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"Hi ! Thanks for reporting. I opened a PR to fix this issue: #2201",
"Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.",
"Hi :) We're indeed working on tutorials that we will add to the docs... | 1,617,179,409,000 | 1,630,077,838,000 | null | NONE | null | First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything.
I'm using datasets 1.5.0
As you can see, when I load the dataset, the C... | {
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"@lhoestq I am going to implement the consolidation step you mentioned in #1870.",
"@lhoestq I was thinking that the order of the TableBlocks is not relevant, isn't it?\r\n\r\nI mean, in order to consolidate _consecutive_ in-memory table blocks, in this case:\r\n```\r\nblocks = [in_memory_1, memory_mapped, in_mem... | 1,617,123,524,000 | 1,624,470,062,000 | 1,618,848,438,000 | MEMBER | null | Add support for `axis` (0 or 1) in `concatenate_datasets`.
Close #853. | {
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"following_url"... | [] | 1,617,119,516,000 | 1,617,187,035,000 | 1,617,187,034,000 | MEMBER | null | This should fix issue #2134
Pickling is limited to <4GiB objects, it's not possible to pickle a big arrow table (for multiprocessing for example).
For big tables, we have to write them on disk and only pickle the path to the table. | {
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"Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this"
] | 1,617,117,994,000 | 1,617,791,015,000 | null | CONTRIBUTOR | null | from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.
but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
lang3_dict ... | {
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"Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `impor... | 1,617,116,646,000 | 1,618,494,586,000 | 1,618,494,586,000 | CONTRIBUTOR | null | Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation).
However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs... | {
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See e.g. current docs for `Dataset.to_csv`. | {
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"Hi ! In the arrow file we store all the integers as uint8.\r\nSo your arrow file should weigh around `height x width x n_channels x n_images` bytes.\r\n\r\nWhat feature type do your TFDS dataset have ?\r\n\r\nIf it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for exampl... | 1,617,115,569,000 | 1,618,578,422,000 | null | NONE | null | Hi,
I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8.
The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`.
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"#2274 has been merged. You can now merge master into this branch and use `assert_arrow_metadata_are_synced_with_dataset_features(dset)` to make sure that the metadata are good :)"
] | 1,617,112,934,000 | 1,619,707,844,000 | 1,619,707,843,000 | MEMBER | null | Implement `Dataset.add_column`.
Close #1954. | {
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"That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')\r\n```",
"Hi ! It looks like the arrow file in the folder\r\n`/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa... | 1,617,100,711,000 | 1,617,268,877,000 | null | NONE | null | **Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.
**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikiped... | {
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not satisfied with the API, it means as a dataset implementer I need to write a function with boilerplate and write classes for each `<dataset><task>` "facet". | {
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"following_url"... | [] | 1,617,061,622,000 | 1,617,063,002,000 | 1,617,063,002,000 | MEMBER | null | This branch updates the GEM benchmark to its 1.1 version which includes:
- challenge sets for most tasks
- detokenized TurkCorpus to match the rest of the text simplification subtasks
- fixed inputs for TurkCorpus and ASSET test sets
- 18 languages in WikiLingua
cc @sebastianGehrmann | {
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"Hi @lhoestq \r\nThanks a lot for taking time checking it. I update \"dataset_infos.json\", I added description to the function of _generate_samples in wikiann.py but I was not sure about the format to write in README. thanks. ",
"Thanks !\r\n\r\nFor the fields description in the dataset card, something like thi... | 1,617,061,106,000 | 1,617,197,270,000 | 1,617,197,270,000 | CONTRIBUTOR | null | Hi @lhoestq
I tried to add spans to the wikiann datasets.
Thanks a lot for kindly having a look.
This addresses https://github.com/huggingface/datasets/issues/2130.
Best regards
Rabeeh | {
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"@lhoestq I updated files"
] | 1,617,053,543,000 | 1,617,960,738,000 | 1,617,960,738,000 | CONTRIBUTOR | null | Intent classification/detection dataset from banking category with 77 unique intents. | {
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"Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```",
"Hi!\r\n\r\nUsing that version of the code solves the issue. Thanks!"
] | 1,617,042,234,000 | 1,617,095,573,000 | 1,617,095,573,000 | NONE | null | Hi,
Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.
Here is the minimal reproducible example:
```python
from datasets import load_dataset
from dat... | {
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"following_url"... | [] | 1,617,033,147,000 | 1,617,725,771,000 | 1,617,693,278,000 | CONTRIBUTOR | null | Add Character Error Rate (CER) metric that is used in evaluation in ASR. I also have written unittests (hopefully thorough enough) but I'm not sure how to integrate them into the existed codebase.
```python
from cer import CER
cer = CER()
class TestCER(unittest.TestCase):
def test_cer_case_senstive(self)... | {
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"following_url"... | [] | 1,617,032,772,000 | 1,617,186,956,000 | 1,617,186,955,000 | MEMBER | null | This should fix issue #2131
When calling `load_dataset` at the same time from 2 workers, one of the worker could have missing split infos when reloading the dataset from the cache.
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"Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?",
"Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, ... | 1,617,014,870,000 | 1,617,099,623,000 | 1,617,099,623,000 | CONTRIBUTOR | null | Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. | {
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"Hi !\r\nIndeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:\r\n```python\r\nimport pyarrow as pa\r\nimport pickle\r\n\r\narr = pa.array([0] * ((4 * 8 << 30) // 64))\r\ntable = pa.Table.from_... | 1,617,014,595,000 | 1,620,064,761,000 | 1,620,064,761,000 | NONE | null | Using Datasets 1.5.0 on Python 3.7.
Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so... | {
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"If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n... \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u064... | 1,617,008,589,000 | 1,617,126,057,000 | 1,617,126,057,000 | NONE | null | Hi
Looking into MLQA dataset for langauge "ar":
```
"question": [
"\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?",
"\u0643\u0... | {
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"You can filter the languages this way:\r\n```python\r\ntydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\r\n```\r\n\r\nOtherwise maybe we can have one configuration per language ?\r\nWhat do you think of this for example ?\r\n\r\n```python\r\nload_dataset(\"tydiqa\", \"primary_task.en\")\... | 1,617,008,181,000 | 1,617,530,235,000 | null | NONE | null | Hi @lhoestq
Currently TydiQA is mixed and user can only access the whole training set of all languages:
https://www.tensorflow.org/datasets/catalog/tydi_qa
for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenien... | {
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"Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue",
"The PR got merged :)\r\nFeel free to try it out on the `master` br... | 1,617,007,558,000 | 1,618,052,935,000 | 1,618,052,935,000 | NONE | null | version: 1.5.0
met a very strange error, I am training large scale language model, and need train on 2 machines(workers).
And sometimes I will get this error `TypeError: 'NoneType' object is not iterable`
This is traceback
```
71 | | Traceback (most recent call last):
-- | -- | --
72 | | File "run_gpt.py"... | {
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https://api.github.com/repos/huggingface/datasets/issues/2130 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2130/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2130/comments | https://api.github.com/repos/huggingface/datasets/issues/2130/events | https://github.com/huggingface/datasets/issues/2130 | 843,111,936 | MDU6SXNzdWU4NDMxMTE5MzY= | 2,130 | wikiann dataset is missing columns | {
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"Here please find TFDS format of this dataset: https://www.tensorflow.org/datasets/catalog/wikiann\r\nwhere there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq ",
"Hi !\r\nApparently you can get the spans from the NER tags using `tags_to_spans` defined ... | 1,617,006,180,000 | 1,630,075,458,000 | 1,630,075,458,000 | NONE | null | Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2129 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2129/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2129/comments | https://api.github.com/repos/huggingface/datasets/issues/2129/events | https://github.com/huggingface/datasets/issues/2129 | 843,033,656 | MDU6SXNzdWU4NDMwMzM2NTY= | 2,129 | How to train BERT model with next sentence prediction? | {
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"Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.",
"Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction... | 1,617,000,483,000 | 1,617,253,120,000 | 1,617,253,120,000 | NONE | null | Hello.
I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction
like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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"Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) "
] | 1,616,999,642,000 | 1,617,194,881,000 | 1,617,194,881,000 | CONTRIBUTOR | null | Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial!
I spot an error that the order of Dialogue action slot names and values are reversed.
https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.p... | {
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https://api.github.com/repos/huggingface/datasets/issues/2127 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2127/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2127/comments | https://api.github.com/repos/huggingface/datasets/issues/2127/events | https://github.com/huggingface/datasets/pull/2127 | 843,017,199 | MDExOlB1bGxSZXF1ZXN0NjAyNDYxMzc3 | 2,127 | make documentation more clear to use different cloud storage | {
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"following_url"... | [] | 1,616,999,046,000 | 1,617,020,184,000 | 1,617,020,184,000 | MEMBER | null | This PR extends the cloud storage documentation. To show you can use a different `fsspec` implementation. | {
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"following_url"... | [] | 1,616,950,650,000 | 1,617,010,034,000 | 1,617,010,033,000 | CONTRIBUTOR | null | The title says it all (motivated by [this issue](https://github.com/pytorch/pytorch/issues/53146) in the pytorch repo). | {
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https://api.github.com/repos/huggingface/datasets/issues/2125 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2125/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2125/comments | https://api.github.com/repos/huggingface/datasets/issues/2125/events | https://github.com/huggingface/datasets/issues/2125 | 842,690,570 | MDU6SXNzdWU4NDI2OTA1NzA= | 2,125 | Is dataset timit_asr broken? | {
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"Hi,\r\n\r\nthanks for the report, but this is a duplicate of #2052. ",
"@mariosasko \r\nThank you for your quick response! Following #2052, I've fixed the problem."
] | 1,616,920,218,000 | 1,616,934,565,000 | 1,616,934,565,000 | NONE | null | Using `timit_asr` dataset, I saw all records are the same.
``` python
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_example... | {
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https://api.github.com/repos/huggingface/datasets/issues/2124 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2124/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2124/comments | https://api.github.com/repos/huggingface/datasets/issues/2124/events | https://github.com/huggingface/datasets/issues/2124 | 842,627,729 | MDU6SXNzdWU4NDI2Mjc3Mjk= | 2,124 | Adding ScaNN library to do MIPS? | {
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"I haven't played with it (yet) but it sounds really cool !\r\n"
] | 1,616,890,020,000 | 1,617,024,223,000 | null | NONE | null | @lhoestq Hi I am thinking of adding this new google library to do the MIPS similar to **add_faiss_idex**. As the paper suggests, it is really fast when it comes to retrieving the nearest neighbors.
https://github.com/google-research/google-research/tree/master/scann
![image](https://user-images.githubusercontent... | {
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https://api.github.com/repos/huggingface/datasets/issues/2123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2123/comments | https://api.github.com/repos/huggingface/datasets/issues/2123/events | https://github.com/huggingface/datasets/issues/2123 | 842,577,285 | MDU6SXNzdWU4NDI1NzcyODU= | 2,123 | Problem downloading GEM wiki_auto_asset_turk dataset | {
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"Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n``` ",
"Thanks for the answer! I updated the library but unfortunately it didn't solve the problem.",
"Is there an... | 1,616,870,488,000 | 1,620,836,118,000 | 1,620,836,117,000 | NONE | null | @yjernite
### Summary
I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.
### Steps to reproduce
Code snippet:
from datasets import load_dataset
#dataset = load_dataset('gem', 'web_nlg_en')
d... | {
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https://api.github.com/repos/huggingface/datasets/issues/2122 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2122/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2122/comments | https://api.github.com/repos/huggingface/datasets/issues/2122/events | https://github.com/huggingface/datasets/pull/2122 | 842,194,588 | MDExOlB1bGxSZXF1ZXN0NjAxODE3MjI0 | 2,122 | Fast table queries with interpolation search | {
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"following_url"... | [] | 1,616,782,160,000 | 1,628,100,719,000 | 1,617,719,581,000 | MEMBER | null | ## Intro
This should fix issue #1803
Currently querying examples in a dataset is O(n) because of the underlying pyarrow ChunkedArrays implementation.
To fix this I implemented interpolation search that is pretty effective since datasets usually verifies the condition of evenly distributed chunks (the default ch... | {
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https://api.github.com/repos/huggingface/datasets/issues/2121 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2121/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2121/comments | https://api.github.com/repos/huggingface/datasets/issues/2121/events | https://github.com/huggingface/datasets/pull/2121 | 842,148,633 | MDExOlB1bGxSZXF1ZXN0NjAxNzc4NDc4 | 2,121 | Add Validation For README | {
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"Good start! Here are some proposed next steps:\r\n- We want the Class structure to reflect the template - so the parser know what section titles to expect and when something has gone wrong\r\n- As a result, we don't need to parse the table of contents, since it will always be the same\r\n- For each section/subsect... | 1,616,778,137,000 | 1,620,652,638,000 | 1,620,639,701,000 | CONTRIBUTOR | null | Hi @lhoestq, @yjernite
This is a simple Readme parser. All classes specific to different sections can inherit `Section` class, and we can define more attributes in each.
Let me know if this is going in the right direction :)
Currently the output looks like this, for `to_dict()` on `FashionMNIST` `README.md`:
... | {
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"Thanks for reporting :) We're looking into it",
"Back up. "
] | 1,616,764,933,000 | 1,616,773,942,000 | 1,616,773,942,000 | NONE | null | Hi
I normally use this link to see all datasets and how I can load them
https://huggingface.co/datasets/viewer/
Now I am getting
502 Bad Gateway
nginx/1.18.0 (Ubuntu)
could you bring this webpage back ? this was very helpful @lhoestq
thanks for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/2119 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2119/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2119/comments | https://api.github.com/repos/huggingface/datasets/issues/2119/events | https://github.com/huggingface/datasets/pull/2119 | 841,567,199 | MDExOlB1bGxSZXF1ZXN0NjAxMjg2MjIy | 2,119 | copy.deepcopy os.environ instead of copy | {
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"following_url"... | [] | 1,616,731,118,000 | 1,616,771,632,000 | 1,616,771,632,000 | CONTRIBUTOR | null | Fixes: https://github.com/huggingface/datasets/issues/2115
- bug fix: using envrion.copy() returns a dict.
- using deepcopy(environ) returns an `_environ` object
- Changing the datatype of the _environ object can break code, if subsequent libraries perform operations using apis exclusive to the environ object, lik... | {
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"I thought deepcopy on `os.environ` is unsafe (see [this](https://stackoverflow.com/questions/13142972/using-copy-deepcopy-on-os-environ-in-python-appears-broken)), but I can't replicate the behavior described in the linked SO thread.\r\n\r\nClosing this one because #2119 has a much cleaner approach."
] | 1,616,730,497,000 | 1,616,760,203,000 | 1,616,760,005,000 | CONTRIBUTOR | null | Replace `os.environ.copy` with in-place modification
Fixes #2115 | {
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https://api.github.com/repos/huggingface/datasets/issues/2117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2117/comments | https://api.github.com/repos/huggingface/datasets/issues/2117/events | https://github.com/huggingface/datasets/issues/2117 | 841,535,283 | MDU6SXNzdWU4NDE1MzUyODM= | 2,117 | load_metric from local "glue.py" meet error 'NoneType' object is not callable | {
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"@Frankie123421 what was the resolution to this?",
"> @Frankie123421 what was the resolution to this?\r\n\r\nuse glue_metric.py instead of glue.py in load_metric",
"thank you!"
] | 1,616,726,122,000 | 1,629,927,845,000 | 1,616,726,426,000 | NONE | null | actual_task = "mnli" if task == "mnli-mm" else task
dataset = load_dataset(path='/home/glue.py', name=actual_task)
metric = load_metric(path='/home/glue.py', name=actual_task)
---------------------------------------------------------------------------
TypeError Traceback (most recent... | {
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https://api.github.com/repos/huggingface/datasets/issues/2116 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2116/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2116/comments | https://api.github.com/repos/huggingface/datasets/issues/2116/events | https://github.com/huggingface/datasets/issues/2116 | 841,481,292 | MDU6SXNzdWU4NDE0ODEyOTI= | 2,116 | Creating custom dataset results in error while calling the map() function | {
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"Hi,\r\n\r\nthe `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the \"association over... | 1,616,719,066,000 | 1,617,201,032,000 | 1,617,201,032,000 | NONE | null | calling `map()` of `datasets` library results into an error while defining a Custom dataset.
Reproducible example:
```
import datasets
class MyDataset(datasets.Dataset):
def __init__(self, sentences):
"Initialization"
self.samples = sentences
def __len__(self):
"Denotes the ... | {
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"following_url"... | [] | 1,616,704,159,000 | 1,616,771,632,000 | 1,616,771,632,000 | NONE | null | In our testing, we noticed that the datasets.map() implementation is modifying the datatype of python os.environ object from '_Environ' to 'dict'.
This causes following function calls to fail as follows:
`
x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None)
TypeError: get() takes... | {
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"> Awesome thank you :)\r\n> This is really cool\r\n> \r\n> I left a few comments.\r\n> \r\n> Also it looks like the dummy data are quite big (100-200KB each). Can you try to reduce their sizes please ? For example I noticed that all the jsonl files inside the `dummy_data.zip` files have 20 lines. Can you only keep... | 1,616,697,617,000 | 1,617,187,130,000 | 1,617,187,130,000 | CONTRIBUTOR | null | Add support for two legal NLP datasets:
- EURLEX (https://www.aclweb.org/anthology/P19-1636/)
- ECtHR cases (https://arxiv.org/abs/2103.13084)
- EU-REG-IR (https://arxiv.org/abs/2101.10726) | {
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This will avoid
> During handling of the above exception, another exception occurred: | {
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https://api.github.com/repos/huggingface/datasets/issues/2112 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2112/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2112/comments | https://api.github.com/repos/huggingface/datasets/issues/2112/events | https://github.com/huggingface/datasets/pull/2112 | 841,098,008 | MDExOlB1bGxSZXF1ZXN0NjAwODgyMjA0 | 2,112 | Support for legal NLP datasets (EURLEX and ECtHR cases) | {
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- EURLEX (https://www.aclweb.org/anthology/P19-1636/)
- ECtHR cases (https://arxiv.org/abs/2103.13084) | {
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"I discussed with Patrick and I think we could have a nice addition: have a parameter `concatenate_texts` that, if `True`, uses the old implementation.\r\n\r\nBy default `concatenate_texts` would be `False`, so that sentences are evaluated independently, and to save resources (the WER computation has a quadratic co... | 1,616,688,408,000 | 1,617,693,643,000 | 1,617,693,643,000 | MEMBER | null | Compute WER metric iteratively to avoid MemoryError.
Fix #2078. | {
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"Hi ! The SplitInfo is not always available. By default you would get `split_info.num_examples == 0`\r\nSo unfortunately we can't use this assertion you suggested",
"> Hi ! The SplitInfo is not always available. By default you would get `split_info.num_examples == 0`\r\n> So unfortunately we can't use this assert... | 1,616,668,760,000 | 1,618,234,383,000 | 1,618,234,383,000 | CONTRIBUTOR | null | Fix incorrect num_examples comparison assertion in builder.py | {
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"If you agree, I could also add a link to [Discussions](https://github.com/huggingface/datasets/discussions) in order to reinforce the use of Discussion to make Questions (instead of Issues).\r\n\r\nI could also add some other templates: Bug, Feature Request,...",
"@theo-m we wrote our same comments at the same t... | 1,616,665,313,000 | 1,618,813,211,000 | 1,618,813,211,000 | MEMBER | null | When opening an issue, it is not evident for the users how to choose a blank issue template. There is a link at the bottom of all the other issue templates (`Don’t see your issue here? Open a blank issue.`), but this is not very visible for users. This is the reason why many users finally chose the `add-dataset` templa... | {
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"following_url"... | [] | 1,616,621,536,000 | 1,616,653,903,000 | null | NONE | null | Motivation - Some FAISS indexes like IVF consist of the training step that clusters the dataset into a given number of indexes. It would be nice if we can use a GPU to do the training step and covert the index back to CPU as mention in [this faiss example](https://gist.github.com/mdouze/46d6bbbaabca0b9778fca37ed2bcccf6... | {
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"> Also I was wondering this is really needed to have `utils.metadata` as a submodule of `datasets` ? This is only used by the CI so I'm not sure we should have this in the actual `datasets` package.\r\n\r\nI'm unclear on the suggestion, would you rather have a root-level `./metadata.py` file? I think it's well whe... | 1,616,575,961,000 | 1,619,425,634,000 | 1,619,425,633,000 | CONTRIBUTOR | null | - `pydantic` metadata schema with dedicated validators against our taxonomy
- ci script to validate new changes against this schema and start a vertuous loop
- soft validation on tasks ids since we expect the taxonomy to undergo some changes in the near future
for reference with the current validation we have ~365... | {
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"Hi ! Thanks for reporting\r\n\r\nBy looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue.\r\nMoreover these issues are not always the same:\r\n- L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line\r\n- L2897 is on... | 1,616,530,487,000 | 1,616,708,180,000 | null | NONE | null | In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error.
The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here:
> ... | {
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"Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) \r\n\r\nUntil you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?",
"Hi @kyleclo,... | 1,616,528,586,000 | 1,628,104,682,000 | null | NONE | null | Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! | {
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"Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```",
"Thanks a lot! That solved it and I was able to upload a model trained on it as well :)"
] | 1,616,525,994,000 | 1,617,664,646,000 | null | NONE | null | Hello,
I am trying to load_dataset("wiki_movies") and it gives me this error -
`FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingfa... | {
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"Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease co... | 1,616,519,889,000 | 1,617,719,999,000 | 1,617,719,999,000 | NONE | null | This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.
Example result:
```
"citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {... | {
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"Hi !\r\nLooks like there's a unicode error in the new citation in the miam.py file.\r\nCould you try to fix it ? Not sure from which character it comes from though\r\n\r\nYou can test if it works on your side with\r\n```\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_con... | 1,616,496,083,000 | 1,616,522,890,000 | 1,616,522,890,000 | CONTRIBUTOR | null | Hi @lhoestq, I have updated the citations to reference an OpenReview preprint. | {
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"I have a question: what about `dictionary_encode_column_`?\r\n- It is deprecated in Dataset, but it recommends using a non-existing method instead: `Dataset.dictionary_encode_column` does not exist.\r\n- It is NOT deprecated in DatasetDict.",
"`dictionary_encode_column_ ` should be deprecated since it never work... | 1,616,495,272,000 | 1,616,573,981,000 | 1,616,522,629,000 | MEMBER | null | Fix deprecated warnings:
- Use deprecated Sphinx directive in docstring
- Fix format of deprecated message
- Raise FutureWarning | {
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"Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function ?",
"It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a... | 1,616,491,717,000 | 1,616,519,536,000 | 1,616,519,536,000 | NONE | null | I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helpin... | {
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"Hi ! This is 1.1 as specified by the download urls here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/squad/squad.py#L50-L55",
"Got it. Thank you~"
] | 1,616,485,674,000 | 1,616,752,134,000 | 1,616,752,134,000 | NONE | null | Hi~
I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it. | {
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"following_url"... | [] | 1,616,441,036,000 | 1,617,097,535,000 | null | NONE | null | Hello, thanks for all the work on developing and maintaining this amazing platform, which I am enjoying working with!
I was wondering if there is a reason why the German CoNLL 2003 dataset is not included in the [repository](https://github.com/huggingface/datasets/tree/master/datasets/conll2003), since a copy of it ... | {
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"Nice thank you !\r\nThis looks like a pretty simple yet effective fix ;)\r\nCould you just add a test in `test_features.py` to make sure that you can create `features` with a `_type` field and that it is possible to convert it as a dict and reload it ?\r\n```python\r\nfrom datasets import Features, Value\r\n\r\n# ... | 1,616,368,917,000 | 1,616,682,954,000 | 1,616,682,954,000 | CONTRIBUTOR | null | This PR tries to fix issue #1110. Sorry for taking so long to come back to this.
It's a simple fix, but i am not sure if it works for all possible types of `obj`. Let me know what you think @lhoestq | {
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"Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do ... | 1,616,302,207,000 | 1,616,783,860,000 | null | NONE | null | Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? | {
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"Hello dear maintainer, are there any comments or questions about this PR?",
"@iamollas thanks for the feedback. I did not see the template.\r\nI improved it...",
"Should be clean for merge IMO.",
"@lhoestq CI is green. ;-)",
"Thanks again ! this is awesome :)",
"Thanks for merging. :-)"
] | 1,616,246,887,000 | 1,617,024,282,000 | 1,617,022,815,000 | CONTRIBUTOR | null | also see here https://github.com/PhilipMay/stsb-multi-mt | {
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"Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a... | 1,616,240,678,000 | 1,626,111,700,000 | null | CONTRIBUTOR | null | Hi,
the dataset README files have special headers.
Somehow a documenation of the allowed values and tags is missing.
Could you add that?
Just to give some concrete questions that should be answered imo:
- which values can be passted to multilinguality?
- what should be passed to language_creators?
- which valu... | {
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"Trailing whitespace was removed. So more changes in diff than just this fix."
] | 1,616,232,224,000 | 1,616,514,012,000 | 1,616,514,012,000 | CONTRIBUTOR | null | Hi,
IMO when using BibTex Author should be used instead of Authors.
See here: http://www.bibtex.org/Using/de/
Thanks
Philip | {
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"@lhoestq I'll try to add a test later if you think this approach with the wrapper is good.",
"Awesome thank you !\r\nYes this approach with a wrapper is good :)",
"@lhoestq Added a test. To verify that this change fixes the problem, replace:\r\n```\r\n!pip install datasets==1.5\r\n```\r\nwith:\r\n```\r\n!pip i... | 1,616,205,923,000 | 1,617,960,333,000 | 1,617,960,333,000 | CONTRIBUTOR | null | This PR adds a decorator that updates the dataset metadata if a previously executed transform modifies its features.
Fixes #2083
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"I tried this with `ade_corpus_v2` dataset. `ade_corpus_v2-train.arrow` (downloaded dataset) and `cache-25d41a4d3c2d8a25.arrow` (ran a mapping function on the dataset) both had file permission with octal value of `0644`. "
] | 1,616,177,696,000 | 1,616,594,344,000 | 1,616,594,344,000 | CONTRIBUTOR | null | Fix for #2065 | {
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"+1 on this request"
] | 1,616,146,063,000 | 1,618,563,044,000 | 1,618,563,044,000 | CONTRIBUTOR | null | ## Adding a Dataset
- **Name:** CUAD - Contract Understanding Atticus Dataset
- **Description:** As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
- **Paper:** https://arxiv.org/abs/2103.06268
- **Data:** http... | {
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"Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age... | 1,616,142,588,000 | 1,617,960,333,000 | 1,617,960,333,000 | MEMBER | null | Hey,
I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets
and noticed that when the order in which the datasets are concatenated changes an error is thrown where it shou... | {
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https://api.github.com/repos/huggingface/datasets/issues/2082 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2082/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2082/comments | https://api.github.com/repos/huggingface/datasets/issues/2082/events | https://github.com/huggingface/datasets/pull/2082 | 835,401,555 | MDExOlB1bGxSZXF1ZXN0NTk2MDY1NTM0 | 2,082 | Updated card using information from data statement and datasheet | {
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"following_url"... | [] | 1,616,114,378,000 | 1,616,164,149,000 | 1,616,164,149,000 | CONTRIBUTOR | null | I updated and clarified the REFreSD [data card](https://github.com/mcmillanmajora/datasets/blob/refresd_card/datasets/refresd/README.md) with information from the Eleftheria's [website](https://elbria.github.io/post/refresd/). I added brief descriptions where the initial card referred to the paper, and I also recreated... | {
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"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,... | 1,616,084,954,000 | 1,616,676,413,000 | 1,616,676,413,000 | NONE | null | Hi,
I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.
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https://api.github.com/repos/huggingface/datasets/issues/2078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2078/comments | https://api.github.com/repos/huggingface/datasets/issues/2078/events | https://github.com/huggingface/datasets/issues/2078 | 834,694,819 | MDU6SXNzdWU4MzQ2OTQ4MTk= | 2,078 | MemoryError when computing WER metric | {
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"Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compu... | 1,616,067,005,000 | 1,619,857,909,000 | 1,617,693,643,000 | NONE | null | Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:
```
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
However, I receive the following exception:
`Traceback (most recent call last):
File ... | {
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"🔥 "
] | 1,616,064,874,000 | 1,616,067,206,000 | 1,616,067,206,000 | CONTRIBUTOR | null | `0.0.2 => 0.0.6` | {
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https://api.github.com/repos/huggingface/datasets/issues/2076 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2076/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2076/comments | https://api.github.com/repos/huggingface/datasets/issues/2076/events | https://github.com/huggingface/datasets/issues/2076 | 834,445,296 | MDU6SXNzdWU4MzQ0NDUyOTY= | 2,076 | Issue: Dataset download error | {
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"Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.",
"It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and th... | 1,616,049,366,000 | 1,616,413,951,000 | null | NONE | null | The download link in `iwslt2017.py` file does not seem to work anymore.
For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz`
Would be nice if we could modify it script and use the new downloadable link? | {
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"Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?",
"@albertvillanova Thanks! It works well now. "
] | 1,616,030,346,000 | 1,616,236,181,000 | 1,616,236,181,000 | NONE | null | When I run:
from datasets import load_dataset, load_metric
common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation")
common_voice_test = load_dataset("common_voice", "zh-CN", split="test")
Got:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/ma... | {
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"> It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency.\r\n\r\nWe can also update the task lists here: https://github.com/huggingface/dat... | 1,616,025,756,000 | 1,616,519,470,000 | 1,616,519,470,000 | CONTRIBUTOR | null | This PR fixes several `size_categories` in YAML tags and makes them consistent. Additionally, I have added a few more categories after `1M`, up to `1T`. I would like to add that to the streamlit app also.
This PR also adds a couple of infos that I found missing.
The code for generating this:
```python
for datas... | {
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This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068 | {
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"I think I will stop pushing to this PR, so that it can me merged for today release. \r\n\r\nI will open another PR for further fixing docs.\r\n\r\nDo you agree, @lhoestq ?",
"Sounds good thanks !"
] | 1,616,004,824,000 | 1,616,574,057,000 | 1,616,071,281,000 | MEMBER | null | Fix docstring issues. | {
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"dupe of #1992"
] | 1,615,997,338,000 | 1,616,058,623,000 | 1,616,058,623,000 | CONTRIBUTOR | null | ```python
# benchmark_filter.py
import logging
import sys
import time
from datasets import load_dataset, set_caching_enabled
if __name__ == "__main__":
set_caching_enabled(False)
logging.basicConfig(level=logging.DEBUG)
bc = load_dataset("bookcorpus")
now = time.time()
try:
... | {
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"Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a b... | 1,615,989,109,000 | 1,628,099,836,000 | 1,628,099,836,000 | NONE | null | Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original co... | {
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"Maybe we should add some other split classes?"
] | 1,615,987,168,000 | 1,616,063,260,000 | 1,616,063,260,000 | MEMBER | null | Add and fix docstring for `NamedSplit`, which was missing. | {
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https://api.github.com/repos/huggingface/datasets/issues/2068 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2068/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2068/comments | https://api.github.com/repos/huggingface/datasets/issues/2068/events | https://github.com/huggingface/datasets/issues/2068 | 833,602,832 | MDU6SXNzdWU4MzM2MDI4MzI= | 2,068 | PyTorch not available error on SageMaker GPU docker though it is installed | {
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"cc @philschmid ",
"Hey @sivakhno,\r\n\r\nhow does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`",
"Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. \r\nI have also tried using `torch=1.6... | 1,615,975,467,000 | 1,623,646,050,000 | 1,623,646,050,000 | NONE | null | I get en error when running data loading using SageMaker SDK
```
File "main.py", line 34, in <module>
run_training()
File "main.py", line 25, in run_training
dm.setup('fit')
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn
return fn(*a... | {
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"Hi ! Thanks for reporting.\r\nThis looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful !\r\n\r\nOtherwise I can try to run the wav2vec2 code above on my side but probably not this week..",
"```\r\nfrom datasets import load_dataset\r\n\r\ndatase... | 1,615,972,348,000 | 1,628,099,948,000 | 1,628,099,948,000 | NONE | null | As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
When using the num_proc argument on windows the whole Python environment crashes and hanging in loop.
For example at the map_to_array part.
An error occures because the cache file already exists and windows throws and error. After this the log c... | {
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https://api.github.com/repos/huggingface/datasets/issues/2066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2066/comments | https://api.github.com/repos/huggingface/datasets/issues/2066/events | https://github.com/huggingface/datasets/pull/2066 | 833,480,551 | MDExOlB1bGxSZXF1ZXN0NTk0NDcwMjEz | 2,066 | Fix docstring rendering of Dataset/DatasetDict.from_csv args | {
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"Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb... | 1,615,940,422,000 | 1,620,629,129,000 | 1,620,629,129,000 | NONE | null | Hello,
It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you kno... | {
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https://api.github.com/repos/huggingface/datasets/issues/2064 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2064/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2064/comments | https://api.github.com/repos/huggingface/datasets/issues/2064/events | https://github.com/huggingface/datasets/pull/2064 | 833,002,360 | MDExOlB1bGxSZXF1ZXN0NTk0MDczOTQ1 | 2,064 | Fix ted_talks_iwslt version error | {
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"following_url"... | [] | 1,615,913,025,000 | 1,615,917,608,000 | 1,615,917,608,000 | CONTRIBUTOR | null | This PR fixes the bug where the version argument would be passed twice if the dataset configuration was created on the fly.
Fixes #2059 | {
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https://api.github.com/repos/huggingface/datasets/issues/2063 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2063/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2063/comments | https://api.github.com/repos/huggingface/datasets/issues/2063/events | https://github.com/huggingface/datasets/pull/2063 | 832,993,705 | MDExOlB1bGxSZXF1ZXN0NTk0MDY2NzI5 | 2,063 | [Common Voice] Adapt dataset script so that no manual data download is actually needed | {
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https://api.github.com/repos/huggingface/datasets/issues/2062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2062/comments | https://api.github.com/repos/huggingface/datasets/issues/2062/events | https://github.com/huggingface/datasets/pull/2062 | 832,625,483 | MDExOlB1bGxSZXF1ZXN0NTkzNzUyNTMz | 2,062 | docs: fix missing quotation | {
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https://api.github.com/repos/huggingface/datasets/issues/2061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2061/comments | https://api.github.com/repos/huggingface/datasets/issues/2061/events | https://github.com/huggingface/datasets/issues/2061 | 832,596,228 | MDU6SXNzdWU4MzI1OTYyMjg= | 2,061 | Cannot load udpos subsets from xtreme dataset using load_dataset() | {
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"@lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.",
"Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset scr... | 1,615,887,133,000 | 1,624,017,251,000 | 1,624,017,250,000 | NONE | null | Hello,
I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and ... | {
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https://api.github.com/repos/huggingface/datasets/issues/2060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2060/comments | https://api.github.com/repos/huggingface/datasets/issues/2060/events | https://github.com/huggingface/datasets/pull/2060 | 832,588,591 | MDExOlB1bGxSZXF1ZXN0NTkzNzIxNzcx | 2,060 | Filtering refactor | {
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"I thought at first that the multiproc test was not relevant now that we do stuff only in memory, but I think there's something that's actually broken, my tiny benchmark on bookcorpus runs forever (2hrs+) when I add `num_proc=4` as a kwarg, will investigate 👀 \r\n\r\nI'm not familiar with the caching you describe ... | 1,615,886,610,000 | 1,634,116,144,000 | 1,634,116,143,000 | CONTRIBUTOR | null | fix https://github.com/huggingface/datasets/issues/2032
benchmarking is somewhat inconclusive, currently running on `book_corpus` with:
```python
bc = load_dataset("bookcorpus")
now = time.time()
bc.filter(lambda x: len(x["text"]) < 64)
elapsed = time.time() - now
print(elapsed)
```
t... | {
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https://api.github.com/repos/huggingface/datasets/issues/2059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2059/comments | https://api.github.com/repos/huggingface/datasets/issues/2059/events | https://github.com/huggingface/datasets/issues/2059 | 832,579,156 | MDU6SXNzdWU4MzI1NzkxNTY= | 2,059 | Error while following docs to load the `ted_talks_iwslt` dataset | {
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"@skyprince999 as you authored the PR for this dataset, any comments?",
"This has been fixed in #2064 by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)"
] | 1,615,885,939,000 | 1,615,917,631,000 | 1,615,917,607,000 | NONE | null | I am currently trying to load the `ted_talks_iwslt` dataset into google colab.
The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so.
```python
dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
```
Executing it results in the error ... | {
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"following_url"... | [] | 1,615,839,527,000 | 1,615,839,527,000 | null | CONTRIBUTOR | null | I was having some weird bugs with `C4`dataset version of HuggingFace, so I decided to try to download `C4`from `tfds`. I would like to know if it is possible to convert a tfds dataset to HuggingFace dataset format :)
I can also open a new issue reporting the bug I'm receiving with `datasets.load_dataset('c4','en')` ... | {
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https://api.github.com/repos/huggingface/datasets/issues/2056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2056/comments | https://api.github.com/repos/huggingface/datasets/issues/2056/events | https://github.com/huggingface/datasets/issues/2056 | 831,718,397 | MDU6SXNzdWU4MzE3MTgzOTc= | 2,056 | issue with opus100/en-fr dataset | {
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"@lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks ",
"Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers impor... | 1,615,807,962,000 | 1,615,909,740,000 | 1,615,909,739,000 | NONE | null | Hi
I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this?
Thanks a lot @lhoestq for your help in advance.
`
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https://api.github.com/repos/huggingface/datasets/issues/2055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2055/comments | https://api.github.com/repos/huggingface/datasets/issues/2055/events | https://github.com/huggingface/datasets/issues/2055 | 831,684,312 | MDU6SXNzdWU4MzE2ODQzMTI= | 2,055 | is there a way to override a dataset object saved with save_to_disk? | {
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"Hi\r\nYou can rename the arrow file and update the name in `state.json`",
"I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n dataset_with_embedding =csv_da... | 1,615,805,453,000 | 1,616,385,977,000 | 1,616,385,977,000 | NONE | null | At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object? | {
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https://api.github.com/repos/huggingface/datasets/issues/2054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2054/comments | https://api.github.com/repos/huggingface/datasets/issues/2054/events | https://github.com/huggingface/datasets/issues/2054 | 831,597,665 | MDU6SXNzdWU4MzE1OTc2NjU= | 2,054 | Could not find file for ZEST dataset | {
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"The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.",
"This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)",
"Thanks @lhoestq and @matt-peters ",
"I am closing this issue since its ... | 1,615,799,518,000 | 1,620,034,224,000 | 1,620,034,224,000 | CONTRIBUTOR | null | I am trying to use zest dataset from Allen AI using below code in colab,
```
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("zest")
```
I am getting the following error,
```
Using custom data configuration default
Downloading and preparing dataset zest/default (download: ... | {
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https://api.github.com/repos/huggingface/datasets/issues/2053 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2053/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2053/comments | https://api.github.com/repos/huggingface/datasets/issues/2053/events | https://github.com/huggingface/datasets/pull/2053 | 831,151,728 | MDExOlB1bGxSZXF1ZXN0NTkyNTM4ODY2 | 2,053 | Add bAbI QA tasks | {
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"Hi @lhoestq,\r\n\r\nShould I remove the 160 configurations? Is it too much?\r\n\r\nEDIT:\r\nCan you also check the task category? I'm not sure if there is an appropriate tag for the same.",
"Thanks for the changes !\r\n\r\n> Should I remove the 160 configurations? Is it too much?\r\n\r\nYea 160 configuration is ... | 1,615,727,079,000 | 1,617,021,708,000 | 1,617,021,708,000 | CONTRIBUTOR | null | - **Name:** *The (20) QA bAbI tasks*
- **Description:** *The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many mor... | {
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https://api.github.com/repos/huggingface/datasets/issues/2052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2052/comments | https://api.github.com/repos/huggingface/datasets/issues/2052/events | https://github.com/huggingface/datasets/issues/2052 | 831,135,704 | MDU6SXNzdWU4MzExMzU3MDQ= | 2,052 | Timit_asr dataset repeats examples | {
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"Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```",
"Ty!"
] | 1,615,722,223,000 | 1,615,804,636,000 | 1,615,804,636,000 | NONE | null | Summary
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce
As an example, on this code there is the text from the training part:
Code snippet:
```
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
timit['train']['text']... | {
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