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https://api.github.com/repos/huggingface/datasets/issues/741 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/741/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/741/comments | https://api.github.com/repos/huggingface/datasets/issues/741/events | https://github.com/huggingface/datasets/issues/741 | 723,924,275 | MDU6SXNzdWU3MjM5MjQyNzU= | 741 | Creating dataset consumes too much memory | {
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"Thanks for reporting.\r\nIn theory since the dataset script is just made to yield examples to write them into an arrow file, it's not supposed to create memory issues.\r\n\r\nCould you please try to run this exact same loop in a separate script to see if it's not an issue with `PIL` ?\r\nYou can just copy paste wh... | 1,603,001,226,000 | 1,644,944,590,000 | 1,644,944,590,000 | CONTRIBUTOR | null | Moving this issue from https://github.com/huggingface/datasets/pull/722 here, because it seems like a general issue.
Given the following dataset example, where each example saves a sequence of 260x210x3 images (max length 400):
```python
def _generate_examples(self, base_path, split):
""" Yields examp... | {
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I updated the urls to the new ones, since we don't support redirections for downloads.
Fix #737 | {
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https://api.github.com/repos/huggingface/datasets/issues/739 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/739/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/739/comments | https://api.github.com/repos/huggingface/datasets/issues/739/events | https://github.com/huggingface/datasets/pull/739 | 723,044,066 | MDExOlB1bGxSZXF1ZXN0NTA0Njk5NTY3 | 739 | Add wiki dpr multiset embeddings | {
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"I still have to compute the dataset_infos, and build + host the indexes",
"update: I'm computing the metadata, will update the PR soon",
"Finally all green and ready to merge :)"
] | 1,602,839,149,000 | 1,606,399,370,000 | 1,606,399,369,000 | MEMBER | null | There are two DPR encoders, one trained on Natural Questions and one trained on a multiset/hybrid dataset.
Previously only the embeddings from the encoder trained on NQ were available. I'm adding the ones from the encoder trained on the multiset/hybrid dataset.
In the configuration you can now specify `embeddings_nam... | {
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"Hello,\r\n\r\nI ran https://github.com/huggingface/transformers/blob/master/examples/token-classification/run.sh\r\n\r\nAnd received this error:\r\n```\r\n100%|██████████| 407/407 [21:37<00:00, 3.44s/it]Traceback (most recent call last):\r\n File \"run_ner.py\", line 445, in <module>\r\n main()\r\n File \"ru... | 1,602,838,305,000 | 1,611,245,235,000 | 1,603,103,472,000 | CONTRIBUTOR | null | Recently, the original seqeval has enabled us to get per type scores and overall scores as a dictionary.
This PR replaces the current code with the original function(`classification_report`) to simplify it.
Also, the original code has been updated to fix #352.
- Related issue: https://github.com/chakki-works/seq... | {
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"Thanks for reporting.\r\nThat's because the download url has changed. The old url now redirects to the new one but we don't support redirection for downloads.\r\n\r\nI'm opening a PR to update the url"
] | 1,602,777,473,000 | 1,603,097,676,000 | 1,603,097,676,000 | NONE | null | **Datasets Version:**
1.1.2
**Python Version:**
3.6/3.7
**Code:**
```python
from datasets import load_dataset
load_dataset("trec")
```
**Expected behavior:**
Download Trec dataset and load Dataset object
**Current Behavior:**
Get a connection error saying it couldn't reach http://cogcomp.org/Data/... | {
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"can you also reference the `--organization` flag like in https://github.com/huggingface/transformers/blob/master/docs/source/model_sharing.rst#upload-your-model-with-the-cli ?",
"done!",
"Not sure if the changes in `datasets/wmt_t2t/wmt_utils.py` are intentional.\r\nIf you want to add more configs to wmt, coul... | 1,602,769,299,000 | 1,603,458,928,000 | 1,603,458,928,000 | CONTRIBUTOR | null | This is one I did to get the pseudo-labels updated. Not sure if it generalizes, but I figured I would write it down. It was pretty easy because all I had to do was make properly formatted directories and change URLs.
+ In slack @thomwolf called it a `user-namespace` dataset, but the docs call it `community dataset`... | {
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"Thanks for reporting !\r\nWe'll add support for other keys"
] | 1,602,759,327,000 | 1,604,064,232,000 | 1,604,064,232,000 | CONTRIBUTOR | null | I have found that only "train", "validation" and "test" are valid keys in the `data_files` argument. When you use any other ones, those attached files are silently ignored - leading to unexpected behaviour for the users.
So the following, unintuitively, returns only one key (namely `train`).
```python
datasets =... | {
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"Followup: \r\nFrom the info in https://github.com/huggingface/datasets/pull/722, I probably should load the videos as array of frames directly into the database. \r\nThis will make the dataset generation time very long, but will make working with the dataset much easier.",
"When I run:\r\n```\r\npython datasets-... | 1,602,673,302,000 | 1,616,480,383,000 | 1,616,480,383,000 | CONTRIBUTOR | null | takes like 9-10 hours to download all of the videos for the dataset, but it does finish :) | {
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https://api.github.com/repos/huggingface/datasets/issues/731 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/731/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/731/comments | https://api.github.com/repos/huggingface/datasets/issues/731/events | https://github.com/huggingface/datasets/pull/731 | 721,142,985 | MDExOlB1bGxSZXF1ZXN0NTAzMTExNzc4 | 731 | dataset(aslg_pc12): initial loading script | {
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"Thanks @lhoestq \r\nAre there any guidelines for the dummy data?\r\nIn this particular case for example, the dataset fetches from two hardcoded URLs. \r\nDo I just `head -n 10` both files and zip them?\r\n\r\n",
"> Thanks @lhoestq\r\n> Are there any guidelines for the dummy data?\r\n> In this particular case for... | 1,602,652,477,000 | 1,603,898,826,000 | 1,603,898,826,000 | CONTRIBUTOR | null | This contains the only current public part of this corpus.
The rest of the corpus is not yet been made public, but this sample is still being used by researchers. | {
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"Thanks for reporting. That's a bug indeed.\r\nApparently only the `data_files` parameter is taken into account right now in `DatasetBuilder._create_builder_config` but it should also be the case for `config_kwargs` (or at least the instantiated `builder_config`)",
"Hi, does this bug be fixed? when I load JSON fi... | 1,602,640,954,000 | 1,638,109,737,000 | 1,603,964,161,000 | NONE | null | The following code with `test1.txt` containing just "🤗🤗🤗":
```
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="latin_1")
print(dataset[0])
dataset = datasets.load_dataset('text', data_files=['test1.txt'], split="train", encoding="utf-8")
print(dataset[0])
```
produc... | {
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## Reproducer
```python
import datasets
import torch
from datasets import Metric
class GatherMetric(Metric):
... | {
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## Reproducer
```python
import datasets
import torch
from datasets import Metric
class GatherMetric(Metric):
def _info(self):
return datasets.MetricInfo(
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To fix that we could simply specify `position=i` for i=0 to n the number of files to download when instantiating the tqdm progress bar.
Another way would be to have one "... | {
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"Hi try, to provide more information please.\r\n\r\nExample code in a colab to reproduce the error, details on what you are trying to do and what you were expected and details on your environment (OS, PyPi packages version).",
"> Hi try, to provide more information please.\r\n> \r\n> Example code in a colab to re... | 1,602,503,110,000 | 1,645,120,434,000 | 1,644,921,537,000 | NONE | null | Hi,
I have encountered this problem during loading the openwebtext dataset:
```
>>> dataset = load_dataset('openwebtext')
Downloading and preparing dataset openwebtext/plain_text (download: 12.00 GiB, generated: 37.04 GiB, post-processed: Unknown size, total: 49.03 GiB) to /home/admin/.cache/huggingface/datasets/op... | {
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"Great, as you found it useful I improved the code a bit to automate indentation in the parent class, so that the child repr doesn't need to guess the indentation level, while repr'ing nicely on its own.\r\n\r\n- do we want indent=4 or 2?\r\n- do we want `{` ... `}` or w/o?\r\n\r\ncurrently it's indent4 and w/ curl... | 1,602,468,226,000 | 1,603,470,275,000 | 1,603,443,646,000 | MEMBER | null | Currently, if I do:
```
from datasets import load_dataset
load_dataset("wikihow", 'all', data_dir="/hf/pegasus-datasets/wikihow/")
```
I get:
```
DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None),
'headline': Value(dtype='string', id=None), 'title': Value(dtype='string',
id=None... | {
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"Should be fixed now: \r\n\r\n\r\n\r\nNot sure I understand what you mean by the second part?\r\n",
"Thank you!\r\n\r\n> Not sure I understand what you mean by the second part?\r\n\r\nCompare the 2:\r\n* htt... | 1,602,457,932,000 | 1,602,694,812,000 | 1,602,694,812,000 | MEMBER | null | It looks like the website still has all the `nlp` data, e.g.: https://huggingface.co/nlp/viewer/?dataset=wikihow&config=all
should probably redirect to: https://huggingface.co/datasets/wikihow
also for some reason the new information is slightly borked. If you look at the old one it was nicely formatted and had t... | {
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"Nice ! :)\r\nIt's indeed the first time we have such contributions so we'll have to figure out the appropriate way to integrate them.\r\nCould you add details on what they could be used for ?\r\n",
"They can be used as training data for a smaller model.",
"Sounds just like a regular dataset to me then, no?",
... | 1,602,450,345,000 | 1,627,967,511,000 | 1,627,967,511,000 | CONTRIBUTOR | null | I recently [uploaded pseudo-labels](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/precomputed_pseudo_labels.md) for CNN/DM, XSUM and WMT16-en-ro to s3, and thom mentioned I should add them to this repo.
Since pseudo-labels are just a large model's generations on an existing dataset, what is ... | {
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"This might be interesting to @kayoyin the author of https://github.com/kayoyin/transformer-slt – pinging you just in case :)",
"Thanks Amit, this is a great idea! I'm thinking of porting the SLT models from my paper here as well, having this dataset would be perfect for that :)"
] | 1,602,359,048,000 | 1,609,830,411,000 | null | CONTRIBUTOR | null | This is the first sign language dataset in this repo as far as I know.
Following an old issue I opened https://github.com/huggingface/datasets/issues/302.
I added the dataset official REAMDE file, but I see it's not very standard, so it can be removed.
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https://api.github.com/repos/huggingface/datasets/issues/721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/721/comments | https://api.github.com/repos/huggingface/datasets/issues/721/events | https://github.com/huggingface/datasets/issues/721 | 718,647,147 | MDU6SXNzdWU3MTg2NDcxNDc= | 721 | feat(dl_manager): add support for ftp downloads | {
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"We only support http by default for downloading.\r\nIf you really need to use ftp, then feel free to use a library that allows to download through ftp in your dataset script (I see that you've started working on #722 , that's awesome !). The users will get a message to install the extra library when they load the ... | 1,602,345,020,000 | 1,644,921,884,000 | 1,644,921,883,000 | CONTRIBUTOR | null | I am working on a new dataset (#302) and encounter a problem downloading it.
```python
# This is the official download link from https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/
_URL = "ftp://wasserstoff.informatik.rwth-aachen.de/pub/rwth-phoenix/2016/phoenix-2014-T.v3.tar.gz"
dl_manager.do... | {
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"Same issue here. I will be digging further, but it looks like the [script](https://github.com/huggingface/datasets/blob/master/datasets/wiki_dpr/wiki_dpr.py#L132) is attempting to open a file that is not downloaded yet. \r\n\r\n```\r\n99dcbca09109e58502e6b9271d4d3f3791b43f61f3161a76b25d2775ab1a4498.lock\r\n```\r\n... | 1,602,080,833,000 | 1,608,732,271,000 | 1,608,732,271,000 | NONE | null | ## Environment info
transformers version: 3.3.1
Platform: Linux-4.19
Python version: 3.7.7
PyTorch version (GPU?): 1.6.0
Tensorflow version (GPU?): No
Using GPU in script?: Yes
Using distributed or parallel set-up in script?: No
## To reproduce
Steps to reproduce the behaviour... | {
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https://api.github.com/repos/huggingface/datasets/issues/719 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/719/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/719/comments | https://api.github.com/repos/huggingface/datasets/issues/719/events | https://github.com/huggingface/datasets/pull/719 | 716,492,263 | MDExOlB1bGxSZXF1ZXN0NDk5MjE5Mjg2 | 719 | Fix train_test_split output format | {
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This was due to `column_names` being handled as a List[str] instead of Dict[str, List[str]] when the dataset transform (train_test_split) returns a DatasetDict (one set of column names per split).
This should ... | {
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see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111) for the error details.
For now I just added `<4.50.0` in the setup.py
Hopefully we can find what's wrong with this version soon | {
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"Referencing the wrong issue # in the commit message. Closing this to fix it again."
] | 1,601,912,529,000 | 1,601,912,798,000 | 1,601,912,792,000 | CONTRIBUTOR | null | Fixes the Attribute error in cell 3 of the overview notebook | {
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"One thing though, could we try to read the files in parallel?",
"We could but I'm not sure this would help a lot since the bottleneck is the drive IO if the files are big enough.\r\nIt could make sense for very small files.",
"Looks like windows is not a big fan of this approach\r\nI'm working on a fix",
"I ... | 1,601,891,275,000 | 1,601,903,598,000 | 1,601,903,597,000 | MEMBER | null | As mentioned in #622 the pandas reader used for text dataset doesn't work properly when there are \r characters in the text file.
Instead I switched to pure python using `open` and `read`.
From my benchmark on a 100MB text file, it's the same speed as the previous pandas reader. | {
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"Discussed in #622, fixed in #715. Closing the issue. Thanks @lhoestq, it works now! 👍 "
] | 1,601,867,223,000 | 1,602,223,105,000 | 1,601,905,769,000 | NONE | null | The new pandas-based text reader isn't able to work properly with files that contain carriage return symbols (`\r`).
It fails with the following error message:
```
...
File "pandas/_libs/parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 874, in pandas._l... | {
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"Do this:\r\n``` python\r\nsquad_dataset = list_datasets(with_details=True)[datasets.index('squad')]\r\npprint(squad_dataset.__dict__) # It's a simple python dataclass\r\n```",
"Thanks! This worked. I have created a PR to fix this in the notebook. "
] | 1,601,791,111,000 | 1,601,915,140,000 | 1,601,915,140,000 | CONTRIBUTOR | null | Hi,
I got the following error in **cell number 3** while exploring the **Overview.ipynb** notebook in google colab. I used the [link ](https://colab.research.google.com/github/huggingface/datasets/blob/master/notebooks/Overview.ipynb) provided in the main README file to open it in colab.
```python
# You can acc... | {
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"Datasets does not use elasticsearch API to define custom similarity. If you want to use a custom similarity, the best would be to run a curl request directly to your elasticsearch instance (see sample hereafter, directly from ES documentation), then you should be able to use `my_similarity` in your configuration p... | 1,601,723,929,000 | 1,626,634,975,000 | null | NONE | null | **QUESTION : How should we use other similarity algorithms supported by Elasticsearch other than "BM25" ?**
**ES Reference**
https://www.elastic.co/guide/en/elasticsearch/reference/current/index-modules-similarity.html
**HF doc reference:**
https://huggingface.co/docs/datasets/faiss_and_ea.html
**context :**
=... | {
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"Facing a similar issue here. My model using SQuAD dataset takes about 1h to process with in memory data and more than 2h with datasets directly.",
"And if you use in-memory-data with datasets with `load_dataset(..., keep_in_memory=True)`?",
"Thanks for the tip @thomwolf ! I did not see that flag in the docs. I... | 1,601,707,447,000 | 1,613,139,208,000 | 1,613,139,208,000 | NONE | null | I've been very excited about this amazing datasets project. However, I've noticed that the performance can be substantially slower than using an in-memory dataset.
Now, this is expected I guess, due to memory mapping data using arrow files, and you don't get anything for free. But I was surprised at how much slower.... | {
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"Hello @mathcass I would want to work on this issue. May I do the same? ",
"@punitaojha, certainly. Feel free to work on this. Let me know if you need any help or clarity.",
"Hello @mathcass \r\n1. I did fork the repository and clone the same on my local system. \r\n\r\n2. Then learnt about how we can publish o... | 1,601,681,979,000 | 1,607,070,159,000 | 1,601,844,628,000 | NONE | null | I was looking at the docs on [Perplexity](https://huggingface.co/transformers/perplexity.html) via GPT2. When you load datasets and try to load Wikitext, you get the error,
```
module 'pyarrow' has no attribute 'PyExtensionType'
```
I traced it back to datasets having installed PyArrow 1.0.1 but there's not pinni... | {
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To make sure the hash is unique given a certain combination of files/splits, we sort the split names.
However the `NamedSplit` objects can't be passed to `sorted` and currently it raises an error: we need to sort th... | {
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"Hi !\r\nThanks for reporting :) \r\nIndeed this is an issue on the `datasets` side.\r\nI'm creating a PR",
"Thanks @lhoestq !"
] | 1,601,652,475,000 | 1,601,885,699,000 | 1,601,885,699,000 | NONE | null | ## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
Don't forget to fill out the missing fields in that output! -->
- `transformers` version: 3.3.1 (installed from master)
- `datasets` version: 1.0.2 (installed as a dependency from transformers)
... | {
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To fix that I reverted to the use of the HF API that fetch the available datasets on S3 that is synced with the master branch | {
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"Awesome :) \r\n\r\nDon't pay attention to the RemoteDatasetTest error, I'm fixing it right now",
"You can rebase from master to fix the CI test :)",
"If we're lucky we can even include this dataset in today's release",
"Just thinking since `type` can only be `comparison` or `bridge` and `level` can only be `... | 1,601,639,068,000 | 1,601,643,281,000 | 1,601,643,281,000 | CONTRIBUTOR | null | Added the [HotpotQA](https://github.com/hotpotqa/hotpot) multi-hop question answering dataset.
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"Oups too late, sorry"
] | 1,601,631,346,000 | 1,601,632,514,000 | 1,601,632,338,000 | MEMBER | null | Continuation of #700
Rouge 2 and Rouge Lsum were missing in Rouge's outputs.
Rouge Lsum is also useful to evaluate Rouge L for sentences with `\n`
Fix #617 | {
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"Indeed there's currently a mismatch between the description and what it rouge actually returns.\r\nThanks for proposing this fix :) \r\n\r\nI think it's better to return rouge 1-2-L.\r\nWas there a reason to only include rouge 1 and rouge L @thomwolf ? ",
"rougeLsum is also missing, could you add it ?",
"Addin... | 1,601,627,805,000 | 1,601,636,929,000 | 1,601,632,745,000 | NONE | null | The description of the ROUGE metric says,
```
_KWARGS_DESCRIPTION = """
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for ... | {
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"also i tried below code to solve checksum error \r\n`datasets-cli test ./datasets/xnli --save_infos --all_configs`\r\n\r\nand it shows \r\n\r\n```\r\n2020-10-02 07:06:16.588760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\r\nTraceback (most ... | 1,601,621,596,000 | 1,601,747,152,000 | 1,601,747,017,000 | NONE | null | `dataset = datasets.load_dataset(path='xnli')`
showing below error
```
/opt/conda/lib/python3.7/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
36 if len(bad_urls) > 0:
37 error_msg = "Checksums didn't match" + for_verifi... | {
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Thank you | {
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I also added a `load_elasticsearch_index` method to load an index that has already been built.
I checked the tests for the ES index and we have tests that mock ElasticSearch.
I think this is good for now but at some point it would be cool to have an end-to-end test with a real ES... | {
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Fix #690 | {
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However in the tests, the dummy data were still downloaded from s3.
So I changed that to download them from github instead, in the MockDownloadManager.
Moreover I noticed that `anli`'s dummy data were quite heavy (18MB compressed, i.e. the ent... | {
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"It looks like an outdated PR (we've already added mlsum). Closing it"
] | 1,601,557,270,000 | 1,601,571,673,000 | 1,601,571,673,000 | NONE | null | . | {
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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."
] | 1,600,887,745,000 | 1,603,811,965,000 | 1,603,811,965,000 | NONE | null | {
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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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"Adding a direct link to the rendered markdown:\r\nhttps://github.com/mcmillanmajora/datasets/blob/add_dataset_documentation/datasets/snli/README.md\r\n",
"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"
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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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https://api.github.com/repos/huggingface/datasets/issues/647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/647/comments | https://api.github.com/repos/huggingface/datasets/issues/647/events | https://github.com/huggingface/datasets/issues/647 | 704,734,764 | MDU6SXNzdWU3MDQ3MzQ3NjQ= | 647 | Cannot download dataset_info.json | {
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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",
"I just checked the benchmarks of other PRs and some of them had 300s (!!) for filter. This needs some investigation.."... | 1,600,450,294,000 | 1,600,533,992,000 | 1,600,533,991,000 | MEMBER | null | Fix #615 | {
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] | 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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"Hi @joeddav thanks for adding this! (I did a long webarchive.org session to actually find that dataset a while ago).\r\n\r\nOne question: should we manually correct the labeling scheme to (at least) IOB1?\r\n\r\nThat means \"LOC\" will be converted to \"I-LOC\". IOB1 is not explict. mentioned in the paper, but it ... | 1,600,435,304,000 | 1,600,571,083,000 | 1,600,571,083,000 | CONTRIBUTOR | null | Adds the [Polyglot-NER dataset](https://sites.google.com/site/rmyeid/projects/polylgot-ner) with named entity tags for 40 languages. I include separate configs for each language as well as a `combined` config which lumps them all together. | {
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