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https://api.github.com/repos/huggingface/datasets/issues/692 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/692/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/692/comments | https://api.github.com/repos/huggingface/datasets/issues/692/events | https://github.com/huggingface/datasets/pull/692 | 712,818,968 | MDExOlB1bGxSZXF1ZXN0NDk2MjM4NzIw | 692 | Update README.md | {
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"Hacktoberfest spam",
"To enhance its readability.....not Hacktoberfest spam",
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"Already supported."
] | 1,601,513,778,000 | 1,644,922,010,000 | 1,644,922,010,000 | NONE | null | This is great work, so huge shoutout to contributors and huggingface.
The [/nlp/viewer](https://huggingface.co/nlp/viewer/) is great and the [/datasets](https://huggingface.co/datasets) page is great. I was wondering if in both or either places we can have a filter that selects if a dataset is good for the following... | {
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"Thanks for reporting.\r\nThe data file must have been updated by the host.\r\nI'll update the checksum with the new one.",
"Well actually it looks like the link isn't working anymore :(",
"The new link is https://cims.nyu.edu/~sbowman/xnli/XNLI-1.0.zip\r\nI'll update the dataset script",
"I'll do a release i... | 1,601,488,203,000 | 1,601,572,508,000 | 1,601,560,874,000 | NONE | null | Hi,
I tried to download "xnli" dataset in colab using
`xnli = load_dataset(path='xnli')`
but got 'NonMatchingChecksumError' error
`NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-27-a87bedc82eeb> in <module>()
----> 1 xnli = load_dataset(path='xnli')
3 frames
/usr... | {
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"If the windows tests in the CI pass, today will be a happy day"
] | 1,601,483,292,000 | 1,601,484,332,000 | 1,601,484,331,000 | MEMBER | null | Following the discussion in #622 , it appears that there's no appropriate ways to use the payrrow csv reader to read text files because of the separator.
In this PR I switched to pandas to read the file.
Moreover pandas allows to read the file by chunk, which means that you can build the arrow dataset from a text... | {
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```
The current process just got forked. Disabling parallelism to avoid deadlocks...
To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false)
```
This message is shown when TOKENIZERS_PARALLELISM is... | {
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"Hi !\r\n\r\nThis is because `encode` expects one single text as input (str), or one tokenized text (List[str]).\r\nI believe that you actually wanted to use `encode_batch` which expects a batch of texts.\r\nHowever this method is only available for our \"fast\" tokenizers (ex: BertTokenizerFast).\r\nBertJapanese i... | 1,601,446,610,000 | 1,601,459,583,000 | 1,601,459,583,000 | NONE | null | It seems to fail to process the final batch. This [colab](https://colab.research.google.com/drive/1_byLZRHwGP13PHMkJWo62Wp50S_Z2HMD?usp=sharing) can reproduce the error.
Code:
```python
# train_ds = Dataset(features: {
# 'title': Value(dtype='string', id=None),
# 'score': Value(dtype='float64', id=Non... | {
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"Yes! might do it with @srush one of these days. Hopefully it won't break too many links (we can always redirect from old url to new)",
"This was fixed but forgot to close the issue. cc @lhoestq @yjernite \r\n\r\nThanks @jarednielsen!"
] | 1,601,407,312,000 | 1,610,130,566,000 | 1,610,130,566,000 | CONTRIBUTOR | null | Might be worth updating to https://huggingface.co/datasets/viewer/ | {
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```python
from datasets import load_dataset, Features
features = Features({...})
csv_dataset = load_dataset("csv", data_files=["path/to/my/file.csv"], features=features)
```
I added tests to make sure that it is also compatible with the ca... | {
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However even though features passed to `cast_` had the same order as the dataset features, it could fail because the schema that was built was always in alphabetical order.
This issue was reported by @lewtun in #623
To fix that I fi... | {
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However in the text dataset the delimiter was set to `\b` which is backspace in python, while the bell character is `\a`.
I replace \b by \a
Hopefully it fixes issues mentioned by some users in #622 | {
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It makes the background-color of the chapter titles in the docs navbar darker, to differentiate them from the inner sections.
see changes [here](https://691-250213286-gh.circle-artifacts.com/0/do... | {
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"Hi !\r\n\r\nGood catch, thanks for creating this PR :)\r\n\r\nCould you also regenerate the metadata for this dataset using \r\n```\r\ndatasets-cli test ./datasets/gap --save_infos --all_configs\r\n```\r\n\r\nThat'd be awesome",
"@lhoestq Thank you for your revieing!!!\r\n\r\nI've performed it and have read CONT... | 1,601,282,379,000 | 1,601,394,887,000 | 1,601,394,887,000 | CONTRIBUTOR | null | ### Why I did
The value in `row["A-coref"]` and `row["B-coref"]` is `'TRUE'` or `'FALSE'`.
This type is `string`, then `bool('FALSE')` is equal to `True` in Python.
So, both rows are transformed into `True` now.
So, I modified this problem.
### What I did
I modified `bool(row["A-coref"])` and `bool(row["B-cor... | {
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from datasets import Dataset
d = ds.Dataset.from_dict({"a": range(10)})
print(d[[0, -1]])
# OverflowError
```
raises an error because of the negative id.
This PR fixes that.
Fix #668 | {
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"Good catch !\r\nIndeed the `@property` is missing.\r\n\r\nFeel free to open a PR :)",
"Also not that C4 is a dataset that needs an Apache Beam runtime to be generated.\r\nFor example Dataflow, Spark, Flink etc.\r\n\r\nUsually we generate the dataset on our side once and for all, but we haven't done it for C4 yet... | 1,601,281,854,000 | 1,601,288,769,000 | 1,601,288,769,000 | CONTRIBUTOR | null | The manual download instructions are not clear
```The dataset c4 with config en requires manual data.
Please follow the manual download instructions: <bound method C4.manual_download_instructions of <datasets_modules.datasets.c4.830b0c218bd41fed439812c8dd19dbd4767d2a3faa385eb695cf8666c982b1b3.c4.C4 object at 0x7ff... | {
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Fix #671 | {
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"The problem still exists after removing the cache files.",
"Can you reproduce this example in a Colab so we can investigate? (or give more information on your software/hardware config)",
"Thanks for reporting.\r\nI just found the issue, I'm creating a PR",
"We'll do a release pretty soon to include the fix :... | 1,601,277,573,000 | 1,602,078,393,000 | 1,602,077,886,000 | NONE | null | I try to split my dataset by `train_test_split`, but after that the item in `train` and `test` `Dataset` is empty.
The codes:
```
yelp_data = datasets.load_from_disk('/home/ssd4/huanglianzhe/test_yelp')
print(yelp_data[0])
yelp_data = yelp_data.train_test_split(test_size=0.1)
print(yelp_data)
pri... | {
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"Yes you can have a look here: https://huggingface.co/docs/datasets/loading_datasets.html#csv-files",
"No activity, closing"
] | 1,601,241,770,000 | 1,603,184,929,000 | 1,603,184,929,000 | CONTRIBUTOR | null | Is it possible to add a custom dataset such as a .csv to the NLP library?
Thanks. | {
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"I have the same issue. Tried to download a few of them and not a single one is downloaded successfully.\r\n\r\nThis is the output:\r\n```\r\n>>> dataset = load_dataset('blended_skill_talk', split='train')\r\nUsing custom data configuration default <-- This step never ends\r\n```",
"This was fixed i... | 1,601,178,985,000 | 1,601,886,498,000 | 1,601,886,498,000 | NONE | null | I don't know if this is just me or Windows. Maybe other Windows users can chime in if they don't have this problem. I've been trying to get some of the tutorials working on Windows, but when I use the load_dataset() function, it just stalls and the script keeps running indefinitely without downloading anything. I've wa... | {
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"Thanks for reporting !\r\nWe'll free some memory"
] | 1,601,151,328,000 | 1,644,922,078,000 | 1,644,922,078,000 | NONE | null | This is just to report that When I pick blog_authorship_corpus in
https://huggingface.co/nlp/viewer/?dataset=blog_authorship_corpus
I get this:
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"We should try to regenerate the data using the official script.\r\nBut iirc that's what we used in the first place, so not sure why it didn't match in the first place.\r\n\r\nI'll let you know when the dataset is updated",
"Thanks, looking forward to hearing your update on this thread. \r\n\r\nThis is a blocking... | 1,601,140,584,000 | 1,603,185,367,000 | null | CONTRIBUTOR | null | Hi there ✋
I'm looking into your `xsum` dataset and I have several questions on that.
So here is how I loaded the data:
```
>>> data = datasets.load_dataset('xsum', version='1.0.1')
>>> data['train']
Dataset(features: {'document': Value(dtype='string', id=None), 'summary': Value(dtype='string', id=None)}, nu... | {
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1. Huggingface datasets cache dir does not exist
2. Try to load a local dataset script
builder.py throws an error when trying to create a filelock in a directory (cache/datasets) that does not exist
https://github.com/huggingface/datasets/blob/master/src/datasets/builder.py#L177
Tested o... | {
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This should fix #657
FYI another fix was proposed by @tshrjn in #658 and suggests to remove this field.
However IMO `answer_start` is useful to match the squad dataset format for consistency, even though it is not used in the metric computation. I th... | {
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"Hi @xixiaoyao,\r\nDepending on what you want to do you can:\r\n- use a first step of `filter` to filter out the invalid examples: https://huggingface.co/docs/datasets/processing.html#filtering-rows-select-and-filter\r\n- or directly detect the invalid examples inside the callable used with `map` and return them un... | 1,601,032,673,000 | 1,601,915,293,000 | 1,601,915,293,000 | NONE | null | in processing func, I process examples and detect some invalid examples, which I did not want it to be added into train dataset. However I did not find how to skip this recognized invalid example when doing dataset.map. | {
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from datasets import Dataset
d = ds.Dataset.from_dict({"a": range(10)})
print(d[0])
# {'a': 0}
print(d[-1])
# {'a': 9}
print(d[[0, -1]])
# OverflowError
```
results in
```
---------------------------------------------------------------------------
OverflowError ... | {
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"And I tested it on T5ForConditionalGeneration, that works no problem.",
"Hi did you manage to fix your issue ?\r\n\r\nIf so feel free to share your fix and close this thread"
] | 1,600,960,483,000 | 1,609,531,285,000 | 1,609,531,285,000 | NONE | null | HI,
The following script is used to fine-tune a BertForSequenceClassification model on SST2.
The script is adapted from [this colab](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb) that presents an example of fine-tuning BertForQuestionAnswering using squad data... | {
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"No they are other similar copies but they are not provided by the official Bert models authors."
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"Hi !\r\nIt works on my side with both the LongFormerTokenizer and the LongFormerTokenizerFast.\r\n\r\nWhich version of transformers/datasets are you using ?",
"transformers and datasets are both the latest",
"Then I guess you need to give us more informations on your setup (OS, python, GPU, etc) or a Google Co... | 1,600,835,294,000 | 1,602,149,536,000 | 1,602,149,536,000 | NONE | null | I load squad dataset. Then want to process data use following function with `Huggingface Transformers LongformerTokenizer`.
```
def convert_to_features(example):
# Tokenize contexts and questions (as pairs of inputs)
input_pairs = [example['question'], example['context']]
encodings = tokenizer.encode... | {
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"Hi !\r\nThanks for reporting.\r\nIt looks like no object inherits from `datasets.GeneratorBasedBuilder` (or more generally from `datasets.DatasetBuilder`) in your script.\r\n\r\nCould you check that there exist at least one dataset builder class ?",
"Hi @xixiaoyao did you manage to fix your issue ?",
"No activ... | 1,600,833,216,000 | 1,603,184,773,000 | 1,603,184,773,000 | NONE | null |
version: 1.0.2
```
train_dataset = datasets.load_dataset('squad')
```
The above code can works. However, when I download the squad.py from your server, and saved as `my_squad.py` to local. I run followings raise errors.
```
train_dataset = datasets.load_dataset('./my_squad.py') ... | {
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"It would be amazing if we ended up with this much information on all of our datasets :) \r\n\r\nI don't think there's too much repetition, everything that is ... | 1,600,813,777,000 | 1,602,608,720,000 | 1,602,534,412,000 | CONTRIBUTOR | null | First draft of a dataset card using the SNLI corpus as an example.
This is mostly based on the [Google Doc draft](https://docs.google.com/document/d/1dKPGP-dA2W0QoTRGfqQ5eBp0CeSsTy7g2yM8RseHtos/edit), but I added a few sections and moved some things around.
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"BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.",
"> BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality te... | 1,600,776,322,000 | 1,601,976,010,000 | 1,601,284,046,000 | CONTRIBUTOR | null | This adds [The OpenWebText Corpus](https://skylion007.github.io/OpenWebTextCorpus/), which is a clean and large text corpus for nlp pretraining. It is an open source effort to reproduce OpenAI’s WebText dataset used by GPT-2, and it is also needed to reproduce ELECTRA.
It solves #132 .
### Besides dataset buildin... | {
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It caused `KeyError` issues in #620
I changed the logic to add those new columns to the list that `__getitem__` should return. | {
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"Closing this one in favor of #670 \r\n\r\nThanks again for reporting the issue and proposing this fix !\r\nLet me know if you have other remarks"
] | 1,600,765,792,000 | 1,601,395,110,000 | 1,601,395,110,000 | NONE | null | Resolves issue [657](https://github.com/huggingface/datasets/issues/657). | {
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"Thanks for reporting !\r\nThere indeed a mismatch between the features and the kwargs description\r\n\r\nI believe `answer_start` was added to match the squad dataset format for consistency, even though it is not used in the metric computation. I think I'd rather keep it this way, so that you can just give `refere... | 1,600,765,620,000 | 1,602,555,416,000 | 1,601,395,058,000 | NONE | null | The [description](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L39) doesn't mention `answer_start` in squad. However the `datasets.features` require [it](https://github.com/huggingface/datasets/blob/master/metrics/squad/squad.py#L68). It's also not used in the evaluation. | {
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"We can just install multiprocess actually, I'll change that",
"Just an FYI: I remember that I wanted to try pathos a couple of years back and I ran into issues considering cross-platform; the code would just break on Windows. If I can verify this PR by running CPU tests on Windows, let me know!",
"That's good ... | 1,600,704,739,000 | 1,601,304,340,000 | 1,601,304,339,000 | MEMBER | null | [Multiprocess](https://github.com/uqfoundation/multiprocess) (from the [pathos](https://github.com/uqfoundation/pathos) project) allows to use lambda functions in multiprocessed map.
It was suggested to use it by @kandorm.
We're already using dill which is its only dependency. | {
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"To fix the CI you just have to copy the dummy data to the 1.1.0 folder, and maybe create the dummy ones for the `debiased` configuration",
"Fixed! Thanks @lhoestq "
] | 1,600,699,868,000 | 1,600,705,240,000 | 1,600,704,964,000 | MEMBER | null | The [Winogrande](https://arxiv.org/abs/1907.10641) paper mentions a `debiased` subset that wasn't in the first release; this PR adds it. | {
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The bug came from the type inference that didn't handle a weird case in Pyarrow.
Indeed this code runs without error but alters the data in arrow:
```python
import pyarrow as pa
type = pa.struct({"a": pa.struct({"b": pa.string()})})
array_with_altered_data = pa.array([{"a": {"b": "foo", "c": "bar"}}... | {
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"Currently the `json` dataset doesn't support this format unfortunately.\r\nHowever you could load it with\r\n```python\r\nfrom datasets import Dataset\r\nimport pandas as pd\r\n\r\ndf = pd.read_json(\"path_to_local.json\", orient=\"index\")\r\ndataset = Dataset.from_pandas(df)\r\n```",
"or you can make a custom ... | 1,600,646,234,000 | 1,600,690,464,000 | null | NONE | null | I have a local json dataset with the following form.
{
'id01234': {'key1': value1, 'key2': value2, 'key3': value3},
'id01235': {'key1': value1, 'key2': value2, 'key3': value3},
.
.
.
'id09999': {'key1': value1, 'key2': value2, 'key3': value3}
}
Note that instead of a list of records i... | {
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"Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps",
"Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but ac... | 1,600,513,623,000 | 1,600,775,650,000 | 1,600,775,649,000 | CONTRIBUTOR | null | Hi, I recently want to add a dataset whose source data is like this
```
openwebtext.tar.xz
|__ openwebtext
|__subset000.xz
| |__ ....txt
| |__ ....txt
| ...
|__ subset001.xz
|
....
```
So I wrote `openwebtext.py` like this
```
d... | {
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"Thanks for reporting !\r\n\r\nThis issue must have appeared when we refactored type inference in `nlp`\r\nBy default the library tries to keep the same feature types when applying `map` but apparently it has troubles with nested structures. I'll try to fix that next week"
] | 1,600,504,872,000 | 1,600,704,785,000 | 1,600,704,785,000 | NONE | null | I'm observing inconsistent behavior when applying .map(). This happens specifically when I'm incrementally adding onto a feature that is a nested dictionary. Here's a simple example that reproduces the problem.
```python
import datasets
# Dataset with a single feature called 'field' consisting of two examples
d... | {
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"This should be fixed with #645 ",
"Feel free to re-open if it still occurs"
] | 1,600,481,711,000 | 1,600,534,027,000 | 1,600,533,991,000 | CONTRIBUTOR | null | It only happened when "multiprocessing" + "batched" + "large dataset" at the same time.
```
def bprocess(examples):
examples['len'] = []
for text in examples['text']:
examples['len'].append(len(text))
return examples
wiki.map(brpocess, batched=True, num_proc=8)
```
```
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"Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week",
"Thanks, @lhoestq !\r\nPlease let me know when it is available. ",
"Right now the recommended way is to create the dataset on a server with internet connection and then to save it an... | 1,600,479,315,000 | 1,600,676,922,000 | 1,600,676,922,000 | NONE | null | I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/text... | {
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"I tried lower batch sizes and it didn't accelerate filter (quite the opposite actually).\r\nThe slow-down also appears for pyarrow 0.17.1 for some reason, not sure it comes from these changes",
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"This PR is ready :)\r\nIt brings official support for windows.\r\n\r\nSome tests `AWSDatasetTest` are failing.\r\nThis is because I had to fix a few datasets that were not compatible with windows.\r\nThese test will pass once they got merged on master :)"
] | 1,600,449,456,000 | 1,601,042,550,000 | 1,601,042,548,000 | MEMBER | null | There are a few differences in the behavior of python and pyarrow on windows.
For example there are restrictions when accessing/deleting files that are open
Fix #590 | {
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"Thanks for reporting !\r\nIt uses a temporary file to write the data.\r\nHowever it looks like the temporary file is not placed in the right directory during the processing",
"Well actually I just tested and the temporary file is placed in the same directory, so it should work as expected.\r\nWhich version of `d... | 1,600,443,686,000 | 1,645,023,209,000 | 1,645,023,209,000 | NONE | null | Hi guys, I run this on my Colab (PRO):
```python
from datasets import load_dataset
dataset = load_dataset('text', data_files='/content/corpus.txt', cache_dir='/content/drive/My Drive', split='train')
def encode(examples):
return tokenizer(examples['text'], truncation=True, padding='max_length')
dataset = ... | {
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```python
import dataset as ds
dataset = ...
with ds.temp_seed(42):
shuffled = dataset.shuffle()
```
Now it returns the same one since the seed is set | {
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"Hi ! Sure I'll take a look"
] | 1,600,412,950,000 | 1,600,429,027,000 | 1,600,429,027,000 | CONTRIBUTOR | null | Hi @lhoestq , I know you are busy and there are also other important issues. But if this is easy to be fixed, I am shamelessly wondering if you can give me some help , so I can evaluate my models and restart with my developing cycle asap. 😚
datasets version: editable install of master at 9/17
`datasets.load_data... | {
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I changed the feature names of LinCE and XTREME/PAN-X | {
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"I think it's ready now @stas00, did you want to add something else ?\r\nThis PR includes your changes but with the level set to warning",
"LGTM, thank you, @lhoestq "
] | 1,600,267,073,000 | 1,600,336,339,000 | 1,600,336,338,000 | MEMBER | null | Continuation of #618 | {
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"Not sure what could cause that on the `datasets` side. Could this be a `Trainer` issue ? cc @julien-c @sgugger ?",
"There was a memory leak issue fixed recently in master. You should install from source and see if it fixes your problem.",
"@lhoestq @sgugger Thanks for your comments. I have install from source ... | 1,600,230,795,000 | 1,613,476,921,000 | null | NONE | null | I tried to pretrain Longformer using transformers and datasets. But I got OOM issues with loading a large text file. My script is almost like this:
```python
from datasets import load_dataset
@dataclass
class DataCollatorForDatasetsLanguageModeling(DataCollatorForLanguageModeling):
"""
Data collator u... | {
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"thanks!"
] | 1,600,216,061,000 | 1,600,705,871,000 | 1,600,239,164,000 | CONTRIBUTOR | null | This PR fixes two typos in the loading datasets docs, one of them being a broken link to the `load_dataset` function. | {
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https://api.github.com/repos/huggingface/datasets/issues/631 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/631/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/631/comments | https://api.github.com/repos/huggingface/datasets/issues/631/events | https://github.com/huggingface/datasets/pull/631 | 701,711,255 | MDExOlB1bGxSZXF1ZXN0NDg3MTE3OTA0 | 631 | Fix text delimiter | {
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"Which OS are you using ?@abhi1nandy2",
"> Which OS are you using ?\r\n\r\nPRETTY_NAME=\"Debian GNU/Linux 9 (stretch)\"\r\nNAME=\"Debian GNU/Linux\"\r\nVERSION_ID=\"9\"\r\nVERSION=\"9 (stretch)\"\r\nVERSION_CODENAME=stretch\r\nID=debian\r\nHOME_URL=\"https://www.debian.org/\"\r\nSUPPORT_URL=\"https://www.debian.o... | 1,600,157,322,000 | 1,600,786,986,000 | 1,600,158,385,000 | MEMBER | null | I changed the delimiter in the `text` dataset script.
It should fix the `pyarrow.lib.ArrowInvalid: CSV parse error` from #622
I changed the delimiter to an unused ascii character that is not present in text files : `\b` | {
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"Seems like it works when setting ```block_size=2100000000``` or something arbitrarily large though.",
"Can you give us some stats on the data files you use as inputs?",
"Basically ~600MB txt files(UTF-8) * 59. \r\ncontents like ```안녕하세요, 이것은 예제로 한번 말해보는 텍스트입니다. 그냥 이렇다고요.<|endoftext|>\\n```\r\n\r\nAlso, it gets... | 1,600,149,756,000 | 1,601,072,503,000 | 1,601,072,503,000 | NONE | null | ```
Traceback (most recent call last):
File "examples/language-modeling/run_language_modeling.py", line 333, in <module>
main()
File "examples/language-modeling/run_language_modeling.py", line 262, in main
get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_t... | {
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"sorry it's an apache arrow issue."
] | 1,600,129,846,000 | 1,600,130,177,000 | 1,600,129,937,000 | NONE | null | I am trying to read json data (it's an array with lots of dictionaries) and getting block boundaries issue as below :
I tried calling read_json with readOptions but no luck .
```
table = json.read_json(fn)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pyarrow/_json.pyx", li... | {
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"Thanks!"
] | 1,600,126,039,000 | 1,604,351,003,000 | 1,600,150,775,000 | CONTRIBUTOR | null | Fixed the `add a dataset` and `share a dataset` links in the contribution guideline to refer to the new docs website. | {
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Note: rebased on huggingface/dat... | {
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"Indeed we convert tensors to list to be able to write in arrow format. Because of this conversion we lose the dtype information. We should add the dtype detection when we do type inference. However it would require a bit of refactoring since currently the conversion happens before the type inference..\r\n\r\nAnd t... | 1,600,087,085,000 | 1,629,189,004,000 | 1,629,189,004,000 | CONTRIBUTOR | null | After switching to `datasets` my model just broke. After a weekend of debugging, the issue was that my model could not handle the double that the Dataset provided, as it expected a float (but didn't give a warning, which seems a [PyTorch issue](https://discuss.pytorch.org/t/is-it-required-that-input-and-hidden-for-gru-... | {
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Thank you again for this amazing repo.
Would it be possible for y'all to add the LearningQ dataset - https://github.com/AngusGLChen/LearningQ ?
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"Currently `csv` doesn't support the `features` attribute (unlike `json`).\r\nWhat you can do for now is cast the features using the in-place transform `cast_`\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset('csv', data_files=file_dict, delimiter=';', column_names=['text', 'label... | 1,599,916,894,000 | 1,601,495,503,000 | 1,601,455,194,000 | MEMBER | null | I am trying to load a local file with the `load_dataset` function and I want to predefine the feature types with the `features` argument. However, the types are always the same independent of the value of `features`.
I am working with the local files from the emotion dataset. To get the data you can use the followi... | {
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"Can you give us more information on your os and pip environments (pip list)?",
"@thomwolf Sure. I'll try downgrading to 3.7 now even though Arrow say they support >=3.5.\r\n\r\nLinux (Ubuntu 18.04) - Python 3.8\r\n======================\r\nPackage - Version\r\n---------------------\r\ncertifi 2... | 1,599,914,968,000 | 1,603,883,251,000 | 1,603,883,250,000 | CONTRIBUTOR | null | Trying the following snippet, I get different problems on Linux and Windows.
```python
dataset = load_dataset("text", data_files="data.txt")
# or
dataset = load_dataset("text", data_files=["data.txt"])
```
(ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that ... | {
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https://api.github.com/repos/huggingface/datasets/issues/620 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/620/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/620/comments | https://api.github.com/repos/huggingface/datasets/issues/620/events | https://github.com/huggingface/datasets/issues/620 | 699,815,135 | MDU6SXNzdWU2OTk4MTUxMzU= | 620 | map/filter multiprocessing raises errors and corrupts datasets | {
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"It seems that I ran into the same problem\r\n```\r\ndef tokenize(cols, example):\r\n for in_col, out_col in cols.items():\r\n example[out_col] = hf_tokenizer.convert_tokens_to_ids(hf_tokenizer.tokenize(example[in_col]))\r\n return example\r\ncola = datasets.load_dataset('glue', 'cola')\r\ntokenized_cola = col... | 1,599,863,406,000 | 1,602,174,707,000 | 1,602,174,706,000 | NONE | null | After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing.
```python
...
ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed)
ner_ds_dict["validation"] = ner_ds_dict["test"]
rel_ds_dict = rel_ds.train_test_split(test_si... | {
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"Indeed you're right ! Thanks for reporting that\r\n\r\nCould you open a PR to fix the features names ?"
] | 1,599,857,183,000 | 1,600,239,559,000 | 1,600,239,559,000 | CONTRIBUTOR | null | I think the following features in MLQA shouldn't be named the way they are:
1. `questions` (should be `question`)
2. `ids` (should be `id`)
3. `start` (should be `answer_start`)
The reasons I'm suggesting these features be renamed are:
* To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA et... | {
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https://api.github.com/repos/huggingface/datasets/issues/618 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/618/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/618/comments | https://api.github.com/repos/huggingface/datasets/issues/618/events | https://github.com/huggingface/datasets/pull/618 | 699,684,831 | MDExOlB1bGxSZXF1ZXN0NDg1NDAxMzI5 | 618 | sync logging utils with transformers | {
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"Also, some downloads and dataset processing can be quite long for large datasets like wikipedia/pg19/etc. We probably don't want to user to think that the library is hanging. Happy to reorganize logging between DEBUG/INFO/WARNING to make it less verbose by default though.",
"The problem is that `transformers` im... | 1,599,853,573,000 | 1,600,357,259,000 | 1,600,336,427,000 | MEMBER | null | sync the docs/code with the recent changes in transformers' `logging` utils:
1. change the default level to `WARNING`
2. add `DATASETS_VERBOSITY` env var
3. expand docs | {
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https://api.github.com/repos/huggingface/datasets/issues/617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/617/comments | https://api.github.com/repos/huggingface/datasets/issues/617/events | https://github.com/huggingface/datasets/issues/617 | 699,472,596 | MDU6SXNzdWU2OTk0NzI1OTY= | 617 | Compare different Rouge implementations | {
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"Updates - the differences between the following three\r\n(1) https://github.com/bheinzerling/pyrouge (previously popular. The one I trust the most)\r\n(2) https://github.com/google-research/google-research/tree/master/rouge\r\n(3) https://github.com/pltrdy/files2rouge (used in fairseq)\r\ncan be explained by two t... | 1,599,839,372,000 | 1,617,211,713,000 | 1,601,632,338,000 | NONE | null | I used RougeL implementation provided in `datasets` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https://arxiv.org/pdf/1909.03186.pdf) for example.
Ca... | {
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https://api.github.com/repos/huggingface/datasets/issues/616 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/616/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/616/comments | https://api.github.com/repos/huggingface/datasets/issues/616/events | https://github.com/huggingface/datasets/issues/616 | 699,462,293 | MDU6SXNzdWU2OTk0NjIyOTM= | 616 | UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors | {
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"I have the same issue",
"Same issue here when Trying to load a dataset from disk.",
"I am also experiencing this issue, and don't know if it's affecting my training.",
"Same here. I hope the dataset is not being modified in-place.",
"I think the only way to avoid this warning would be to do a copy of the n... | 1,599,838,756,000 | 1,626,988,341,000 | null | CONTRIBUTOR | null | I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this stra... | {
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https://api.github.com/repos/huggingface/datasets/issues/615 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/615/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/615/comments | https://api.github.com/repos/huggingface/datasets/issues/615/events | https://github.com/huggingface/datasets/issues/615 | 699,410,773 | MDU6SXNzdWU2OTk0MTA3NzM= | 615 | Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0 | {
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"Related: https://issues.apache.org/jira/browse/ARROW-9773\r\n\r\nIt's definitely a size thing. I took a smaller dataset with 87000 rows and did:\r\n```\r\nfor i in range(10,1000,20):\r\n table = pa.concat_tables([dset._data]*i)\r\n table.take([0])\r\n```\r\nand it broke at around i=300.\r\n\r\nAlso when `_in... | 1,599,835,838,000 | 1,600,534,060,000 | 1,600,533,991,000 | MEMBER | null | How to reproduce:
```python
from datasets import load_dataset
wiki = load_dataset("wikipedia", "20200501.en", split="train")
wiki[[0]]
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
<ipython-input-13-38... | {
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https://api.github.com/repos/huggingface/datasets/issues/614 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/614/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/614/comments | https://api.github.com/repos/huggingface/datasets/issues/614/events | https://github.com/huggingface/datasets/pull/614 | 699,177,110 | MDExOlB1bGxSZXF1ZXN0NDg0OTQ2MzA1 | 614 | [doc] Update deploy.sh | {
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https://api.github.com/repos/huggingface/datasets/issues/613 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/613/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/613/comments | https://api.github.com/repos/huggingface/datasets/issues/613/events | https://github.com/huggingface/datasets/pull/613 | 699,117,070 | MDExOlB1bGxSZXF1ZXN0NDg0ODkyMTUx | 613 | Add CoNLL-2003 shared task dataset | {
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"I think we should somewhere mention, that is the dataset in IOB2 tagging scheme, whereas the original dataset uses IOB1 :)",
"Indeed this is something we want to mention.\r\n\r\nIf would want to add more details about the IOB1->2 change, feel free to ignore my suggestions and edit the description + update the da... | 1,599,818,550,000 | 1,601,894,585,000 | 1,600,338,998,000 | CONTRIBUTOR | null | Please consider adding CoNLL-2003 shared task dataset as it's beneficial for token classification tasks. The motivation behind this PR is the [PR](https://github.com/huggingface/transformers/pull/7041) in the transformers project. This dataset would be not only useful for the usual run-of-the-mill NER tasks but also fo... | {
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https://api.github.com/repos/huggingface/datasets/issues/611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/611/comments | https://api.github.com/repos/huggingface/datasets/issues/611/events | https://github.com/huggingface/datasets/issues/611 | 698,863,988 | MDU6SXNzdWU2OTg4NjM5ODg= | 611 | ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 | {
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"Can you give us stats/information on your pandas DataFrame?",
"```\r\n<class 'pandas.core.frame.DataFrame'>\r\nInt64Index: 17136104 entries, 0 to 17136103\r\nData columns (total 6 columns):\r\n # Column Dtype \r\n--- ------ ----- \r\n 0 item_id int64 \r\n 1 item_titl object \r\n... | 1,599,802,152,000 | 1,601,046,895,000 | null | NONE | null | Hi, I'm trying to load a dataset from Dataframe, but I get the error:
```bash
---------------------------------------------------------------------------
ArrowCapacityError Traceback (most recent call last)
<ipython-input-7-146b6b495963> in <module>
----> 1 dataset = Dataset.from_pandas(emb)... | {
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"Could you try\r\n```python\r\nload_dataset('text', data_files='test.txt',cache_dir=\"./\", split=\"train\")\r\n```\r\n?\r\n\r\n`load_dataset` returns a dictionary by default, like {\"train\": your_dataset}",
"Hi @lhoestq\r\nThanks for your suggestion.\r\n\r\nI tried \r\n```\r\ndataset = load_dataset('text', data... | 1,599,763,298,000 | 1,618,044,244,000 | null | NONE | null | I migrate my question from https://github.com/huggingface/transformers/pull/4009#issuecomment-690039444
I tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file.
According to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file.... | {
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"Thanks for opening this PR :) \r\n\r\nWe changed the name of the lib from nlp to datasets yesterday.\r\nCould you rebase from master and re-generate the dataset_info.json file to fix the name changes ?",
"Rebased changes here: https://github.com/huggingface/datasets/pull/626"
] | 1,599,761,792,000 | 1,600,110,362,000 | 1,600,110,361,000 | CONTRIBUTOR | null | NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112. | {
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"Feel free to open the PR ;)\r\nThanks for updating the dataset_info.json file !"
] | 1,599,760,022,000 | 1,600,239,198,000 | 1,600,239,198,000 | CONTRIBUTOR | null | NYU is switching dataset hosting from Google to FB. Initial changes to `datasets` are in https://github.com/jeswan/nlp/commit/b7d4a071d432592ded971e30ef73330529de25ce. What tests do you suggest I run before opening a PR?
See: https://github.com/jiant-dev/jiant/issues/161 and https://github.com/nyu-mll/jiant/pull/111... | {
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"Closing as #607 was merged"
] | 1,599,741,018,000 | 1,599,754,521,000 | 1,599,754,521,000 | MEMBER | null | Transmit format to children obtained when processing a dataset.
Added a test.
When concatenating datasets, if the formats are disparate, the concatenated dataset has a format reset to defaults. | {
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```
pip install git+http://github.com/huggingface/nlp.git
```
or
```
git clone http://github.com/huggingface/nlp.git
pip install -e ./nlp
```
will use the latest scripts, and not the ones from the previous version. | {
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I fixed the indices by applying an offset.
I added the case to our tests to make sure it doesn't happen again.
I also added the message proposed by @thomwolf in #597
```python
>>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2, load_from_cache_file=False)
Done writing 10 ... | {
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"When I change from python3.6 to python3.8, it works! ",
"Does it work when you install `nlp` from source on python 3.6?",
"No, still the pickling error.",
"I wasn't able to reproduce on google colab (python 3.6.9 as well) with \r\n\r\npickle==4.0\r\ndill=0.3.2\r\ntransformers==3.1.0\r\ndatasets=1.0.1 (also t... | 1,599,719,288,000 | 1,601,044,314,000 | 1,601,044,314,000 | NONE | null | Hi,
I modified line 136 in the original [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) as:
```
# line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size)
dataset = load_da... | {
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"Hi ! sorry for the late response\r\n\r\nCould you try to rebase from master ? We changed the named of the library last week so you have to include this change in your code.\r\n\r\nCan you give me more details about the error you get when running the cli command ?\r\n\r\nNote that in case of a manual download you h... | 1,599,708,669,000 | 1,600,345,045,000 | 1,600,345,045,000 | MEMBER | null | @lhoestq The command to create metadata failed. I guess it's because the zip is not downloaded from a remote address? How to solve that? Also the CI fails and I don't know how to fix that :( | {
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"Thanks for reporting !\r\nWhich version of transformers are you using ?\r\nIt looks like it doesn't have the PreTrainedTokenizerBase class",
"I was using transformer 2.9. And I switch to the latest transformer package. Everything works just fine!!\r\n\r\nThanks for helping! I should look more carefully next time... | 1,599,685,403,000 | 1,599,719,121,000 | 1,599,692,248,000 | NONE | null | Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine):
To recreate the error:
First, installing nlp directly from source:
```
git clone https://github.com/huggingface/nlp.git
cd nlp
pip install -e .
``... | {
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"I fixed a bug that could cause this issue earlier today. Could you pull the latest version and try again ?",
"Still the case on master.\r\nI guess we should have an offset in the multi-procs indeed (hopefully it's enough).\r\n\r\nAlso, side note is that we should add some logging before the \"test\" to say we ar... | 1,599,681,056,000 | 1,599,735,817,000 | 1,599,735,817,000 | CONTRIBUTOR | null | When `num_proc` > 1, the indices argument passed to the map function is incorrect:
```python
d = load_dataset('imdb', split='test[:1%]')
def fn(x, inds):
print(inds)
return x
d.select(range(10)).map(fn, with_indices=True, batched=True)
# [0, 1]
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
d.select(range(10... | {
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"Ready for review @lhoestq, just updated a few 156 files here"
] | 1,599,666,441,000 | 1,599,732,304,000 | 1,599,732,303,000 | MEMBER | null | Move the repo to isort 5.0.0.
Also start testing style/quality on datasets and metrics.
Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies.
Maybe we could add this in datasets but while cleaning this I've seen many example of really unused i... | {
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"`pip install git+https://github.com/huggingface/nlp.git` should have done the job.\r\n\r\nDid you uninstall `nlp` before installing from github ?",
"> Did you uninstall `nlp` before installing from github ?\r\n\r\nI did not. I created a new environment and installed `nlp` directly from `github` and it worked!\r\... | 1,599,663,712,000 | 1,599,668,419,000 | 1,599,668,418,000 | NONE | null | Hi,
As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method. While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.p... | {
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"/cc: @vblagoje",
"Closing this one as #594 is merged (same changes except the dummy data hack)",
"Awesome @stefan-it ! @lhoestq how soon can I use the fixed GermEval dataset in HF token classification examples?",
"I've manually updated the script on S3, so you can actually use it right now with\r\n```python\... | 1,599,646,049,000 | 1,599,661,014,000 | 1,599,658,515,000 | CONTRIBUTOR | null | Hi,
unfortunately, the download links for the GermEval 2014 dataset have changed: they're now located on a Google Drive.
I changed the URLs and bump version from 1.0.0 to 2.0.0. | {
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