727,460 | PR_kwDODunzps5QfojD | 5,860 | Minor tqdm optim | [] | closed | false | null | 3 | 2023-05-15T09:49:37Z | 2023-05-17T18:46:46Z | 2023-05-17T18:39:35Z | null | Don't create a tqdm progress bar when `disable_tqdm` is passed to `map_nested`.
On my side it sped up some iterable datasets by ~30% when `map_nested` is used extensively to recursively tensorize python dicts. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/3175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3175/comments | https://api.github.com/repos/huggingface/datasets/issues/3175/events | https://github.com/huggingface/datasets/pull/3175 | 1,038,945,271 | PR_kwDODunzps4t0bXw | 3,175 | Add docs for `to_tf_dataset` | [
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] | closed | false | null | 2 | 2021-10-28T20:55:22Z | 2021-11-03T15:39:36Z | 2021-11-03T10:07:23Z | null | This PR adds some documentation for new features released in v1.13.0, with the main addition being `to_tf_dataset`:
- Show how to use `to_tf_dataset` in the tutorial, and move `set_format(type='tensorflow'...)` to the Process section (let me know if I'm missing anything @Rocketknight1 😅).
- Add an example for loading dataset from multiple zipped CSV files to the Load section.
- Add an example for removing columns for an `IterableDataset`.
- Add graphic for visualizing streaming. | {
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"This looks great, thank you!",
"Thanks !\r\n\r\nFor some reason the new GIF is 6MB, which is a bit heavy for an image on a website. The previous one was around 200KB though which is perfect. For a good experience we usually expect images to be less than 500KB - otherwise for users with poor connection it takes t... |
https://api.github.com/repos/huggingface/datasets/issues/4249 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4249/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4249/comments | https://api.github.com/repos/huggingface/datasets/issues/4249/events | https://github.com/huggingface/datasets/pull/4249 | 1,218,524,424 | PR_kwDODunzps42742y | 4,249 | Support streaming XGLUE dataset | [] | closed | false | null | 1 | 2022-04-28T10:27:23Z | 2022-05-06T08:38:21Z | 2022-04-28T16:08:03Z | null | Support streaming XGLUE dataset.
Fix #4247.
CC: @severo | {
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