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"my implementation of sharding per TPU core: https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/trainers/t5_trainer.py#L316 \r\nmy implementation of dataloader for this case https://github.com/google-research/ruse/blob/d4dd58a2d8efe0ffb1a9e9e77e3228d6824d3c3c/seq2seq/tasks... | 2020-12-02T08:45:15 | 2022-10-05T13:00:42 | 2022-10-05T13:00:42 | CONTRIBUTOR | null | null | null | null | Hi
I am dealing with large-scale datasets which I need to train distributedly, I used the shard function to divide the dataset across the cores, without any sampler, this does not work for distributed training and does not become any faster than 1 TPU core. 1) how I can make sure data is not loaded in memory 2) in case of distributed training with iterative datasets which measures needs to be taken? Is this all sharding the data only. I was wondering if there can be possibility for me to discuss this with someone with distributed training with iterative datasets using dataset library. thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/961 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/961/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/961/comments | https://api.github.com/repos/huggingface/datasets/issues/961/events | https://github.com/huggingface/datasets/issues/961 | 754,434,398 | MDU6SXNzdWU3NTQ0MzQzOTg= | 961 | sample multiple datasets | {
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"here I share my dataloader currently for multiple tasks: https://gist.github.com/rabeehkarimimahabadi/39f9444a4fb6f53dcc4fca5d73bf8195 \r\n\r\nI need to train my model distributedly with this dataloader, \"MultiTasksataloader\", currently this does not work in distributed fasion,\r\nto save on memory I tried to us... | 2020-12-01T14:20:02 | 2024-06-17T08:23:20 | 2023-07-20T14:08:57 | CONTRIBUTOR | null | null | null | null | Hi
I am dealing with multiple datasets, I need to have a dataloader over them with a condition that in each batch data samples are coming from one of the datasets. My main question is:
- I need to have a way to sample the datasets first with some weights, lets say 2x dataset1 1x dataset2, could you point me how I can do it
sub-questions:
- I want to concat sampled datasets and define one dataloader on it, then I need a way to make sure batches come from 1 dataset in each iteration, could you assist me how I can do?
- I use iterative-type of datasets, but I need a method of shuffling still since it brings accuracy performance issues if not doing it, thanks for the help. | {
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- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). | {
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"I tried to make it run once on the SparkRunner but it seems that this runner has some issues when it is run locally.\r\nFrom my experience the DirectRunner is fine though, even if it's clearly not memory efficient.\r\n\r\nIt would be awesome though to make it work locally on a SparkRunner !\r\nDid you manage to ma... | 2020-12-01T01:11:43 | 2024-03-15T16:05:14 | 2024-03-15T16:05:14 | NONE | null | null | null | null | Hi,
I'm wondering if https://huggingface.co/docs/datasets/beam_dataset.html has an non-GCP or non-Dataflow version example/tutorial? I tried to migrate it to run on DirectRunner and SparkRunner, however, there were way too many runtime errors that I had to fix during the process, and even so I wasn't able to get either runner correctly producing the desired output.
Thanks!
Shang | {
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https://api.github.com/repos/huggingface/datasets/issues/927 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/927/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/927/comments | https://api.github.com/repos/huggingface/datasets/issues/927/events | https://github.com/huggingface/datasets/issues/927 | 753,679,020 | MDU6SXNzdWU3NTM2NzkwMjA= | 927 | Hello | {
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"Also, I cannot first convert it to torch format, since huggingface seq2seq_trainer codes process the datasets afterwards during datacollector function to make it optimize for TPUs. ",
"sorry I misunderstood length of dataset with dataloader, closed. thanks "
] | 2020-11-30T12:23:39 | 2020-11-30T12:37:27 | 2020-11-30T12:37:26 | CONTRIBUTOR | null | null | null | null | Hi
I have a MRPC dataset which I convert it to seq2seq format, then this is of this format:
`Dataset(features: {'src_texts': Value(dtype='string', id=None), 'tgt_texts': Value(dtype='string', id=None)}, num_rows: 10)
`
I feed it to a dataloader:
```
dataloader = DataLoader(
train_dataset,
batch_size=self.args.train_batch_size,
sampler=train_sampler,
collate_fn=self.data_collator,
drop_last=self.args.dataloader_drop_last,
num_workers=self.args.dataloader_num_workers,
)
```
now if I type len(dataloader) this is 1, which is wrong, and this needs to be 10. could you assist me please? thanks
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"This is an interesting idea !\r\nDo you have ideas about how to approach the decoding and the normalization ?",
"@lhoestq\r\nI think we first need to save the transformation chain to a list in `self._fingerprint`. Then we can\r\n- decode all the current saved datasets to see if there is already one that is equiv... | 2020-11-30T03:50:46 | 2020-12-24T05:11:49 | null | NONE | null | null | null | null | Hi there. For now, we are using `xxhash` to hash the transformations to fingerprint and we will save a copy of the processed dataset to disk if there is a new hash value. However, there are some transformations that are idempotent or commutative to each other. I think that encoding the transformation chain as the fingerprint may help in those cases, for example, use `base64.urlsafe_b64encode`. In this way, before we want to save a new copy, we can decode the transformation chain and normalize it to prevent omit potential reuse. As the main targets of this project are the really large datasets that cannot be loaded entirely in memory, I believe it would save a lot of time if we can avoid some write.
If you have interest in this, I'd love to help :). | null | {
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"nvm, I'd made an assumption that the library gets installed with transformers. "
] | 2020-11-29T01:24:15 | 2020-11-29T14:33:09 | 2020-11-29T14:33:09 | NONE | null | null | null | null | Currently, running `from datasets import load_dataset` will throw a `ModuleNotFoundError: No module named 'datasets'` error.
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} | [] | closed | false | null | [] | [] | 2020-11-28T21:36:23 | 2020-11-29T10:11:51 | 2020-11-29T10:11:51 | NONE | null | null | null | null | ## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). | {
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"Thanks for reporting ! I'm looking into it."
] | 2020-11-27T12:18:53 | 2020-11-29T22:48:53 | 2020-11-29T22:48:53 | NONE | null | null | null | null | Hello,
I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to
`~/.cache`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
from pathlib import Path
validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data"))
```
## The output:
* The dataset is downloaded to my home directory's `.cache`
* A new empty directory named "`natural_questions` is created in the specified directory `.data`
* `tree data` in the shell outputs:
```
data
└── natural_questions
└── default
└── 0.0.2
3 directories, 0 files
```
The output:
```
Downloading: 8.61kB [00:00, 5.11MB/s]
Downloading: 13.6kB [00:00, 7.89MB/s]
Using custom data configuration default
Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8
3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531...
Downloading: 100%|██████████████████████████████████████████████████| 13.6k/13.6k [00:00<00:00, 1.51MB/s]
Downloading: 7%|███▎ | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s]
```
## Expected behaviour:
The dataset "Natural Questions" should be downloaded to the directory "./data"
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"Thanks for reporting !\r\ncc @srush for the empty feature list issue and the encoding issue\r\ncc @julien-c maybe we can update the url and just have a redirection from the old url to the new one ?",
"Ok, I redirected on our side to a new url. ⚠️ @srush: if you update the Streamlit config too to `/datasets/viewe... | 2020-11-27T09:14:34 | 2021-10-31T09:12:01 | 2021-10-31T09:12:01 | CONTRIBUTOR | null | null | null | null | I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:
- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user
```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
_marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
* (len(clabels[0]) - len(hidden_columns))
```
- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
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https://api.github.com/repos/huggingface/datasets/issues/888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/888/comments | https://api.github.com/repos/huggingface/datasets/issues/888/events | https://github.com/huggingface/datasets/issues/888 | 750,944,422 | MDU6SXNzdWU3NTA5NDQ0MjI= | 888 | Nested lists are zipped unexpectedly | {
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"Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https://huggingface.co/docs/datasets/features.html?highlight=features) for more details",
"Thanks.\r\nThis is a bit (very) confusing, but I guess if ... | 2020-11-25T16:07:46 | 2020-11-25T17:30:39 | 2020-11-25T17:30:39 | CONTRIBUTOR | null | null | null | null | I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
"middle": datasets.features.Sequence({
"bottom": datasets.Value("int32")
})
})
```
And I then create an example:
```python
yield 1, {
"top": [{
"middle": [
{"bottom": 1},
{"bottom": 2}
]
}]
}
```
I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```
and expect to be able to access `data[0]["top"][0]["middle"][0]`.
That is not the case. Here is `data[0]` as JSON:
```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```
Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/887 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/887/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/887/comments | https://api.github.com/repos/huggingface/datasets/issues/887/events | https://github.com/huggingface/datasets/issues/887 | 750,868,831 | MDU6SXNzdWU3NTA4Njg4MzE= | 887 | pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> | {
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] | open | false | null | [] | [
"Yes right now `ArrayXD` can only be used as a column feature type, not a subtype.\r\nWith the current Arrow limitations I don't think we'll be able to make it work as a subtype, however it should be possible to allow dimensions of dynamic sizes (`Array3D(shape=(None, 137, 2), dtype=\"float32\")` for example since ... | 2020-11-25T14:32:21 | 2021-09-09T17:03:40 | null | CONTRIBUTOR | null | null | null | null | I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic)
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _generate_examples(self):
""" Yields examples. """
yield 1, {
"pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
}
```
But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> | null | {
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https://api.github.com/repos/huggingface/datasets/issues/885 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/885/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/885/comments | https://api.github.com/repos/huggingface/datasets/issues/885/events | https://github.com/huggingface/datasets/issues/885 | 750,789,052 | MDU6SXNzdWU3NTA3ODkwNTI= | 885 | Very slow cold-start | {
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"id": 2067376369,
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] | closed | false | null | [] | [
"Good point!",
"Yes indeed. We can probably improve that by using lazy imports",
"#1690 added fast start-up of the library "
] | 2020-11-25T12:47:58 | 2021-01-13T11:31:25 | 2021-01-13T11:31:25 | CONTRIBUTOR | null | null | null | null | Hi,
I expect when importing `datasets` that nothing major happens in the background, and so the import should be insignificant.
When I load a metric, or a dataset, its fine that it takes time.
The following ranges from 3 to 9 seconds:
```
python -m timeit -n 1 -r 1 'from datasets import load_dataset'
```
edit:
sorry for the mis-tag, not sure how I added it. | {
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https://api.github.com/repos/huggingface/datasets/issues/883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/883/comments | https://api.github.com/repos/huggingface/datasets/issues/883/events | https://github.com/huggingface/datasets/issues/883 | 749,750,801 | MDU6SXNzdWU3NDk3NTA4MDE= | 883 | Downloading/caching only a part of a datasets' dataset. | {
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"default": true... | open | false | null | [] | [
"Not at the moment but we could likely support this feature.",
"?",
"I think it would be a very helpful feature, because sometimes one only wants to evaluate models on the dev set, and the whole training data may be many times bigger.\r\nThis makes the task impossible with limited memory resources."
] | 2020-11-24T14:25:18 | 2020-11-27T13:51:55 | null | NONE | null | null | null | null | Hi,
I want to use the validation data *only* (of natural question).
I don't want to have the whole dataset cached in my machine, just the dev set.
Is this possible? I can't find a way to do it in the docs.
Thank you,
Sapir | null | {
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https://api.github.com/repos/huggingface/datasets/issues/880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/880/comments | https://api.github.com/repos/huggingface/datasets/issues/880/events | https://github.com/huggingface/datasets/issues/880 | 748,949,606 | MDU6SXNzdWU3NDg5NDk2MDY= | 880 | Add SQA | {
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"I’ll take this one to test the workflow for the sprint next week cc @yjernite @lhoestq ",
"@thomwolf here's a slightly adapted version of the code from the [official Tapas repository](https://github.com/google-research/tapas/blob/master/tapas/utils/interaction_utils.py) that is used to turn the `answer_coordinat... | 2020-11-23T16:31:55 | 2020-12-23T13:58:24 | 2020-12-23T13:58:23 | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** SQA (Sequential Question Answering) by Microsoft.
- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.
- **Paper:** https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/
- **Data:** https://www.microsoft.com/en-us/download/details.aspx?id=54253
- **Motivation:** currently, the [Tapas](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https://github.com/huggingface/transformers/pull/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).
Note 1: this dataset actually consists of 2 types of files:
1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)
2) a folder of csv files, which contain the actual tabular data
Note 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.
Adding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https://github.com/ppasupat/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https://github.com/wenhuchen/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/879 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/879/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/879/comments | https://api.github.com/repos/huggingface/datasets/issues/879/events | https://github.com/huggingface/datasets/issues/879 | 748,848,847 | MDU6SXNzdWU3NDg4NDg4NDc= | 879 | boolq does not load | {
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"Hi ! It runs on my side without issues. I tried\r\n```python\r\nfrom datasets import load_dataset\r\nload_dataset(\"boolq\")\r\n```\r\n\r\nWhat version of datasets and tensorflow are your runnning ?\r\nAlso if you manage to get a minimal reproducible script (on google colab for example) that would be useful.",
"... | 2020-11-23T14:28:28 | 2022-10-05T12:23:32 | 2022-10-05T12:23:32 | CONTRIBUTOR | null | null | null | null | Hi
I am getting these errors trying to load boolq thanks
Traceback (most recent call last):
File "test.py", line 5, in <module>
data = AutoTask().get("boolq").get_dataset("train", n_obs=10)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 42, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 38, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 150, in download_custom
get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 472, in get_from_cache
f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
FileNotFoundError: Cannot find the requested files in the cached path at /idiap/home/rkarimi/.cache/huggingface/datasets/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.
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https://api.github.com/repos/huggingface/datasets/issues/878 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/878/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/878/comments | https://api.github.com/repos/huggingface/datasets/issues/878/events | https://github.com/huggingface/datasets/issues/878 | 748,621,981 | MDU6SXNzdWU3NDg2MjE5ODE= | 878 | Loading Data From S3 Path in Sagemaker | {
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"This would be a neat feature",
"> neat feature\r\n\r\nI dint get these clearly, can you please elaborate like how to work on these ",
"It could maybe work almost out of the box just by using `cached_path` in the text/csv/json scripts, no?",
"Thanks thomwolf and julien-c\r\n\r\nI'm still confusion on what you... | 2020-11-23T09:17:22 | 2020-12-23T09:53:08 | null | NONE | null | null | null | null | In Sagemaker Im tring to load the data set from S3 path as follows
`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
data_files = {}
data_files["train"] = train_path
data_files["validation"] = valid_path
data_files["test"] = test_path
extension = train_path.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
print(datasets)`
I getting an error of
`algo-1-7plil_1 | File "main.py", line 21, in <module>
algo-1-7plil_1 | datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1 | m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1 | return os.stat(filename).st_mtime
algo-1-7plil_1 | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`
But when im trying with pandas , it is able to load from S3
Does the datasets library support S3 path to load | {
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https://api.github.com/repos/huggingface/datasets/issues/877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/877/comments | https://api.github.com/repos/huggingface/datasets/issues/877/events | https://github.com/huggingface/datasets/issues/877 | 748,234,438 | MDU6SXNzdWU3NDgyMzQ0Mzg= | 877 | DataLoader(datasets) become more and more slowly within iterations | {
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"Hi ! Thanks for reporting.\r\nDo you have the same slowdown when you iterate through the raw dataset object as well ? (no dataloader)\r\nIt would be nice to know whether it comes from the dataloader or not",
"> Hi ! Thanks for reporting.\r\n> Do you have the same slowdown when you iterate through the raw dataset... | 2020-11-22T12:41:10 | 2024-11-22T03:02:53 | 2020-11-29T15:45:12 | NONE | null | null | null | null | Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!
```
dataset = load_from_disk(dataset_path) # around 21,000,000 lines
lineloader = tqdm(DataLoader(dataset, batch_size=1))
for idx, line in enumerate(lineloader):
# do some thing for each line
```
In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s.
And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s.
Could you please help me with this problem?
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https://api.github.com/repos/huggingface/datasets/issues/876 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/876/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/876/comments | https://api.github.com/repos/huggingface/datasets/issues/876/events | https://github.com/huggingface/datasets/issues/876 | 748,195,104 | MDU6SXNzdWU3NDgxOTUxMDQ= | 876 | imdb dataset cannot be loaded | {
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"It looks like there was an issue while building the imdb dataset.\r\nCould you provide more information about your OS and the version of python and `datasets` ?\r\n\r\nAlso could you try again with \r\n```python\r\ndataset = datasets.load_dataset(\"imdb\", split=\"train\", download_mode=\"force_redownload\")\r\n``... | 2020-11-22T08:24:43 | 2024-05-10T03:03:29 | 2020-12-24T17:38:47 | CONTRIBUTOR | null | null | null | null | Hi
I am trying to load the imdb train dataset
`dataset = datasets.load_dataset("imdb", split="train")`
getting following errors, thanks for your help
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
>>> dataset = datasets.load_dataset("imdb", split="train")
```
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https://api.github.com/repos/huggingface/datasets/issues/875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/875/comments | https://api.github.com/repos/huggingface/datasets/issues/875/events | https://github.com/huggingface/datasets/issues/875 | 748,194,311 | MDU6SXNzdWU3NDgxOTQzMTE= | 875 | bug in boolq dataset loading | {
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"I just opened a PR to fix this.\r\nThanks for reporting !"
] | 2020-11-22T08:18:34 | 2020-11-24T10:12:33 | 2020-11-24T10:12:33 | CONTRIBUTOR | null | null | null | null | Hi
I am trying to load boolq dataset:
```
import datasets
datasets.load_dataset("boolq")
```
I am getting the following errors, thanks for your help
```
>>> import datasets
2020-11-22 09:16:30.070332: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2020-11-22 09:16:30.070389: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
>>> datasets.load_dataset("boolq")
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets
Using custom data configuration default
Downloading and preparing dataset boolq/default (download: 8.36 MiB, generated: 7.47 MiB, post-processed: Unknown size, total: 15.83 MiB) to /idiap/temp/rkarimi/cache_home/datasets/boolq/default/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/874 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/874/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/874/comments | https://api.github.com/repos/huggingface/datasets/issues/874/events | https://github.com/huggingface/datasets/issues/874 | 748,193,140 | MDU6SXNzdWU3NDgxOTMxNDA= | 874 | trec dataset unavailable | {
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"This was fixed in #740 \r\nCould you try to update `datasets` and try again ?",
"This has been fixed in datasets 1.1.3"
] | 2020-11-22T08:09:36 | 2020-11-27T13:56:42 | 2020-11-27T13:56:42 | CONTRIBUTOR | null | null | null | null | Hi
when I try to load the trec dataset I am getting these errors, thanks for your help
`datasets.load_dataset("trec", split="train")
`
```
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/873/comments | https://api.github.com/repos/huggingface/datasets/issues/873/events | https://github.com/huggingface/datasets/issues/873 | 747,959,523 | MDU6SXNzdWU3NDc5NTk1MjM= | 873 | load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error | {
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} | [] | closed | false | null | [] | [
"I get the same error. It was fixed some days ago, but again it appears",
"Hi @mrm8488 it's working again today without any fix so I am closing this issue.",
"I see the issue happening again today - \r\n\r\n[nltk_data] Downloading package stopwords to /root/nltk_data...\r\n[nltk_data] Package stopwords is alr... | 2020-11-21T06:30:45 | 2023-08-03T12:07:03 | 2020-11-22T12:18:05 | NONE | null | null | null | null | ```
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
Stack trace:
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-6-2e06a8332652> in <module>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
608 download_config=download_config,
609 download_mode=download_mode,
--> 610 ignore_verifications=ignore_verifications,
611 )
612
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
513 if not downloaded_from_gcs:
514 self._download_and_prepare(
--> 515 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
516 )
517 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
568 split_dict = SplitDict(dataset_name=self.name)
569 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 570 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
571
572 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
I have ran the code on Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/871 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/871/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/871/comments | https://api.github.com/repos/huggingface/datasets/issues/871/events | https://github.com/huggingface/datasets/issues/871 | 747,470,136 | MDU6SXNzdWU3NDc0NzAxMzY= | 871 | terminate called after throwing an instance of 'google::protobuf::FatalException' | {
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"Loading the iwslt2017-en-nl config of iwslt2017 works fine on my side. \r\nMaybe you can open an issue on transformers as well ? And also add more details about your environment (OS, python version, version of transformers and datasets etc.)",
"closing now, figured out this is because the max length of decoder w... | 2020-11-20T12:56:24 | 2020-12-12T21:16:32 | 2020-12-12T21:16:32 | CONTRIBUTOR | null | null | null | null | Hi
I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks
100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
run_t5_base_eval.sh: line 19: 5795 Aborted | {
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https://api.github.com/repos/huggingface/datasets/issues/870 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/870/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/870/comments | https://api.github.com/repos/huggingface/datasets/issues/870/events | https://github.com/huggingface/datasets/issues/870 | 747,021,996 | MDU6SXNzdWU3NDcwMjE5OTY= | 870 | [Feature Request] Add optional parameter in text loading script to preserve linebreaks | {
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"Hi ! Thanks for your message.\r\nIndeed it's a free feature we can add and that can be useful.\r\nIf you want to contribute, feel free to open a PR to add it to the text dataset script :)",
"Resolved via #1913."
] | 2020-11-19T23:51:31 | 2022-06-01T15:25:53 | 2022-06-01T15:25:52 | NONE | null | null | null | null | I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data.
I recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great.
But the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines.
Once I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned.
But if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`. | {
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https://api.github.com/repos/huggingface/datasets/issues/866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/866/comments | https://api.github.com/repos/huggingface/datasets/issues/866/events | https://github.com/huggingface/datasets/issues/866 | 745,719,222 | MDU6SXNzdWU3NDU3MTkyMjI= | 866 | OSCAR from Inria group | {
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"PR is already open here : #348 \r\nThe only thing remaining is to compute the metadata of each subdataset (one per language + shuffled/unshuffled).\r\nAs soon as #863 is merged we can start computing them. This will take a bit of time though",
"Grand, thanks for this!"
] | 2020-11-18T14:40:54 | 2020-11-18T15:01:30 | 2020-11-18T15:01:30 | NONE | null | null | null | null | ## Adding a Dataset
- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https://oscar-corpus.com/).
- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*
- **Paper:** *[here](https://hal.inria.fr/hal-02148693)*
- **Data:** *[here](https://oscar-corpus.com/)*
- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*
I am aware that you do offer the "colossal" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.
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"I'm sorry, this was a problem with my environment.\r\nNow that I have identified the cause of environmental dependency, I would like to fix it and try it.\r\nExcuse me for making a noise."
] | 2020-11-18T08:04:41 | 2020-11-18T08:16:35 | 2020-11-18T08:16:35 | CONTRIBUTOR | null | null | null | null | I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.
I cloned the newest version of datasets (master branch), and do `pip install -e .`.
Then, `import datasets` causes the error below.
```
~/workspace/Clone/datasets/src/datasets/utils/file_utils.py in <module>
116 sys.path.append(str(HF_MODULES_CACHE))
117
--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)
119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, "__init__.py")):
120 with open(os.path.join(HF_MODULES_CACHE, "__init__.py"), "w"):
~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/os.py in makedirs(name, mode, exist_ok)
221 return
222 try:
--> 223 mkdir(name, mode)
224 except OSError:
225 # Cannot rely on checking for EEXIST, since the operating system
FileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>/.cache/huggingface/modules'
```
The error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.
(I use Python 3.8, so `exist_ok` is expected to work.)
I've checked some environment variables, and they are set as below.
```
*** NameError: name 'HF_MODULES_CACHE' is not defined
*** NameError: name 'hf_cache_home' is not defined
*** NameError: name 'XDG_CACHE_HOME' is not defined
```
Should I set some environment variables before using this library?
And, do you have any idea why "No such file or directory" occurs even though the `exist_ok = True` option is set?
Thank you in advance. | {
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"Same error here!\r\n",
"Same here! My kaggle notebook stopped working like yesterday. It's strange because I have fixed version of datasets==1.1.2",
"I'm looking at it right now",
"I couldn't reproduce unfortunately. I tried\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"cnn_dailymai... | 2020-11-18T04:38:02 | 2020-11-20T05:22:11 | 2020-11-20T05:22:10 | NONE | null | null | null | null | ### Script to reproduce the error
```
from datasets import load_dataset
train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
```
### Error
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-8-47c39c228935> in <module>()
1 from datasets import load_dataset
2
----> 3 train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
4 valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
609 download_config=download_config,
610 download_mode=download_mode,
--> 611 ignore_verifications=ignore_verifications,
612 )
613
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
469 if not downloaded_from_gcs:
470 self._download_and_prepare(
--> 471 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
472 )
473 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
524 split_dict = SplitDict(dataset_name=self.name)
525 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 526 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
527
528 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
Thanks for any suggestions. | {
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https://api.github.com/repos/huggingface/datasets/issues/861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/861/comments | https://api.github.com/repos/huggingface/datasets/issues/861/events | https://github.com/huggingface/datasets/issues/861 | 744,753,458 | MDU6SXNzdWU3NDQ3NTM0NTg= | 861 | Possible Bug: Small training/dataset file creates gigantic output | {
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"The preprocessing tokenizes the input text. Tokenization outputs `input_ids`, `attention_mask`, `token_type_ids` and `special_tokens_mask`. All those are of length`max_seq_length` because of padding. Therefore for each sample it generate 4 *`max_seq_length` integers. Currently they're all saved as int64. This is w... | 2020-11-17T13:48:59 | 2021-03-30T14:04:04 | 2021-03-22T12:04:55 | NONE | null | null | null | null | Hey guys,
I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.
I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?
I've used the following CMD:
`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`
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https://api.github.com/repos/huggingface/datasets/issues/860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/860/comments | https://api.github.com/repos/huggingface/datasets/issues/860/events | https://github.com/huggingface/datasets/issues/860 | 744,750,691 | MDU6SXNzdWU3NDQ3NTA2OTE= | 860 | wmt16 cs-en does not donwload | {
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"We know host this file, so downloading should be more robust."
] | 2020-11-17T13:45:35 | 2022-10-05T12:27:00 | 2022-10-05T12:26:59 | CONTRIBUTOR | null | null | null | null | Hi
I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "finetune_t5_trainer.py", line 109, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset
dataset = load_dataset("wmt16", self.pair, split=split)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz | {
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https://api.github.com/repos/huggingface/datasets/issues/854 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/854/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/854/comments | https://api.github.com/repos/huggingface/datasets/issues/854/events | https://github.com/huggingface/datasets/issues/854 | 743,675,376 | MDU6SXNzdWU3NDM2NzUzNzY= | 854 | wmt16 does not download | {
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"Hi,I also posted it to the forum, but this is a bug, perhaps it needs to be reported here? thanks ",
"It looks like the official OPUS server for WMT16 doesn't provide the data files anymore (503 error).\r\nI searched a bit and couldn't find a mirror except maybe http://nlp.ffzg.hr/resources/corpora/setimes/ (the... | 2020-11-16T09:31:51 | 2022-10-05T12:27:42 | 2022-10-05T12:27:42 | CONTRIBUTOR | null | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
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https://api.github.com/repos/huggingface/datasets/issues/853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/853/comments | https://api.github.com/repos/huggingface/datasets/issues/853/events | https://github.com/huggingface/datasets/issues/853 | 743,426,583 | MDU6SXNzdWU3NDM0MjY1ODM= | 853 | concatenate_datasets support axis=0 or 1 ？ | {
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"Unfortunately `concatenate_datasets` only supports concatenating the rows, while what you want to achieve is concatenate the columns.\r\nCurrently to add more columns to a dataset, one must use `map`.\r\nWhat you can do is somehting like this:\r\n```python\r\n# suppose you have datasets d1, d2, d3\r\ndef add_colum... | 2020-11-16T02:46:23 | 2021-04-19T16:07:18 | 2021-04-19T16:07:18 | NONE | null | null | null | null | I want to achieve the following result
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https://api.github.com/repos/huggingface/datasets/issues/852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/852/comments | https://api.github.com/repos/huggingface/datasets/issues/852/events | https://github.com/huggingface/datasets/issues/852 | 743,396,240 | MDU6SXNzdWU3NDMzOTYyNDA= | 852 | wmt cannot be downloaded | {
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] | closed | false | null | [] | [] | 2020-11-16T01:04:41 | 2020-11-16T09:31:58 | 2020-11-16T09:31:58 | CONTRIBUTOR | null | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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https://api.github.com/repos/huggingface/datasets/issues/849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/849/comments | https://api.github.com/repos/huggingface/datasets/issues/849/events | https://github.com/huggingface/datasets/issues/849 | 742,263,333 | MDU6SXNzdWU3NDIyNjMzMzM= | 849 | Load amazon dataset | {
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"Thanks for reporting !\r\nWe plan to show information about the different configs of the datasets on the website, with the corresponding `load_dataset` calls.\r\n\r\nAlso I think the bullet points formatting has been fixed"
] | 2020-11-13T08:34:24 | 2020-11-17T07:22:59 | 2020-11-17T07:22:59 | CONTRIBUTOR | null | null | null | null | Hi,
I was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset.
Eg. what API usage is on the [website](https://huggingface.co/datasets/amazon_us_reviews)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews")
```
How it is when I tried (the error generated does point me to the right direction though)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews", 'Books_v1_00')
```
Also, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it? | {
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https://api.github.com/repos/huggingface/datasets/issues/848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/848/comments | https://api.github.com/repos/huggingface/datasets/issues/848/events | https://github.com/huggingface/datasets/issues/848 | 742,240,942 | MDU6SXNzdWU3NDIyNDA5NDI= | 848 | Error when concatenate_datasets | {
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"As you can see in the error the test checks if `indices_mappings_in_memory` is True or not, which is different from the test you do in your script. In a dataset, both the data and the indices mapping can be either on disk or in memory.\r\n\r\nThe indices mapping correspond to a mapping on top of the data table tha... | 2020-11-13T07:56:02 | 2020-11-13T17:40:59 | 2020-11-13T15:55:10 | NONE | null | null | null | null | Hello, when I concatenate two dataset loading from disk, I encountered a problem:
```
test_dataset = load_from_disk('data/test_dataset')
trn_dataset = load_from_disk('data/train_dataset')
train_dataset = concatenate_datasets([trn_dataset, test_dataset])
```
And it reported ValueError blow:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-74fa525512ca> in <module>
----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])
/opt/miniconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py in concatenate_datasets(dsets, info, split)
2547 "However datasets' indices {} come from memory and datasets' indices {} come from disk.".format(
2548 [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],
-> 2549 [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],
2550 )
2551 )
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
But it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:
```
trn_dataset._data_files
# output
[{'filename': 'data/train_dataset/csv-train.arrow', 'skip': 0, 'take': 593264}]
test_dataset._data_files
# output
[{'filename': 'data/test_dataset/csv-test.arrow', 'skip': 0, 'take': 424383}]
print([not dset._data_files for dset in [trn_dataset, test_dataset]])
# [False, False]
# And I tested the code the same as arrow_dataset, but nothing happened
dsets = [trn_dataset, test_dataset]
dsets_in_memory = [not dset._data_files for dset in dsets]
if any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):
raise ValueError(
"Datasets should ALL come from memory, or should ALL come from disk.\n"
"However datasets {} come from memory and datasets {} come from disk.".format(
[i for i in range(len(dsets)) if dsets_in_memory[i]],
[i for i in range(len(dsets)) if not dsets_in_memory[i]],
)
)
```
Any suggestions would be greatly appreciated!
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/847/comments | https://api.github.com/repos/huggingface/datasets/issues/847/events | https://github.com/huggingface/datasets/issues/847 | 742,179,495 | MDU6SXNzdWU3NDIxNzk0OTU= | 847 | multiprocessing in dataset map "can only test a child process" | {
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"It looks like an issue with wandb/tqdm here.\r\nWe're using the `multiprocess` library instead of the `multiprocessing` builtin python package to support various types of mapping functions. Maybe there's some sort of incompatibility.\r\n\r\nCould you make a minimal script to reproduce or a google colab ?",
"It l... | 2020-11-13T06:01:04 | 2022-10-05T12:22:51 | 2022-10-05T12:22:51 | NONE | null | null | null | null | Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.
```
def tokenizer_fn(example):
return tokenizer.batch_encode_plus(example['text'])
ds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])
```
```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 156, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1510, in _map_single
for i in pbar:
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 228, in __iter__
for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1186, in __iter__
self.close()
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 251, in close
super(tqdm_notebook, self).close(*args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1291, in close
fp_write('')
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1288, in fp_write
self.fp.write(_unicode(s))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 91, in new_write
cb(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 598, in _console_callback
self._backend.interface.publish_output(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 146, in publish_output
self._publish_output(o)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 151, in _publish_output
self._publish(rec)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 431, in _publish
if self._process and not self._process.is_alive():
File "/usr/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
"""
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/846/comments | https://api.github.com/repos/huggingface/datasets/issues/846/events | https://github.com/huggingface/datasets/issues/846 | 741,885,174 | MDU6SXNzdWU3NDE4ODUxNzQ= | 846 | Add HoVer multi-hop fact verification dataset | {
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{
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"default": false,
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"Hi @yjernite I'm new but wanted to contribute. Has anyone already taken this problem and do you think it is suitable for newbies?",
"Hi @tenjjin! This dataset is still up for grabs! Here's the link with the guide to add it. You should play around with the library first (download and look at a few datasets), the... | 2020-11-12T19:55:46 | 2020-12-10T21:47:33 | 2020-12-10T21:47:33 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** HoVer
- **Description:** https://twitter.com/YichenJiang9/status/1326954363806429186 contains 20K claim verification examples
- **Paper:** https://arxiv.org/abs/2011.03088
- **Data:** https://hover-nlp.github.io/
- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/843 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/843/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/843/comments | https://api.github.com/repos/huggingface/datasets/issues/843/events | https://github.com/huggingface/datasets/issues/843 | 741,531,121 | MDU6SXNzdWU3NDE1MzExMjE= | 843 | use_custom_baseline still produces errors for bertscore | {
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{
"color": "25b21e",
"default": false,
"description": "A bug in a metric script",
"id": 2067393914,
"name": "metric bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/metric%20bug"
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] | closed | false | null | [] | [
"Thanks for reporting ! That's a bug indeed\r\nIf you want to contribute, feel free to fix this issue and open a PR :)",
"This error is because of a mismatch between `datasets` and `bert_score`. With `datasets=1.1.2` and `bert_score>=0.3.6` it works ok. So `pip install -U bert_score` should fix the problem. ",
... | 2020-11-12T11:44:32 | 2024-05-28T16:30:17 | 2021-02-09T14:21:48 | NONE | null | null | null | null | `metric = load_metric('bertscore')`
`a1 = "random sentences"`
`b1 = "random sentences"`
`metric.compute(predictions = [a1], references = [b1], lang = 'en')`
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/stephen_chan/.cache/huggingface/modules/datasets_modules/metrics/bertscore/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363/bertscore.py", line 108, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`
Adding 'use_custom_baseline = False' as an argument produces this error
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
TypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`
This is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/842 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/842/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/842/comments | https://api.github.com/repos/huggingface/datasets/issues/842/events | https://github.com/huggingface/datasets/issues/842 | 741,208,428 | MDU6SXNzdWU3NDEyMDg0Mjg= | 842 | How to enable `.map()` pre-processing pipelines to support multi-node parallelism? | {
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"Right now multiprocessing only runs on single node.\r\n\r\nHowever it's probably possible to extend it to support multi nodes. Indeed we're using the `multiprocess` library from the `pathos` project to do multiprocessing in `datasets`, and `pathos` is made to support parallelism on several nodes. More info about p... | 2020-11-12T02:04:38 | 2025-03-26T09:10:22 | null | NONE | null | null | null | null | Hi,
Currently, multiprocessing can be enabled for the `.map()` stages on a single node. However, in the case of multi-node training, (since more than one node would be available) I'm wondering if it's possible to extend the parallel processing among nodes, instead of only 1 node running the `.map()` while the other node is waiting for it to finish?
Thanks! | null | {
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"It seems the process needs '/datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py'\r\nWhere and how to assign this ```wikipedia.py``` after I manually download it ?",
"\r\ndownload the ```wikipedia.py``` at the working directory and go with ```dataset = load_dataset('wikipedia.py', '20200501.en')``` ... | 2020-11-11T12:42:15 | 2020-11-11T18:17:16 | 2020-11-11T18:17:16 | NONE | null | null | null | null | Hello,
I need to connect to a frontal node (with http proxy, no gpu) before connecting to a gpu node (but no http proxy, so can not use wget so on).
I successfully downloaded and reuse the wikipedia datasets in a frontal node.
When I connect to the gpu node, I supposed to use the downloaded datasets from cache, but failed and end with time out error.
On frontal node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Reusing dataset wikipedia (/linkhome/rech/genini01/uua34ms/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/f92599dfccab29832c442b82870fa8f6983e5b4ebbf5e6e2dcbe894e325339cd)
/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)
return torch._C._cuda_getDeviceCount() > 0
```
On gpu node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 160, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
TimeoutError: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 677, in urlopen
chunked=chunked,
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 172, in _new_conn
self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 727, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/retry.py", line 446, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 590, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 264, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return requests.head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 516, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
```
Any advice?Thanks!
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https://api.github.com/repos/huggingface/datasets/issues/839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/839/comments | https://api.github.com/repos/huggingface/datasets/issues/839/events | https://github.com/huggingface/datasets/issues/839 | 740,355,270 | MDU6SXNzdWU3NDAzNTUyNzA= | 839 | XSum dataset missing spaces between sentences | {
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} | [] | open | false | null | [] | [] | 2020-11-11T00:34:43 | 2020-11-11T00:34:43 | null | NONE | null | null | null | null | I noticed that the XSum dataset has no space between sentences. This could lead to worse results for anyone training or testing on it. Here's an example (0th entry in the test set):
`The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!"` | null | {
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"Which version of pyarrow do you have ? Could you try to update pyarrow and try again ?",
"Thanks for the fast response. I have the latest version '2.0.0' (I tried to update)\r\nI am working with Python 3.8.5",
"I think that the issue is similar to this one:https://issues.apache.org/jira/browse/ARROW-9612\r\nTh... | 2020-11-10T19:35:40 | 2021-11-24T16:59:19 | 2020-11-19T17:35:38 | NONE | null | null | null | null | Hi All
I am trying to load a custom dataset and I am trying to load a single file to make sure the file is loading correctly:
dataset = load_dataset('csv', data_files=files)
When I run it I get:
Downloading and preparing dataset csv/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache/huggingface/datasets/csv/default-35575a1051604c88/0.0.0/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...
I am getting this error:
6a4ac4/csv.py in _generate_tables(self, files)
78 def _generate_tables(self, files):
79 for i, file in enumerate(files):
---> 80 pa_table = pac.read_csv(
81 file,
82 read_options=self.config.pa_read_options,
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**
The size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.
There is no issue reading the file with pandas. any idea what could be the issue?
When I am running a different CSV I do not get this line:
(download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)
Any ideas?
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"Hi @bminixhofer ! Parsing WikiMedia is notoriously difficult: this processing used [mwparserfromhell](https://github.com/earwig/mwparserfromhell) which is pretty good but not perfect.\r\n\r\nAs an alternative, you can also use the Wiki40b dataset which was pre-processed using an un-released Google internal tool",
... | 2020-11-10T17:26:38 | 2020-11-10T18:23:20 | 2020-11-10T17:49:21 | NONE | null | null | null | null | Hi, thanks for this library!
Running this code:
```py
import datasets
wikipedia = datasets.load_dataset("wikipedia", "20200501.de")
print(wikipedia['train']['text'][0])
```
I get:
```
mini|Ricardo Flores Magón
mini|Mexikanische Revolutionäre, Magón in der Mitte anführend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des Gemälde „Tierra y Libertad“ von Idelfonso Carrara (?) von 1930.
Ricardo Flores Magón (* 16. September 1874 in San Antonio Eloxochitlán im mexikanischen Bundesstaat Oaxaca; † 22. November 1922 im Bundesgefängnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein führender anarchistischer Theoretiker und Aktivist, der die revolutionäre mexikanische Bewegung radikal beeinflusste. Magón war Gründer der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World.
Politische Biografie
Journalistisch und politisch kämpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung „Land und Freiheit“ (Tierra y Libertad) populär. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte großen Einfluss auf die Landarbeiter. 1904 floh er in die USA und gründete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in Gefängnissen und im Exil und wurde 1918 in den USA wegen „Behinderung der Kriegsanstrengungen“ zu zwanzig Jahren Gefängnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass Magón von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM veröffentlichte 1923 einen Beitrag, nachdem Magón von einem Gefängniswärter erschlagen wurde.
mini|Die Brüder Ricardo (links) und Enrique Flores Magón (rechts) vor dem Los Angeles County Jail, 1917
[...]
```
so some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup?
Apologies if this has been asked before. | {
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https://api.github.com/repos/huggingface/datasets/issues/834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/834/comments | https://api.github.com/repos/huggingface/datasets/issues/834/events | https://github.com/huggingface/datasets/issues/834 | 740,082,890 | MDU6SXNzdWU3NDAwODI4OTA= | 834 | [GEM] add WikiLingua cross-lingual abstractive summarization dataset | {
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"Hey @yjernite. This is a very interesting dataset. Would love to work on adding it but I see that the link to the data is to a gdrive folder. Can I just confirm wether dlmanager can handle gdrive urls or would this have to be a manual dl?",
"Hi @KMFODA ! A version of WikiLingua is actually already accessible in ... | 2020-11-10T17:00:43 | 2021-04-15T12:04:09 | 2021-04-15T12:01:38 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** WikiLingua
- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.
- **Paper:** https://arxiv.org/pdf/2010.03093.pdf
- **Data:** https://github.com/esdurmus/Wikilingua
- **Motivation:** Included in the GEM shared task. Multilingual.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/833/comments | https://api.github.com/repos/huggingface/datasets/issues/833/events | https://github.com/huggingface/datasets/issues/833 | 740,079,692 | MDU6SXNzdWU3NDAwNzk2OTI= | 833 | [GEM] add ASSET text simplification dataset | {
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- **Name:** ASSET
- **Description:** ASSET is a crowdsourced
multi-reference corpus for assessing sentence simplification in English where each simplification was produced by executing several rewriting transformations.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.424.pdf
- **Data:** https://github.com/facebookresearch/asset
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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] | closed | false | null | [] | [] | 2020-11-10T16:53:23 | 2020-12-03T13:38:08 | 2020-12-03T13:38:08 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** WikiAuto
- **Description:** Sentences in English Wikipedia and their corresponding sentences in Simple English Wikipedia that are written with simpler grammar and word choices. A lot of lexical and syntactic paraphrasing.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.709.pdf
- **Data:** https://github.com/chaojiang06/wiki-auto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/831/comments | https://api.github.com/repos/huggingface/datasets/issues/831/events | https://github.com/huggingface/datasets/issues/831 | 740,071,697 | MDU6SXNzdWU3NDAwNzE2OTc= | 831 | [GEM] Add WebNLG dataset | {
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- **Name:** WebNLG
- **Description:** WebNLG consists of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation of these triples (16,095 data inputs and 42,873 data-text pairs). The data is available in English and Russian
- **Paper:** https://www.aclweb.org/anthology/P17-1017.pdf
- **Data:** https://webnlg-challenge.loria.fr/download/
- **Motivation:** Included in the GEM shared task, multilingual
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/830/comments | https://api.github.com/repos/huggingface/datasets/issues/830/events | https://github.com/huggingface/datasets/issues/830 | 740,065,376 | MDU6SXNzdWU3NDAwNjUzNzY= | 830 | [GEM] add ToTTo Table-to-text dataset | {
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"closed via #1098 "
] | 2020-11-10T16:38:34 | 2020-12-10T13:06:02 | 2020-12-10T13:06:01 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** ToTTo
- **Description:** ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
- **Paper:** https://arxiv.org/abs/2004.14373
- **Data:** https://github.com/google-research-datasets/totto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/829/comments | https://api.github.com/repos/huggingface/datasets/issues/829/events | https://github.com/huggingface/datasets/issues/829 | 740,061,699 | MDU6SXNzdWU3NDAwNjE2OTk= | 829 | [GEM] add Schema-Guided Dialogue | {
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] | closed | false | null | [] | [] | 2020-11-10T16:33:44 | 2020-12-03T13:37:50 | 2020-12-03T13:37:50 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** The Schema-Guided Dialogue Dataset
- **Description:** The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather.
- **Paper:** https://arxiv.org/pdf/2002.01359.pdf https://arxiv.org/pdf/2004.15006.pdf
- **Data:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/827/comments | https://api.github.com/repos/huggingface/datasets/issues/827/events | https://github.com/huggingface/datasets/issues/827 | 739,983,024 | MDU6SXNzdWU3Mzk5ODMwMjQ= | 827 | [GEM] MultiWOZ dialogue dataset | {
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"Hi @yjernite can I help in adding this dataset? \r\n\r\nI am excited about this because this will be my first contribution to the datasets library as well as to hugginface.",
"Resolved via https://github.com/huggingface/datasets/pull/979"
] | 2020-11-10T14:57:50 | 2022-10-05T12:31:13 | 2022-10-05T12:31:13 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)
- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts – there are no annotations from the user side.
- **Paper:** https://arxiv.org/pdf/2007.12720.pdf
- **Data:** https://github.com/budzianowski/multiwoz
- **Motivation:** Will likely be part of the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/826/comments | https://api.github.com/repos/huggingface/datasets/issues/826/events | https://github.com/huggingface/datasets/issues/826 | 739,976,716 | MDU6SXNzdWU3Mzk5NzY3MTY= | 826 | [GEM] Add E2E dataset | {
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- **Name:** E2E NLG dataset (for End-to-end natural language generation)
- **Description:**a dataset for training end-to-end, datadriven natural language generation systems in the restaurant domain, the datasets consists of 5,751 dialogue-act Meaning Representations (structured data) and 8.1 reference free-text utterances per dialogue-act on average
- **Paper:** https://arxiv.org/pdf/1706.09254.pdf https://arxiv.org/abs/1901.07931
- **Data:** http://www.macs.hw.ac.uk/InteractionLab/E2E/#data
- **Motivation:** This dataset will likely be included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/824/comments | https://api.github.com/repos/huggingface/datasets/issues/824/events | https://github.com/huggingface/datasets/issues/824 | 739,896,526 | MDU6SXNzdWU3Mzk4OTY1MjY= | 824 | Discussion using datasets in offline mode | {
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"No comments ?",
"I think it would be very cool. I'm currently working on a cluster from Compute Canada, and I have internet access only when I'm not in the nodes where I run the scripts. So I was expecting to be able to use the wmt14 dataset until I realized I needed internet connection even if I downloaded the ... | 2020-11-10T13:10:51 | 2023-10-26T09:26:26 | 2022-02-15T10:32:36 | NONE | null | null | null | null | `datasets.load_dataset("csv", ...)` breaks if you have no connection (There is already this issue https://github.com/huggingface/datasets/issues/761 about it). It seems to be the same for metrics too.
I create this ticket to discuss a bit and gather what you have in mind or other propositions.
Here are some points to open discussion:
- if you want to prepare your code/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.
- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset("MY_PATH/csv.py", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.
- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least "freeze" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.
WDYT? (thks)
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https://api.github.com/repos/huggingface/datasets/issues/823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/823/comments | https://api.github.com/repos/huggingface/datasets/issues/823/events | https://github.com/huggingface/datasets/issues/823 | 739,815,763 | MDU6SXNzdWU3Mzk4MTU3NjM= | 823 | how processing in batch works in datasets | {
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"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
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"Hi I don’t think this is a request for a dataset like you labeled it.\r\n\r\nI also think this would be better suited for the forum at https://discuss.huggingface.co. we try to keep the issue for the repo for bug reports and new features/dataset requests and have usage questions discussed on the forum. Thanks.",
... | 2020-11-10T11:11:17 | 2020-11-10T13:11:10 | 2020-11-10T13:11:09 | NONE | null | null | null | null | Hi,
I need to process my datasets before it is passed to dataloader in batch,
here is my codes
```
class AbstractTask(ABC):
task_name: str = NotImplemented
preprocessor: Callable = NotImplemented
split_to_data_split: Mapping[str, str] = NotImplemented
tokenizer: Callable = NotImplemented
max_source_length: str = NotImplemented
max_target_length: str = NotImplemented
# TODO: should not be a task item, but cannot see other ways.
tpu_num_cores: int = None
# The arguments set are for all tasks and needs to be kept common.
def __init__(self, config):
self.max_source_length = config['max_source_length']
self.max_target_length = config['max_target_length']
self.tokenizer = config['tokenizer']
self.tpu_num_cores = config['tpu_num_cores']
def _encode(self, batch) -> Dict[str, torch.Tensor]:
batch_encoding = self.tokenizer.prepare_seq2seq_batch(
[x["src_texts"] for x in batch],
tgt_texts=[x["tgt_texts"] for x in batch],
max_length=self.max_source_length,
max_target_length=self.max_target_length,
padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack
return_tensors="pt"
)
return batch_encoding.data
def data_split(self, split):
return self.split_to_data_split[split]
def get_dataset(self, split, n_obs=None):
split = self.data_split(split)
if n_obs is not None:
split = split+"[:{}]".format(n_obs)
dataset = load_dataset(self.task_name, split=split)
dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
dataset.set_format(type="torch", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])
return dataset
```
I call it like
`AutoTask.get(task, train_dataset_config).get_dataset(split="train", n_obs=data_args.n_train)
`
This gives the following error, to me because the data inside the dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks
File "finetune_multitask_trainer.py", line 192, in main
if training_args.do_train else None
File "finetune_multitask_trainer.py", line 191, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in get_dataset
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1236, in map
update_data = does_function_return_dict(test_inputs, test_indices)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1207, in does_function_return_dict
function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in <lambda>
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in _encode
[x["src_texts"] for x in batch],
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in <listcomp>
[x["src_texts"] for x in batch],
TypeError: string indices must be integers
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https://api.github.com/repos/huggingface/datasets/issues/822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/822/comments | https://api.github.com/repos/huggingface/datasets/issues/822/events | https://github.com/huggingface/datasets/issues/822 | 739,579,314 | MDU6SXNzdWU3Mzk1NzkzMTQ= | 822 | datasets freezes | {
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"id": 2067388877,
"name": "dataset bug",
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"Pytorch is unable to convert strings to tensors unfortunately.\r\nYou can use `set_format(type=\"torch\")` on columns that can be converted to tensors, such as token ids.\r\n\r\nThis makes me think that we should probably raise an error or at least a warning when one tries to create pytorch tensors out of text col... | 2020-11-10T05:10:19 | 2023-07-20T16:08:14 | 2023-07-20T16:08:13 | NONE | null | null | null | null | Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks
dataset1 = load_dataset("squad", split="train[:10]")
dataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])
dataset2 = load_dataset("imdb", split="train[:10]")
dataset2 = dataset2.set_format(type="torch", columns=["text", "label"])
print(len(dataset1))
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https://api.github.com/repos/huggingface/datasets/issues/821 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/821/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/821/comments | https://api.github.com/repos/huggingface/datasets/issues/821/events | https://github.com/huggingface/datasets/issues/821 | 739,506,859 | MDU6SXNzdWU3Mzk1MDY4NTk= | 821 | `kor_nli` dataset doesn't being loaded properly | {
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} | [] | closed | false | null | [] | [] | 2020-11-10T02:04:12 | 2020-11-16T13:59:12 | 2020-11-16T13:59:12 | NONE | null | null | null | null | There are two issues from `kor_nli` dataset
1. csv.DictReader failed to split features by tab
- Should not exist `None` value in label feature, but there it is.
```python
kor_nli_train['train'].unique('gold_label')
# ['neutral', 'entailment', 'contradiction', None]
```
- I found a reason why there is `None` values in label feature as following code
```python
from datasets import load_dataset
kor_nli_train = load_dataset('kor_nli', 'multi_nli')
for idx, example in enumerate(kor_nli_train['train']):
if example['gold_label'] is None:
print(idx, example)
break
# 16835 {'gold_label': None, 'sentence1': '그는 전쟁 전에 가벼운 벅스킨 암말을 가지고 달리기 위해 우유처럼 하얀 스터드를 넣었다.\t전쟁 전에 다인종 여성들과 함께 있는 백인 남자가 있었다.\tentailment\n슬림은 재빨리 옷을 입었고, 순간적으로 미지근한 물을 뿌릴 수 있는 아침 세탁물을 기꺼이 가두었다.\t슬림은 직장에 늦었다.\tneutral\n뉴욕에서 그 식사를 해봤는데, 거기서 소고기의 멋진 소고기 부분을 요리하고 바베큐로 만든 널빤지 같은 걸 가져왔는데, 정말 대단해.\t그들이 거기서 요리하는 쇠고기는 역겹다. 거기서 절대 먹지 마라.\tcontradiction\n판매원의 죽음에서 브라이언 데네히... 크리스 켈리\t크리스 켈리는 세일즈맨의 죽음을 언급하지 않는다.\tcontradiction\n그러는 동안 요리사는 그냥 화가 났어.\t스튜가 끓는 동안 요리사는 화가 났다.\tneutral\n마지막 로마의 맹공격 전날 밤, 900명 이상의 유대인 수비수들이 로마인들에게 그들을 사로잡는 승리를 주기 보다는 대량 자살을 저질렀다.\t로마인들이 그들의 포획에 승리하도록 내버려두기 보다는 900명의 유대인 수비수들이 자살했다.\tentailment\n앞으로 발사하라.\t발사.\tneutral\n그리고 당신은 우리 땅이 에이커에 있다는 것을 알고 있다. 우리 사람들은 어떤 것이 얼마나 많은지 이해하지 못할 것이다.\t모든 사람들은 우리의 측정 시스템이 어떻게 작동하는지 알고 이해합니다.\tcontradiction\n주미게스\tJumiyges는 도시의 이름이다.\tneutral\n사람은 자기 민족을 돌봐야 한다...\t사람은 조국에 공감해야 한다.\tentailment\n또한 PDD 63은 정부와 업계가 컴퓨터 기반 공격에 대해 경고하고 방어할 준비를 더 잘할 수 있도록 시스템 취약성, 위협, 침입 및 이상에 대한 정보를 공유하는 메커니즘을 수립하는 것이 중요하다는 것을 인식했습니다.\t정보 전송 프로토콜을 만드는 것은 중요하다.\tentailment\n카페 링 피아자 델라 레퓌블리카 바로 남쪽에는 피렌체가 알려진 짚 제품 때문에 한때 스트로 마켓이라고 불렸던 16세기 로지아인 메르카토 누오보(Mercato Nuovo)가 있다.\t피아자 델라 레퓌블리카에는 카페가 많이 있다.\tentailment\n우리가 여기 있는 한 트린판이 뭘 주웠는지 살펴봐야겠어\t우리는 트린판이 무엇을 주웠는지 보는 데 시간을 낭비하지 않을 것이다.\tcontradiction\n그러나 켈트족의 문화적 기반을 가진 아일랜드 교회는 유럽의 신흥 기독교 세계와는 다르게 발전했고 결국 로마와 중앙집권적 행정으로 대체되었다.\t아일랜드 교회에는 켈트족의 기지가 있었다.\tentailment\n글쎄, 넌 선택의 여지가 없어\t글쎄, 너에겐 많은 선택권이 있어.\tcontradiction\n사실, 공식적인 보장은 없다.\t내가 산 물건에 대한 보증이 없었다.\tneutral\n덜 활기차긴 하지만, 안시와 르 부르젯의 사랑스러운 호수에서도 삶은 똑같이 상쾌하다.\t안시와 르 부르겟에서는 호수에서의 활동이 서두르고 바쁜 분위기를 연출한다.\tcontradiction\n그의 여행 소식이 이미 퍼졌다면 공격 소식도 퍼졌을 테지만 마을에서는 전혀 공황의 기미가 보이지 않았다.\t그는 왜 마을이 당황하지 않았는지 알 수 없었다.\tneutral\n과거에는 죽음의 위협이 토지의 판매를 막는 데 거의 도움이 되지 않았다.\t토지 판매는 어떠한 위협도 교환하지 않고 이루어진다.\tcontradiction\n어느 시점에 이르러 나는 지금 다가오는 새로운 것들과 나오는 많은 새로운 것들이 내가 늙어가고 있다고 말하는 시대로 접어들고 있다.\t나는 여전히 내가 보는 모든 새로운 것을 사랑한다.\tcontradiction\n뉴스위크는 물리학자들이 경기장 행사에서 고속도로의 자동차 교통과 보행자 교통을 개선하기 위해 새떼의 움직임을 연구하고 있다고 말한다.\t고속도로의 자동차 교통 흐름을 개선하는 것은 물리학자들이 새떼를 연구하는 이유 중 하나이다.\tentailment\n얼마나 다른가? 그는 잠시 말을 멈추었다가 말을 이었다.\t그는 그 소녀가 어디에 있는지 알고 싶었다.\tentailment\n글쎄, 그에게 너무 많은 것을 주지마.\t그는 훨씬 더 많은 것을 요구할 것이다.\tneutral\n아무리 그의 창작물이 완벽해 보인다고 해도, 그들을 믿는 것은 아마도 좋은 생각이 아닐 것이다.\'\t도자기를 잘 만든다고 해서 누군가를 믿는 것은 아마 좋지 않을 것이다.\tneutral\n버스틀링 그란 비아(Bustling Gran Via)는 호텔, 상점, 극장, 나이트클럽, 카페 등이 어우러져 산책과 창가를 볼 수 있다.\tGran Via는 호텔, 상점, 극장, 나이트클럽, 카페의 번화한 조합이다.\tentailment\n정부 인쇄소\t그 사무실은 워싱턴에 위치해 있다.\tneutral\n실제 문화 전쟁이 어디 있는지 알고 싶다면 학원을 잊어버리고 실리콘 밸리와 레드몬드를 생각해 보라.\t실제 문화 전쟁은 레드몬드에서 일어난다.\tentailment\n그리고 페니실린을 주지 않기 위해 침대 위에 올려놨어\t그녀의 방에는 페니실린이 없다는 징후가 전혀 없었다.\tcontradiction\nL.A.의 야외 시장을 활보하는 것은 맛있고 저렴한 그루브를 잡고, 끝이 없는 햇빛을 즐기고, 신선한 농산물, 꽃, 향, 그리고 가젯 갈로어를 구입하면서 현지인들과 어울릴 수 있는 훌륭한 방법이다.\tLA의 야외 시장을 돌아다니는 것은 시간 낭비다.\tcontradiction\n안나는 밖으로 나와 안도의 한숨을 내쉬었다. 단 한 번, 그리고 마리후아쉬 맛의 술로 끝내자는 결심이 뒤섞여 있었다.\t안나는 안심하고 마리후아쉬 맛의 술을 다 마시기로 결심했다.\tentailment\n5 월에 Vajpayee는 핵 실험의 성공적인 완료를 발표했는데, 인도인들은 주권의 표시로 선전했지만 이웃 국가와 서구와의 인도 관계를 복잡하게 만들 수 있습니다.\t인도는 성공적인 핵실험을 한 적이 없다.\tcontradiction\n플라노 원에서 보통 얼마나 많은 것을 가지고 있는가?\t저 사람들 중에 플라노 원에 가본 사람 있어?\tcontradiction\n그것의 전체적인 형태의 우아함은 운하 건너편에서 가장 잘 볼 수 있다. 왜냐하면, 로마에 있는 성 베드로처럼, 돔은 길쭉한 본당 뒤로 더 가까운 곳에 사라지기 때문이다.\t성 베드로의 길쭉한 본당은 돔을 가린다.\tentailment\n당신은 수틴이 살에 강박적인 기쁨을 가지고 누드를 그릴 것이라고 생각하겠지만, 아니오; 그는 그의 모든 경력에서 단 한 점만을 그렸고, 그것은 사소한 그림이다.\t그는 그것이 그를 불편하게 만들었기 때문에 하나만 그렸다.\tneutral\n이 인상적인 풍경은 원래 나포 레온이 루브르 박물관의 침실에서 볼 수 있도록 계획되었는데, 그 당시 궁전이었습니다.\t나폴레옹은 그의 모든 궁전에 있는 그의 침실에서 보는 경치에 많은 관심을 가졌다.\tneutral\n그는 우리에게 문 열쇠를 건네주고는 급히 떠났다.\t그는 긴장해서 우리에게 열쇠를 빨리 주었다.\tneutral\n위원회는 또한 최종 규칙을 OMB에 제출했다.\t위원회는 또한 이 규칙을 다른 그룹에 제출했지만 최종 규칙은 OMB가 평가하기 위한 것이 었습니다.\tneutral\n정원가게에 가보면 올리비아의 복제 화합물 같은 유쾌한 이름을 가진 제품들을 찾을 수 있을 겁니다.이 제품이 뿌리를 내리도록 돕기 위해 촬영의 절단된 끝에 덩크슛을 하는 호르몬의 혼합물이죠.\t정원 가꾸기 가게의 제품들은 종종 그들의 목적을 설명하기 위해 기술적으로나 과학적으로 파생된 이름(올리비아의 복제 화합물처럼)을 부여받는다.\tneutral\n스타는 스틸 자신이나 왜 그녀의 이야기를 바꾸었는지에 훨씬 더 관심이 있을 것이다.\t스틸의 이야기는 조금도 변하지 않았다.\tcontradiction\n남편과의 마지막 대결로 맥티어는 노라의 변신을 너무나 능숙하게 예고해 왔기 때문에, 그녀에게는 당황스러울 정도로 갑작스러운 것처럼 보이지만, 우리에게는 감정적으로 불가피해 보인다.\t노라의 변신은 분명하고 필연적이었다.\tcontradiction\n이집트 최남단 도시인 아스완은 오랜 역사를 통해 중요한 역할을 해왔다.\t아스완은 이집트 국경 바로 위에 위치해 있습니다.\tneutral\n그러나 훨씬 더 우아한 건축적 터치는 신성한 춤인 Bharatanatyam에서 수행된 108 가지 기본 포즈를 시바 패널에서 볼 수 있습니다.\t패널에 대한 시바의 묘사는 일반적인 모티브다.\tneutral\n호화롭게 심어진 계단식 정원은 이탈리아 형식의 가장 훌륭한 앙상블 중 하나입니다.\t아름다운 정원과 희귀한 꽃꽂이 모두 이탈리아의 형식적인 스타일을 보여준다.\tneutral\n음, 그랬으면 좋았을 텐데\t나는 그것을 다르게 할 기회를 몹시 갈망한다.\tentailment\n폐허가 된 성의 기슭에 자리잡고 있는 예쁜 중세 도시 케이서스버그는 노벨 평화상 수상자 알버트 슈바이처(1875년)의 출생지로 널리 알려져 있다.\t알버트 슈바이처는 둘 다 케이서스버그 마을에 있었다.\tentailment\n고감도는 문제가 있는 대부분의 환자들이 발견될 것을 보장한다.\t장비 민감도는 문제 탐지와 관련이 없습니다.\tcontradiction\n오늘은 확실히 반바지 같은 날이었어\t오늘 사무실에 있는 모든 사람들은 반바지를 입었다.\tneutral\n못생긴 턱시도를 입고.\t그것은 분홍색과 주황색입니다.\tneutral\n이주 노동 수용소 오 마이 갓 그들은 판지 상자에 산다.\t노동 수용소에는 판지 상자에 사는 이주 노동자들의 사진이 있다.\tneutral\n그래, 그가 전 세계를 여행한 후에 그런 거야\t그것은 사람들의 세계 여행을 따른다.\tentailment\n건너편에 크고 큰 참나무 몇 그루가 있다.\t우리는 여기 오크나 어떤 종류의 미국 나무도 없다.\tcontradiction\nFort-de-France에서 출발하는 자동차나 여객선으로, 당신은 안세 ? 바다 포도가 그늘을 제공하는 쾌적한 갈색 모래 해변과 피크닉 테이블, 어린이 미끄럼틀, 식당이 있는 안느에 도착할 수 있다.\t프랑스 요새에서 자동차나 페리를 타고 안세로 갈 수 있다.\tentailment\n그리고 그것은 앨라배마주가 예상했던 대로 예산에서 50만 달러를 삭감하지 않을 것이라는 것을 의미한다.\t앨라배마 주는 예산 삭감을 하지 않았다. 왜냐하면 그렇게 하는 것에 대한 초기 정당성이 정밀 조사에 맞서지 않았기 때문이다.\tneutral\n알았어 먼저 어 .. 어 .. 노인이나 가족을 요양원에 보내는 것에 대해 어떻게 생각하니?\t가족을 요양원에 보내서 사는 것에 대해 어떻게 생각하는지 알 필요가 없다.\tcontradiction\n나머지는 너에게 달렸어.\t나머지는 너에게 달렸지만 시간이 많지 않다.\tneutral\n음-흠, 3월에 햇볕에 타는 것에 대해 걱정하면 안 된다는 것을 알고 있는 3월이야.\t3월은 그렇게 덥지 않다.\tneutral\n그리고 어, 그런 작은 것들로 다시 시작해봐. 아직 훨씬 싸. 어, 그 특별한 모델 차는 150달러야.\t그 모형차는 4천 달러가 든다.\tcontradiction\n내일 돌아가야 한다면, 칼이 말했다.\t돌아갈 수 없어. 오늘은 안 돼. 내일은 안 돼. 절대 안 돼." 칼이 말했다.', 'sentence2': 'contradiction'}
```
2. (Optional) Preferred to change the name of the features for the compatibility with `run_glue.py` in 🤗 Transformers
- `kor_nli` dataset has same data structure of multi_nli, xnli
- Changing the name of features and the feature type of 'gold_label' to ClassLabel might be helpful
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
```
If you don't mind, I would like to fix this.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/817/comments | https://api.github.com/repos/huggingface/datasets/issues/817/events | https://github.com/huggingface/datasets/issues/817 | 739,145,369 | MDU6SXNzdWU3MzkxNDUzNjk= | 817 | Add MRQA dataset | {
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"Done! cf #1117 and #1022"
] | 2020-11-09T15:52:19 | 2020-12-04T15:44:42 | 2020-12-04T15:44:41 | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** MRQA
- **Description:** Collection of different (subsets of) QA datasets all converted to the same format to evaluate out-of-domain generalization (the datasets come from different domains, distributions, etc.). Some datasets are used for training and others are used for evaluation. This dataset was collected as part of MRQA 2019's shared task
- **Paper:** https://arxiv.org/abs/1910.09753
- **Data:** https://github.com/mrqa/MRQA-Shared-Task-2019
- **Motivation:** Out-of-domain generalization is becoming (has become) a de-factor evaluation for NLU systems
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). | {
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https://api.github.com/repos/huggingface/datasets/issues/816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/816/comments | https://api.github.com/repos/huggingface/datasets/issues/816/events | https://github.com/huggingface/datasets/issues/816 | 739,102,686 | MDU6SXNzdWU3MzkxMDI2ODY= | 816 | [Caching] Dill globalvars() output order is not deterministic and can cause cache issues. | {
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"To show the issue:\r\n```\r\npython -c \"from datasets.fingerprint import Hasher; a=[]; func = lambda : len(a); print(Hasher.hash(func))\"\r\n```\r\ndoesn't always return the same ouput since `globs` is a dictionary with \"a\" and \"len\" as keys but sometimes not in the same order"
] | 2020-11-09T15:01:20 | 2020-11-11T15:20:50 | 2020-11-11T15:20:50 | MEMBER | null | null | null | null | Dill uses `dill.detect.globalvars` to get the globals used by a function in a recursive dump. `globalvars` returns a dictionary of all the globals that a dumped function needs. However the order of the keys in this dict is not deterministic and can cause caching issues.
To fix that one could register an implementation of dill's `save_function` in the `datasets` pickler that sorts the globals keys before dumping a function. | {
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https://api.github.com/repos/huggingface/datasets/issues/815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/815/comments | https://api.github.com/repos/huggingface/datasets/issues/815/events | https://github.com/huggingface/datasets/issues/815 | 738,842,092 | MDU6SXNzdWU3Mzg4NDIwOTI= | 815 | Is dataset iterative or not? | {
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"Hello !\r\nCould you give more details ?\r\n\r\nIf you mean iter through one dataset then yes, `Dataset` object does implement the `__iter__` method so you can use \r\n```python\r\nfor example in dataset:\r\n # do something\r\n```\r\n\r\nIf you want to iter through several datasets you can first concatenate the... | 2020-11-09T09:11:48 | 2020-11-10T10:50:03 | 2020-11-10T10:50:03 | NONE | null | null | null | null | Hi
I want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?
could you provide me with example how I can use datasets as iterative datasets?
thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/814/comments | https://api.github.com/repos/huggingface/datasets/issues/814/events | https://github.com/huggingface/datasets/issues/814 | 738,500,443 | MDU6SXNzdWU3Mzg1MDA0NDM= | 814 | Joining multiple datasets | {
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"found a solution here https://discuss.pytorch.org/t/train-simultaneously-on-two-datasets/649/35, closed for now, thanks "
] | 2020-11-08T16:19:30 | 2020-11-08T19:38:48 | 2020-11-08T19:38:48 | NONE | null | null | null | null | Hi
I have multiple iterative datasets from your library with different size and I want to join them in a way that each datasets is sampled equally, so smaller datasets more, larger one less, could you tell me how to implement this in pytorch? thanks | {
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"Hi Apparently I need to shard the data and give one host a chunk, could you provide me please with examples on how to do it? I want to use it jointly with finetune_trainer.py in huggingface repo seq2seq examples. thanks. ",
"Hey @rabeehkarimimahabadi I'm actually looking for the same feature. Did you manage to g... | 2020-11-08T15:27:11 | 2022-10-05T12:54:23 | 2022-10-05T12:54:23 | NONE | null | null | null | null | Hi,
I am using your datasets to define my dataloaders, and I am training finetune_trainer.py in huggingface repo on them.
I need a distributedSampler to be able to train the models on TPUs being able to distribute the load across the TPU cores. Could you tell me how I can implement the distribued sampler when using datasets in which datasets are iterative? To give you more context, I have multiple of datasets and I need to write sampler for this case. thanks. | {
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https://api.github.com/repos/huggingface/datasets/issues/812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/812/comments | https://api.github.com/repos/huggingface/datasets/issues/812/events | https://github.com/huggingface/datasets/issues/812 | 738,340,217 | MDU6SXNzdWU3MzgzNDAyMTc= | 812 | Too much logging | {
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"Hi ! Thanks for reporting :) \r\nI agree these one should be hidden when the logging level is warning, we'll fix that",
"+1, the amount of logging is excessive.\r\n\r\nMost of it indeed comes from `filelock.py`, though there are occasionally messages from other sources too. Below is an example (all of these mess... | 2020-11-07T23:56:30 | 2021-01-26T14:31:34 | 2020-11-16T17:06:42 | NONE | null | null | null | null | I'm doing this in the beginning of my script:
from datasets.utils import logging as datasets_logging
datasets_logging.set_verbosity_warning()
but I'm still getting these logs:
[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock
[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock
using datasets version = 1.1.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/811/comments | https://api.github.com/repos/huggingface/datasets/issues/811/events | https://github.com/huggingface/datasets/issues/811 | 738,280,132 | MDU6SXNzdWU3MzgyODAxMzI= | 811 | nlp viewer error | {
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}
] | closed | false | null | [] | [
"and also for 'blog_authorship_corpus'\r\nhttps://huggingface.co/nlp/viewer/?dataset=blog_authorship_corpus\r\n\r\n",
"Is this the problem of my local computer or ??",
"Related to:\r\n- #673"
] | 2020-11-07T17:08:58 | 2022-02-15T10:51:44 | 2022-02-14T15:24:20 | NONE | null | null | null | null | Hello,
when I select amazon_us_reviews in nlp viewer, it shows error.
https://huggingface.co/nlp/viewer/?dataset=amazon_us_reviews
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https://api.github.com/repos/huggingface/datasets/issues/809 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/809/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/809/comments | https://api.github.com/repos/huggingface/datasets/issues/809/events | https://github.com/huggingface/datasets/issues/809 | 737,832,701 | MDU6SXNzdWU3Mzc4MzI3MDE= | 809 | Add Google Taskmaster dataset | {
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{
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
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"Hey @yjernite. Was going to start working on this but found taskmaster 1,2 & 3 in the datasets library already so think this can be closed now?",
"You are absolutely right :) \r\n\r\nClosed by https://github.com/huggingface/datasets/pull/1193 https://github.com/huggingface/datasets/pull/1197 https://github.com/h... | 2020-11-06T15:10:41 | 2021-04-20T13:09:26 | 2021-04-20T13:09:26 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** Taskmaster
- **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations)
- **Paper:** https://arxiv.org/abs/1909.05358
- **Data:** https://github.com/google-research-datasets/Taskmaster
- **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/807/comments | https://api.github.com/repos/huggingface/datasets/issues/807/events | https://github.com/huggingface/datasets/issues/807 | 737,509,954 | MDU6SXNzdWU3Mzc1MDk5NTQ= | 807 | load_dataset for LOCAL CSV files report CONNECTION ERROR | {
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"Hi !\r\nThe url works on my side.\r\n\r\nIs the url working in your navigator ?\r\nAre you connected to internet ? Does your network block access to `raw.githubusercontent.com` ?",
"> Hi !\r\n> The url works on my side.\r\n> \r\n> Is the url working in your navigator ?\r\n> Are you connected to internet ? Does y... | 2020-11-06T06:33:04 | 2021-01-11T01:30:27 | 2020-11-14T05:30:34 | NONE | null | null | null | null | ## load_dataset for LOCAL CSV files report CONNECTION ERROR
- **Description:**
A local demo csv file:
```
import pandas as pd
import numpy as np
from datasets import load_dataset
import torch
import transformers
df = pd.DataFrame(np.arange(1200).reshape(300,4))
df.to_csv('test.csv', header=False, index=False)
print('datasets version: ', datasets.__version__)
print('pytorch version: ', torch.__version__)
print('transformers version: ', transformers.__version__)
# output:
datasets version: 1.1.2
pytorch version: 1.5.0
transformers version: 3.2.0
```
when I load data through `dataset`:
```
dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
```
Error infos:
```
ConnectionError Traceback (most recent call last)
<ipython-input-17-bbdadb9a0c78> in <module>
----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
588 # Download/copy dataset processing script
589 module_path, hash = prepare_module(
--> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
591 )
592
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
270 if script_version is not None:
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
306 user_agent=download_config.user_agent,
307 local_files_only=download_config.local_files_only,
--> 308 use_etag=download_config.use_etag,
309 )
310 elif os.path.exists(url_or_filename):
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)
473 elif response is not None and response.status_code == 404:
474 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 475 raise ConnectionError("Couldn't reach {}".format(url))
476
477 # Try a second time
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py
```
And I try to connect to the site with requests:
```
import requests
requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
```
Similarly Error occurs:
```
---------------------------------------------------------------------------
ConnectionRefusedError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
159 conn = connection.create_connection(
--> 160 (self._dns_host, self.port), self.timeout, **extra_kw
161 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
NewConnectionError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
676 headers=headers,
--> 677 chunked=chunked,
678 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
380 try:
--> 381 self._validate_conn(conn)
382 except (SocketTimeout, BaseSSLError) as e:
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 976 conn.connect()
977
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self)
307 # Add certificate verification
--> 308 conn = self._new_conn()
309 hostname = self.host
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
171 raise NewConnectionError(
--> 172 self, "Failed to establish a new connection: %s" % e
173 )
NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 retries=self.max_retries,
--> 449 timeout=timeout
450 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
724 retries = retries.increment(
--> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
726 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
438 if new_retry.is_exhausted():
--> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause))
440
MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
During handling of the above exception, another exception occurred:
ConnectionError Traceback (most recent call last)
<ipython-input-20-18cc3eb4a049> in <module>
1 import requests
2
----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs)
102
103 kwargs.setdefault('allow_redirects', False)
--> 104 return request('head', url, **kwargs)
105
106
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
528 }
529 send_kwargs.update(settings)
--> 530 resp = self.send(prep, **send_kwargs)
531
532 return resp
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs)
641
642 # Send the request
--> 643 r = adapter.send(request, **kwargs)
644
645 # Total elapsed time of the request (approximately)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
514 raise SSLError(e, request=request)
515
--> 516 raise ConnectionError(e, request=request)
517
518 except ClosedPoolError as e:
ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/806/comments | https://api.github.com/repos/huggingface/datasets/issues/806/events | https://github.com/huggingface/datasets/issues/806 | 737,215,430 | MDU6SXNzdWU3MzcyMTU0MzA= | 806 | Quail dataset urls are out of date | {
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"Hi ! Thanks for reporting.\r\nWe should fix the urls and use quail 1.3.\r\nIf you want to contribute feel free to fix the urls and open a PR :) ",
"Done! PR [https://github.com/huggingface/datasets/pull/820](https://github.com/huggingface/datasets/pull/820)\r\n\r\nUpdated links and also regenerated the metadata ... | 2020-11-05T19:40:19 | 2020-11-10T14:02:51 | 2020-11-10T14:02:51 | CONTRIBUTOR | null | null | null | null | <h3>Code</h3>
```
from datasets import load_dataset
quail = load_dataset('quail')
```
<h3>Error</h3>
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml
```
As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. | {
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https://api.github.com/repos/huggingface/datasets/issues/805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/805/comments | https://api.github.com/repos/huggingface/datasets/issues/805/events | https://github.com/huggingface/datasets/issues/805 | 737,019,360 | MDU6SXNzdWU3MzcwMTkzNjA= | 805 | On loading a metric from datasets, I get the following error | {
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"Hi ! We support only pyarrow > 0.17.1 so that we have access to the `PyExtensionType` object.\r\nCould you update pyarrow and try again ?\r\n```\r\npip install --upgrade pyarrow\r\n```"
] | 2020-11-05T15:14:38 | 2022-02-14T15:32:59 | 2022-02-14T15:32:59 | NONE | null | null | null | null | `from datasets import load_metric`
`metric = load_metric('bleurt')`
Traceback:
210 class _ArrayXDExtensionType(pa.PyExtensionType):
211
212 ndims: int = None
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType'
Any help will be appreciated. Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/804/comments | https://api.github.com/repos/huggingface/datasets/issues/804/events | https://github.com/huggingface/datasets/issues/804 | 736,858,507 | MDU6SXNzdWU3MzY4NTg1MDc= | 804 | Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') | {
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"cc @yjernite is this expected ?",
"Yes: TriviaQA has a private test set for the leaderboard [here](https://competitions.codalab.org/competitions/17208)\r\n\r\nFor the KILT training and validation portions, you need to link the examples from the TriviaQA dataset as detailed here:\r\nhttps://github.com/huggingface... | 2020-11-05T11:38:01 | 2020-11-09T14:14:59 | 2020-11-09T14:14:58 | CONTRIBUTOR | null | null | null | null | # The issue
It's all in the title, it appears to be fine on the train and validation sets.
Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ?
# How to reproduce
```py
from datasets import load_dataset
kilt_tasks = load_dataset("kilt_tasks")
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
# both in "kilt_tasks"
In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])
Out[18]: False
# and "trivia_qa"
In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])
Out[13]: True
# appears to be fine on the train and validation sets.
In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])
Out[14]: False
In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])
Out[15]: False
In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])
Out[16]: True
In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])
Out[17]: True
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/801 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/801/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/801/comments | https://api.github.com/repos/huggingface/datasets/issues/801/events | https://github.com/huggingface/datasets/issues/801 | 735,790,876 | MDU6SXNzdWU3MzU3OTA4NzY= | 801 | How to join two datasets? | {
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"Hi this is also my question. thanks ",
"Hi ! Currently the only way to add new fields to a dataset is by using `.map` and picking items from the other dataset\r\n",
"Closing this one. Feel free to re-open if you have other questions about this issue.\r\n\r\nAlso linking another discussion about joining dataset... | 2020-11-04T03:53:11 | 2020-12-23T14:02:58 | 2020-12-23T14:02:58 | NONE | null | null | null | null | Hi,
I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels?
I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/798/comments | https://api.github.com/repos/huggingface/datasets/issues/798/events | https://github.com/huggingface/datasets/issues/798 | 735,518,805 | MDU6SXNzdWU3MzU1MTg4MDU= | 798 | Cannot load TREC dataset: ConnectionError | {
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{
"color": "2edb81",
"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
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"Hi ! Indeed there's an issue with those links.\r\nWe should probably use the target urls of the redirections instead",
"Hi, the same issue here, could you tell me how to download it through datasets? thanks ",
"Same issue. ",
"Actually it's already fixed on the master branch since #740 \r\nI'll do the 1.1.3 ... | 2020-11-03T17:45:22 | 2022-02-14T15:34:22 | 2022-02-14T15:34:22 | NONE | null | null | null | null | ## Problem
I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally.
* `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>.
* `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`
* Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address
* Increasing max_redirects to 100 doesn't help
Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.
* datasets.__version__ == '1.1.2'
* requests.__version__ == '2.24.0'
## Error trace
```
>>> import datasets
>>> datasets.__version__
'1.1.2'
>>> dataset = load_dataset("trec", split="train")
Using custom data configuration default
Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
```
I would appreciate some suggestions here. | {
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https://api.github.com/repos/huggingface/datasets/issues/797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/797/comments | https://api.github.com/repos/huggingface/datasets/issues/797/events | https://github.com/huggingface/datasets/issues/797 | 735,420,332 | MDU6SXNzdWU3MzU0MjAzMzI= | 797 | Token classification labels are strings and we don't have the list of labels | {
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"default": fals... | closed | false | null | [] | [
"Indeed. Pinging @stefan-it here if he want to give an expert opinion :)",
"Related is https://github.com/huggingface/datasets/pull/636",
"Should definitely be a ClassLabel 👍 ",
"Already done."
] | 2020-11-03T15:33:30 | 2022-02-14T15:41:54 | 2022-02-14T15:41:53 | CONTRIBUTOR | null | null | null | null | Not sure if this is an issue we want to fix or not, putting it here so it's not forgotten. Right now, in token classification datasets, the labels for NER, POS and the likes are typed as `Sequence` of `strings`, which is wrong in my opinion. These should be `Sequence` of `ClassLabel` or some types that gives easy access to the underlying labels.
The main problem for preprocessing those datasets is that the list of possible labels is not stored inside the `Dataset` object which makes converting the labels to IDs quite difficult (you either have to know the list of labels in advance or run a full pass through the dataset to get the list of labels, the `unique` method being useless with the type `Sequence[str]`). | {
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https://api.github.com/repos/huggingface/datasets/issues/795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/795/comments | https://api.github.com/repos/huggingface/datasets/issues/795/events | https://github.com/huggingface/datasets/issues/795 | 735,198,265 | MDU6SXNzdWU3MzUxOTgyNjU= | 795 | Descriptions of raw and processed versions of wikitext are inverted | {
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"default": false,
"description": "A bug in a dataset script provided in the library",
"id": 2067388877,
"name": "dataset bug",
"node_id": "MDU6TGFiZWwyMDY3Mzg4ODc3",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug"
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] | closed | false | null | [] | [
"Yes indeed ! Thanks for reporting",
"Fixed by:\r\n- #3241"
] | 2020-11-03T10:24:51 | 2022-02-14T15:46:21 | 2022-02-14T15:46:21 | NONE | null | null | null | null | Nothing of importance, but it looks like the descriptions of wikitext-n-v1 and wikitext-n-raw-v1 are inverted for both n=2 and n=103. I just verified by loading them and the `<unk>` tokens are present in the non-raw versions, which confirms that it's a mere inversion of the descriptions and not of the datasets themselves.
Also it would be nice if those descriptions appeared in the dataset explorer.
https://github.com/huggingface/datasets/blob/87bd0864845ea0a1dd7167918dc5f341bf807bd3/datasets/wikitext/wikitext.py#L52 | {
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https://api.github.com/repos/huggingface/datasets/issues/794 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/794/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/794/comments | https://api.github.com/repos/huggingface/datasets/issues/794/events | https://github.com/huggingface/datasets/issues/794 | 735,158,725 | MDU6SXNzdWU3MzUxNTg3MjU= | 794 | self.options cannot be converted to a Python object for pickling | {
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"Hi ! Thanks for reporting that's a bug on master indeed.\r\nWe'll fix that soon"
] | 2020-11-03T09:27:34 | 2020-11-19T17:35:38 | 2020-11-19T17:35:38 | NONE | null | null | null | null | Hi,
Currently I am trying to load csv file with customized read_options. And the latest master seems broken if we pass the ReadOptions object.
Here is a code snippet
```python
from datasets import load_dataset
from pyarrow.csv import ReadOptions
load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024))
```
error is `self.options cannot be converted to a Python object for pickling`
Would you mind to take a look? Thanks!
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-ab83fec2ded4> in <module>
----> 1 load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024))
/tmp/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
602 hash=hash,
603 features=features,
--> 604 **config_kwargs,
605 )
606
/tmp/datasets/src/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
162 name,
163 custom_features=features,
--> 164 **config_kwargs,
165 )
166
/tmp/datasets/src/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
281 )
282 else:
--> 283 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
284
285 if builder_config.data_files is not None:
/tmp/datasets/src/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
/tmp/datasets/src/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
/tmp/datasets/src/datasets/utils/py_utils.py in dumps(obj)
365 file = StringIO()
366 with _no_cache_fields(obj):
--> 367 dump(obj, file)
368 return file.getvalue()
369
/tmp/datasets/src/datasets/utils/py_utils.py in dump(obj, file)
337 def dump(obj, file):
338 """pickle an object to a file"""
--> 339 Pickler(file, recurse=True).dump(obj)
340 return
341
~/.local/lib/python3.6/site-packages/dill/_dill.py in dump(self, obj)
444 raise PicklingError(msg)
445 else:
--> 446 StockPickler.dump(self, obj)
447 stack.clear() # clear record of 'recursion-sensitive' pickled objects
448 return
/usr/lib/python3.6/pickle.py in dump(self, obj)
407 if self.proto >= 4:
408 self.framer.start_framing()
--> 409 self.save(obj)
410 self.write(STOP)
411 self.framer.end_framing()
/usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)
474 f = self.dispatch.get(t)
475 if f is not None:
--> 476 f(self, obj) # Call unbound method with explicit self
477 return
478
~/.local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/usr/lib/python3.6/pickle.py in save_dict(self, obj)
819
820 self.memoize(obj)
--> 821 self._batch_setitems(obj.items())
822
823 dispatch[dict] = save_dict
/usr/lib/python3.6/pickle.py in _batch_setitems(self, items)
850 k, v = tmp[0]
851 save(k)
--> 852 save(v)
853 write(SETITEM)
854 # else tmp is empty, and we're done
/usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)
494 reduce = getattr(obj, "__reduce_ex__", None)
495 if reduce is not None:
--> 496 rv = reduce(self.proto)
497 else:
498 reduce = getattr(obj, "__reduce__", None)
~/.local/lib/python3.6/site-packages/pyarrow/_csv.cpython-36m-x86_64-linux-gnu.so in pyarrow._csv.ReadOptions.__reduce_cython__()
TypeError: self.options cannot be converted to a Python object for pickling
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/792 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/792/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/792/comments | https://api.github.com/repos/huggingface/datasets/issues/792/events | https://github.com/huggingface/datasets/issues/792 | 734,693,652 | MDU6SXNzdWU3MzQ2OTM2NTI= | 792 | KILT dataset: empty string in triviaqa input field | {
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"Just found out about https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md\r\n(Not very clear in https://huggingface.co/datasets/kilt_tasks links to http://github.com/huggingface/datasets/datasets/kilt_tasks/README.md which is dead, closing the issue though :))"
] | 2020-11-02T17:33:54 | 2020-11-05T10:34:59 | 2020-11-05T10:34:59 | CONTRIBUTOR | null | null | null | null | # What happened
Both train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark)
# Versions
KILT version is `1.0.0`
`datasets` version is `1.1.2`
[more here](https://gist.github.com/PaulLerner/3768c8d25f723edbac20d99b6a4056c1)
# How to reproduce
```py
In [1]: from datasets import load_dataset
In [4]: dataset = load_dataset("kilt_tasks")
# everything works fine, removed output for a better readibility
Dataset kilt_tasks downloaded and prepared to /people/lerner/.cache/huggingface/datasets/kilt_tasks/all_tasks/1.0.0/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data.
# empty string in triviaqa input field
In [36]: dataset['train_triviaqa'][0]
Out[36]:
{'id': 'dpql_5197',
'input': '',
'meta': {'left_context': '',
'mention': '',
'obj_surface': {'text': []},
'partial_evidence': {'end_paragraph_id': [],
'meta': [],
'section': [],
'start_paragraph_id': [],
'title': [],
'wikipedia_id': []},
'right_context': '',
'sub_surface': {'text': []},
'subj_aliases': {'text': []},
'template_questions': {'text': []}},
'output': {'answer': ['five £', '5 £', '£5', 'five £'],
'meta': [],
'provenance': [{'bleu_score': [1.0],
'end_character': [248],
'end_paragraph_id': [30],
'meta': [],
'section': ['Section::::Question of legal tender.\n'],
'start_character': [246],
'start_paragraph_id': [30],
'title': ['Banknotes of the pound sterling'],
'wikipedia_id': ['270680']}]}}
In [35]: dataset['train_triviaqa']['input'][:10]
Out[35]: ['', '', '', '', '', '', '', '', '', '']
# same with test set
In [37]: dataset['test_triviaqa']['input'][:10]
Out[37]: ['', '', '', '', '', '', '', '', '', '']
# works fine with natural questions
In [34]: dataset['train_nq']['input'][:10]
Out[34]:
['how i.met your mother who is the mother',
'who had the most wins in the nfl',
'who played mantis guardians of the galaxy 2',
'what channel is the premier league on in france',
"god's not dead a light in the darkness release date",
'who is the current president of un general assembly',
'when do the eclipse supposed to take place',
'what is the name of the sea surrounding dubai',
'who holds the nba record for most points in a career',
'when did the new maze runner movie come out']
```
Stay safe :) | {
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https://api.github.com/repos/huggingface/datasets/issues/790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/790/comments | https://api.github.com/repos/huggingface/datasets/issues/790/events | https://github.com/huggingface/datasets/issues/790 | 734,470,197 | MDU6SXNzdWU3MzQ0NzAxOTc= | 790 | Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist | {
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"I saw that `faiss-cpu` 1.6.4.post2 was released recently to fix the installation on macos. It should work now",
"Closing this one.\r\nFeel free to re-open if you still have issues"
] | 2020-11-02T12:36:35 | 2020-11-10T14:05:02 | 2020-11-10T14:05:02 | NONE | null | null | null | null | I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error.
```sh
git clone https://github.com/huggingface/datasets
cd datasets
virtualenv venv -p python3 --system-site-packages
source venv/bin/activate
pip install -e ".[dev]"
```
Python 3.7.7
| {
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https://api.github.com/repos/huggingface/datasets/issues/788 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/788/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/788/comments | https://api.github.com/repos/huggingface/datasets/issues/788/events | https://github.com/huggingface/datasets/issues/788 | 734,136,124 | MDU6SXNzdWU3MzQxMzYxMjQ= | 788 | failed to reuse cache | {
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} | [] | closed | false | null | [] | [] | 2020-11-02T02:42:36 | 2020-11-02T12:26:15 | 2020-11-02T12:26:15 | NONE | null | null | null | null | I packed the `load_dataset ` in a function of class, and cached data in a directory. But when I import the class and use the function, the data still have to be downloaded again. The information (Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to ******) which logged to terminal shows the path is right to the cache directory, but the files still have to be downloaded again. | {
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https://api.github.com/repos/huggingface/datasets/issues/786 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/786/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/786/comments | https://api.github.com/repos/huggingface/datasets/issues/786/events | https://github.com/huggingface/datasets/issues/786 | 733,761,717 | MDU6SXNzdWU3MzM3NjE3MTc= | 786 | feat(dataset): multiprocessing _generate_examples | {
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"I agree that would be cool :)\r\nRight now the only distributed dataset builder is based on Apache Beam so you can use distributed processing frameworks like Dataflow, Spark, Flink etc. to build your dataset but it's not really well suited for single-worker parallel processing afaik",
"`_generate_examples` can n... | 2020-10-31T16:52:16 | 2023-01-16T10:59:13 | 2023-01-16T10:59:13 | CONTRIBUTOR | null | null | null | null | forking this out of #741, this issue is only regarding multiprocessing
I'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool.
In my use case, I would instead of:
```python
for datum in data:
yield self.load_datum(datum)
```
do:
```python
return pool.map(self.load_datum, data)
```
As the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset.
If this was a larger dataset (and many such datasets exist), it would take multiple days to complete.
Using multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour. | {
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https://api.github.com/repos/huggingface/datasets/issues/784 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/784/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/784/comments | https://api.github.com/repos/huggingface/datasets/issues/784/events | https://github.com/huggingface/datasets/issues/784 | 733,700,463 | MDU6SXNzdWU3MzM3MDA0NjM= | 784 | Issue with downloading Wikipedia data for low resource language | {
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"Hello, maybe you could ty to use another date for the wikipedia dump (see the available [dates](https://dumps.wikimedia.org/jvwiki) here for `jv`) ?",
"@lhoestq\r\n\r\nI've tried `load_dataset('wikipedia', '20200501.zh', beam_runner='DirectRunner')` and got the same `FileNotFoundError` as @SamuelCahyawijaya.\r\n... | 2020-10-31T11:40:00 | 2022-02-09T17:50:16 | 2020-11-25T15:42:13 | NONE | null | null | null | null | Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet
```
jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')
su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')
```
And I get the following error for these two languages:
Javanese
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json
```
Sundanese
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json
```
I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid.
Any suggestions on how to handle this issue? Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/778 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/778/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/778/comments | https://api.github.com/repos/huggingface/datasets/issues/778/events | https://github.com/huggingface/datasets/issues/778 | 732,449,652 | MDU6SXNzdWU3MzI0NDk2NTI= | 778 | Unexpected behavior when loading cached csv file? | {
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"Hi ! Thanks for reporting.\r\nThe same issue was reported in #730 (but with the encodings instead of the delimiter). It was fixed by #770 .\r\nThe fix will be available in the next release :)",
"Thanks for the prompt reply and terribly sorry for the spam! \r\nLooking forward to the new release! "
] | 2020-10-29T16:06:10 | 2020-10-29T21:21:27 | 2020-10-29T21:21:27 | CONTRIBUTOR | null | null | null | null | I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.
Small snippet to reproduce the behavior:
```python
import datasets
with open("dummy_data.csv", "w") as file:
file.write("test,this;text\n")
print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names)
# ["test", "this;text"]
print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names)
# still ["test", "this;text"]
```
By the way, thanks a lot for this amazing library! :) | {
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"cc @aconneau ;) ",
"These dataset files are no longer available. https://data.statmt.org/cc-100/ files provided in this link are no longer available. Can anybody fix that issue?\r\n@abhishekkrthakur @yjernite ",
"Hi ! Can you open an issue to report this problem ? This will help keep track of the fix :)",
"... | 2020-10-28T18:20:41 | 2022-01-26T13:22:54 | 2020-12-14T10:20:07 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** CC-100: Monolingual Datasets from Web Crawl Data
- **Description:** https://twitter.com/alex_conneau/status/1321507120848625665
- **Paper:** https://arxiv.org/abs/1911.02116
- **Data:** http://data.statmt.org/cc-100/
- **Motivation:** A large scale multi-lingual language modeling dataset. Text is de-duplicated and filtered by how "Wikipedia-like" it is, hopefully helping avoid some of the worst parts of the common crawl.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/771 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/771/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/771/comments | https://api.github.com/repos/huggingface/datasets/issues/771/events | https://github.com/huggingface/datasets/issues/771 | 731,482,213 | MDU6SXNzdWU3MzE0ODIyMTM= | 771 | Using `Dataset.map` with `n_proc>1` print multiple progress bars | {
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"Yes it allows to monitor the speed of each process. Currently each process takes care of one shard of the dataset.\r\n\r\nAt one point we can consider using streaming batches to a pool of processes instead of sharding the dataset in `num_proc` parts. At that point it will be easy to use only one progress bar",
"... | 2020-10-28T14:13:27 | 2023-02-13T20:16:39 | 2023-02-13T20:16:39 | CONTRIBUTOR | null | null | null | null | When using `Dataset.map` with `n_proc > 1`, only one of the processes should print a progress bar (to make the output readable). Right now, `n_proc` progress bars are printed. | {
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https://api.github.com/repos/huggingface/datasets/issues/769 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/769/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/769/comments | https://api.github.com/repos/huggingface/datasets/issues/769/events | https://github.com/huggingface/datasets/issues/769 | 731,257,104 | MDU6SXNzdWU3MzEyNTcxMDQ= | 769 | How to choose proper download_mode in function load_dataset? | {
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"`download_mode=datasets.GenerateMode.FORCE_REDOWNLOAD` should work.\r\nThis makes me think we we should rename this to DownloadMode.FORCE_REDOWNLOAD. Currently that's confusing",
"Can we just use `features=...` in `load_dataset` for this @lhoestq?",
"Indeed you should use `features` in this case. \r\n```python... | 2020-10-28T09:16:19 | 2022-02-22T12:22:52 | 2022-02-22T12:22:52 | NONE | null | null | null | null | Hi, I am a beginner to datasets and I try to use datasets to load my csv file.
my csv file looks like this
```
text,label
"Effective but too-tepid biopic",3
"If you sometimes like to go to the movies to have fun , Wasabi is a good place to start .",4
"Emerges as something rare , an issue movie that 's so honest and keenly observed that it does n't feel like one .",5
```
First I try to use this command to load my csv file .
``` python
dataset=load_dataset('csv', data_files=['sst_test.csv'])
```
It seems good, but when i try to overwrite the convert_options to convert 'label' columns from int64 to float32 like this.
``` python
import pyarrow as pa
from pyarrow import csv
read_options = csv.ReadOptions(block_size=1024*1024)
parse_options = csv.ParseOptions()
convert_options = csv.ConvertOptions(column_types={'text': pa.string(), 'label': pa.float32()})
dataset = load_dataset('csv', data_files=['sst_test.csv'], read_options=read_options,
parse_options=parse_options, convert_options=convert_options)
```
It keeps the same:
```shell
Dataset(features: {'text': Value(dtype='string', id=None), 'label': Value(dtype='int64', id=None)}, num_rows: 2210)
```
I think this issue is caused by the parameter "download_mode" Default to REUSE_DATASET_IF_EXISTS because after I delete the cache_dir, it seems right.
Is it a bug? How to choose proper download_mode to avoid this issue?
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"This is cool! I think some aspects to think about and decide in terms of API are:\r\n- do we allow several methods (chained i guess)\r\n- how do we inspect the currently set method(s)\r\n- how do we control/reset them"
] | 2020-10-27T22:33:03 | 2020-10-28T08:58:13 | null | CONTRIBUTOR | null | null | null | null | The library is great, but it would be even more awesome with a `lazy_map` method implemented on `Dataset` and `DatasetDict`. This would apply a function on a give item but when the item is requested. Two use cases:
1. load image on the fly
2. apply a random function and get different outputs at each epoch (like data augmentation or randomly masking a part of a sentence for BERT-like objectives). | null | {
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"Yes definitely we should give more flexibility to control the name of the splits outputted by `train_test_split`.\r\n\r\nRelated is the very interesting feedback from @bramvanroy on how we should improve this method: https://discuss.huggingface.co/t/how-to-split-main-dataset-into-train-dev-test-as-datasetdict/1090... | 2020-10-27T19:59:44 | 2020-11-10T14:05:21 | null | CONTRIBUTOR | null | null | null | null | ### Feature Request 🚀
Can we add a way to name your splits when using the `.train_test_split` function?
In almost every use case I've come across, I have a `train` and a `test` split in my `DatasetDict`, and I want to create a `validation` split. Therefore, its kinda useless to get a `test` split back from `train_test_split`, as it'll just overwrite my real `test` split that I intended to keep.
### Workaround
this is my hack for dealin with this, for now :slightly_smiling_face:
```python
from datasets import load_dataset
ds = load_dataset('imdb')
ds['train'], ds['validation'] = ds['train'].train_test_split(.1).values()
```
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"Is this a duplicate of #924 ?",
"Yup, closing! Haven't been keeping track of the solved issues during the sprint."
] | 2020-10-27T17:34:04 | 2020-12-03T13:37:18 | 2020-12-03T13:37:18 | MEMBER | null | null | null | null | ## Adding a Dataset
- **Name:** DART
- **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
- **Paper:** https://arxiv.org/abs/2007.02871v1
- **Data:** https://github.com/Yale-LILY/dart
- **Motivation:** the dataset will likely be included in the GEM benchmark
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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- **Name:** DART
- **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
- **Paper:** https://arxiv.org/abs/2007.02871v1
- **Data:** https://github.com/Yale-LILY/dart
- **Motivation:** It will likely be included in the GEM generation evaluation benchmark
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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- Data: https://github.com/UFAL-DSG/cs_restaurant_dataset
- The dataset will likely be part of the GEM benchmark | {
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"Yes currently you need an internet connection because the lib tries to check for the etag of the dataset script online to see if you don't have it locally already.\r\n\r\nIf we add a way to store the etag/hash locally after the first download, it would allow users to first download the dataset with an internet con... | 2020-10-26T20:54:46 | 2022-02-15T10:32:28 | 2022-02-15T10:32:28 | CONTRIBUTOR | null | null | null | null | I've been trying to use the IMDB dataset offline, but after downloading it and turning off the internet it still raises an error from the ```requests``` library trying to reach for the online dataset.
Is this the intended behavior ?
(Sorry, I wrote the the first version of this issue while still on nlp 0.3.0). | {
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"following_url": "https://api.github.com/users/TevenLeScao/following{/other_user}"... | [] | 2020-10-26T14:56:53 | 2020-12-03T13:38:34 | 2020-12-03T13:38:34 | MEMBER | null | null | null | null | The current version of the [HANS dataset](https://github.com/huggingface/datasets/blob/master/datasets/hans/hans.py) is missing the additional information provided for each example, including the sentence parses, heuristic and subcase. | {
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https://api.github.com/repos/huggingface/datasets/issues/759 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/759/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/759/comments | https://api.github.com/repos/huggingface/datasets/issues/759/events | https://github.com/huggingface/datasets/issues/759 | 729,046,916 | MDU6SXNzdWU3MjkwNDY5MTY= | 759 | (Load dataset failure) ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py | {
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"Are you running the script on a machine with an internet connection ?",
"Yes , I can browse the url through Google Chrome.",
"Does this HEAD request return 200 on your machine ?\r\n```python\r\nimport requests ... | 2020-10-25T15:34:57 | 2023-09-13T23:56:51 | 2021-08-04T18:10:09 | NONE | null | null | null | null | Hey, I want to load the cnn-dailymail dataset for fine-tune.
I write the code like this
from datasets import load_dataset
test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“train”)
And I got the following errors.
Traceback (most recent call last):
File “test.py”, line 7, in
test_dataset = load_dataset(“cnn_dailymail”, “3.0.0”, split=“test”)
File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 589, in load_dataset
module_path, hash = prepare_module(
File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\load.py”, line 268, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 300, in cached_path
output_path = get_from_cache(
File “C:\Users\666666\AppData\Local\Programs\Python\Python38\lib\site-packages\datasets\utils\file_utils.py”, line 475, in get_from_cache
raise ConnectionError(“Couldn’t reach {}”.format(url))
ConnectionError: Couldn’t reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/cnn_dailymail/cnn_dailymail.py
How can I fix this ? | {
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https://api.github.com/repos/huggingface/datasets/issues/758 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/758/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/758/comments | https://api.github.com/repos/huggingface/datasets/issues/758/events | https://github.com/huggingface/datasets/issues/758 | 728,638,559 | MDU6SXNzdWU3Mjg2Mzg1NTk= | 758 | Process 0 very slow when using num_procs with map to tokenizer | {
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"Hi ! Thanks for reporting.\r\nIs the distribution of text length of your data evenly distributed across your dataset ? I mean, could it be because the examples in the first part of your dataset are slower to process ?\r\nAlso could how many CPUs can you use for multiprocessing ?\r\n```python\r\nimport multiprocess... | 2020-10-24T02:40:20 | 2020-10-28T03:59:46 | 2020-10-28T03:59:45 | NONE | null | null | null | null | <img width="721" alt="image" src="https://user-images.githubusercontent.com/17930170/97066109-776d0d00-15ed-11eb-8bba-bb4d2e0fcc33.png">
The code I am using is
```
dataset = load_dataset("text", data_files=[file_path], split='train')
dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True,
truncation=True, max_length=args.block_size), num_proc=8)
dataset.set_format(type='torch', columns=['input_ids'])
dataset.save_to_disk(file_path+'.arrow')
```
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https://api.github.com/repos/huggingface/datasets/issues/757 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/757/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/757/comments | https://api.github.com/repos/huggingface/datasets/issues/757/events | https://github.com/huggingface/datasets/issues/757 | 728,241,494 | MDU6SXNzdWU3MjgyNDE0OTQ= | 757 | CUDA out of memory | {
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"Could you provide more details ? What's the code you ran ?",
"```python\r\ntokenizer = FunnelTokenizer.from_pretrained('funnel-transformer/small')\r\n\r\ndef tokenize(batch):\r\n return tokenizer(batch['text'], padding='max_length', truncation=True,max_length=512)\r\n\r\ndataset = load_dataset(\"bookcorpus\",... | 2020-10-23T13:57:00 | 2020-12-23T14:06:29 | 2020-12-23T14:06:29 | NONE | null | null | null | null | In your dataset ,cuda run out of memory as long as the trainer begins:
however, without changing any other element/parameter,just switch dataset to `LineByLineTextDataset`,everything becames OK.
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https://api.github.com/repos/huggingface/datasets/issues/752 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/752/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/752/comments | https://api.github.com/repos/huggingface/datasets/issues/752/events | https://github.com/huggingface/datasets/issues/752 | 726,917,801 | MDU6SXNzdWU3MjY5MTc4MDE= | 752 | Clicking on a metric in the search page points to datasets page giving "Missing dataset" warning | {
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"Thanks for the report, can reproduce. Will fix",
"Fixed now @ogabrielluiz "
] | 2020-10-21T22:56:23 | 2020-10-22T16:19:42 | 2020-10-22T16:19:42 | NONE | null | null | null | null | Hi! Sorry if this isn't the right place to talk about the website, I just didn't exactly where to write this.
Searching a metric in https://huggingface.co/metrics gives the right results but clicking on a metric (E.g ROUGE) points to https://huggingface.co/datasets/rouge. Clicking on a metric without searching points to the right page.
Thanks for all the great work! | {
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https://api.github.com/repos/huggingface/datasets/issues/751 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/751/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/751/comments | https://api.github.com/repos/huggingface/datasets/issues/751/events | https://github.com/huggingface/datasets/issues/751 | 726,820,191 | MDU6SXNzdWU3MjY4MjAxOTE= | 751 | Error loading ms_marco v2.1 using load_dataset() | {
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"There was a similar issue in #294 \r\nClearing the cache and download again the dataset did the job. Could you try to clear your cache and download the dataset again ?",
"I was able to load the dataset successfully, I'm pretty sure it's just a cache issue that you have.\r\nLet me know if clearing your cache fixe... | 2020-10-21T19:54:43 | 2020-11-05T01:31:57 | 2020-11-05T01:31:57 | NONE | null | null | null | null | Code:
`dataset = load_dataset('ms_marco', 'v2.1')`
Error:
```
`---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
<ipython-input-16-34378c057212> in <module>()
9
10 # Downloading and loading a dataset
---> 11 dataset = load_dataset('ms_marco', 'v2.1')
10 frames
/usr/lib/python3.6/json/decoder.py in raw_decode(self, s, idx)
353 """
354 try:
--> 355 obj, end = self.scan_once(s, idx)
356 except StopIteration as err:
357 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Unterminated string starting at: line 1 column 388988661 (char 388988660)
`
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/750 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/750/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/750/comments | https://api.github.com/repos/huggingface/datasets/issues/750/events | https://github.com/huggingface/datasets/issues/750 | 726,589,446 | MDU6SXNzdWU3MjY1ODk0NDY= | 750 | load_dataset doesn't include `features` in its hash | {
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Example: some models on the hub have a different ordering for the labels than what `datasets` uses for MNLI so I'd like to do something along the lines of:
```
dataset = load_dataset("glue", "mnli")
features = dataset["train"].features
features["label"] = ClassLabel(names = ['entailment', 'contradiction', 'neutral']) # new label order
dataset = load_dataset("glue", "mnli", features=features)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/749 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/749/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/749/comments | https://api.github.com/repos/huggingface/datasets/issues/749/events | https://github.com/huggingface/datasets/issues/749 | 726,366,062 | MDU6SXNzdWU3MjYzNjYwNjI= | 749 | [XGLUE] Adding new dataset | {
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"Amazing! ",
"Small poll @thomwolf @yjernite @lhoestq @JetRunner @qiweizhen .\r\n\r\nAs stated in the XGLUE paper: https://arxiv.org/pdf/2004.01401.pdf , for each of the 11 down-stream tasks training data is only available in English, whereas development and test data is available in multiple different language ... | 2020-10-21T10:51:36 | 2022-09-30T11:35:30 | 2021-01-06T10:02:55 | CONTRIBUTOR | null | null | null | null | XGLUE is a multilingual GLUE like dataset propesed in this [paper](https://arxiv.org/pdf/2004.01401.pdf).
I'm planning on adding the dataset to the library myself in a couple of weeks.
Also tagging @JetRunner @qiweizhen in case I need some guidance | {
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https://api.github.com/repos/huggingface/datasets/issues/744 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/744/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/744/comments | https://api.github.com/repos/huggingface/datasets/issues/744/events | https://github.com/huggingface/datasets/issues/744 | 724,918,448 | MDU6SXNzdWU3MjQ5MTg0NDg= | 744 | Dataset Explorer Doesn't Work for squad_es and squad_it | {
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"Oups wrong click.\r\nThis one is for you @srush"
] | 2020-10-19T19:34:12 | 2020-10-26T16:36:17 | 2020-10-26T16:36:17 | NONE | null | null | null | null | https://huggingface.co/nlp/viewer/?dataset=squad_es
https://huggingface.co/nlp/viewer/?dataset=squad_it
Both pages show "OSError: [Errno 28] No space left on device". | {
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https://api.github.com/repos/huggingface/datasets/issues/743 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/743/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/743/comments | https://api.github.com/repos/huggingface/datasets/issues/743/events | https://github.com/huggingface/datasets/issues/743 | 724,703,980 | MDU6SXNzdWU3MjQ3MDM5ODA= | 743 | load_dataset for CSV files not working | {
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"Thank you !\r\nCould you provide a csv file that reproduces the error ?\r\nIt doesn't have to be one of your dataset. As long as it reproduces the error\r\nThat would help a lot !",
"I think another good example is the following:\r\n`\r\nfrom datasets import load_dataset\r\n`\r\n`\r\ndataset = load_dataset(\"csv... | 2020-10-19T14:53:51 | 2025-04-24T06:35:25 | null | CONTRIBUTOR | null | null | null | null | Similar to #622, I've noticed there is a problem when trying to load a CSV file with datasets.
`
from datasets import load_dataset
`
`
dataset = load_dataset("csv", data_files=["./sample_data.csv"], delimiter="\t", column_names=["title", "text"], script_version="master")
`
Displayed error:
`
...
ArrowInvalid: CSV parse error: Expected 2 columns, got 1
`
I should mention that when I've tried to read data from `https://github.com/lhoestq/transformers/tree/custom-dataset-in-rag-retriever/examples/rag/test_data/my_knowledge_dataset.csv` it worked without a problem. I've read that there might be some problems with /r character, so I've removed them from the custom dataset, but the problem still remains.
I've added a colab reproducing the bug, but unfortunately I cannot provide the dataset.
https://colab.research.google.com/drive/1Qzu7sC-frZVeniiWOwzoCe_UHZsrlxu8?usp=sharing
Are there any work around for it ?
Thank you | null | {
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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... | 2020-10-18T06:07:06 | 2022-02-15T17:03:10 | 2022-02-15T17:03:10 | CONTRIBUTOR | null | null | null | 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 examples. """
filepath = os.path.join(base_path, "annotations", "manual", "PHOENIX-2014-T." + split + ".corpus.csv")
images_path = os.path.join(base_path, "features", "fullFrame-210x260px", split)
with open(filepath, "r", encoding="utf-8") as f:
data = csv.DictReader(f, delimiter="|", quoting=csv.QUOTE_NONE)
for row in data:
frames_path = os.path.join(images_path, row["video"])[:-7]
np_frames = []
for frame_name in os.listdir(frames_path):
frame_path = os.path.join(frames_path, frame_name)
im = Image.open(frame_path)
np_frames.append(np.asarray(im))
im.close()
yield row["name"], {"video": np_frames}
```
The dataset creation process goes out of memory on a machine with 500GB RAM.
I was under the impression that the "generator" here is exactly for that, to avoid memory constraints.
However, even if you want the entire dataset in memory, it would be in the worst case
`260x210x3 x 400 max length x 7000 samples` in bytes (uint8) = 458.64 gigabytes
So I'm not sure why it's taking more than 500GB.
And the dataset creation fails after 170 examples on a machine with 120gb RAM, and after 672 examples on a machine with 500GB RAM.
---
## Info that might help:
Iterating over examples is extremely slow.
If I perform this iteration in my own, custom loop (Without saving to file), it runs at 8-9 examples/sec
And you can see at this state it is using 94% of the memory:
And it is only using one CPU core, which is probably why it's so slow:
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https://api.github.com/repos/huggingface/datasets/issues/737 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/737/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/737/comments | https://api.github.com/repos/huggingface/datasets/issues/737/events | https://github.com/huggingface/datasets/issues/737 | 722,463,923 | MDU6SXNzdWU3MjI0NjM5MjM= | 737 | Trec Dataset Connection Error | {
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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"
] | 2020-10-15T15:57:53 | 2020-10-19T08:54:36 | 2020-10-19T08:54:36 | NONE | null | null | null | 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/QA/QC/train_5500.label (but the link doesn't seem broken)
<details>
<summary>Error Logs</summary>
Using custom data configuration default
Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /root/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-8-66bf1242096e> in <module>()
----> 1 load_dataset("trec")
10 frames
/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)
473 elif response is not None and response.status_code == 404:
474 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 475 raise ConnectionError("Couldn't reach {}".format(url))
476
477 # Try a second time
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
</details> | {
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https://api.github.com/repos/huggingface/datasets/issues/735 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/735/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/735/comments | https://api.github.com/repos/huggingface/datasets/issues/735/events | https://github.com/huggingface/datasets/issues/735 | 722,225,270 | MDU6SXNzdWU3MjIyMjUyNzA= | 735 | Throw error when an unexpected key is used in data_files | {
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"Thanks for reporting !\r\nWe'll add support for other keys"
] | 2020-10-15T10:55:27 | 2020-10-30T13:23:52 | 2020-10-30T13:23:52 | CONTRIBUTOR | null | null | null | 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 = load_dataset("text", data_files={"train": train_f, "valid": valid_f})
print(datasets.keys())
# dict_keys(['train'])
```
whereas using `validation` instead, does return the expected result:
```python
datasets = load_dataset("text", data_files={"train": train_f, "validation": valid_f})
print(datasets.keys())
# dict_keys(['train', 'validation'])
```
I would like to see more freedom in which keys one can use, but if that is not possible at least an error should be thrown when using an unexpected key. | {
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