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https://api.github.com/repos/huggingface/datasets/issues/2530 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2530/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2530/comments | https://api.github.com/repos/huggingface/datasets/issues/2530/events | https://github.com/huggingface/datasets/pull/2530 | 927,013,773 | MDExOlB1bGxSZXF1ZXN0Njc1MjMyNDk0 | 2,530 | Fixed label parsing in the ProductReviews dataset | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-22T09:12:45" | "2021-06-22T12:55:20" | "2021-06-22T12:52:40" | CONTRIBUTOR | null | Fixed issue with parsing dataset labels. | {
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https://api.github.com/repos/huggingface/datasets/issues/2529 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2529/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2529/comments | https://api.github.com/repos/huggingface/datasets/issues/2529/events | https://github.com/huggingface/datasets/pull/2529 | 926,378,812 | MDExOlB1bGxSZXF1ZXN0Njc0NjkxNjA5 | 2,529 | Add summarization template | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-21T16:08:31" | "2021-06-23T14:22:11" | "2021-06-23T13:30:10" | MEMBER | null | This PR adds a task template for text summarization. As far as I can tell, we do not need to distinguish between "extractive" or "abstractive" summarization - both can be handled with this template.
Usage:
```python
from datasets import load_dataset
from datasets.tasks import Summarization
ds = load_dataset("xsum", split="train")
# Dataset({
# features: ['document', 'summary', 'id'],
# num_rows: 204045
# })
summarization = Summarization(text_column="document", summary_column="summary")
ds.prepare_for_task(summarization)
# Dataset({
# features: ['text', 'summary'],
# num_rows: 204045
# })
```
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"Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`."
] | "2021-06-21T15:04:54" | "2021-06-24T14:42:47" | "2021-06-24T14:42:47" | NONE | null | ## Describe the bug
In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449)
`disable=bool(logging.get_verbosity() == logging.NOTSET)`
and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493)
`not_verbose = bool(logger.getEffectiveLevel() > WARNING)`
## Steps to reproduce the bug
```python
import datasets
import logging
datasets.logging.get_verbosity = lambda : logging.NOTSET
datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
```
## Expected results
The code should download and load the dataset as normal without displaying progress bars
## Actual results
```ImportError Traceback (most recent call last)
<ipython-input-4-aec65c0509c6> in <module>
----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
~/venv/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)
713 dataset=True,
714 return_resolved_file_path=True,
--> 715 use_auth_token=use_auth_token,
716 )
717 # Set the base path for downloads as the parent of the script location
~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
350 file_path = hf_bucket_url(path, filename=name, dataset=False)
351 try:
--> 352 local_path = cached_path(file_path, download_config=download_config)
353 except FileNotFoundError:
354 raise FileNotFoundError(
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
289 use_etag=download_config.use_etag,
290 max_retries=download_config.max_retries,
--> 291 use_auth_token=download_config.use_auth_token,
292 )
293 elif os.path.exists(url_or_filename):
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
668 headers=headers,
669 cookies=cookies,
--> 670 max_retries=max_retries,
671 )
672
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)
493 initial=resume_size,
494 desc="Downloading",
--> 495 disable=not_verbose,
496 )
497 for chunk in response.iter_content(chunk_size=1024):
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs)
217 total = self.total * unit_scale if self.total else self.total
218 self.container = self.status_printer(
--> 219 self.fp, total, self.desc, self.ncols)
220 self.sp = self.display
221
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols)
95 if IProgress is None: # #187 #451 #558 #872
96 raise ImportError(
---> 97 "IProgress not found. Please update jupyter and ipywidgets."
98 " See https://ipywidgets.readthedocs.io/en/stable"
99 "/user_install.html")
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8
- Python version: 3.7.10
- PyArrow version: 3.0.0
I am running this code on Deepnote and which important to this issue **does not** support IPywidgets
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https://api.github.com/repos/huggingface/datasets/issues/2527 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2527/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2527/comments | https://api.github.com/repos/huggingface/datasets/issues/2527/events | https://github.com/huggingface/datasets/pull/2527 | 926,031,525 | MDExOlB1bGxSZXF1ZXN0Njc0MzkzNjQ5 | 2,527 | Replace bad `n>1M` size tag | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-21T09:42:35" | "2021-06-21T15:06:50" | "2021-06-21T15:06:49" | MEMBER | null | Some datasets were still using the old `n>1M` tag which has been replaced with tags `1M<n<10M`, etc.
This resulted in unexpected results when searching for datasets bigger than 1M on the hub, since it was only showing the ones with the tag `n>1M`. | {
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https://api.github.com/repos/huggingface/datasets/issues/2525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2525/comments | https://api.github.com/repos/huggingface/datasets/issues/2525/events | https://github.com/huggingface/datasets/pull/2525 | 925,896,358 | MDExOlB1bGxSZXF1ZXN0Njc0Mjc5MTgy | 2,525 | Use scikit-learn package rather than sklearn in setup.py | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-21T07:04:25" | "2021-06-21T10:01:13" | "2021-06-21T08:57:33" | CONTRIBUTOR | null | The sklearn package is an historical thing and should probably not be used by anyone, see https://github.com/scikit-learn/scikit-learn/issues/8215#issuecomment-344679114 for some caveats.
Note: this affects only TESTS_REQUIRE so I guess only developers not end users. | {
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https://api.github.com/repos/huggingface/datasets/issues/2523 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2523/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2523/comments | https://api.github.com/repos/huggingface/datasets/issues/2523/events | https://github.com/huggingface/datasets/issues/2523 | 925,421,008 | MDU6SXNzdWU5MjU0MjEwMDg= | 2,523 | Fr | {
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https://api.github.com/repos/huggingface/datasets/issues/2522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2522/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2522/comments | https://api.github.com/repos/huggingface/datasets/issues/2522/events | https://github.com/huggingface/datasets/issues/2522 | 925,334,379 | MDU6SXNzdWU5MjUzMzQzNzk= | 2,522 | Documentation Mistakes in Dataset: emotion | {
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"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.",
"The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```",
"@GDGauravDutta the dataset author replied in their issue: https://github.com/dair-ai/emotion_dataset/issues/2\r\n> The dataset released is a preprocessed six emotion variant released for educational and research purposes.\r\n\r\n@albertovilla the dataset card was fixed with 6 emotions."
] | "2021-06-19T07:08:57" | "2023-01-02T12:04:58" | "2023-01-02T12:04:58" | NONE | null | As per documentation,
Dataset: emotion
Homepage: https://github.com/dair-ai/emotion_dataset
Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py
Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion
Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.
But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust. | {
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https://api.github.com/repos/huggingface/datasets/issues/2521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2521/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2521/comments | https://api.github.com/repos/huggingface/datasets/issues/2521/events | https://github.com/huggingface/datasets/pull/2521 | 925,030,685 | MDExOlB1bGxSZXF1ZXN0NjczNTgxNzQ4 | 2,521 | Insert text classification template for Emotion dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/2519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2519/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2519/comments | https://api.github.com/repos/huggingface/datasets/issues/2519/events | https://github.com/huggingface/datasets/pull/2519 | 924,903,240 | MDExOlB1bGxSZXF1ZXN0NjczNDcyMzYy | 2,519 | Improve performance of pandas arrow extractor | {
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https://api.github.com/repos/huggingface/datasets/issues/2518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2518/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2518/comments | https://api.github.com/repos/huggingface/datasets/issues/2518/events | https://github.com/huggingface/datasets/pull/2518 | 924,654,100 | MDExOlB1bGxSZXF1ZXN0NjczMjU5Nzg1 | 2,518 | Add task templates for tydiqa and xquad | {
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Notes:
* I could not test the tydiqa implementation since I don't have enough disk space 😢 . But I am confident the template works :)
* there exist other datasets like `fquad` and `mlqa` which are candidates for question-answering templates, but some work is needed to handle the ordering of nested column described in #2434
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https://api.github.com/repos/huggingface/datasets/issues/2515 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2515/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2515/comments | https://api.github.com/repos/huggingface/datasets/issues/2515/events | https://github.com/huggingface/datasets/pull/2515 | 924,435,447 | MDExOlB1bGxSZXF1ZXN0NjczMDc3NTIx | 2,515 | CRD3 dataset card | {
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"Hi @colbym-MM, thanks for reporting. We are fixing it."
] | "2021-06-17T17:28:48" | "2021-06-18T08:43:55" | "2021-06-18T08:43:55" | NONE | null | https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66 | {
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https://api.github.com/repos/huggingface/datasets/issues/2512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2512/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2512/comments | https://api.github.com/repos/huggingface/datasets/issues/2512/events | https://github.com/huggingface/datasets/issues/2512 | 924,069,353 | MDU6SXNzdWU5MjQwNjkzNTM= | 2,512 | seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict' | {
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"Sorry, I was using an old version of sequeval"
] | "2021-06-17T15:36:02" | "2021-06-17T15:46:07" | "2021-06-17T15:46:07" | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
seqeval = load_metric("seqeval")
seqeval.compute(predictions=[['A']], references=[['A']])
```
## Expected results
The function computes a dict with metrics
## Actual results
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-69a57f5cf06f> in <module>
1 from datasets import load_dataset, load_metric
2 seqeval = load_metric("seqeval")
----> 3 seqeval.compute(predictions=[['A']], references=[['A']])
~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix)
95
96 def _compute(self, predictions, references, suffix=False):
---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True)
98 report.pop("macro avg")
99 report.pop("weighted avg")
TypeError: classification_report() got an unexpected keyword argument 'output_dict'
```
## Environment info
sklearn=0.24
datasets=1.1.3
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https://api.github.com/repos/huggingface/datasets/issues/2511 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2511/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2511/comments | https://api.github.com/repos/huggingface/datasets/issues/2511/events | https://github.com/huggingface/datasets/issues/2511 | 923,762,133 | MDU6SXNzdWU5MjM3NjIxMzM= | 2,511 | Add C4 | {
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"Update on this: I'm computing the checksums of the data files. It will be available soon",
"Added in #2575 :)"
] | "2021-06-17T10:31:04" | "2021-07-05T12:36:58" | "2021-07-05T12:36:57" | MEMBER | null | ## Adding a Dataset
- **Name:** *C4*
- **Description:** *https://github.com/allenai/allennlp/discussions/5056*
- **Paper:** *https://arxiv.org/abs/1910.10683*
- **Data:** *https://huggingface.co/datasets/allenai/c4*
- **Motivation:** *Used a lot for pretraining*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Should fix https://github.com/huggingface/datasets/issues/1710 | {
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In this PR I also added `DatasetDict.align_labels_with_mapping` | {
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I fixed that by setting the fingerprint to be the hash of:
- the relative cache dir (dataset_name/version/config_id)
- the requested split
Close #2496
I had to fix an issue with the filelock filename that was too long (>255). It prevented the tests to run on my machine. I just added `hash_filename_if_too_long` in case this happens, to not get filenames longer than 255.
We usually have long filenames for filelocks because they are named after the path that is being locked. In case the path is a cache directory that has long directory names, then the filelock filename could en up being very long. | {
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"Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path/to/data/dir\")\r\n```\r\n\r\nLet me know what you think",
"Yep that would be sweet - closing for now as we found a workaround. ",
"@lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to seeing.",
"Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw/image-folder', data_files='PetImages/')\r\n```",
"Bumping this one following our recent discussion @mariosasko @nateraw :)"
] | "2021-06-15T22:43:33" | "2022-03-01T16:29:44" | "2022-03-01T16:29:44" | NONE | null | **Is your feature request related to a problem? Please describe.**
Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader.
**Describe the solution you'd like**
Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10".
**Describe alternatives you've considered**
Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)
Write custom data loader logic
**Additional context**
We're training ViT on custom dataset
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} | [] | "2021-06-15T14:10:02" | "2021-06-16T10:47:49" | "2021-06-16T10:47:49" | MEMBER | null | This PR depends on PR #2453 (which must be merged first).
Close #2366. | {
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https://api.github.com/repos/huggingface/datasets/issues/2506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2506/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2506/comments | https://api.github.com/repos/huggingface/datasets/issues/2506/events | https://github.com/huggingface/datasets/pull/2506 | 921,435,598 | MDExOlB1bGxSZXF1ZXN0NjcwNDM4NTgx | 2,506 | Add course banner | {
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This could make the numpy/torch/tf/jax formatting faster | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-14T17:24:23" | "2021-06-21T16:15:50" | "2021-06-21T16:15:49" | MEMBER | null | Hi !
I just added the "jax" formatting, as we already have for pytorch, tensorflow, numpy (and also pandas and arrow).
It does pretty much the same thing as the pytorch formatter except it creates jax.numpy.ndarray objects.
```python
from datasets import Dataset
d = Dataset.from_dict({"foo": [[0., 1., 2.]]})
d = d.with_format("jax")
d[0]
# {'foo': DeviceArray([0., 1., 2.], dtype=float32)}
```
A few details:
- The default integer precision for jax depends on the jax configuration `jax_enable_x64` (see [here](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), I took that into account. Unless `jax_enable_x64` is specified, it is int32 by default
- AFAIK it's not possible to do a full conversion from arrow data to jax data. We are doing arrow -> numpy -> jax but the numpy -> jax part doesn't do zero copy unfortutanely (see [here](https://github.com/google/jax/issues/4486))
- the env var for disabling JAX is `USE_JAX`. However I noticed that in `transformers` it is `USE_FLAX`. This is not an issue though IMO
I also updated `convert_to_python_objects` to allow users to pass jax.numpy.ndarray objects to build a dataset.
Since the `convert_to_python_objects` method became slow because it's the time when pytorch, tf (and now jax) are imported, I fixed it by checking the `sys.modules` to avoid unecessary import of pytorch, tf or jax.
Close #2495 | {
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} | [] | "2021-06-14T16:28:12" | "2021-06-14T16:49:42" | "2021-06-14T16:49:42" | MEMBER | null | This Zenodo metadata file fixes the name of the `Datasets` license appearing in the DOI as `"Apache-2.0"`, which otherwise by default is `"other-open"`.
Close #2472. | {
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TODOs:
- [x] Add docstring and entry in the docs
- [x] Add tests
Closes #2484
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} | [] | "2021-06-14T10:02:47" | "2021-06-15T18:06:18" | "2021-06-14T14:24:37" | MEMBER | null | From pyarrow version 4, it is supported to cast sliced lists.
This PR uses default pyarrow cast in Datasets to cast sliced list arrays if pyarrow version is >= 4.
In relation with PR #2461 and #2490.
cc: @lhoestq, @abhi1thakur, @SBrandeis | {
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However the fingerprint seems to change when someone moves the cache directory of the dataset.
This is because it uses the default fingerprint generation:
1. the dataset path is used to get the fingerprint
2. the modification times of the arrow file is also used to get the fingerprint
To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory. | {
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Here is my PR for the text classification dataset in Mongolian.
Please do let me know in case you have anything to clarify.
Thanks & Regards,
Enod | {
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Close #2489. | {
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- it was fixed on 3 May 2021
- version 4.0.1 was released on 19 May 2021 with the bug fix | {
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I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib.
1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset?
You can see the datasets available for Rico here: http://interactionmining.org/rico
2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset?
2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image?
3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently?
4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string?
I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: ! | {
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"#self-assign"
] | "2021-06-11T18:47:22" | "2021-07-05T10:45:57" | "2021-07-05T10:45:57" | MEMBER | null | As discussed with @stas00 and @lhoestq, this would allow things like:
```python
from datasets import load_dataset_builder
dataset_name = "openwebtext"
builder = load_dataset_builder(dataset_name)
print(builder.cache_dir)
``` | {
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However calling gc.collect too often causes significant slow downs (the CI run time doubled).
So I just moved the gc.collect call to the exact place where it's actually needed: when post-processing a dataset | {
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They were due to open arrow files not properly closed by pyarrow.
Since https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 gc.collect is called each time we don't need an arrow file to make sure that the files are closed.
close https://github.com/huggingface/datasets/issues/2471
cc @lewtun | {
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"My suggestion for this would be to have this enabled by default.\r\n\r\nPlus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:\r\n\r\n1. uncompress a handful of files via a generator enough to generate one arrow file\r\n2. process arrow file 1\r\n3. delete all the files that went in and aren't needed anymore.\r\n\r\nrinse and repeat.\r\n\r\n1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project\r\n2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing\r\n3. It would already include deleting temp files this issue is talking about\r\n\r\nI wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders."
] | "2021-06-11T12:21:52" | "2021-07-19T09:08:18" | "2021-07-19T09:08:18" | MEMBER | null | As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user. | {
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To-Do: Update README.md and add YAML tags | {
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https://api.github.com/repos/huggingface/datasets/issues/2475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2475/comments | https://api.github.com/repos/huggingface/datasets/issues/2475/events | https://github.com/huggingface/datasets/issues/2475 | 917,650,882 | MDU6SXNzdWU5MTc2NTA4ODI= | 2,475 | Issue in timit_asr database | {
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"This bug was fixed in #1995. Upgrading datasets to version 1.6 fixes the issue!",
"Indeed was a fixed bug.\r\nWorks on version 1.8\r\nThanks "
] | "2021-06-10T18:05:29" | "2021-06-13T08:13:50" | "2021-06-13T08:13:13" | NONE | null | ## Describe the bug
I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows).
I am using the next code line
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
The above code result with the same sentence duplicated ten times.
It also happens when I use the dataset viewer at Streamlit .
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
data = dataset.to_pandas()
# Sample code to reproduce the bug
```
## Expected results
table with different row information
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.4.1 (also occur in the latest version)
- Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.8.1+cu102 (False)
- Tensorflow version (GPU?): 1.15.3 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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"Hi ! `load_from_disk` doesn't move the data. If you specify a local path to your mounted drive, then the dataset is going to be loaded directly from the arrow file in this directory. The cache files that result from `map` operations are also stored in the same directory by default.\r\n\r\nHowever note than writing data to your google drive actually fills the VM's disk (see https://github.com/huggingface/datasets/issues/643)\r\n\r\nGiven that, I don't think that changing the cache directory changes anything.\r\n\r\nLet me know what you think",
"Thanks for your answer! I am a little surprised since I just want to read the dataset.\r\n\r\nAfter debugging a bit, I noticed that the VM’s disk fills up when the tables (generator) are converted to a list:\r\n\r\nhttps://github.com/huggingface/datasets/blob/5ba149773d23369617563d752aca922081277ec2/src/datasets/table.py#L850\r\n\r\nIf I try to iterate through the table’s generator e.g.: \r\n\r\n`length = sum(1 for x in tables)`\r\n\r\nthe VM’s disk fills up as well.\r\n\r\nI’m running out of Ideas 😄 ",
"Indeed reading the data shouldn't increase the VM's disk. Not sure what google colab does under the hood for that to happen",
"Apparently, Colab uses a local cache of the data files read/written from Google Drive. See:\r\n- https://github.com/googlecolab/colabtools/issues/2087#issuecomment-860818457\r\n- https://github.com/googlecolab/colabtools/issues/1915#issuecomment-804234540\r\n- https://github.com/googlecolab/colabtools/issues/2147#issuecomment-885052636"
] | "2021-06-10T17:39:36" | "2022-02-16T14:55:01" | "2022-02-16T14:55:00" | NONE | null | **Is your feature request related to a problem? Please describe.**
When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:
`
from datasets import load_from_disk
myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData")
`
I know that chaching on google drive could slow down learning. But at least it would run.
**Describe the solution you'd like**
Add cache_Dir parameter to the load_from_disk function.
**Describe alternatives you've considered**
It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?
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To-Do: Update README.md and add YAML tags | {
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"I have received a reply from Zenodo support:\r\n> We are currently investigating and fixing this issue related to GitHub releases. As soon as we have solved it we will reach back to you.",
"Other repo maintainers had the same problem with Zenodo. \r\n\r\nThere is an open issue on their GitHub repo: zenodo/zenodo#2181",
"I have received the following request from Zenodo support:\r\n> Could you send us the link to the repository as well as the release tag?\r\n\r\nMy reply:\r\n> Sure, here it is:\r\n> - Link to the repository: https://github.com/huggingface/datasets\r\n> - Link to the repository at the release tag: https://github.com/huggingface/datasets/releases/tag/1.8.0\r\n> - Release tag: 1.8.0",
"Zenodo issue has been fixed. The 1.8.0 release DOI can be found here: https://zenodo.org/record/4946100#.YMd6vKj7RPY"
] | "2021-06-10T15:15:46" | "2021-06-14T16:49:42" | "2021-06-14T16:49:42" | MEMBER | null | After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published".
I have contacted Zenodo support to fix this issue.
TODO:
- [x] Check with Zenodo to fix the issue
- [x] Check BibTeX entry is right | {
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```
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process
``` | {
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"Hi ! It looks like the issue comes from pyarrow. What version of pyarrow are you using ? How did you install it ?",
"Thank you for the quick reply! I have `pyarrow==4.0.0`, and I am installing with `pip`. It's not one of my explicit dependencies, so I assume it came along with something else.",
"Could you trying reinstalling pyarrow with pip ?\r\nI'm not sure why it would check in your multicurtural-sc directory for source files.",
"Sure! I tried reinstalling to get latest. pip was mad because it looks like Datasets currently wants <4.0.0 (which is interesting, because apparently I ended up with 4.0.0 already?), but I gave it a shot anyway:\r\n\r\n```bash\r\n$ pip install --upgrade --force-reinstall pyarrow\r\nCollecting pyarrow\r\n Downloading pyarrow-4.0.1-cp39-cp39-manylinux2014_x86_64.whl (21.9 MB)\r\n |████████████████████████████████| 21.9 MB 23.8 MB/s\r\nCollecting numpy>=1.16.6\r\n Using cached numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.4 MB)\r\nInstalling collected packages: numpy, pyarrow\r\n Attempting uninstall: numpy\r\n Found existing installation: numpy 1.20.3\r\n Uninstalling numpy-1.20.3:\r\n Successfully uninstalled numpy-1.20.3\r\n Attempting uninstall: pyarrow\r\n Found existing installation: pyarrow 3.0.0\r\n Uninstalling pyarrow-3.0.0:\r\n Successfully uninstalled pyarrow-3.0.0\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\ndatasets 1.8.0 requires pyarrow<4.0.0,>=1.0.0, but you have pyarrow 4.0.1 which is incompatible.\r\nSuccessfully installed numpy-1.20.3 pyarrow-4.0.1\r\n```\r\n\r\nTrying it, the same issue:\r\n\r\n\r\n\r\nI tried installing `\"pyarrow<4.0.0\"`, which gave me 3.0.0. Running, still, same issue.\r\n\r\nI agree it's weird that pyarrow is checking the source code directory for its files. (There is no `pyarrow/` directory there.) To me, that makes it seem like an issue with how pyarrow is called.\r\n\r\nOut of curiosity, I tried running this with fewer workers to see when the error arises:\r\n\r\n- 1: ✅\r\n- 2: ✅\r\n- 4: ✅\r\n- 8: ✅\r\n- 10: ✅\r\n- 11: ❌ 🤔\r\n- 12: ❌\r\n- 16: ❌\r\n- 32: ❌\r\n\r\nchecking my datasets:\r\n\r\n```python\r\n>>> datasets\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['text'],\r\n num_rows: 389290\r\n })\r\n validation.sc: Dataset({\r\n features: ['text'],\r\n num_rows: 10 # 🤔\r\n })\r\n validation.wvs: Dataset({\r\n features: ['text'],\r\n num_rows: 93928\r\n })\r\n})\r\n```\r\n\r\nNew hypothesis: crash if `num_proc` > length of a dataset? 😅\r\n\r\nIf so, this might be totally my fault, as the caller. Could be a docs fix, or maybe this library could do a check to limit `num_proc` for this case?",
"Good catch ! Not sure why it could raise such a weird issue from pyarrow though\r\nWe should definitely reduce num_proc to the length of the dataset if needed and log a warning.",
"This has been fixed in #2566, thanks @connor-mccarthy !\r\nWe'll make a new release soon that includes the fix ;)"
] | "2021-06-09T22:40:22" | "2021-07-01T09:34:54" | "2021-07-01T09:11:13" | NONE | null | ## Describe the bug
Crash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.
I believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.
## Steps to reproduce the bug
```python
# this function will be applied with map()
def tokenize_function(examples):
return tokenizer(
examples["text"],
padding=PaddingStrategy.DO_NOT_PAD,
truncation=True,
)
# data_files is a Dict[str, str] mapping name -> path
datasets = load_dataset("text", data_files={...})
# this is where the error happens if num_proc = 16,
# but is fine if num_proc = 1
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=num_workers,
)
```
## Expected results
The `map()` function succeeds with `num_proc` > 1.
## Actual results
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31
- Python version: 3.9.5
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes, but I think N/A for this issue
- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N/A for this issue
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https://api.github.com/repos/huggingface/datasets/issues/2466 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2466/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2466/comments | https://api.github.com/repos/huggingface/datasets/issues/2466/events | https://github.com/huggingface/datasets/pull/2466 | 915,914,098 | MDExOlB1bGxSZXF1ZXN0NjY1NjY1MjQy | 2,466 | change udpos features structure | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-09T08:03:31" | "2021-06-18T11:55:09" | "2021-06-16T10:41:37" | CONTRIBUTOR | null | The structure is change such that each example is a sentence
The change is done for issues:
#2061
#2444
Close #2061 , close #2444. | {
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https://api.github.com/repos/huggingface/datasets/issues/2465 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2465/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2465/comments | https://api.github.com/repos/huggingface/datasets/issues/2465/events | https://github.com/huggingface/datasets/pull/2465 | 915,525,071 | MDExOlB1bGxSZXF1ZXN0NjY1MzMxMDMz | 2,465 | adding masahaner dataset | {
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@lhoestq , can you please review | {
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https://api.github.com/repos/huggingface/datasets/issues/2464 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2464/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2464/comments | https://api.github.com/repos/huggingface/datasets/issues/2464/events | https://github.com/huggingface/datasets/pull/2464 | 915,485,601 | MDExOlB1bGxSZXF1ZXN0NjY1Mjk1MDE5 | 2,464 | fix: adjusting indexing for the labels. | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T20:47:25" | "2021-06-09T10:15:46" | "2021-06-09T09:10:28" | CONTRIBUTOR | null | The labels index were mismatching the actual ones used in the dataset. Specifically `0` is used for `SUPPORTS` and `1` is used for `REFUTES`
After this change, the `README.md` now reflects the content of `dataset_infos.json`.
Signed-off-by: Matteo Manica <drugilsberg@gmail.com> | {
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https://api.github.com/repos/huggingface/datasets/issues/2463 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2463/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2463/comments | https://api.github.com/repos/huggingface/datasets/issues/2463/events | https://github.com/huggingface/datasets/pull/2463 | 915,454,788 | MDExOlB1bGxSZXF1ZXN0NjY1MjY3NTA2 | 2,463 | Fix proto_qa download link | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T20:23:16" | "2021-06-10T12:49:56" | "2021-06-10T08:31:10" | CONTRIBUTOR | null | Fixes #2459
Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/2461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2461/comments | https://api.github.com/repos/huggingface/datasets/issues/2461/events | https://github.com/huggingface/datasets/pull/2461 | 915,286,150 | MDExOlB1bGxSZXF1ZXN0NjY1MTE3MTY4 | 2,461 | Support sliced list arrays in cast | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T17:38:47" | "2021-06-08T17:56:24" | "2021-06-08T17:56:23" | MEMBER | null | There is this issue in pyarrow:
```python
import pyarrow as pa
arr = pa.array([[i * 10] for i in range(4)])
arr.cast(pa.list_(pa.int32())) # works
arr = arr.slice(1)
arr.cast(pa.list_(pa.int32())) # fails
# ArrowNotImplementedError("Casting sliced lists (non-zero offset) not yet implemented")
```
However in `Dataset.cast` we slice tables to cast their types (it's memory intensive), so we have the same issue.
Because of this it is currently not possible to cast a Dataset with a Sequence feature type (unless the table is small enough to not be sliced).
In this PR I fixed this by resetting the offset of `pyarrow.ListArray` arrays to zero in the table before casting.
I used `pyarrow.compute.subtract` function to update the offsets of the ListArray.
cc @abhi1thakur @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/2460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2460/comments | https://api.github.com/repos/huggingface/datasets/issues/2460/events | https://github.com/huggingface/datasets/pull/2460 | 915,268,536 | MDExOlB1bGxSZXF1ZXN0NjY1MTAyMjA4 | 2,460 | Revert default in-memory for small datasets | {
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https://api.github.com/repos/huggingface/datasets/issues/2459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2459/comments | https://api.github.com/repos/huggingface/datasets/issues/2459/events | https://github.com/huggingface/datasets/issues/2459 | 915,222,015 | MDU6SXNzdWU5MTUyMjIwMTU= | 2,459 | `Proto_qa` hosting seems to be broken | {
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"@VictorSanh , I think @mariosasko is already working on it. "
] | "2021-06-08T16:16:32" | "2021-06-10T08:31:09" | "2021-06-10T08:31:09" | MEMBER | null | ## Describe the bug
The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now.
@zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py`
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("proto_qa")
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators
train_fpath = dl_manager.download(_URLs[self.config.name]["train"])
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download
num_proc=download_config.num_proc,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl
``` | {
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"cc: @krandiash (pinged in reverted PR)."
] | "2021-06-08T15:51:41" | "2021-06-08T18:57:11" | "2021-06-08T17:55:43" | MEMBER | null | Users are reporting issues and confusion about setting default in-memory to True for small datasets.
We see 2 clear use cases of Datasets:
- the "canonical" way, where you can work with very large datasets, as they are memory-mapped and cached (after every transformation)
- some edge cases (speed benchmarks, interactive/exploratory analysis,...), where default in-memory can explicitly be enabled, and no caching will be done
After discussing with @lhoestq we have agreed to:
- revert this feature (implemented in #2182)
- explain in the docs how to optimize speed/performance by setting default in-memory
cc: @stas00 https://github.com/huggingface/datasets/pull/2409#issuecomment-856210552 | {
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https://api.github.com/repos/huggingface/datasets/issues/2457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2457/comments | https://api.github.com/repos/huggingface/datasets/issues/2457/events | https://github.com/huggingface/datasets/pull/2457 | 915,079,441 | MDExOlB1bGxSZXF1ZXN0NjY0OTQwMzQ0 | 2,457 | Add align_labels_with_mapping function | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T13:54:00" | "2022-01-12T08:57:41" | "2021-06-17T09:56:52" | MEMBER | null | This PR adds a helper function to align the `label2id` mapping between a `datasets.Dataset` and a classifier (e.g. a transformer with a `PretrainedConfig.label2id` dict), with the alignment performed on the dataset itself.
This will help us with the Hub evaluation, where we won't know in advance whether a model that is fine-tuned on say MNLI has the same mappings as the MNLI dataset we load from `datasets`.
An example where this is needed is if we naively try to evaluate `microsoft/deberta-base-mnli` on `mnli` because the model config has the following mappings:
```python
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
},
"label2id": {
"CONTRADICTION": 0,
"ENTAILMENT": 2,
"NEUTRAL": 1
}
```
while the `mnli` dataset has the `contradiction` and `neutral` labels swapped:
```python
id2label = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
label2id = {'contradiction': 2, 'entailment': 0, 'neutral': 1}
```
As a result, we get a much lower accuracy during evaluation:
```python
from datasets import load_dataset
from transformers.trainer_utils import EvalPrediction
from transformers import AutoModelForSequenceClassification, Trainer
# load dataset for evaluation
mnli = load_dataset("glue", "mnli", split="test")
# load model
model_ckpt = "microsoft/deberta-base-mnli"
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
# preprocess, create trainer ...
mnli_enc = ...
trainer = Trainer(model, args=args, tokenizer=tokenizer)
# generate preds
preds = trainer.predict(mnli_enc)
# preds.label_ids misalinged with model.config => returns wrong accuracy (too low)!
compute_metrics(EvalPrediction(preds.predictions, preds.label_ids))
```
The fix is to use the helper function before running the evaluation to make sure the label IDs are aligned:
```python
mnli_enc_aligned = mnli_enc.align_labels_with_mapping(label2id=config.label2id, label_column="label")
# preds now aligned and everyone is happy :)
preds = trainer.predict(mnli_enc_aligned)
```
cc @thomwolf @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2456/comments | https://api.github.com/repos/huggingface/datasets/issues/2456/events | https://github.com/huggingface/datasets/pull/2456 | 914,709,293 | MDExOlB1bGxSZXF1ZXN0NjY0NjAwOTk1 | 2,456 | Fix cross-reference typos in documentation | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T09:45:14" | "2021-06-08T17:41:37" | "2021-06-08T17:41:36" | MEMBER | null | Fix some minor typos in docs that avoid the creation of cross-reference links. | {
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https://api.github.com/repos/huggingface/datasets/issues/2455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2455/comments | https://api.github.com/repos/huggingface/datasets/issues/2455/events | https://github.com/huggingface/datasets/pull/2455 | 914,177,468 | MDExOlB1bGxSZXF1ZXN0NjY0MTEzNjg2 | 2,455 | Update version in xor_tydi_qa.py | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-08T02:23:45" | "2021-06-14T15:35:25" | "2021-06-14T15:35:25" | CONTRIBUTOR | null | Fix #2449
@lhoestq Should I revert to the old `dummy/1.0.0` or delete it and keep only `dummy/1.1.0`? | {
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https://api.github.com/repos/huggingface/datasets/issues/2454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2454/comments | https://api.github.com/repos/huggingface/datasets/issues/2454/events | https://github.com/huggingface/datasets/pull/2454 | 913,883,631 | MDExOlB1bGxSZXF1ZXN0NjYzODUyODU1 | 2,454 | Rename config and environment variable for in memory max size | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-07T19:21:08" | "2021-06-07T20:43:46" | "2021-06-07T20:43:46" | MEMBER | null | As discussed in #2409, both config and environment variable have been renamed.
cc: @stas00, huggingface/transformers#12056 | {
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https://api.github.com/repos/huggingface/datasets/issues/2453 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2453/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2453/comments | https://api.github.com/repos/huggingface/datasets/issues/2453/events | https://github.com/huggingface/datasets/pull/2453 | 913,729,258 | MDExOlB1bGxSZXF1ZXN0NjYzNzE3NTk2 | 2,453 | Keep original features order | {
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} | [] | "2021-06-07T16:26:38" | "2021-06-15T18:05:36" | "2021-06-15T15:43:48" | MEMBER | null | When loading a Dataset from a JSON file whose column names are not sorted alphabetically, we should get the same column name order, whether we pass features (in the same order as in the file) or not.
I found this issue while working on #2366. | {
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https://api.github.com/repos/huggingface/datasets/issues/2452 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2452/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2452/comments | https://api.github.com/repos/huggingface/datasets/issues/2452/events | https://github.com/huggingface/datasets/issues/2452 | 913,603,877 | MDU6SXNzdWU5MTM2MDM4Nzc= | 2,452 | MRPC test set differences between torch and tensorflow datasets | {
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"Realized that `tensorflow_datasets` is not provided by Huggingface and should therefore raise the issue there."
] | "2021-06-07T14:20:26" | "2021-06-07T14:34:32" | "2021-06-07T14:34:32" | NONE | null | ## Describe the bug
When using `load_dataset("glue", "mrpc")` to load the MRPC dataset, the test set includes the labels. When using `tensorflow_datasets.load('glue/{}'.format('mrpc'))` to load the dataset the test set does not contain the labels. There should be consistency between torch and tensorflow ways of importing the GLUE datasets.
## Steps to reproduce the bug
Minimal working code
```python
from datasets import load_dataset
import tensorflow as tf
import tensorflow_datasets
# torch
dataset = load_dataset("glue", "mrpc")
# tf
data = tensorflow_datasets.load('glue/{}'.format('mrpc'))
data = list(data['test'].as_numpy_iterator())
for i in range(40,50):
tf_sentence1 = data[i]['sentence1'].decode("utf-8")
tf_sentence2 = data[i]['sentence2'].decode("utf-8")
tf_label = data[i]['label']
index = data[i]['idx']
print('Index {}'.format(index))
torch_sentence1 = dataset['test']['sentence1'][index]
torch_sentence2 = dataset['test']['sentence2'][index]
torch_label = dataset['test']['label'][index]
print('Tensorflow: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(tf_sentence1, tf_sentence2, tf_label))
print('Torch: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(torch_sentence1, torch_sentence2, torch_label))
```
Sample output
```
Index 954
Tensorflow:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label -1
Torch:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label 1
Index 711
Tensorflow:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label -1
Torch:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label 0
```
## Expected results
I would expect the datasets to be independent of whether I am working with torch or tensorflow.
## Actual results
Test set labels are provided in the `datasets.load_datasets()` for MRPC. However MRPC is the only task where the test set labels are not -1.
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2451/comments | https://api.github.com/repos/huggingface/datasets/issues/2451/events | https://github.com/huggingface/datasets/pull/2451 | 913,263,340 | MDExOlB1bGxSZXF1ZXN0NjYzMzIwNDY1 | 2,451 | Mention that there are no answers in adversarial_qa test set | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-07T08:13:57" | "2021-06-07T08:34:14" | "2021-06-07T08:34:13" | MEMBER | null | As mention in issue https://github.com/huggingface/datasets/issues/2447, there are no answers in the test set | {
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https://api.github.com/repos/huggingface/datasets/issues/2450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2450/comments | https://api.github.com/repos/huggingface/datasets/issues/2450/events | https://github.com/huggingface/datasets/issues/2450 | 912,890,291 | MDU6SXNzdWU5MTI4OTAyOTE= | 2,450 | BLUE file not found | {
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"Hi ! The `blue` metric doesn't exist, but the `bleu` metric does.\r\nYou can get the full list of metrics [here](https://github.com/huggingface/datasets/tree/master/metrics) or by running\r\n```python\r\nfrom datasets import list_metrics\r\n\r\nprint(list_metrics())\r\n```",
"Ah, my mistake. Thanks for correcting"
] | "2021-06-06T17:01:54" | "2021-06-07T10:46:15" | "2021-06-07T10:46:15" | NONE | null | Hi, I'm having the following issue when I try to load the `blue` metric.
```shell
import datasets
metric = datasets.load_metric('blue')
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 332, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 605, in load_metric
dataset=False,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 343, in prepare_module
combined_path, github_file_path
FileNotFoundError: Couldn't find file locally at blue/blue.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py.
The file is also not present on the master branch on github.
```
Here is dataset installed version info
```shell
pip freeze | grep datasets
datasets==1.7.0
```
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https://api.github.com/repos/huggingface/datasets/issues/2449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2449/comments | https://api.github.com/repos/huggingface/datasets/issues/2449/events | https://github.com/huggingface/datasets/pull/2449 | 912,751,752 | MDExOlB1bGxSZXF1ZXN0NjYyODg1ODUz | 2,449 | Update `xor_tydi_qa` url to v1.1 | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-06T09:44:58" | "2021-06-07T15:16:21" | "2021-06-07T08:31:04" | CONTRIBUTOR | null | The dataset is updated and the old url no longer works. So I updated it.
I faced a bug while trying to fix this. Documenting the solution here. Maybe we can add it to the doc (`CONTRIBUTING.md` and `ADD_NEW_DATASET.md`).
> And to make the command work without the ExpectedMoreDownloadedFiles error, you just need to use the --ignore_verifications flag.
https://github.com/huggingface/datasets/issues/2076#issuecomment-803904366 | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-05T17:30:24" | "2021-06-08T20:02:58" | "2021-06-07T08:18:25" | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2447 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2447/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2447/comments | https://api.github.com/repos/huggingface/datasets/issues/2447/events | https://github.com/huggingface/datasets/issues/2447 | 912,299,527 | MDU6SXNzdWU5MTIyOTk1Mjc= | 2,447 | dataset adversarial_qa has no answers in the "test" set | {
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"Hi ! I'm pretty sure that the answers are not made available for the test set on purpose because it is part of the DynaBench benchmark, for which you can submit your predictions on the website.\r\nIn any case we should mention this in the dataset card of this dataset.",
"Makes sense, but not intuitive for someone searching through the datasets. Thanks for adding the note to clarify."
] | "2021-06-05T14:57:38" | "2021-06-07T11:13:07" | "2021-06-07T11:13:07" | NONE | null | ## Describe the bug
When loading the adversarial_qa dataset the 'test' portion has no answers. Only the 'train' and 'validation' portions do. This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')
## Steps to reproduce the bug
```
from datasets import load_dataset
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['test']
print('Loaded {:,} examples'.format(len(examples)))
has_answers = 0
for e in examples:
if e['answers']['text']:
has_answers += 1
print('{:,} have answers'.format(has_answers))
>>> Loaded 3,000 examples
>>> 0 have answers
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['validation']
<...code above...>
>>> Loaded 3,000 examples
>>> 3,000 have answers
```
## Expected results
If 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no "no answer" questions with this set (not sure if this is correct or not).
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyArrow version: 1.0.0
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"```\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py\", line 332, in _run_script\r\n exec(code, module.__dict__)\r\nFile \"/home/sasha/nlp-viewer/run.py\", line 233, in <module>\r\n configs = get_confs(option)\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py\", line 604, in wrapped_func\r\n return get_or_create_cached_value()\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py\", line 588, in get_or_create_cached_value\r\n return_value = func(*args, **kwargs)\r\nFile \"/home/sasha/nlp-viewer/run.py\", line 148, in get_confs\r\n builder_cls = nlp.load.import_main_class(module_path[0], dataset=True)\r\nFile \"/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/datasets/load.py\", line 85, in import_main_class\r\n module = importlib.import_module(module_path)\r\nFile \"/usr/lib/python3.7/importlib/__init__.py\", line 127, in import_module\r\n return _bootstrap._gcd_import(name[level:], package, level)\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _gcd_import\r\nFile \"<frozen importlib._bootstrap>\", line 983, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 967, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 677, in _load_unlocked\r\nFile \"<frozen importlib._bootstrap_external>\", line 728, in exec_module\r\nFile \"<frozen importlib._bootstrap>\", line 219, in _call_with_frames_removed\r\nFile \"/home/sasha/.cache/huggingface/modules/datasets_modules/datasets/yelp_polarity/a770787b2526bdcbfc29ac2d9beb8e820fbc15a03afd3ebc4fb9d8529de57544/yelp_polarity.py\", line 36, in <module>\r\n from datasets.tasks import TextClassification\r\n```",
"Solved by updating the `nlpviewer`"
] | "2021-06-04T15:44:29" | "2021-06-04T18:56:47" | "2021-06-04T18:56:47" | CONTRIBUTOR | null | 
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https://api.github.com/repos/huggingface/datasets/issues/2444 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2444/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2444/comments | https://api.github.com/repos/huggingface/datasets/issues/2444/events | https://github.com/huggingface/datasets/issues/2444 | 911,297,139 | MDU6SXNzdWU5MTEyOTcxMzk= | 2,444 | Sentence Boundaries missing in Dataset: xtreme / udpos | {
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"Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.",
"Closed by #2466."
] | "2021-06-04T09:10:26" | "2021-06-18T11:53:43" | "2021-06-18T11:53:43" | CONTRIBUTOR | null | I was browsing through annotation guidelines, as suggested by the datasets introduction.
The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments)
But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter.
PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array.
You may compare in PAN-x.en and udpos.English in the explorer:
https://huggingface.co/datasets/viewer/?dataset=xtreme | {
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"Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that",
"Unfortunately, I know this problem very well... 😅 \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning/error at startup for that case: #2224",
"@albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https://stackoverflow.com/a/46546731/14095927). As mentioned in the comment, this code relies on an undocumented function and Win10-specific."
] | "2021-06-03T00:27:30" | "2021-06-28T08:47:39" | "2021-06-28T08:47:39" | CONTRIBUTOR | null | Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English... | {
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"Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n ...\r\n yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)",
"Yup, I indeed was generating duplicate keys. Fixed it and now it's working."
] | "2021-06-01T17:59:41" | "2021-06-04T23:50:03" | "2021-06-04T23:50:03" | NONE | null | ## Describe the bug
Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script.
Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')`
Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets.
## Steps to reproduce the bug
I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested.
## Expected results
For my data to be loaded.
## Actual results
**DuplicatedKeysError** exception is raised
```
Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655...
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
<ipython-input-6-c342ea0dae9d> in <module>()
----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')
5 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs)
749 try_from_hf_gcs=try_from_hf_gcs,
750 base_path=base_path,
--> 751 use_auth_token=use_auth_token,
752 )
753
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
573 if not downloaded_from_gcs:
574 self._download_and_prepare(
--> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
576 )
577 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
650 try:
651 # Prepare split will record examples associated to the split
--> 652 self._prepare_split(split_generator, **prepare_split_kwargs)
653 except OSError as e:
654 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
990 writer.write(example, key)
991 finally:
--> 992 num_examples, num_bytes = writer.finalize()
993
994 split_generator.split_info.num_examples = num_examples
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream)
407 # In case current_examples < writer_batch_size, but user uses finalize()
408 if self._check_duplicates:
--> 409 self.check_duplicate_keys()
410 # Re-intializing to empty list for next batch
411 self.hkey_record = []
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
347 for hash, key in self.hkey_record:
348 if hash in tmp_record:
--> 349 raise DuplicatedKeysError(key)
350 else:
351 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 0
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2440/comments | https://api.github.com/repos/huggingface/datasets/issues/2440/events | https://github.com/huggingface/datasets/issues/2440 | 908,521,954 | MDU6SXNzdWU5MDg1MjE5NTQ= | 2,440 | Remove `extended` field from dataset tagger | {
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"The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too",
"Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/huggingface/datasets-tagging\r\nOtherwise I can probably fix it next week",
"I've opened a PR on `datasets-tagging` to fix the issue 🚀 ",
"thanks ! this is fixed now"
] | "2021-06-01T17:18:42" | "2021-06-09T09:06:31" | "2021-06-09T09:06:30" | MEMBER | null | ## Describe the bug
While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:
```
dataset_name = 'arcd'
@pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path))
def test_changed_dataset_card(dataset_name):
card_path = repo_path / "datasets" / dataset_name / "README.md"
assert card_path.exists()
error_messages = []
try:
ReadMe.from_readme(card_path)
except Exception as readme_error:
error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}")
try:
DatasetMetadata.from_readme(card_path)
except Exception as metadata_error:
error_messages.append(
f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}"
)
if error_messages:
> raise ValueError("\n".join(error_messages))
E ValueError: The following issues have been found in the dataset cards:
E YAML tags:
E __init__() got an unexpected keyword argument 'extended'
tests/test_dataset_cards.py:70: ValueError
```
Consider either removing this tag from the tagger or including it as part of the validation step in the CI.
cc @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/2439 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2439/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2439/comments | https://api.github.com/repos/huggingface/datasets/issues/2439/events | https://github.com/huggingface/datasets/pull/2439 | 908,511,983 | MDExOlB1bGxSZXF1ZXN0NjU5MTkzMDE3 | 2,439 | Better error message when trying to access elements of a DatasetDict without specifying the split | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-01T17:04:32" | "2021-06-15T16:03:23" | "2021-06-07T08:54:35" | MEMBER | null | As mentioned in #2437 it'd be nice to to have an indication to the users when they try to access an element of a DatasetDict without specifying the split name.
cc @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2438/comments | https://api.github.com/repos/huggingface/datasets/issues/2438/events | https://github.com/huggingface/datasets/pull/2438 | 908,461,914 | MDExOlB1bGxSZXF1ZXN0NjU5MTQ5Njg0 | 2,438 | Fix NQ features loading: reorder fields of features to match nested fields order in arrow data | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-01T16:09:30" | "2021-06-04T09:02:31" | "2021-06-04T09:02:31" | MEMBER | null | As mentioned in #2401, there is an issue when loading the features of `natural_questions` since the order of the nested fields in the features don't match. The order is important since it matters for the underlying arrow schema.
To fix that I re-order the features based on the arrow schema:
```python
inferred_features = Features.from_arrow_schema(arrow_table.schema)
self.info.features = self.info.features.reorder_fields_as(inferred_features)
assert self.info.features.type == inferred_features.type
```
The re-ordering is a recursive function. It takes into account that the `Sequence` feature type is a struct of list and not a list of struct.
Now it's possible to load `natural_questions` again :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2437/comments | https://api.github.com/repos/huggingface/datasets/issues/2437/events | https://github.com/huggingface/datasets/pull/2437 | 908,108,882 | MDExOlB1bGxSZXF1ZXN0NjU4ODUwNTkw | 2,437 | Better error message when using the wrong load_from_disk | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-01T09:43:22" | "2021-06-08T18:03:50" | "2021-06-08T18:03:50" | MEMBER | null | As mentioned in #2424, the error message when one tries to use `Dataset.load_from_disk` to load a DatasetDict object (or _vice versa_) can be improved. I added a suggestion in the error message to let users know that they should use the other one. | {
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https://api.github.com/repos/huggingface/datasets/issues/2436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2436/comments | https://api.github.com/repos/huggingface/datasets/issues/2436/events | https://github.com/huggingface/datasets/pull/2436 | 908,100,211 | MDExOlB1bGxSZXF1ZXN0NjU4ODQzMzQy | 2,436 | Update DatasetMetadata and ReadMe | {
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} | [] | closed | false | null | [] | null | [] | "2021-06-01T09:32:37" | "2021-06-14T13:23:27" | "2021-06-14T13:23:26" | CONTRIBUTOR | null | This PR contains the changes discussed in #2395.
**Edit**:
In addition to those changes, I'll be updating the `ReadMe` as follows:
Currently, `Section` has separate parsing and validation error lists. In `.validate()`, we add these lists to the final lists and throw errors.
One way to make `ReadMe` consistent with `DatasetMetadata` and add a separate `.validate()` method is to throw separate parsing and validation errors.
This way, we don't have to throw validation errors, but only parsing errors in `__init__ ()`. We can have an option in `__init__()` to suppress parsing errors so that an object is created for validation. Doing this will allow the user to get all the errors in one go.
In `test_dataset_cards` , we are already catching error messages and appending to a list. This can be done for `ReadMe()` for parsing errors, and `ReadMe(...,suppress_errors=True); readme.validate()` for validation, separately.
**Edit 2**:
The only parsing issue we have as of now is multiple headings at the same level with the same name. I assume this will happen very rarely, but it is still better to throw an error than silently pick one of them. It should be okay to separate it this way.
Wdyt @lhoestq ?
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} | [] | closed | false | null | [] | null | [] | "2021-05-31T14:09:11" | "2021-06-03T14:34:30" | "2021-06-03T14:32:27" | MEMBER | null | This PR adds task templates for 9 SQuAD-like templates with the following properties:
* 1 config
* A schema that matches the `squad` one (i.e. same column names, especially for the nested `answers` column because the current implementation does not support casting with mismatched columns. see #2434)
* Less than 20GB (my laptop can't handle more right now)
The aim of this PR is to provide a few datasets to experiment with the task template integration in other libraries / services.
PR #2429 should be merged before this one.
cc @abhi1thakur | {
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https://api.github.com/repos/huggingface/datasets/issues/2434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2434/comments | https://api.github.com/repos/huggingface/datasets/issues/2434/events | https://github.com/huggingface/datasets/issues/2434 | 907,503,557 | MDU6SXNzdWU5MDc1MDM1NTc= | 2,434 | Extend QuestionAnsweringExtractive template to handle nested columns | {
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"this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n",
"The current task API is somewhat deprecated (we plan to align it with `train eval index` at some point), so I think we can close this issue."
] | "2021-05-31T14:06:51" | "2022-10-05T17:06:28" | "2022-10-05T17:06:28" | MEMBER | null | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-12-50e5b8f69c20> in <module>
----> 1 ds.prepare_for_task("question-answering-extractive")[0]
~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task)
1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`
1437 dataset.info.task_templates = None
-> 1438 dataset = dataset.cast(features=template.features)
1439 return dataset
1440
~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)
977 format = self.format
978 dataset = self.with_format("arrow")
--> 979 dataset = dataset.map(
980 lambda t: t.cast(schema),
981 batched=True,
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1600
1601 if num_proc is None or num_proc == 1:
-> 1602 return self._map_single(
1603 function=function,
1604 with_indices=with_indices,
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
176 }
177 # apply actual function
--> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
180 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)
1940 ) # Something simpler?
1941 try:
-> 1942 batch = apply_function_on_filtered_inputs(
1943 batch,
1944 indices,
~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1837 processed_inputs = (
-> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1839 )
1840 if update_data is None:
~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t)
978 dataset = self.with_format("arrow")
979 dataset = dataset.map(
--> 980 lambda t: t.cast(schema),
981 batched=True,
982 batch_size=batch_size,
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe)
241 else:
242 options = CastOptions.unsafe(target_type)
--> 243 return call_function("cast", [arr], options)
244
245
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2433/comments | https://api.github.com/repos/huggingface/datasets/issues/2433/events | https://github.com/huggingface/datasets/pull/2433 | 907,488,711 | MDExOlB1bGxSZXF1ZXN0NjU4MzI5MDQ4 | 2,433 | Fix DuplicatedKeysError in adversarial_qa | {
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} | [] | closed | false | null | [] | null | [] | "2021-05-31T13:48:47" | "2021-06-01T08:52:11" | "2021-06-01T08:52:11" | CONTRIBUTOR | null | Fixes #2431 | {
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https://api.github.com/repos/huggingface/datasets/issues/2432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2432/comments | https://api.github.com/repos/huggingface/datasets/issues/2432/events | https://github.com/huggingface/datasets/pull/2432 | 907,462,881 | MDExOlB1bGxSZXF1ZXN0NjU4MzA3MTE1 | 2,432 | Fix CI six installation on linux | {
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} | [] | closed | false | null | [] | null | [] | "2021-05-31T13:15:36" | "2021-05-31T13:17:07" | "2021-05-31T13:17:06" | MEMBER | null | For some reason we end up with this error in the linux CI when running pip install .[tests]
```
pip._vendor.resolvelib.resolvers.InconsistentCandidate: Provided candidate AlreadyInstalledCandidate(six 1.16.0 (/usr/local/lib/python3.6/site-packages)) does not satisfy SpecifierRequirement('six>1.9'), SpecifierRequirement('six>1.9'), SpecifierRequirement('six>=1.11'), SpecifierRequirement('six~=1.15'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0'), SpecifierRequirement('six>=1.11.0'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.6.1'), SpecifierRequirement('six>=1.9'), SpecifierRequirement('six>=1.5'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six'), SpecifierRequirement('six'), SpecifierRequirement('six~=1.15.0'), SpecifierRequirement('six'), SpecifierRequirement('six<2.0,>=1.6.1'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0')
```
example CI failure here:
https://app.circleci.com/pipelines/github/huggingface/datasets/6200/workflows/b64fdec9-f9e6-431c-acd7-e9f2c440c568/jobs/38247
The main version requirement comes from tensorflow: `six~=1.15.0`
So I pinned the six version to this. | {
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https://api.github.com/repos/huggingface/datasets/issues/2431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2431/comments | https://api.github.com/repos/huggingface/datasets/issues/2431/events | https://github.com/huggingface/datasets/issues/2431 | 907,413,691 | MDU6SXNzdWU5MDc0MTM2OTE= | 2,431 | DuplicatedKeysError when trying to load adversarial_qa | {
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"name": "bug",
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"Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`"
] | "2021-05-31T12:11:19" | "2021-06-01T08:54:03" | "2021-06-01T08:52:11" | NONE | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual results
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
>
>
>During handling of the above exception, another exception occurred:
>
>DuplicatedKeysError Traceback (most recent call last)
>
>/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
> 347 for hash, key in self.hkey_record:
> 348 if hash in tmp_record:
>--> 349 raise DuplicatedKeysError(key)
> 350 else:
> 351 tmp_record.add(hash)
>
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2430 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2430/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2430/comments | https://api.github.com/repos/huggingface/datasets/issues/2430/events | https://github.com/huggingface/datasets/pull/2430 | 907,322,595 | MDExOlB1bGxSZXF1ZXN0NjU4MTg3Njkw | 2,430 | Add version-specific BibTeX | {
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} | [] | closed | false | null | [] | null | [] | "2021-05-31T10:05:42" | "2021-06-08T07:53:22" | "2021-06-08T07:53:22" | MEMBER | null | As pointed out by @lhoestq in #2411, after the creation of the Zenodo DOI for Datasets, a new BibTeX entry is created with each release.
This PR adds a version-specific BibTeX entry, besides the existing one which is generic for the project.
See version-specific BibTeX entry here: https://zenodo.org/record/4817769/export/hx#.YLSyd6j7RPY | {
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https://api.github.com/repos/huggingface/datasets/issues/2429 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2429/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2429/comments | https://api.github.com/repos/huggingface/datasets/issues/2429/events | https://github.com/huggingface/datasets/pull/2429 | 907,321,665 | MDExOlB1bGxSZXF1ZXN0NjU4MTg2ODc0 | 2,429 | Rename QuestionAnswering template to QuestionAnsweringExtractive | {
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https://api.github.com/repos/huggingface/datasets/issues/2428 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2428/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2428/comments | https://api.github.com/repos/huggingface/datasets/issues/2428/events | https://github.com/huggingface/datasets/pull/2428 | 907,169,746 | MDExOlB1bGxSZXF1ZXN0NjU4MDU2MjI3 | 2,428 | Add copyright info for wiki_lingua dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/2427 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2427/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2427/comments | https://api.github.com/repos/huggingface/datasets/issues/2427/events | https://github.com/huggingface/datasets/pull/2427 | 907,162,923 | MDExOlB1bGxSZXF1ZXN0NjU4MDUwMjAx | 2,427 | Add copyright info to MLSUM dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/2426 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2426/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2426/comments | https://api.github.com/repos/huggingface/datasets/issues/2426/events | https://github.com/huggingface/datasets/issues/2426 | 906,473,546 | MDU6SXNzdWU5MDY0NzM1NDY= | 2,426 | Saving Graph/Structured Data in Datasets | {
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"It should probably work out of the box to save structured data. If you want to show an example we can help you.",
"An example of a toy dataset is like:\r\n```json\r\n[\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.",
"Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.",
"Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?",
"If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https://huggingface.co/docs/datasets/master/loading_datasets.html#from-local-files) in the docs for more info.",
"Nice! Thanks for your help."
] | "2021-05-29T13:35:21" | "2021-06-02T01:21:03" | "2021-06-02T01:21:03" | NONE | null | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.
Although I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets.
Thank you very much for your help. | {
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https://api.github.com/repos/huggingface/datasets/issues/2425 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2425/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2425/comments | https://api.github.com/repos/huggingface/datasets/issues/2425/events | https://github.com/huggingface/datasets/pull/2425 | 906,385,457 | MDExOlB1bGxSZXF1ZXN0NjU3NDAwMjM3 | 2,425 | Fix Docstring Mistake: dataset vs. metric | {
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https://api.github.com/repos/huggingface/datasets/issues/2424 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2424/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2424/comments | https://api.github.com/repos/huggingface/datasets/issues/2424/events | https://github.com/huggingface/datasets/issues/2424 | 906,193,679 | MDU6SXNzdWU5MDYxOTM2Nzk= | 2,424 | load_from_disk and save_to_disk are not compatible with each other | {
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"Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.",
"Thanks it worked!",
"Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova ",
"We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path/to/dataset/dict\")\r\nsingle_dataset = load_from_disk(\"path/to/single/dataset\")\r\n```",
"I just opened #2437 to improve the error message",
"Superseded by #2462 "
] | "2021-05-28T23:07:10" | "2021-06-08T19:22:32" | "2021-06-08T19:22:32" | NONE | null | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("art")
dataset.save_to_disk("mydir")
d = Dataset.load_from_disk("mydir")
```
## Expected results
It is expected that these two functions be the reverse of each other without more manipulation
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: 'mydir/art/state.json'
## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
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https://api.github.com/repos/huggingface/datasets/issues/2423 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2423/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2423/comments | https://api.github.com/repos/huggingface/datasets/issues/2423/events | https://github.com/huggingface/datasets/pull/2423 | 905,935,753 | MDExOlB1bGxSZXF1ZXN0NjU2OTc5MjA5 | 2,423 | add `desc` in `map` for `DatasetDict` object | {
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https://api.github.com/repos/huggingface/datasets/issues/2422 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2422/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2422/comments | https://api.github.com/repos/huggingface/datasets/issues/2422/events | https://github.com/huggingface/datasets/pull/2422 | 905,568,548 | MDExOlB1bGxSZXF1ZXN0NjU2NjM3MzY1 | 2,422 | Fix save_to_disk nested features order in dataset_info.json | {
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The order of the nested features matters (pyarrow limitation), but the save_to_disk method was saving the features types as JSON with `sort_keys=True`, which was breaking the order of the nested features. | {
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} | [] | closed | false | null | [] | null | [] | "2021-05-28T14:52:10" | "2021-06-04T09:52:45" | "2021-06-04T09:52:45" | CONTRIBUTOR | null | MAX_MEMORY_DATASET_SIZE_IN_BYTES should be HF_MAX_MEMORY_DATASET_SIZE_IN_BYTES | {
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https://api.github.com/repos/huggingface/datasets/issues/2420 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2420/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2420/comments | https://api.github.com/repos/huggingface/datasets/issues/2420/events | https://github.com/huggingface/datasets/pull/2420 | 904,821,772 | MDExOlB1bGxSZXF1ZXN0NjU1OTQ1ODgw | 2,420 | Updated Dataset Description | {
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} | [] | closed | false | null | [] | null | [] | "2021-05-28T07:10:51" | "2021-06-10T12:11:35" | "2021-06-10T12:11:35" | CONTRIBUTOR | null | Added Point of contact information and several other details about the dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/2419 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2419/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2419/comments | https://api.github.com/repos/huggingface/datasets/issues/2419/events | https://github.com/huggingface/datasets/pull/2419 | 904,347,339 | MDExOlB1bGxSZXF1ZXN0NjU1NTA1OTM1 | 2,419 | adds license information for DailyDialog. | {
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https://api.github.com/repos/huggingface/datasets/issues/2418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2418/comments | https://api.github.com/repos/huggingface/datasets/issues/2418/events | https://github.com/huggingface/datasets/pull/2418 | 904,051,497 | MDExOlB1bGxSZXF1ZXN0NjU1MjM2OTEz | 2,418 | add utf-8 while reading README | {
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https://api.github.com/repos/huggingface/datasets/issues/2417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2417/comments | https://api.github.com/repos/huggingface/datasets/issues/2417/events | https://github.com/huggingface/datasets/pull/2417 | 903,956,071 | MDExOlB1bGxSZXF1ZXN0NjU1MTU3NTI4 | 2,417 | Make datasets PEP-561 compliant | {
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PEP-561: https://www.python.org/dev/peps/pep-0561
MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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