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https://api.github.com/repos/huggingface/datasets/issues/6057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6057/comments | https://api.github.com/repos/huggingface/datasets/issues/6057/events | https://github.com/huggingface/datasets/issues/6057 | 1,815,100,151 | I_kwDODunzps5sMDr3 | 6,057 | Why is the speed difference of gen example so big? | [] | open | false | null | 1 | 2023-07-21T03:34:49Z | 2023-07-21T16:41:09Z | null | null | ```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
with open(metadata_path, 'r') as file:
metadata = json.load(file)
for idx, item in enumerate(metadata):
image_path = item.get('image_path')
text_content = item.get('text_content')
image_data = open(image_path, "rb").read()
yield idx, {
"text": text_content,
"image": {
"path": image_path,
"bytes": image_data,
},
"conditioning_image": {
"path": image_path,
"bytes": image_data,
},
}
```
Hello,
I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**
I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.
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"Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profi... |
https://api.github.com/repos/huggingface/datasets/issues/209 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/209/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/209/comments | https://api.github.com/repos/huggingface/datasets/issues/209/events | https://github.com/huggingface/datasets/pull/209 | 626,405,849 | MDExOlB1bGxSZXF1ZXN0NDI0NDAwOTc4 | 209 | Add a Google Drive exception for small files | [] | closed | false | null | 3 | 2020-05-28T10:40:17Z | 2020-05-28T15:15:04Z | 2020-05-28T15:15:04Z | null | I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive.
One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly.
Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` :
```
elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url)
```
I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing)
I avoid the error by adding an exception but there is maybe a proper way to do it.
Many thanks :hugs:
Best, | {
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"Can you run the style formatting tools to pass the code quality test?\r\n\r\nYou can find all the details in CONTRIBUTING.md: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp",
"Nice ! ",
"``make style`` done! Thanks for the approvals."
] |
https://api.github.com/repos/huggingface/datasets/issues/4896 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4896/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4896/comments | https://api.github.com/repos/huggingface/datasets/issues/4896/events | https://github.com/huggingface/datasets/pull/4896 | 1,351,180,409 | PR_kwDODunzps49z4fU | 4,896 | Fix missing tags in dataset cards | [] | closed | false | null | 1 | 2022-08-25T16:41:43Z | 2022-09-22T14:37:16Z | 2022-08-26T04:41:48Z | null | Fix missing tags in dataset cards:
- anli
- coarse_discourse
- commonsense_qa
- cos_e
- ilist
- lc_quad
- web_questions
- xsum
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/69 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/69/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/69/comments | https://api.github.com/repos/huggingface/datasets/issues/69/events | https://github.com/huggingface/datasets/pull/69 | 615,450,534 | MDExOlB1bGxSZXF1ZXN0NDE1NzYyNTQ4 | 69 | fix cache dir in builder tests | [] | closed | false | null | 2 | 2020-05-10T18:39:21Z | 2020-05-11T07:19:30Z | 2020-05-11T07:19:28Z | null | minor fix | {
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"Nice, is that the reason one cannot rerun the tests without deleting the cache? \r\n",
"Yes exactly. It was not using the temporary dir for tests."
] |
https://api.github.com/repos/huggingface/datasets/issues/5435 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5435/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5435/comments | https://api.github.com/repos/huggingface/datasets/issues/5435/events | https://github.com/huggingface/datasets/issues/5435 | 1,536,099,300 | I_kwDODunzps5bjwPk | 5,435 | Wrong statement in "Load a Dataset in Streaming mode" leads to data leakage | [] | closed | false | null | 4 | 2023-01-17T10:04:16Z | 2023-01-19T09:56:03Z | 2023-01-19T09:56:03Z | null | ### Describe the bug
In the [Split your dataset with take and skip](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#split-your-dataset-with-take-and-skip), it states:
> Using take (or skip) prevents future calls to shuffle from shuffling the dataset shards order, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer. Therefore it is advised to shuffle the dataset before splitting using take or skip. See more details in the [Shuffling the dataset: shuffle](https://huggingface.co/docs/datasets/v1.10.2/dataset_streaming.html#iterable-dataset-shuffling) section.`
>> \# You can also create splits from a shuffled dataset
>> train_dataset = shuffled_dataset.skip(1000)
>> eval_dataset = shuffled_dataset.take(1000)
Where the shuffled dataset comes from:
`shuffled_dataset = dataset.shuffle(buffer_size=10_000, seed=42)`
At least in Tensorflow 2.9/2.10/2.11, [docs](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) states the `reshuffle_each_iteration` argument is `True` by default. This means the dataset would be shuffled after each epoch, and as a result **the validation data would leak into training test**.
### Steps to reproduce the bug
N/A
### Expected behavior
The `reshuffle_each_iteration` argument should be set to `False`.
### Environment info
Tensorflow 2.9/2.10/2.11 | {
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"Just for your information, Tensorflow confirmed this issue [here.](https://github.com/tensorflow/tensorflow/issues/59279)",
"Thanks for reporting, @HaoyuYang59.\r\n\r\nPlease note that these are different \"dataset\" objects: our docs refer to Hugging Face `datasets.Dataset` and not to TensorFlow `tf.data.Datase... |
https://api.github.com/repos/huggingface/datasets/issues/4398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4398/comments | https://api.github.com/repos/huggingface/datasets/issues/4398/events | https://github.com/huggingface/datasets/issues/4398 | 1,246,666,749 | I_kwDODunzps5KTp_9 | 4,398 | Calling `cast_column`/`remove_columns` and a sequence of `map` operations ends up making `faiss` fail with `ValueError` | [
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] | closed | false | null | 4 | 2022-05-24T14:41:34Z | 2022-06-14T16:01:56Z | 2022-06-14T16:01:56Z | null | First of all, sorry in advance for the unclear title, but this bug is weird to explain (at least for me), so I tried my best to summarize all the information in this issue.
## Describe the bug
Calling a certain combination of operations over a 🤗 `Dataset` and then trying to calculate the `faiss` index with `.add_faiss_index` ends up throwing an exception while trying to set the format back of a previously removed column. But this just happens over certain conditions... I'll present some scenarios below!
## Steps to reproduce the bug
Assuming the following dataset named `sample.csv` with some IMDb data:
```csv
id,title,summary
1877830,"The Batman","When a sadistic serial killer begins murdering key political figures in Gotham, Batman is forced to investigate the city's hidden corruption and question his family's involvement."
9419884,"Doctor Strange in the Multiverse of Madness","Doctor Strange teams up with a mysterious teenage girl from his dreams who can travel across multiverses, to battle multiple threats, including other-universe versions of himself, which threaten to wipe out millions across the multiverse. They seek help from Wanda the Scarlet Witch, Wong and others."
11138512,"The Northman","From visionary director Robert Eggers comes The Northman, an action-filled epic that follows a young Viking prince on his quest to avenge his father's murder."
1745960,"Top Gun: Maverick","After more than thirty years of service as one of the Navy's top aviators, Pete Mitchell is where he belongs, pushing the envelope as a courageous test pilot and dodging the advancement in rank that would ground him."
```
We'll be able to reproduce the bug using the following piece of code:
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset, Value
ds = load_dataset("csv", data_files=["sample.csv"], split="train")
ds = ds.cast_column("id", Value("int32")) # from `int64` to `int32`
ds = ds.map(lambda x: {"inputs": f"{ctx_tokenizer.sep_token}".join(["title", "summary"])})
ds = ds.remove_columns(["title", "summary"])
def generate_embeddings(x):
return {"embeddings": ctx_encoder(**ctx_tokenizer(x["inputs"], return_tensors="pt"))[0][0].numpy()}
ds = ds.map(generate_embeddings)
ds = ds.remove_columns("inputs")
ds.add_faiss_index(column="embeddings") # It fails here!
```
The code above is an adaptation of https://huggingface.co/docs/datasets/faiss_es, for the sake of presenting the bug with a simple example.
## Expected results
Ideally, the `faiss` index should be calculated over the 🤗 `Dataset` and no exception should be triggered.
## Actual results
But what happens instead is that a `ValueError: Columns ['inputs'] not in the dataset. Current columns in the dataset: ['id', 'embeddings']`, which makes no sense as that column has been previously dropped.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1074-azure-x86_64-with-glibc2.31
- Python version: 3.9.5
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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"It works if we either remove the `ds = ds.cast_column(\"id\", Value(\"int32\"))` line from the code above, or if instead calling `ds.remove_columns()` we remove the columns inside each mapping as `ds.map(..., remove_columns=[...])` instead of right after the mapping.\r\n\r\nBoth of those solutions seem to fix the ... |
https://api.github.com/repos/huggingface/datasets/issues/3571 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3571/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3571/comments | https://api.github.com/repos/huggingface/datasets/issues/3571/events | https://github.com/huggingface/datasets/pull/3571 | 1,100,519,604 | PR_kwDODunzps4w3fVQ | 3,571 | Add missing tasks to MuchoCine dataset | [] | closed | false | null | 0 | 2022-01-12T16:07:32Z | 2022-01-20T16:51:08Z | 2022-01-20T16:51:07Z | null | Addresses the 2nd bullet point in #2520.
I'm also removing the licensing information, because I couldn't verify that it is correct. | {
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https://api.github.com/repos/huggingface/datasets/issues/268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/268/comments | https://api.github.com/repos/huggingface/datasets/issues/268/events | https://github.com/huggingface/datasets/pull/268 | 637,848,056 | MDExOlB1bGxSZXF1ZXN0NDMzNzU5NzQ1 | 268 | add Rotten Tomatoes Movie Review sentences sentiment dataset | [] | closed | false | null | 1 | 2020-06-12T15:53:59Z | 2020-06-18T07:46:24Z | 2020-06-18T07:46:23Z | null | Sentence-level movie reviews v1.0 from here: http://www.cs.cornell.edu/people/pabo/movie-review-data/ | {
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"@jplu @thomwolf @patrickvonplaten @lhoestq -- How do I request reviewers? Thanks."
] |
https://api.github.com/repos/huggingface/datasets/issues/4203 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4203/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4203/comments | https://api.github.com/repos/huggingface/datasets/issues/4203/events | https://github.com/huggingface/datasets/pull/4203 | 1,212,431,067 | PR_kwDODunzps42oNrS | 4,203 | Add Precision Metric Card | [] | closed | false | null | 1 | 2022-04-22T14:23:48Z | 2022-05-03T14:23:40Z | 2022-05-03T14:16:46Z | null | What this PR mainly does:
- add metric card for precision metric
- update docs in precision python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!) | {
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https://api.github.com/repos/huggingface/datasets/issues/1104 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1104/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1104/comments | https://api.github.com/repos/huggingface/datasets/issues/1104/events | https://github.com/huggingface/datasets/pull/1104 | 757,020,934 | MDExOlB1bGxSZXF1ZXN0NTMyNDY1NzA4 | 1,104 | add TLC | [] | closed | false | null | 0 | 2020-12-04T11:14:58Z | 2020-12-04T14:29:23Z | 2020-12-04T14:29:23Z | null | Added TLC dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/4400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4400/comments | https://api.github.com/repos/huggingface/datasets/issues/4400/events | https://github.com/huggingface/datasets/issues/4400 | 1,247,404,237 | I_kwDODunzps5KWeDN | 4,400 | load dataset wikitext-2-raw-v1 failed. Could not reach wikitext-2-raw-v1.py. | [
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"url": "https://api.github.com/repos/huggingface/datasets/labels/bug"
}
] | closed | false | null | 1 | 2022-05-25T03:10:44Z | 2022-10-24T06:10:27Z | 2022-05-25T03:26:36Z | null | ## Describe the bug
Could not reach wikitext-2-raw-v1.py
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("wikitext-2-raw-v1")
```
## Expected results
Download `wikitext-2-raw-v1` dataset successfully.
## Actual results
```
File "load_datasets.py", line 13, in <module>
load_dataset("wikitext-2-raw-v1")
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1715, in load_dataset
**config_kwargs,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1536, in load_dataset_builder
data_files=data_files,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1282, in dataset_module_factory
raise e1 from None
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 1224, in dataset_module_factory
dynamic_modules_path=dynamic_modules_path,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 559, in get_module
local_path = self.download_loading_script(revision)
File "/root/miniconda3/lib/python3.6/site-packages/datasets/load.py", line 539, in download_loading_script
return cached_path(file_path, download_config=download_config)
File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 246, in cached_path
download_desc=download_config.download_desc,
File "/root/miniconda3/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 582, in get_from_cache
raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.2.2/datasets/wikitext-2-raw-v1/wikitext-2-raw-v1.py (ReadTimeout(ReadTimeoutError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Read timed out. (read timeout=100)",),))
```
I tried to download wikitext-2-raw-v1.py by chrome and got:
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.2.2
- Platform: CentOS 7
- Python version: 3.6
- PyArrow version: 3.0.0
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"I tried in this way.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(path=\"wikitext\", name=\"wikitext-103-v1\", split=\"train\")\r\n```"
] |
https://api.github.com/repos/huggingface/datasets/issues/4256 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4256/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4256/comments | https://api.github.com/repos/huggingface/datasets/issues/4256/events | https://github.com/huggingface/datasets/pull/4256 | 1,221,379,625 | PR_kwDODunzps43F9Zw | 4,256 | Create metric card for MSE | [] | closed | false | null | 1 | 2022-04-29T18:21:22Z | 2022-05-02T14:55:42Z | 2022-05-02T14:48:47Z | null | Proposing a metric card for Mean Squared Error | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/1239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1239/comments | https://api.github.com/repos/huggingface/datasets/issues/1239/events | https://github.com/huggingface/datasets/pull/1239 | 758,339,593 | MDExOlB1bGxSZXF1ZXN0NTMzNTI4NTU5 | 1,239 | add yelp_review_full dataset | [] | closed | false | null | 1 | 2020-12-07T09:35:36Z | 2020-12-08T15:43:24Z | 2020-12-08T15:00:50Z | null | This corresponds to the Yelp-5 requested in https://github.com/huggingface/datasets/issues/353 | {
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} | true | [
"Moved to https://github.com/huggingface/datasets/pull/1315"
] |
https://api.github.com/repos/huggingface/datasets/issues/2710 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2710/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2710/comments | https://api.github.com/repos/huggingface/datasets/issues/2710/events | https://github.com/huggingface/datasets/pull/2710 | 951,723,326 | MDExOlB1bGxSZXF1ZXN0Njk2MDYyNjAy | 2,710 | Update WikiANN data URL | [] | closed | false | null | 1 | 2021-07-23T16:29:21Z | 2021-07-26T09:34:23Z | 2021-07-26T09:34:23Z | null | WikiANN data source URL is no longer accessible: 404 error from Dropbox.
We have decided to host it at Hugging Face. This PR updates the data source URL, the metadata JSON file and the dataset card.
Close #2691. | {
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"We have to update the URL in the XTREME benchmark as well:\r\n\r\nhttps://github.com/huggingface/datasets/blob/0dfc639cec450ed8762a997789a2ed63e63cdcf2/datasets/xtreme/xtreme.py#L411-L411\r\n\r\n"
] |
https://api.github.com/repos/huggingface/datasets/issues/2353 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2353/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2353/comments | https://api.github.com/repos/huggingface/datasets/issues/2353/events | https://github.com/huggingface/datasets/pull/2353 | 890,296,262 | MDExOlB1bGxSZXF1ZXN0NjQzMzM4MDcz | 2,353 | Update README vallidation rules | [] | closed | false | null | 0 | 2021-05-12T16:57:26Z | 2021-05-14T08:56:06Z | 2021-05-14T08:56:06Z | null | This PR allows unexpected subsections under third-level headings. All except `Contributions`.
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/3577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3577/comments | https://api.github.com/repos/huggingface/datasets/issues/3577/events | https://github.com/huggingface/datasets/issues/3577 | 1,102,598,241 | I_kwDODunzps5BuFBh | 3,577 | Add The Mexican Emotional Speech Database (MESD) | [
{
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
},
{
"color": "d93f0b",... | open | false | null | 0 | 2022-01-13T23:49:36Z | 2022-01-27T14:14:38Z | null | null | ## Adding a Dataset
- **Name:** *The Mexican Emotional Speech Database (MESD)*
- **Description:** *Contains 864 voice recordings with six different prosodies: anger, disgust, fear, happiness, neutral, and sadness. Furthermore, three voice categories are included: female adult, male adult, and child. *
- **Paper:** *[Paper](https://ieeexplore.ieee.org/abstract/document/9629934/authors#authors)*
- **Data:** *[link to the Github repository or current dataset location](https://data.mendeley.com/datasets/cy34mh68j9/3)*
- **Motivation:** *Would add Spanish speech data to the HF datasets :) *
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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https://api.github.com/repos/huggingface/datasets/issues/1525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1525/comments | https://api.github.com/repos/huggingface/datasets/issues/1525/events | https://github.com/huggingface/datasets/pull/1525 | 764,530,582 | MDExOlB1bGxSZXF1ZXN0NTM4NTUwMzI2 | 1,525 | Adding a second branch for Atomic to fix git errors | [] | closed | false | null | 0 | 2020-12-12T22:54:50Z | 2020-12-28T15:51:11Z | 2020-12-28T15:51:11Z | null | Adding the Atomic common sense dataset.
See https://homes.cs.washington.edu/~msap/atomic/ | {
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https://api.github.com/repos/huggingface/datasets/issues/4036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4036/comments | https://api.github.com/repos/huggingface/datasets/issues/4036/events | https://github.com/huggingface/datasets/pull/4036 | 1,183,126,893 | PR_kwDODunzps41I854 | 4,036 | Fix building of documentation | [] | closed | false | null | 2 | 2022-03-28T09:09:12Z | 2022-03-28T11:18:31Z | 2022-03-28T11:13:22Z | null | Documentation building is failing:
- https://github.com/huggingface/datasets/runs/5716300989?check_suite_focus=true
```
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Unable to find datasets.filesystems.S3FileSystem in datasets. Make sure the path to that object is correct.
```
Fix #4037. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Superseded by huggingface/doc-builder@31fe6c8bc7225810e281c2f6c6cd32f38828c504"
] |
https://api.github.com/repos/huggingface/datasets/issues/650 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/650/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/650/comments | https://api.github.com/repos/huggingface/datasets/issues/650/events | https://github.com/huggingface/datasets/issues/650 | 704,861,844 | MDU6SXNzdWU3MDQ4NjE4NDQ= | 650 | dummy data testing can't test datasets using `dl_manager.extract` in `_split_generators` | [] | closed | false | null | 4 | 2020-09-19T11:07:03Z | 2020-09-22T11:54:10Z | 2020-09-22T11:54:09Z | null | Hi, I recently want to add a dataset whose source data is like this
```
openwebtext.tar.xz
|__ openwebtext
|__subset000.xz
| |__ ....txt
| |__ ....txt
| ...
|__ subset001.xz
|
....
```
So I wrote `openwebtext.py` like this
```
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_URL)
owt_dir = os.path.join(dl_dir, 'openwebtext')
subset_xzs = [
os.path.join(owt_dir, file_name) for file_name in os.listdir(owt_dir) if file_name.endswith('xz') # filter out ...xz.lock
]
ex_dirs = dl_manager.extract(subset_xzs, num_proc=round(os.cpu_count()*0.75))
nested_txt_files = [
[
os.path.join(ex_dir,txt_file_name) for txt_file_name in os.listdir(ex_dir) if txt_file_name.endswith('txt')
] for ex_dir in ex_dirs
]
txt_files = chain(*nested_txt_files)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"txt_files": txt_files}
),
]
```
All went good, I can load and use real openwebtext, except when I try to test with dummy data. The problem is `MockDownloadManager.extract` do nothing, so `ex_dirs = dl_manager.extract(subset_xzs)` won't decompress `subset_xxx.xz`s for me.
How should I do ? Or you can modify `MockDownloadManager` to make it like a real `DownloadManager` ? | {
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"Hi :) \r\nIn your dummy data zip file you can just have `subset000.xz` as directories instead of compressed files.\r\nLet me know if it helps",
"Thanks for your comment @lhoestq ,\r\nJust for confirmation, changing dummy data like this won't make dummy test test the functionality to extract `subsetxxx.xz` but ac... |
https://api.github.com/repos/huggingface/datasets/issues/2002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2002/comments | https://api.github.com/repos/huggingface/datasets/issues/2002/events | https://github.com/huggingface/datasets/pull/2002 | 823,955,744 | MDExOlB1bGxSZXF1ZXN0NTg2MjgwNzE3 | 2,002 | MOROCO | [] | closed | false | null | 1 | 2021-03-07T16:22:17Z | 2021-03-19T09:52:06Z | 2021-03-19T09:52:06Z | null | Add MOROCO to huggingface datasets. | {
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"@lhoestq Thank you for all the feedback. I've added the suggested changes in my last commit."
] |
https://api.github.com/repos/huggingface/datasets/issues/2484 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2484/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2484/comments | https://api.github.com/repos/huggingface/datasets/issues/2484/events | https://github.com/huggingface/datasets/issues/2484 | 919,092,635 | MDU6SXNzdWU5MTkwOTI2MzU= | 2,484 | Implement loading a dataset builder | [
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"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
}
] | closed | false | null | 1 | 2021-06-11T18:47:22Z | 2021-07-05T10:45:57Z | 2021-07-05T10:45:57Z | null | As discussed with @stas00 and @lhoestq, this would allow things like:
```python
from datasets import load_dataset_builder
dataset_name = "openwebtext"
builder = load_dataset_builder(dataset_name)
print(builder.cache_dir)
``` | {
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"#self-assign"
] |
https://api.github.com/repos/huggingface/datasets/issues/5570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5570/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5570/comments | https://api.github.com/repos/huggingface/datasets/issues/5570/events | https://github.com/huggingface/datasets/issues/5570 | 1,597,190,926 | I_kwDODunzps5fMzMO | 5,570 | load_dataset gives FileNotFoundError on imagenet-1k if license is not accepted on the hub | [] | closed | false | null | 2 | 2023-02-23T16:44:32Z | 2023-07-24T15:18:50Z | 2023-07-24T15:18:50Z | null | ### Describe the bug
When calling ```load_dataset('imagenet-1k')``` FileNotFoundError is raised, if not logged in and if logged in with huggingface-cli but not having accepted the licence on the hub. There is no error once accepting.
### Steps to reproduce the bug
```
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True)
FileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub
```
tested on a colab notebook.
### Expected behavior
I would expect a specific error indicating that I have to login then accept the dataset licence.
I find this bug very relevant as this code is on a guide on the [Huggingface documentation for Datasets](https://huggingface.co/docs/datasets/about_mapstyle_vs_iterable)
### Environment info
google colab cpu-only instance | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5570/timeline | null | completed | null | null | false | [
"Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?",
"The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the sa... |
https://api.github.com/repos/huggingface/datasets/issues/1865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1865/comments | https://api.github.com/repos/huggingface/datasets/issues/1865/events | https://github.com/huggingface/datasets/pull/1865 | 806,388,290 | MDExOlB1bGxSZXF1ZXN0NTcxODE2ODI2 | 1,865 | Updated OPUS Open Subtitles Dataset with metadata information | [] | closed | false | null | 2 | 2021-02-11T13:26:26Z | 2021-02-19T12:38:09Z | 2021-02-12T16:59:44Z | null | Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic... | {
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"Hi !\r\nAbout the problems you mentioned:\r\n- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.\r\n- Moreover when you're on your branch, please specify the path to your local version of th... |
https://api.github.com/repos/huggingface/datasets/issues/5066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5066/comments | https://api.github.com/repos/huggingface/datasets/issues/5066/events | https://github.com/huggingface/datasets/pull/5066 | 1,396,086,745 | PR_kwDODunzps5AIDWj | 5,066 | Support streaming gzip.open | [] | closed | false | null | 1 | 2022-10-04T11:20:05Z | 2022-10-06T15:13:51Z | 2022-10-06T15:11:29Z | null | This PR implements support for streaming out-of-the-box dataset scripts containing `gzip.open`.
This has been a recurring issue. See, e.g.:
- #5060
- #3191 | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/6057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6057/comments | https://api.github.com/repos/huggingface/datasets/issues/6057/events | https://github.com/huggingface/datasets/issues/6057 | 1,815,100,151 | I_kwDODunzps5sMDr3 | 6,057 | Why is the speed difference of gen example so big? | [] | open | false | null | 1 | 2023-07-21T03:34:49Z | 2023-07-21T16:41:09Z | null | null | ```python
def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
with open(metadata_path, 'r') as file:
metadata = json.load(file)
for idx, item in enumerate(metadata):
image_path = item.get('image_path')
text_content = item.get('text_content')
image_data = open(image_path, "rb").read()
yield idx, {
"text": text_content,
"image": {
"path": image_path,
"bytes": image_data,
},
"conditioning_image": {
"path": image_path,
"bytes": image_data,
},
}
```
Hello,
I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, **sometimes 1000examples/s, sometimes only 10examples/s.**
I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.
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"Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profi... |
https://api.github.com/repos/huggingface/datasets/issues/5525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5525/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5525/comments | https://api.github.com/repos/huggingface/datasets/issues/5525/events | https://github.com/huggingface/datasets/issues/5525 | 1,580,342,729 | I_kwDODunzps5eMh3J | 5,525 | TypeError: Couldn't cast array of type string to null | [] | closed | false | null | 6 | 2023-02-10T21:12:36Z | 2023-02-14T17:41:08Z | 2023-02-14T09:35:49Z | null | ### Describe the bug
Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error.
I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors.
I suspect that the error may be in this direction:
We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for.
### Steps to reproduce the bug
Here I link a colab notebook to reproduce the error:
https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA
### Expected behavior
Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5 | {
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"Thanks for reporting, @TJ-Solergibert.\r\n\r\nWe cannot access your Colab notebook: `There was an error loading this notebook. Ensure that the file is accessible and try again.`\r\nCould you please make it publicly accessible?\r\n",
"I swear it's public, I've checked the settings and I've been able to open it in... |
https://api.github.com/repos/huggingface/datasets/issues/395 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/395/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/395/comments | https://api.github.com/repos/huggingface/datasets/issues/395/events | https://github.com/huggingface/datasets/issues/395 | 657,454,983 | MDU6SXNzdWU2NTc0NTQ5ODM= | 395 | Memory issue when doing select | [] | closed | false | null | 1 | 2020-07-15T15:43:38Z | 2020-07-16T08:07:31Z | 2020-07-16T08:07:31Z | null | As noticed in #389, the following code loads the entire wikipedia in memory.
```python
import nlp
w = nlp.load_dataset("wikipedia", "20200501.en", split="train")
w.select([0])
```
This is caused by [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626) for some reason, that tries to serialize the function with all the wikipedia data with it.
It's not the case with `.map` or `.filter`.
However functions that are based on `.select` like `.shuffle`, `.shard`, `.train_test_split`, `.sort` are affected.
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https://api.github.com/repos/huggingface/datasets/issues/3401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3401/comments | https://api.github.com/repos/huggingface/datasets/issues/3401/events | https://github.com/huggingface/datasets/issues/3401 | 1,073,603,508 | I_kwDODunzps4__eO0 | 3,401 | Add Wikimedia pre-processed datasets | [
{
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
"node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5",
"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
}
] | open | false | null | 0 | 2021-12-07T17:33:19Z | 2021-12-07T17:43:37Z | null | null | ## Adding a Dataset
- **Name:** Add pre-processed data to:
- *wikimedia/wikipedia*: https://huggingface.co/datasets/wikimedia/wikipedia
- *wikimedia/wikisource*: https://huggingface.co/datasets/wikimedia/wikisource
- **Description:** Add pre-processed data to the Hub for all languages
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** This will be very useful for the NLP community, as the pre-processing has a high cost for lot of researchers (both in computation and in knowledge)
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
CC: @geohci, @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/6040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6040/comments | https://api.github.com/repos/huggingface/datasets/issues/6040/events | https://github.com/huggingface/datasets/pull/6040 | 1,807,410,238 | PR_kwDODunzps5VptVf | 6,040 | Fix legacy_dataset_infos | [] | closed | false | null | 3 | 2023-07-17T09:56:21Z | 2023-07-17T10:24:34Z | 2023-07-17T10:16:03Z | null | was causing transformers CI to fail
https://circleci.com/gh/huggingface/transformers/855105 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/5727 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5727/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5727/comments | https://api.github.com/repos/huggingface/datasets/issues/5727/events | https://github.com/huggingface/datasets/issues/5727 | 1,661,536,363 | I_kwDODunzps5jCQhr | 5,727 | load_dataset fails with FileNotFound error on Windows | [] | closed | false | null | 4 | 2023-04-10T23:21:12Z | 2023-07-21T14:08:20Z | 2023-07-21T14:08:19Z | null | ### Describe the bug
Although I can import and run the datasets library in a Colab environment, I cannot successfully load any data on my own machine (Windows 10) despite following the install steps:
(1) create conda environment
(2) activate environment
(3) install with: ``conda` install -c huggingface -c conda-forge datasets`
Then
```
from datasets import load_dataset
# this or any other example from the website fails with the FileNotFoundError
glue = load_dataset("glue", "ax")
```
**Below I have pasted the error omitting the full path**:
```
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at C:\Users\...\glue\glue.py or any data file in the same directory. Couldn't find 'glue' on the Hugging Face Hub either: FileNotFoundError: [WinError 3] The system cannot find the path specified:
'C:\\Users\\...\\.cache\\huggingface'
```
### Steps to reproduce the bug
On Windows 10
1) create a minimal conda environment (with just Python)
(2) activate environment
(3) install datasets with: ``conda` install -c huggingface -c conda-forge datasets`
(4) import load_dataset and follow example usage from any dataset card.
### Expected behavior
The expected behavior is to load the file into the Python session running on my machine without error.
### Environment info
```
# Name Version Build Channel
aiohttp 3.8.4 py311ha68e1ae_0 conda-forge
aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge
arrow-cpp 11.0.0 h57928b3_13_cpu conda-forge
async-timeout 4.0.2 pyhd8ed1ab_0 conda-forge
attrs 22.2.0 pyh71513ae_0 conda-forge
aws-c-auth 0.6.26 h1262f0c_1 conda-forge
aws-c-cal 0.5.21 h7cda486_2 conda-forge
aws-c-common 0.8.14 hcfcfb64_0 conda-forge
aws-c-compression 0.2.16 h8a79959_5 conda-forge
aws-c-event-stream 0.2.20 h5f78564_4 conda-forge
aws-c-http 0.7.6 h2545be9_0 conda-forge
aws-c-io 0.13.19 h0d2781e_3 conda-forge
aws-c-mqtt 0.8.6 hd211e0c_12 conda-forge
aws-c-s3 0.2.7 h8113e7b_1 conda-forge
aws-c-sdkutils 0.1.8 h8a79959_0 conda-forge
aws-checksums 0.1.14 h8a79959_5 conda-forge
aws-crt-cpp 0.19.8 he6d3b81_12 conda-forge
aws-sdk-cpp 1.10.57 h64004b3_8 conda-forge
brotlipy 0.7.0 py311ha68e1ae_1005 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
c-ares 1.19.0 h2bbff1b_0
ca-certificates 2023.01.10 haa95532_0
certifi 2022.12.7 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py311h7d9ee11_3 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
cryptography 40.0.1 py311h28e9c30_0 conda-forge
dataclasses 0.8 pyhc8e2a94_3 conda-forge
datasets 2.11.0 py_0 huggingface
dill 0.3.6 pyhd8ed1ab_1 conda-forge
filelock 3.11.0 pyhd8ed1ab_0 conda-forge
frozenlist 1.3.3 py311ha68e1ae_0 conda-forge
fsspec 2023.4.0 pyh1a96a4e_0 conda-forge
gflags 2.2.2 ha925a31_1004 conda-forge
glog 0.6.0 h4797de2_0 conda-forge
huggingface_hub 0.13.4 py_0 huggingface
idna 3.4 pyhd8ed1ab_0 conda-forge
importlib-metadata 6.3.0 pyha770c72_0 conda-forge
importlib_metadata 6.3.0 hd8ed1ab_0 conda-forge
intel-openmp 2023.0.0 h57928b3_25922 conda-forge
krb5 1.20.1 heb0366b_0 conda-forge
libabseil 20230125.0 cxx17_h63175ca_1 conda-forge
libarrow 11.0.0 h04c43f8_13_cpu conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 hcfcfb64_8 conda-forge
libbrotlidec 1.0.9 hcfcfb64_8 conda-forge
libbrotlienc 1.0.9 hcfcfb64_8 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libcrc32c 1.1.2 h0e60522_0 conda-forge
libcurl 7.88.1 h68f0423_1 conda-forge
libexpat 2.5.0 h63175ca_1 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgoogle-cloud 2.8.0 hf2ff781_1 conda-forge
libgrpc 1.52.1 h32da247_1 conda-forge
libhwloc 2.9.0 h51c2c0f_0 conda-forge
libiconv 1.17 h8ffe710_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libprotobuf 3.21.12 h12be248_0 conda-forge
libsqlite 3.40.0 hcfcfb64_0 conda-forge
libssh2 1.10.0 h9a1e1f7_3 conda-forge
libthrift 0.18.1 h9ce19ad_0 conda-forge
libutf8proc 2.8.0 h82a8f57_0 conda-forge
libxml2 2.10.3 hc3477c8_6 conda-forge
libzlib 1.2.13 hcfcfb64_4 conda-forge
lz4-c 1.9.4 hcfcfb64_0 conda-forge
mkl 2022.1.0 h6a75c08_874 conda-forge
multidict 6.0.4 py311ha68e1ae_0 conda-forge
multiprocess 0.70.14 py311ha68e1ae_3 conda-forge
numpy 1.24.2 py311h0b4df5a_0 conda-forge
openssl 3.1.0 hcfcfb64_0 conda-forge
orc 1.8.3 hada7b9e_0 conda-forge
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 2.0.0 py311hf63dbb6_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
pip 23.0.1 pyhd8ed1ab_0 conda-forge
pthreads-win32 2.9.1 hfa6e2cd_3 conda-forge
pyarrow 11.0.0 py311h6a6099b_13_cpu conda-forge
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyopenssl 23.1.1 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 pyh0701188_6 conda-forge
python 3.11.3 h2628c8c_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge
python-xxhash 3.2.0 py311ha68e1ae_0 conda-forge
python_abi 3.11 3_cp311 conda-forge
pytz 2023.3 pyhd8ed1ab_0 conda-forge
pyyaml 6.0 py311ha68e1ae_5 conda-forge
re2 2023.02.02 h63175ca_0 conda-forge
requests 2.28.2 pyhd8ed1ab_1 conda-forge
setuptools 67.6.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hfb803bf_0 conda-forge
tbb 2021.8.0 h91493d7_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
tqdm 4.65.0 pyhd8ed1ab_1 conda-forge
typing-extensions 4.5.0 hd8ed1ab_0 conda-forge
typing_extensions 4.5.0 pyha770c72_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 1.26.15 pyhd8ed1ab_0 conda-forge
vc 14.3 hb6edc58_10 conda-forge
vs2015_runtime 14.34.31931 h4c5c07a_10 conda-forge
wheel 0.40.0 pyhd8ed1ab_0 conda-forge
win_inet_pton 1.1.0 pyhd8ed1ab_6 conda-forge
xxhash 0.8.1 hcfcfb64_0 conda-forge
xz 5.2.10 h8cc25b3_1
yaml 0.2.5 h8ffe710_2 conda-forge
yarl 1.8.2 py311ha68e1ae_0 conda-forge
zipp 3.15.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hcfcfb64_4 conda-forge
zstd 1.5.4 hd43e919_0
``` | {
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"Hi! Can you please paste the entire error stack trace, not only the last few lines?",
"`----> 1 dataset = datasets.load_dataset(\"glue\", \"ax\")\r\n\r\nFile ~\\anaconda3\\envs\\huggingface\\Lib\\site-packages\\datasets\\load.py:1767, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, ... |
https://api.github.com/repos/huggingface/datasets/issues/370 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/370/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/370/comments | https://api.github.com/repos/huggingface/datasets/issues/370/events | https://github.com/huggingface/datasets/pull/370 | 654,304,193 | MDExOlB1bGxSZXF1ZXN0NDQ3MDU3NTIw | 370 | Allow indexing Dataset via np.ndarray | [] | closed | false | null | 1 | 2020-07-09T19:43:15Z | 2020-07-10T14:05:44Z | 2020-07-10T14:05:43Z | null | {
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"Looks like a flaky CI, failed download from S3."
] | |
https://api.github.com/repos/huggingface/datasets/issues/1287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1287/comments | https://api.github.com/repos/huggingface/datasets/issues/1287/events | https://github.com/huggingface/datasets/issues/1287 | 759,300,992 | MDU6SXNzdWU3NTkzMDA5OTI= | 1,287 | 'iwslt2017-ro-nl', cannot be downloaded | [
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] | closed | false | null | 4 | 2020-12-08T09:56:55Z | 2022-06-13T10:41:33Z | 2022-06-13T10:41:33Z | null | Hi
I am trying
`>>> datasets.load_dataset("iwslt2017", 'iwslt2017-ro-nl', split="train")`
getting this error thank you for your help
```
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
Downloading and preparing dataset iwsl_t217/iwslt2017-ro-nl (download: 314.07 MiB, generated: 39.92 MiB, post-processed: Unknown size, total: 354.00 MiB) to /idiap/temp/rkarimi/cache_home_1/datasets/iwsl_t217/iwslt2017-ro-nl/1.0.0/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd...
cahce dir /idiap/temp/rkarimi/cache_home_1/datasets
cahce dir /idiap/temp/rkarimi/cache_home_1/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/iwslt2017/cca6935a0851a8ceac1202a62c958738bdfa23c57a51bc52ac1c5ebd2aa172cd/iwslt2017.py", line 118, in _split_generators
dl_dir = dl_manager.download_and_extract(MULTI_URL)
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 216, in 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 https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz
``` | {
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"the same issue with datasets.load_dataset(\"iwslt2017\", 'iwslt2017-en-nl', split=split), ..... ",
"even with setting master like the following command, still remains \r\n\r\ndatasets.load_dataset(\"iwslt2017\", 'iwslt2017-en-nl', split=\"train\", script_version=\"master\")\r\n",
"Looks like the data has been ... |
https://api.github.com/repos/huggingface/datasets/issues/6088 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6088/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6088/comments | https://api.github.com/repos/huggingface/datasets/issues/6088/events | https://github.com/huggingface/datasets/issues/6088 | 1,825,665,235 | I_kwDODunzps5s0XDT | 6,088 | Loading local data files initiates web requests | [] | closed | false | null | 0 | 2023-07-28T04:06:26Z | 2023-07-28T05:02:22Z | 2023-07-28T05:02:22Z | null | As documented in the [official docs](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/loading_methods#datasets.load_dataset.example-2), I tried to load datasets from local files by
```python
# Load a JSON file
from datasets import load_dataset
ds = load_dataset('json', data_files='path/to/local/my_dataset.json')
```
But this failed on a web request because I'm executing the script on a machine without Internet access. Stacktrace shows
```
in PackagedDatasetModuleFactory.__init__(self, name, data_dir, data_files, download_config, download_mode)
940 self.download_config = download_config
941 self.download_mode = download_mode
--> 942 increase_load_count(name, resource_type="dataset")
```
I've read from the source code that this can be fixed by setting environment variable to run in offline mode. I'm just wondering that is this an expected behaviour that even loading a LOCAL JSON file requires Internet access by default? And what's the point of requesting to `increase_load_count` on some server when loading just LOCAL data files? | {
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https://api.github.com/repos/huggingface/datasets/issues/413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/413/comments | https://api.github.com/repos/huggingface/datasets/issues/413/events | https://github.com/huggingface/datasets/issues/413 | 660,063,655 | MDU6SXNzdWU2NjAwNjM2NTU= | 413 | Is there a way to download only NQ dev? | [] | closed | false | null | 3 | 2020-07-18T10:28:23Z | 2022-02-11T09:50:21Z | 2022-02-11T09:50:21Z | null | Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)?
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data.
I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", beam_runner="DirectRunner")
```
But this still triggered a big download of presumably the whole dataset. Is there any way of doing this or are splits / slicing options only available after downloading?
Thanks! | {
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"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.",
"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially al... |
https://api.github.com/repos/huggingface/datasets/issues/895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/895/comments | https://api.github.com/repos/huggingface/datasets/issues/895/events | https://github.com/huggingface/datasets/pull/895 | 751,782,295 | MDExOlB1bGxSZXF1ZXN0NTI4MjMyMjU3 | 895 | Better messages regarding split naming | [] | closed | false | null | 0 | 2020-11-26T18:55:46Z | 2020-11-27T13:31:00Z | 2020-11-27T13:30:59Z | null | I made explicit the error message when a bad split name is used.
Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol. | {
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https://api.github.com/repos/huggingface/datasets/issues/3094 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3094/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3094/comments | https://api.github.com/repos/huggingface/datasets/issues/3094/events | https://github.com/huggingface/datasets/issues/3094 | 1,027,328,633 | I_kwDODunzps49O8p5 | 3,094 | Support loading a dataset from SQLite files | [
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"default": fals... | closed | false | null | 2 | 2021-10-15T10:58:41Z | 2022-10-03T16:32:29Z | 2022-10-03T16:32:29Z | null | As requested by @julien-c, we could eventually support loading a dataset from SQLite files, like it is the case for JSON/CSV files. | {
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"for reference Kaggle has a good number of open source datasets stored in sqlite\r\n\r\nAlternatively a tutorial or tool on how to convert from sqlite to parquet would be cool too",
"Hello, could we leverage [`pandas.read_sql`](https://pandas.pydata.org/docs/reference/api/pandas.read_sql.html) for this? \r\n\r\nT... |
https://api.github.com/repos/huggingface/datasets/issues/4478 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4478/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4478/comments | https://api.github.com/repos/huggingface/datasets/issues/4478/events | https://github.com/huggingface/datasets/issues/4478 | 1,268,358,213 | I_kwDODunzps5LmZxF | 4,478 | Dataset slow during model training | [
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] | open | false | null | 5 | 2022-06-11T19:40:19Z | 2022-06-14T12:04:31Z | null | null | ## Describe the bug
While migrating towards 🤗 Datasets, I encountered an odd performance degradation: training suddenly slows down dramatically. I train with an image dataset using Keras and execute a `to_tf_dataset` just before training.
First, I have optimized my dataset following https://discuss.huggingface.co/t/solved-image-dataset-seems-slow-for-larger-image-size/10960/6, which actually improved the situation from what I had before but did not completely solve it.
Second, I saved and loaded my dataset using `tf.data.experimental.save` and `tf.data.experimental.load` before training (for which I would have expected no performance change). However, I ended up with the performance I had before tinkering with 🤗 Datasets.
Any idea what's the reason for this and how to speed-up training with 🤗 Datasets?
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
import os
dataset_dir = "./dataset"
prep_dataset_dir = "./prepdataset"
model_dir = "./model"
# Load Data
dataset = load_dataset("Lehrig/Monkey-Species-Collection", "downsized")
def read_image_file(example):
with open(example["image"].filename, "rb") as f:
example["image"] = {"bytes": f.read()}
return example
dataset = dataset.map(read_image_file)
dataset.save_to_disk(dataset_dir)
# Preprocess
from datasets import (
Array3D,
DatasetDict,
Features,
load_from_disk,
Sequence,
Value
)
import numpy as np
from transformers import ImageFeatureExtractionMixin
dataset = load_from_disk(dataset_dir)
num_classes = dataset["train"].features["label"].num_classes
one_hot_matrix = np.eye(num_classes)
feature_extractor = ImageFeatureExtractionMixin()
def to_pixels(image):
image = feature_extractor.resize(image, size=size)
image = feature_extractor.to_numpy_array(image, channel_first=False)
image = image / 255.0
return image
def process(examples):
examples["pixel_values"] = [
to_pixels(image) for image in examples["image"]
]
examples["label"] = [
one_hot_matrix[label] for label in examples["label"]
]
return examples
features = Features({
"pixel_values": Array3D(dtype="float32", shape=(size, size, 3)),
"label": Sequence(feature=Value(dtype="int32"), length=num_classes)
})
prep_dataset = dataset.map(
process,
remove_columns=["image"],
batched=True,
batch_size=batch_size,
num_proc=2,
features=features,
)
prep_dataset = prep_dataset.with_format("numpy")
# Split
train_dev_dataset = prep_dataset['test'].train_test_split(
test_size=test_size,
shuffle=True,
seed=seed
)
train_dev_test_dataset = DatasetDict({
'train': train_dev_dataset['train'],
'dev': train_dev_dataset['test'],
'test': prep_dataset['test'],
})
train_dev_test_dataset.save_to_disk(prep_dataset_dir)
# Train Model
import datetime
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.applications import InceptionV3
from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D, BatchNormalization
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping
from transformers import DefaultDataCollator
dataset = load_from_disk(prep_data_dir)
data_collator = DefaultDataCollator(return_tensors="tf")
train_dataset = dataset["train"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=True,
batch_size=batch_size,
collate_fn=data_collator
)
validation_dataset = dataset["dev"].to_tf_dataset(
columns=['pixel_values'],
label_cols=['label'],
shuffle=False,
batch_size=batch_size,
collate_fn=data_collator
)
print(f'{datetime.datetime.now()} - Saving Data')
tf.data.experimental.save(train_dataset, model_dir+"/train")
tf.data.experimental.save(validation_dataset, model_dir+"/val")
print(f'{datetime.datetime.now()} - Loading Data')
train_dataset = tf.data.experimental.load(model_dir+"/train")
validation_dataset = tf.data.experimental.load(model_dir+"/val")
shape = np.shape(dataset["train"][0]["pixel_values"])
backbone = InceptionV3(
include_top=False,
weights='imagenet',
input_shape=shape
)
for layer in backbone.layers:
layer.trainable = False
model = Sequential()
model.add(backbone)
model.add(GlobalAveragePooling2D())
model.add(Dense(128, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(64, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
print(model.summary())
earlyStopping = EarlyStopping(
monitor='val_loss',
patience=10,
verbose=0,
mode='min'
)
mcp_save = ModelCheckpoint(
f'{model_dir}/best_model.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min'
)
reduce_lr_loss = ReduceLROnPlateau(
monitor='val_loss',
factor=0.1,
patience=7,
verbose=1,
min_delta=0.0001,
mode='min'
)
hist = model.fit(
train_dataset,
epochs=epochs,
validation_data=validation_dataset,
callbacks=[earlyStopping, mcp_save, reduce_lr_loss]
)
```
## Expected results
Same performance when training without my "save/load hack" or a good explanation/recommendation about the issue.
## Actual results
Performance slower without my "save/load hack".
**Epoch Breakdown (without my "save/load hack"):**
- Epoch 1/10
41s 2s/step - loss: 1.6302 - accuracy: 0.5048 - val_loss: 1.4713 - val_accuracy: 0.3273 - lr: 0.0010
- Epoch 2/10
32s 2s/step - loss: 0.5357 - accuracy: 0.8510 - val_loss: 1.0447 - val_accuracy: 0.5818 - lr: 0.0010
- Epoch 3/10
36s 3s/step - loss: 0.3547 - accuracy: 0.9231 - val_loss: 0.6245 - val_accuracy: 0.7091 - lr: 0.0010
- Epoch 4/10
36s 3s/step - loss: 0.2721 - accuracy: 0.9231 - val_loss: 0.3395 - val_accuracy: 0.9091 - lr: 0.0010
- Epoch 5/10
32s 2s/step - loss: 0.1676 - accuracy: 0.9856 - val_loss: 0.2187 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 6/10
42s 3s/step - loss: 0.2066 - accuracy: 0.9615 - val_loss: 0.1635 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 7/10
32s 2s/step - loss: 0.1814 - accuracy: 0.9423 - val_loss: 0.1418 - val_accuracy: 0.9636 - lr: 0.0010
- Epoch 8/10
32s 2s/step - loss: 0.1301 - accuracy: 0.9856 - val_loss: 0.1388 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 9/10
loss: 0.1102 - accuracy: 0.9856 - val_loss: 0.1185 - val_accuracy: 0.9818 - lr: 0.0010
- Epoch 10/10
32s 2s/step - loss: 0.1013 - accuracy: 0.9808 - val_loss: 0.0978 - val_accuracy: 0.9818 - lr: 0.0010
**Epoch Breakdown (with my "save/load hack"):**
- Epoch 1/10
13s 625ms/step - loss: 3.0478 - accuracy: 0.1146 - val_loss: 2.3061 - val_accuracy: 0.0727 - lr: 0.0010
- Epoch 2/10
0s 80ms/step - loss: 2.3105 - accuracy: 0.2656 - val_loss: 2.3085 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 3/10
0s 77ms/step - loss: 1.8608 - accuracy: 0.3542 - val_loss: 2.3130 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 4/10
1s 98ms/step - loss: 1.8677 - accuracy: 0.3750 - val_loss: 2.3157 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 5/10
1s 204ms/step - loss: 1.5561 - accuracy: 0.4583 - val_loss: 2.3049 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 6/10
1s 210ms/step - loss: 1.4657 - accuracy: 0.4896 - val_loss: 2.2944 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 7/10
1s 205ms/step - loss: 1.4018 - accuracy: 0.5312 - val_loss: 2.2917 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 8/10
1s 207ms/step - loss: 1.2370 - accuracy: 0.5729 - val_loss: 2.2814 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 9/10
1s 214ms/step - loss: 1.1190 - accuracy: 0.6250 - val_loss: 2.2733 - val_accuracy: 0.0909 - lr: 0.0010
- Epoch 10/10
1s 207ms/step - loss: 1.1484 - accuracy: 0.6302 - val_loss: 2.2624 - val_accuracy: 0.0909 - lr: 0.0010
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
- TensorFlow: 2.8.0
- GPU (used during training): Tesla V100-SXM2-32GB
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"Hi ! cc @Rocketknight1 maybe you know better ?\r\n\r\nI'm not too familiar with `tf.data.experimental.save`. Note that `datasets` uses memory mapping, so depending on your hardware and the disk you are using you can expect performance differences with a dataset loaded in RAM",
"Hi @lehrig, I suspect what's happe... |
https://api.github.com/repos/huggingface/datasets/issues/2558 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2558/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2558/comments | https://api.github.com/repos/huggingface/datasets/issues/2558/events | https://github.com/huggingface/datasets/pull/2558 | 931,736,647 | MDExOlB1bGxSZXF1ZXN0Njc5MTg0Njk1 | 2,558 | Update: WebNLG - update checksums | [] | closed | false | null | 0 | 2021-06-28T16:16:37Z | 2021-06-28T17:23:17Z | 2021-06-28T17:23:16Z | null | The master branch changed so I computed the new checksums.
I also pinned a specific revision so that it doesn't happen again in the future.
Fix https://github.com/huggingface/datasets/issues/2553 | {
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https://api.github.com/repos/huggingface/datasets/issues/3919 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3919/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3919/comments | https://api.github.com/repos/huggingface/datasets/issues/3919/events | https://github.com/huggingface/datasets/issues/3919 | 1,169,497,210 | I_kwDODunzps5FtRx6 | 3,919 | AttributeError: 'DatasetDict' object has no attribute 'features' | [
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"default": true,
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"name": "bug",
"node_id": "MDU6TGFiZWwxOTM1ODkyODU3",
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}
] | closed | false | null | 2 | 2022-03-15T10:46:59Z | 2022-03-17T04:16:14Z | 2022-03-17T04:16:14Z | null | ## Describe the bug
Receiving the error when trying to check for Dataset features
## Steps to reproduce the bug
from datasets import Dataset
dataset = Dataset.from_pandas(df[['id', 'words', 'bboxes', 'ner_tags', 'image_path']])
dataset.features
## Expected results
A clear and concise description of the expected results.
## Actual results
Getting the following errror
AttributeError: 'DatasetDict' object has no attribute 'features'
## Environment info
Copy-and-paste the text below in your GitHub issue.
- `datasets` version: 1.18.4
- Platform: Linux-4.14.252-131.483.amzn1.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.13
- PyArrow version: 6.0.1
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} | https://api.github.com/repos/huggingface/datasets/issues/3919/timeline | null | completed | null | null | false | [
"You are likely trying to get the `features` from a `DatasetDict`, a dictionary containing `Datasets`. You probably first want to index into a particular split from your `DatasetDict` i.e. `dataset['train'].features`. \r\n\r\nFor example \r\n\r\n```python \r\nds = load_dataset('mnist')\r\nds.features\r\n```\r\nRetu... |
https://api.github.com/repos/huggingface/datasets/issues/1671 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1671/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1671/comments | https://api.github.com/repos/huggingface/datasets/issues/1671/events | https://github.com/huggingface/datasets/issues/1671 | 776,652,193 | MDU6SXNzdWU3NzY2NTIxOTM= | 1,671 | connection issue | [] | closed | false | null | 2 | 2020-12-30T21:56:20Z | 2022-10-05T12:42:12Z | 2022-10-05T12:42:12Z | null | Hi
I am getting this connection issue, resulting in large failure on cloud, @lhoestq I appreciate your help on this.
If I want to keep the codes the same, so not using save_to_disk, load_from_disk, but save the datastes in the way load_dataset reads from and copy the files in the same folder the datasets library reads from, could you assist me how this can be done, thanks
I tried to do read the data, save it to a path and then set HF_HOME, which does not work and this is still not reading from the old set path, could you assist me how to save the datasets in a path, and let dataset library read from this path to avoid connection issue. thanks
```
imdb = datasets.load_dataset("imdb")
imdb.save_to_disk("/idiap/temp/rkarimi/hf_datasets/imdb")
>>> os.environ["HF_HOME"]="/idiap/temp/rkarimi/hf_datasets/"
>>> imdb = datasets.load_dataset("imdb")
Reusing dataset imdb (/idiap/temp/rkarimi/cache_home_2/datasets/imdb/plain_text/1.0.0/90099cb476936b753383ba2ae6ab2eae419b2e87f71cd5189cb9c8e5814d12a3)
```
I tried afterwards to set HF_HOME in bash, this makes it read from it, but it cannot let dataset library load from the saved path and still downloading data. could you tell me how to fix this issue @lhoestq thanks
Also this is on cloud, so I save them in a path, copy it to "another machine" to load the data
### Error stack
```
Traceback (most recent call last):
File "./finetune_t5_trainer.py", line 344, in <module>
main()
File "./finetune_t5_trainer.py", line 232, in main
for task in data_args.eval_tasks} if training_args.do_test else None
File "./finetune_t5_trainer.py", line 232, in <dictcomp>
for task in data_args.eval_tasks} if training_args.do_test else None
File "/workdir/seq2seq/data/tasks.py", line 136, in get_dataset
split = self.get_sampled_split(split, n_obs)
File "/workdir/seq2seq/data/tasks.py", line 64, in get_sampled_split
dataset = self.load_dataset(split)
File "/workdir/seq2seq/data/tasks.py", line 454, in load_dataset
split=split, script_version="master")
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 263, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return http_head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 403, in http_head
url, proxies=proxies, headers=headers, cookies=cookies, allow_redirects=allow_redirects, timeout=timeout
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 542, in request
resp = self.send(prep, **send_kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/sessions.py", line 655, in send
r = adapter.send(request, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/requests/adapters.py", line 504, in send
raise ConnectTimeout(e, request=request)
requests.exceptions.ConnectTimeout: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/glue/glue.py (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7ff6d6c60a20>, 'Connection to s3.amazonaws.com timed out. (connect timeout=10)'))
```
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"Also, mayjor issue for me is the format issue, even if I go through changing the whole code to use load_from_disk, then if I do \r\n\r\nd = datasets.load_from_disk(\"imdb\")\r\nd = d[\"train\"][:10] => the format of this is no more in datasets format\r\nthis is different from you call load_datasets(\"train[10]\")\... |
https://api.github.com/repos/huggingface/datasets/issues/2611 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2611/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2611/comments | https://api.github.com/repos/huggingface/datasets/issues/2611/events | https://github.com/huggingface/datasets/pull/2611 | 940,307,053 | MDExOlB1bGxSZXF1ZXN0Njg2Mzk5MjU3 | 2,611 | More consistent naming | [] | closed | false | null | 0 | 2021-07-09T00:09:17Z | 2021-07-13T17:13:19Z | 2021-07-13T16:08:30Z | null | As per @stas00's suggestion in #2500, this PR inserts a space between the logo and the lib name (`🤗Datasets` -> `🤗 Datasets`) for consistency with the Transformers lib. Additionally, more consistent names are used for Datasets Hub, etc. | {
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https://api.github.com/repos/huggingface/datasets/issues/2859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2859/comments | https://api.github.com/repos/huggingface/datasets/issues/2859/events | https://github.com/huggingface/datasets/issues/2859 | 984,324,500 | MDU6SXNzdWU5ODQzMjQ1MDA= | 2,859 | Loading allenai/c4 in streaming mode does too many HEAD requests | [
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"name": "enhancement",
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"default": fals... | closed | false | null | 2 | 2021-08-31T21:11:04Z | 2021-10-12T07:35:52Z | 2021-10-11T11:05:51Z | null | This does 60,000+ HEAD requests to get all the ETags of all the data files:
```python
from datasets import load_dataset
load_dataset("allenai/c4", streaming=True)
```
It makes loading the dataset completely impractical.
The ETags are used to compute the config id (it must depend on the data files being used).
Instead of using the ETags, we could simply use the commit hash of the dataset repository on the hub, as well and the glob pattern used to resolve the files (here it's `*` by default, to load all the files of the repository) | {
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"https://github.com/huggingface/datasets/blob/6c766f9115d686182d76b1b937cb27e099c45d68/src/datasets/builder.py#L179-L186",
"Thanks a lot!!!"
] |
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 | [] | closed | false | null | 8 | 2020-10-23T13:57:00Z | 2020-12-23T14:06:29Z | 2020-12-23T14:06:29Z | 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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"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\",... |
https://api.github.com/repos/huggingface/datasets/issues/240 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/240/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/240/comments | https://api.github.com/repos/huggingface/datasets/issues/240/events | https://github.com/huggingface/datasets/issues/240 | 631,434,677 | MDU6SXNzdWU2MzE0MzQ2Nzc= | 240 | Deterministic dataset loading | [] | closed | false | null | 4 | 2020-06-05T09:03:26Z | 2020-06-08T09:18:14Z | 2020-06-08T09:18:14Z | null | When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180
which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered
I think we should go through all the dataset scripts and make sure to have deterministic behavior.
A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name.
What do you think @lhoestq? | {
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"Yes good point !",
"I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok w... |
https://api.github.com/repos/huggingface/datasets/issues/2830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2830/comments | https://api.github.com/repos/huggingface/datasets/issues/2830/events | https://github.com/huggingface/datasets/pull/2830 | 977,563,947 | MDExOlB1bGxSZXF1ZXN0NzE4MjkyMTM2 | 2,830 | Add imagefolder dataset | [] | closed | false | null | 15 | 2021-08-23T23:34:06Z | 2022-03-01T16:29:44Z | 2022-03-01T16:29:44Z | null | A generic imagefolder dataset inspired by `torchvision.datasets.ImageFolder`.
Resolves #2508
---
Example Usage:
[](https://colab.research.google.com/gist/nateraw/954fa8cba4ff806f6147a782fa9efd1a/imagefolder-official-example.ipynb) | {
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"@lhoestq @albertvillanova it would be super cool if we could get the Image Classification task to work with this. I'm not sure how to have the dataset find the unique label names _after_ the dataset has been loaded. Is that even possible? \r\n\r\nMy hacky community version [here](https://huggingface.co/datasets/na... |
https://api.github.com/repos/huggingface/datasets/issues/4708 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4708/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4708/comments | https://api.github.com/repos/huggingface/datasets/issues/4708/events | https://github.com/huggingface/datasets/pull/4708 | 1,308,279,700 | PR_kwDODunzps47lewm | 4,708 | Fix require torchaudio and refactor test requirements | [] | closed | false | null | 1 | 2022-07-18T17:24:28Z | 2022-07-22T06:30:56Z | 2022-07-22T06:18:11Z | null | Currently there is a bug in `require_torchaudio` (indeed it is requiring `sox` instead):
```python
def require_torchaudio(test_case):
if find_spec("sox") is None:
...
```
The bug was introduced by:
- #3685
- Commit: https://github.com/huggingface/datasets/pull/3685/commits/b5a3e7122d49c4dcc9333b1d8d18a833fc04b940
which moved
```python
require_sndfile = pytest.mark.skipif(
# In Windows and OS X, soundfile installs sndfile
(sys.platform != "linux" and find_spec("soundfile") is None)
# In Linux, soundfile throws RuntimeError if sndfile not installed with distribution package manager
or (sys.platform == "linux" and find_library("sndfile") is None),
reason="Test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)
require_sox = pytest.mark.skipif(
find_library("sox") is None,
reason="Test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`",
)
require_torchaudio = pytest.mark.skipif(find_spec("torchaudio") is None, reason="Test requires 'torchaudio'")
```
to
```python
def require_sndfile(test_case):
"""
Decorator marking a test that requires soundfile.
These tests are skipped when soundfile isn't installed.
"""
if (sys.platform != "linux" and find_spec("soundfile") is None) or (
sys.platform == "linux" and find_library("sndfile") is None
):
test_case = unittest.skip(
"test requires 'sndfile': `pip install soundfile`; "
"Linux requires sndfile installed with distribution package manager, e.g.: `sudo apt-get install libsndfile1`",
)(test_case)
return test_case
def require_sox(test_case):
"""
Decorator marking a test that requires sox.
These tests are skipped when sox isn't installed.
"""
if find_library("sox") is None:
return unittest.skip("test requires 'sox'; only available in non-Windows, e.g.: `sudo apt-get install sox`")(
test_case
)
return test_case
def require_torchaudio(test_case):
"""
Decorator marking a test that requires torchaudio.
These tests are skipped when torchaudio isn't installed.
"""
if find_spec("sox") is None:
return unittest.skip("test requires 'torchaudio'")(test_case)
return test_case
```
This PR;
- fixes the bug in `require_torchaudio`
- refactors the test requirements back to using `pytest` instead of `unittest`
- the text in `pytest.skipif` `reason` can be used if needed in a test body: `require_torchaudio.kwargs["reason"]` | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/5820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5820/comments | https://api.github.com/repos/huggingface/datasets/issues/5820/events | https://github.com/huggingface/datasets/issues/5820 | 1,695,892,811 | I_kwDODunzps5lFUVL | 5,820 | Incomplete docstring for `BuilderConfig` | [
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] | closed | false | null | 1 | 2023-05-04T12:14:34Z | 2023-05-05T12:31:56Z | 2023-05-05T12:31:56Z | null | Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.
https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117 | {
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"Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.\r\n\r\nThis class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core... |
https://api.github.com/repos/huggingface/datasets/issues/4799 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4799/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4799/comments | https://api.github.com/repos/huggingface/datasets/issues/4799/events | https://github.com/huggingface/datasets/issues/4799 | 1,330,889,854 | I_kwDODunzps5PU8R- | 4,799 | video dataset loader/parser | [
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] | closed | false | null | 2 | 2022-08-07T01:54:12Z | 2022-08-09T16:42:51Z | 2022-08-09T16:42:51Z | null | you know how you can [use `load_dataset` with any arbitrary csv file](https://huggingface.co/docs/datasets/loading#csv)? and you can also [use it to load a local image dataset](https://huggingface.co/docs/datasets/image_load#local-files)?
could you please add functionality to load a video dataset? it would be really cool if i could point it to a bunch of video files and use pytorch to start looping through batches of videos. like if my batch size is 16, each sample in the batch is a frame from a video. i'm competing in the [minerl challenge](https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition), and it would be awesome to use the HF ecosystem. | {
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"Hi! We've just started discussing the video support in `datasets` (decoding backends, video feature type, etc.), so I believe we should have something tangible by the end of this year.\r\n\r\nAlso, if you have additional video features in mind that you would like to see, feel free to let us know",
"Coool thanks ... |
https://api.github.com/repos/huggingface/datasets/issues/212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/212/comments | https://api.github.com/repos/huggingface/datasets/issues/212/events | https://github.com/huggingface/datasets/pull/212 | 626,580,198 | MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy | 212 | have 'add' and 'add_batch' for metrics | [] | closed | false | null | 0 | 2020-05-28T14:56:47Z | 2020-05-29T10:41:05Z | 2020-05-29T10:41:04Z | null | This should fix #116
Previously the `.add` method of metrics expected a batch of examples.
Now `.add` expects one prediction/reference and `.add_batch` expects a batch.
I think it is more coherent with the way the ArrowWriter works. | {
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https://api.github.com/repos/huggingface/datasets/issues/5559 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5559/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5559/comments | https://api.github.com/repos/huggingface/datasets/issues/5559/events | https://github.com/huggingface/datasets/pull/5559 | 1,593,676,489 | PR_kwDODunzps5KcKSb | 5,559 | Fix map suffix_template | [] | closed | false | null | 4 | 2023-02-21T15:26:26Z | 2023-02-21T17:21:37Z | 2023-02-21T17:14:29Z | null | #5455 introduced a small bug that lead `map` to ignore the `suffix_template` argument and not put suffixes to cached files in multiprocessing.
I fixed this and also improved a few things:
- regarding logging: "Loading cached processed dataset" is now logged only once even in multiprocessing (it used to be logged `num_proc` times)
- regarding new_fingerprint: I made sure that the returned dataset satisfies `ds._fingerprint==new_fingerprint` if `new_fingerprint` is passed to `map` | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/1633 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1633/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1633/comments | https://api.github.com/repos/huggingface/datasets/issues/1633/events | https://github.com/huggingface/datasets/issues/1633 | 774,422,603 | MDU6SXNzdWU3NzQ0MjI2MDM= | 1,633 | social_i_qa wrong format of labels | [] | closed | false | null | 2 | 2020-12-24T13:11:54Z | 2020-12-30T17:18:49Z | 2020-12-30T17:18:49Z | null | Hi,
there is extra "\n" in labels of social_i_qa datasets, no big deal, but I was wondering if you could remove it to make it consistent.
so label is 'label': '1\n', not '1'
thanks
```
>>> import datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset(
... 'social_i_qa')
cahce dir /julia/cache/datasets
Downloading: 4.72kB [00:00, 3.52MB/s]
cahce dir /julia/cache/datasets
Downloading: 2.19kB [00:00, 1.81MB/s]
Using custom data configuration default
Reusing dataset social_i_qa (/julia/datasets/social_i_qa/default/0.1.0/4a4190cc2d2482d43416c2167c0c5dccdd769d4482e84893614bd069e5c3ba06)
>>> dataset['train'][0]
{'answerA': 'like attending', 'answerB': 'like staying home', 'answerC': 'a good friend to have', 'context': 'Cameron decided to have a barbecue and gathered her friends together.', 'label': '1\n', 'question': 'How would Others feel as a result?'}
```
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"@lhoestq, should I raise a PR for this? Just a minor change while reading labels text file",
"Sure feel free to open a PR thanks !"
] |
https://api.github.com/repos/huggingface/datasets/issues/2030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2030/comments | https://api.github.com/repos/huggingface/datasets/issues/2030/events | https://github.com/huggingface/datasets/pull/2030 | 829,110,803 | MDExOlB1bGxSZXF1ZXN0NTkwODI4NzQ4 | 2,030 | Implement Dataset from text | [] | closed | false | null | 1 | 2021-03-11T12:34:50Z | 2021-03-18T13:29:29Z | 2021-03-18T13:29:29Z | null | Implement `Dataset.from_text`.
Analogue to #1943, #1946. | {
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"I am wondering why only one test of \"keep_in_memory=True\" fails, when there are many other tests that test the same and it happens only in pyarrow_1..."
] |
https://api.github.com/repos/huggingface/datasets/issues/640 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/640/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/640/comments | https://api.github.com/repos/huggingface/datasets/issues/640/events | https://github.com/huggingface/datasets/pull/640 | 704,311,758 | MDExOlB1bGxSZXF1ZXN0NDg5MjYwNTc1 | 640 | Make shuffle compatible with temp_seed | [] | closed | false | null | 0 | 2020-09-18T11:38:58Z | 2020-09-18T11:47:51Z | 2020-09-18T11:47:50Z | null | This code used to return different dataset at each run
```python
import dataset as ds
dataset = ...
with ds.temp_seed(42):
shuffled = dataset.shuffle()
```
Now it returns the same one since the seed is set | {
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https://api.github.com/repos/huggingface/datasets/issues/911 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/911/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/911/comments | https://api.github.com/repos/huggingface/datasets/issues/911/events | https://github.com/huggingface/datasets/issues/911 | 752,806,215 | MDU6SXNzdWU3NTI4MDYyMTU= | 911 | datasets module not found | [] | closed | false | null | 1 | 2020-11-29T01:24:15Z | 2020-11-29T14:33:09Z | 2020-11-29T14:33:09Z | null | Currently, running `from datasets import load_dataset` will throw a `ModuleNotFoundError: No module named 'datasets'` error.
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"nvm, I'd made an assumption that the library gets installed with transformers. "
] |
https://api.github.com/repos/huggingface/datasets/issues/3378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3378/comments | https://api.github.com/repos/huggingface/datasets/issues/3378/events | https://github.com/huggingface/datasets/pull/3378 | 1,070,580,126 | PR_kwDODunzps4vXF1D | 3,378 | Add The Pile subsets | [] | closed | false | null | 0 | 2021-12-03T13:14:54Z | 2021-12-09T18:11:25Z | 2021-12-09T18:11:23Z | null | Add The Pile subsets:
- pubmed
- ubuntu_irc
- europarl
- hacker_news
- nih_exporter
Close bigscience-workshop/data_tooling#301.
CC: @StellaAthena | {
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https://api.github.com/repos/huggingface/datasets/issues/2875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2875/comments | https://api.github.com/repos/huggingface/datasets/issues/2875/events | https://github.com/huggingface/datasets/issues/2875 | 989,919,398 | MDU6SXNzdWU5ODk5MTkzOTg= | 2,875 | Add Congolese Swahili speech datasets | [
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"color": "d93f0b",... | open | false | null | 0 | 2021-09-07T12:13:50Z | 2021-09-07T12:13:50Z | null | null | ## Adding a Dataset
- **Name:** Congolese Swahili speech corpora
- **Data:** https://gamayun.translatorswb.org/data/
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Also related: https://mobile.twitter.com/OktemAlp/status/1435196393631764482 | {
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https://api.github.com/repos/huggingface/datasets/issues/2743 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2743/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2743/comments | https://api.github.com/repos/huggingface/datasets/issues/2743/events | https://github.com/huggingface/datasets/issues/2743 | 958,119,251 | MDU6SXNzdWU5NTgxMTkyNTE= | 2,743 | Dataset JSON is incorrect | [
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] | closed | false | null | 2 | 2021-08-02T13:01:26Z | 2021-08-03T10:06:57Z | 2021-08-03T09:25:33Z | null | ## Describe the bug
The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.
The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Steps to reproduce the bug
Look at the files.
## Expected results
The first key should be `plain_text`:
```json
{
"plain_text": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Actual results
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
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"As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:\r\n> Indeed there is some problem/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...\r\nIn the meanwhile, in orde... |
https://api.github.com/repos/huggingface/datasets/issues/2550 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2550/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2550/comments | https://api.github.com/repos/huggingface/datasets/issues/2550/events | https://github.com/huggingface/datasets/issues/2550 | 930,951,287 | MDU6SXNzdWU5MzA5NTEyODc= | 2,550 | Allow for incremental cumulative metric updates in a distributed setup | [
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] | closed | false | null | 0 | 2021-06-27T15:00:58Z | 2021-09-26T13:42:39Z | 2021-09-26T13:42:39Z | null | Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.
If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411
If this change is ok with you, I'll open a pull request.
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https://api.github.com/repos/huggingface/datasets/issues/164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/164/comments | https://api.github.com/repos/huggingface/datasets/issues/164/events | https://github.com/huggingface/datasets/issues/164 | 620,540,250 | MDU6SXNzdWU2MjA1NDAyNTA= | 164 | Add Spanish POR and NER Datasets | [
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] | closed | false | null | 2 | 2020-05-18T22:18:21Z | 2020-05-25T16:28:45Z | 2020-05-25T16:28:45Z | null | Hi guys,
In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks.
I can provide it in raw and preprocessed formats. | {
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"Hello @mrm8488, are these datasets official datasets published in an NLP/CL/ML venue?",
"What about this one: https://github.com/ccasimiro88/TranslateAlignRetrieve?"
] |
https://api.github.com/repos/huggingface/datasets/issues/892 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/892/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/892/comments | https://api.github.com/repos/huggingface/datasets/issues/892/events | https://github.com/huggingface/datasets/pull/892 | 751,658,262 | MDExOlB1bGxSZXF1ZXN0NTI4MTMxNTE1 | 892 | Add a few datasets of reference in the documentation | [] | closed | false | null | 3 | 2020-11-26T15:02:39Z | 2020-11-27T18:08:45Z | 2020-11-27T18:08:44Z | null | I started making a small list of various datasets of reference in the documentation.
Since many datasets share a lot in common I think it's good to have a list of datasets scripts to get some inspiration from.
Let me know what you think, and if you have ideas of other datasets that we may add to this list, please let me know. | {
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"Looks good to me. Do we also support TSV in this helper (explain if it should be text or CSV) and in the dummy-data creator?",
"snli is basically based on tsv files (but named as .txt) and it is in the list of datasets of reference.\r\nThe dummy data creator supports tsv",
"merging this one.\r\nIf you think of... |
https://api.github.com/repos/huggingface/datasets/issues/5579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5579/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5579/comments | https://api.github.com/repos/huggingface/datasets/issues/5579/events | https://github.com/huggingface/datasets/pull/5579 | 1,599,732,211 | PR_kwDODunzps5Kwgo4 | 5,579 | Add instructions to create `DataLoader` from augmented dataset in object detection guide | [] | closed | false | null | 3 | 2023-02-25T14:53:17Z | 2023-03-23T19:24:59Z | 2023-03-23T19:24:50Z | null | The following adds instructions on how to create a `DataLoader` from the guide on how to use object detection with augmentations (#4710). I am open to hearing any suggestions for improvement ! | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.",
"I'm not sure we need this part as we provide a link to the notebook that shows how to train an object detection model, and this notebook instantiat... |
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? | [] | closed | false | null | 5 | 2020-10-28T09:16:19Z | 2022-02-22T12:22:52Z | 2022-02-22T12:22:52Z | 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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"`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... |
https://api.github.com/repos/huggingface/datasets/issues/4439 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4439/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4439/comments | https://api.github.com/repos/huggingface/datasets/issues/4439/events | https://github.com/huggingface/datasets/issues/4439 | 1,258,434,111 | I_kwDODunzps5LAi4_ | 4,439 | TIMIT won't load after manual download: Errors about files that don't exist | [
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] | closed | false | null | 3 | 2022-06-02T16:35:56Z | 2022-06-03T08:44:17Z | 2022-06-03T08:44:16Z | null | ## Describe the bug
I get the message from HuggingFace that it must be downloaded manually. From the URL provided in the message, I got to UPenn page for manual download. (UPenn apparently want $250? for the dataset??) ...So, ok, I obtained a copy from a friend and also a smaller version from Kaggle. But in both cases the HF dataloader fails; it is looking for files that don't exist anywhere in the dataset: it is looking for files with lower-case letters like "**test*" (all the filenames in both my copies are uppercase) and certain file extensions that exclude the .DOC which is provided in TIMIT:
## Steps to reproduce the bug
```python
data = load_dataset('timit_asr', 'clean')['train']
```
## Expected results
The dataset should load with no errors.
## Actual results
This error message:
```
File "/home/ubuntu/envs/data2vec/lib/python3.9/site-packages/datasets/data_files.py", line 201, in resolve_patterns_locally_or_by_urls
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to resolve any data file that matches '['**test*', '**eval*']' at /home/ubuntu/datasets/timit with any supported extension ['csv', 'tsv', 'json', 'jsonl', 'parquet', 'txt', 'blp', 'bmp', 'dib', 'bufr', 'cur', 'pcx', 'dcx', 'dds', 'ps', 'eps', 'fit', 'fits', 'fli', 'flc', 'ftc', 'ftu', 'gbr', 'gif', 'grib', 'h5', 'hdf', 'png', 'apng', 'jp2', 'j2k', 'jpc', 'jpf', 'jpx', 'j2c', 'icns', 'ico', 'im', 'iim', 'tif', 'tiff', 'jfif', 'jpe', 'jpg', 'jpeg', 'mpg', 'mpeg', 'msp', 'pcd', 'pxr', 'pbm', 'pgm', 'ppm', 'pnm', 'psd', 'bw', 'rgb', 'rgba', 'sgi', 'ras', 'tga', 'icb', 'vda', 'vst', 'webp', 'wmf', 'emf', 'xbm', 'xpm', 'zip']
```
But this is a strange sort of error: why is it looking for lower-case file names when all the TIMIT dataset filenames are uppercase? Why does it exclude .DOC files when the only parts of the TIMIT data set with "TEST" in them have ".DOC" extensions? ...I wonder, how was anyone able to get this to work in the first place?
The files in the dataset look like the following:
```
³ PHONCODE.DOC
³ PROMPTS.TXT
³ SPKRINFO.TXT
³ SPKRSENT.TXT
³ TESTSET.DOC
```
...so why are these being excluded by the dataset loader?
## Environment info
- `datasets` version: 2.2.2
- Platform: Linux-5.4.0-1060-aws-x86_64-with-glibc2.27
- Python version: 3.9.9
- PyArrow version: 8.0.0
- Pandas version: 1.4.2
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"To have some context, please see:\r\n- #4145\r\n\r\nPlease, also note that we have recently made some fixes to the script, which are in our GitHub master branch but not yet released:\r\n- #4422\r\n- #4425 \r\n- #4436",
"Thanks Albert! I'll try pulling `datasets` from the git repo instead of PyPI, and/or just wai... |
https://api.github.com/repos/huggingface/datasets/issues/1940 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1940/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1940/comments | https://api.github.com/repos/huggingface/datasets/issues/1940/events | https://github.com/huggingface/datasets/issues/1940 | 815,770,012 | MDU6SXNzdWU4MTU3NzAwMTI= | 1,940 | Side effect when filtering data due to `does_function_return_dict` call in `Dataset.map()` | [
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] | closed | false | null | 2 | 2021-02-24T19:18:56Z | 2021-03-23T15:26:49Z | 2021-03-23T15:26:49Z | null | Hi there!
In my codebase I have a function to filter rows in a dataset, selecting only a certain number of examples per class. The function passes a extra argument to maintain a counter of the number of dataset rows/examples already selected per each class, which are the ones I want to keep in the end:
```python
def fill_train_examples_per_class(example, per_class_limit: int, counter: collections.Counter):
label = int(example['label'])
current_counter = counter.get(label, 0)
if current_counter < per_class_limit:
counter[label] = current_counter + 1
return True
return False
```
At some point I invoke it through the `Dataset.filter()` method in the `arrow_dataset.py` module like this:
```python
...
kwargs = {"per_class_limit": train_examples_per_class_limit, "counter": Counter()}
datasets['train'] = datasets['train'].filter(fill_train_examples_per_class, num_proc=1, fn_kwargs=kwargs)
...
```
The problem is that, passing a stateful container (the counter,) provokes a side effect in the new filtered dataset obtained. This is due to the fact that at some point in `filter()`, the `map()`'s function `does_function_return_dict` is invoked in line [1290](https://github.com/huggingface/datasets/blob/96578adface7e4bc1f3e8bafbac920d72ca1ca60/src/datasets/arrow_dataset.py#L1290).
When this occurs, the state of the counter is initially modified by the effects of the function call on the 1 or 2 rows selected in lines 1288 and 1289 of the same file (which are marked as `test_inputs` & `test_indices` respectively in lines 1288 and 1289. This happens out of the control of the user (which for example can't reset the state of the counter before continuing the execution,) provoking in the end an undesired side effect in the results obtained.
In my case, the resulting dataset -despite of the counter results are ok- lacks an instance of the classes 0 and 1 (which happen to be the classes of the first two examples of my dataset.) The rest of the classes I have in my dataset, contain the right number of examples as they were not affected by the effects of `does_function_return_dict` call.
I've debugged my code extensively and made a workaround myself hardcoding the necessary stuff (basically putting `update_data=True` in line 1290,) and then I obtain the results I expected without the side effect.
Is there a way to avoid that call to `does_function_return_dict` in map()'s line 1290 ? (e.g. extracting the required information that `does_function_return_dict` returns without making the testing calls to the user function on dataset rows 0 & 1)
Thanks in advance,
Francisco Perez-Sorrosal
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"Thanks for the report !\r\n\r\nCurrently we don't have a way to let the user easily disable this behavior.\r\nHowever I agree that we should support stateful processing functions, ideally by removing `does_function_return_dict`.\r\n\r\nWe needed this function in order to know whether the `map` functions needs to w... |
https://api.github.com/repos/huggingface/datasets/issues/617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/617/comments | https://api.github.com/repos/huggingface/datasets/issues/617/events | https://github.com/huggingface/datasets/issues/617 | 699,472,596 | MDU6SXNzdWU2OTk0NzI1OTY= | 617 | Compare different Rouge implementations | [] | closed | false | null | 7 | 2020-09-11T15:49:32Z | 2023-03-22T12:08:44Z | 2020-10-02T09:52:18Z | null | I used RougeL implementation provided in `datasets` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https://arxiv.org/pdf/1909.03186.pdf) for example.
Can you make sure the google-research implementation you are using matches the official perl implementation?
There are a couple of python wrappers around the perl implementation, [this](https://pypi.org/project/pyrouge/) has been commonly used, and [this](https://github.com/pltrdy/files2rouge) is used in fairseq).
There's also a python reimplementation [here](https://github.com/pltrdy/rouge) but its RougeL numbers are way off.
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"Updates - the differences between the following three\r\n(1) https://github.com/bheinzerling/pyrouge (previously popular. The one I trust the most)\r\n(2) https://github.com/google-research/google-research/tree/master/rouge\r\n(3) https://github.com/pltrdy/files2rouge (used in fairseq)\r\ncan be explained by two t... |
https://api.github.com/repos/huggingface/datasets/issues/688 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/688/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/688/comments | https://api.github.com/repos/huggingface/datasets/issues/688/events | https://github.com/huggingface/datasets/pull/688 | 711,804,828 | MDExOlB1bGxSZXF1ZXN0NDk1MzkwMTc1 | 688 | Disable tokenizers parallelism in multiprocessed map | [] | closed | false | null | 0 | 2020-09-30T09:53:34Z | 2020-10-01T08:45:46Z | 2020-10-01T08:45:45Z | null | It was reported in #620 that using multiprocessing with a tokenizers shows this message:
```
The current process just got forked. Disabling parallelism to avoid deadlocks...
To disable this warning, please explicitly set TOKENIZERS_PARALLELISM=(true | false)
```
This message is shown when TOKENIZERS_PARALLELISM is unset.
Moreover if it is set to `true`, then the program just hangs.
To hide the message (if TOKENIZERS_PARALLELISM is unset) and avoid hanging (if TOKENIZERS_PARALLELISM is `true`), then I set TOKENIZERS_PARALLELISM to `false` when forking the process. After forking is gets back to its original value.
Also I added a warning if TOKENIZERS_PARALLELISM was `true` and is set to `false`:
```
Setting TOKENIZERS_PARALLELISM=false for forked processes.
```
cc @n1t0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5427 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5427/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5427/comments | https://api.github.com/repos/huggingface/datasets/issues/5427/events | https://github.com/huggingface/datasets/issues/5427 | 1,535,162,889 | I_kwDODunzps5bgLoJ | 5,427 | Unable to download dataset id_clickbait | [] | closed | false | null | 1 | 2023-01-16T16:05:36Z | 2023-01-18T09:51:28Z | 2023-01-18T09:25:19Z | null | ### Describe the bug
I tried to download dataset `id_clickbait`, but receive this error message.
```
FileNotFoundError: Couldn't find file at https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip
```
When i open the link using browser, i got this XML data.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>NoSuchBucket</Code><Message>The specified bucket does not exist</Message><BucketName>md-datasets-cache-zipfiles-prod</BucketName><RequestId>NVRM6VEEQD69SD00</RequestId><HostId>W/SPDxLGvlCGi0OD6d7mSDvfOAUqLAfvs9nTX50BkJrjMny+X9Jnqp/Li2lG9eTUuT4MUkAA2jjTfCrCiUmu7A==</HostId></Error>
```
### Steps to reproduce the bug
Code snippet:
```
from datasets import load_dataset
load_dataset('id_clickbait', 'annotated')
load_dataset('id_clickbait', 'raw')
```
Link to Kaggle notebook: https://www.kaggle.com/code/ilosvigil/bug-check-on-id-clickbait-dataset
### Expected behavior
Successfully download and load `id_newspaper` dataset.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid
- Python version: 3.7.12
- PyArrow version: 8.0.0
- Pandas version: 1.3.5 | {
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"Thanks for reporting, @ilos-vigil.\r\n\r\nWe have transferred this issue to the corresponding dataset on the Hugging Face Hub: https://huggingface.co/datasets/id_clickbait/discussions/1 "
] |
https://api.github.com/repos/huggingface/datasets/issues/2959 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2959/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2959/comments | https://api.github.com/repos/huggingface/datasets/issues/2959/events | https://github.com/huggingface/datasets/pull/2959 | 1,005,547,632 | PR_kwDODunzps4sMihl | 2,959 | Added computer vision tasks | [] | closed | false | null | 5 | 2021-09-23T15:07:27Z | 2022-03-01T17:41:51Z | 2022-03-01T17:41:51Z | null | Added various image processing/computer vision tasks. | {
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"Looks great, thanks ! If the 3d ones are really rare we can remove them for now.\r\n\r\nAnd I can see that `object-detection` and `semantic-segmentation` are both task categories (top-level) and task ids (bottom-level). Maybe there's a way to group them and have less granularity for the task categories. For exampl... |
https://api.github.com/repos/huggingface/datasets/issues/2569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2569/comments | https://api.github.com/repos/huggingface/datasets/issues/2569/events | https://github.com/huggingface/datasets/issues/2569 | 933,015,797 | MDU6SXNzdWU5MzMwMTU3OTc= | 2,569 | Weights of model checkpoint not initialized for RobertaModel for Bertscore | [
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] | closed | false | null | 2 | 2021-06-29T18:55:23Z | 2021-07-01T07:08:59Z | 2021-06-30T07:35:49Z | null | When applying bertscore out of the box,
```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```
Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html
```
from datasets import load_metric
metric = load_metric('bertscore')
# Example of typical usage
for batch in dataset:
inputs, references = batch
predictions = model(inputs)
metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```
I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version: 3.9.5
- PyArrow version: 3.0.0
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"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei... |
https://api.github.com/repos/huggingface/datasets/issues/4713 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4713/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4713/comments | https://api.github.com/repos/huggingface/datasets/issues/4713/events | https://github.com/huggingface/datasets/pull/4713 | 1,309,184,756 | PR_kwDODunzps47ojC1 | 4,713 | Document installation of sox OS dependency for audio | [] | closed | false | null | 1 | 2022-07-19T08:42:35Z | 2022-07-21T08:16:59Z | 2022-07-21T08:04:15Z | null | The `sox` OS package needs being installed manually using the distribution package manager.
This PR adds this explanation to the docs. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/2594 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2594/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2594/comments | https://api.github.com/repos/huggingface/datasets/issues/2594/events | https://github.com/huggingface/datasets/pull/2594 | 937,294,772 | MDExOlB1bGxSZXF1ZXN0NjgzODc0NjIz | 2,594 | Fix BibTeX entry | [] | closed | false | null | 0 | 2021-07-05T18:24:10Z | 2021-07-06T04:59:38Z | 2021-07-06T04:59:38Z | null | Fix BibTeX entry. | {
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https://api.github.com/repos/huggingface/datasets/issues/1188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1188/comments | https://api.github.com/repos/huggingface/datasets/issues/1188/events | https://github.com/huggingface/datasets/pull/1188 | 757,827,407 | MDExOlB1bGxSZXF1ZXN0NTMzMTI2MTcw | 1,188 | adding hind_encorp dataset | [] | closed | false | null | 13 | 2020-12-06T02:18:45Z | 2020-12-11T17:40:41Z | 2020-12-11T17:40:41Z | null | adding Hindi_Encorp05 dataset | {
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"help needed in dummy data",
"extension of the file is .plaintext so dummy data generation is failing\r\n",
"you can add the `--match_text_file \"*.plaintext\"` flag when generating the dummy data\r\n\r\nalso it looks like the PR is empty, is this expected ?",
"yes it is expected because I made all my change... |
https://api.github.com/repos/huggingface/datasets/issues/4372 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4372/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4372/comments | https://api.github.com/repos/huggingface/datasets/issues/4372/events | https://github.com/huggingface/datasets/pull/4372 | 1,241,703,826 | PR_kwDODunzps44HeYC | 4,372 | Check if dataset features match before push in `DatasetDict.push_to_hub` | [] | closed | false | null | 1 | 2022-05-19T12:32:30Z | 2022-05-20T15:23:36Z | 2022-05-20T15:15:30Z | null | Fix #4211 | {
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] |
https://api.github.com/repos/huggingface/datasets/issues/524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/524/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/524/comments | https://api.github.com/repos/huggingface/datasets/issues/524/events | https://github.com/huggingface/datasets/issues/524 | 683,686,359 | MDU6SXNzdWU2ODM2ODYzNTk= | 524 | Some docs are missing parameter names | [] | closed | false | null | 1 | 2020-08-21T16:47:34Z | 2020-08-25T09:04:03Z | 2020-08-25T09:04:03Z | null | See https://huggingface.co/nlp/master/package_reference/main_classes.html#nlp.Dataset.map. I believe this is because the parameter names are enclosed in backticks in the docstrings, maybe it's an old docstring format that doesn't work with the current Sphinx version. | {
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"Indeed, good catch!"
] |
https://api.github.com/repos/huggingface/datasets/issues/2616 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2616/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2616/comments | https://api.github.com/repos/huggingface/datasets/issues/2616/events | https://github.com/huggingface/datasets/pull/2616 | 940,799,038 | MDExOlB1bGxSZXF1ZXN0Njg2ODE3NjYz | 2,616 | Support remote data files | [
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} | 2 | 2021-07-09T14:07:38Z | 2021-07-09T16:13:41Z | 2021-07-09T16:13:41Z | null | Add support for (streaming) remote data files:
```python
data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{relative_file_path}"
ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
```
cc: @thomwolf | {
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"@lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?",
"> @lhoestq maybe we could also use (if available) the ETag of the remote file in `create_config_id`?\r\n\r\nSure ! We can get the ETag with\r\n```python\r\nheaders = get_authentication_headers_for_url(url, use_a... |
https://api.github.com/repos/huggingface/datasets/issues/1844 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1844/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1844/comments | https://api.github.com/repos/huggingface/datasets/issues/1844/events | https://github.com/huggingface/datasets/issues/1844 | 803,588,125 | MDU6SXNzdWU4MDM1ODgxMjU= | 1,844 | Update Open Subtitles corpus with original sentence IDs | [
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] | closed | false | null | 6 | 2021-02-08T13:55:13Z | 2021-02-12T17:38:58Z | 2021-02-12T17:38:58Z | null | Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles).
I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.
I think I should tag @abhishekkrthakur as he's the one who added it in the first place.
Thanks! | {
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"Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles... |
https://api.github.com/repos/huggingface/datasets/issues/29 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/29/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/29/comments | https://api.github.com/repos/huggingface/datasets/issues/29/events | https://github.com/huggingface/datasets/pull/29 | 610,243,997 | MDExOlB1bGxSZXF1ZXN0NDExNzIwODMx | 29 | Hf_api small changes | [] | closed | false | null | 1 | 2020-04-30T17:06:43Z | 2020-04-30T19:51:45Z | 2020-04-30T19:51:44Z | null | From Patrick:
```python
from nlp import hf_api
api = hf_api.HfApi()
api.dataset_list()
```
works :-) | {
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"Ok merging! I think it's good now"
] |
https://api.github.com/repos/huggingface/datasets/issues/3330 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3330/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3330/comments | https://api.github.com/repos/huggingface/datasets/issues/3330/events | https://github.com/huggingface/datasets/pull/3330 | 1,065,176,619 | PR_kwDODunzps4vFtF7 | 3,330 | Change TriviaQA license (#3313) | [] | closed | false | null | 0 | 2021-11-28T03:26:45Z | 2021-11-29T11:24:21Z | 2021-11-29T11:24:21Z | null | Fixes (#3313) | {
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https://api.github.com/repos/huggingface/datasets/issues/3741 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3741/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3741/comments | https://api.github.com/repos/huggingface/datasets/issues/3741/events | https://github.com/huggingface/datasets/pull/3741 | 1,141,132,649 | PR_kwDODunzps4y-syt | 3,741 | Rm sphinx doc | [] | closed | false | null | 0 | 2022-02-17T10:11:37Z | 2022-02-17T10:15:17Z | 2022-02-17T10:15:12Z | null | Checklist
- [x] Update circle ci yaml
- [x] Delete sphinx static & python files in docs dir
- [x] Update readme in docs dir
- [ ] Update docs config in setup.py | {
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https://api.github.com/repos/huggingface/datasets/issues/2434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2434/comments | https://api.github.com/repos/huggingface/datasets/issues/2434/events | https://github.com/huggingface/datasets/issues/2434 | 907,503,557 | MDU6SXNzdWU5MDc1MDM1NTc= | 2,434 | Extend QuestionAnsweringExtractive template to handle nested columns | [
{
"color": "a2eeef",
"default": true,
"description": "New feature or request",
"id": 1935892871,
"name": "enhancement",
"node_id": "MDU6TGFiZWwxOTM1ODkyODcx",
"url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement"
}
] | closed | false | null | 2 | 2021-05-31T14:06:51Z | 2022-10-05T17:06:28Z | 2022-10-05T17:06:28Z | null | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-12-50e5b8f69c20> in <module>
----> 1 ds.prepare_for_task("question-answering-extractive")[0]
~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task)
1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`
1437 dataset.info.task_templates = None
-> 1438 dataset = dataset.cast(features=template.features)
1439 return dataset
1440
~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)
977 format = self.format
978 dataset = self.with_format("arrow")
--> 979 dataset = dataset.map(
980 lambda t: t.cast(schema),
981 batched=True,
~/git/datasets/src/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)
1600
1601 if num_proc is None or num_proc == 1:
-> 1602 return self._map_single(
1603 function=function,
1604 with_indices=with_indices,
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
176 }
177 # apply actual function
--> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
180 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, desc)
1940 ) # Something simpler?
1941 try:
-> 1942 batch = apply_function_on_filtered_inputs(
1943 batch,
1944 indices,
~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1837 processed_inputs = (
-> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1839 )
1840 if update_data is None:
~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t)
978 dataset = self.with_format("arrow")
979 dataset = dataset.map(
--> 980 lambda t: t.cast(schema),
981 batched=True,
982 batch_size=batch_size,
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe)
241 else:
242 options = CastOptions.unsafe(target_type)
--> 243 return call_function("cast", [arr], options)
244
245
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct
``` | {
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"this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n",
"The current task API is somewhat deprecated (we plan to align it with `train eval index` at some point), so I think we can close this issue."
] |
https://api.github.com/repos/huggingface/datasets/issues/3944 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3944/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3944/comments | https://api.github.com/repos/huggingface/datasets/issues/3944/events | https://github.com/huggingface/datasets/pull/3944 | 1,171,209,510 | PR_kwDODunzps40iu4n | 3,944 | Create README.md | [] | closed | false | null | 1 | 2022-03-16T15:46:26Z | 2022-03-17T17:50:54Z | 2022-03-17T17:47:05Z | null | Proposing COMET metric card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] |
https://api.github.com/repos/huggingface/datasets/issues/2721 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2721/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2721/comments | https://api.github.com/repos/huggingface/datasets/issues/2721/events | https://github.com/huggingface/datasets/pull/2721 | 954,238,230 | MDExOlB1bGxSZXF1ZXN0Njk4MTY0Njg3 | 2,721 | Deal with the bad check in test_load.py | [] | closed | false | null | 1 | 2021-07-27T20:23:23Z | 2021-07-28T09:58:34Z | 2021-07-28T08:53:18Z | null | This PR removes a check that's been added in #2684. My intention with this check was to capture an URL in the error message, but instead, it captures a substring of the previous regex match in the test function. Another option would be to replace this check with:
```python
m_paths = re.findall(r"\S*_dummy/_dummy.py\b", str(exc_info.value)) # on Linux this will match an URL as well as a local_path due to different os.sep, so take the last element (an URL always comes last in the list)
assert len(m_paths) > 0 and is_remote_url(m_paths[-1]) # is_remote_url comes from datasets.utils.file_utils
```
@lhoestq Let me know which one of these two approaches (delete or replace) do you prefer? | {
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"Hi ! I did a change for this test already in #2662 :\r\n\r\nhttps://github.com/huggingface/datasets/blob/00686c46b7aaf6bfcd4102cec300a3c031284a5a/tests/test_load.py#L312-L316\r\n\r\n(though I have to change the variable name `m_combined_path` to `m_url` or something)\r\n\r\nI guess it's ok to remove this check for... |
https://api.github.com/repos/huggingface/datasets/issues/4516 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4516/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4516/comments | https://api.github.com/repos/huggingface/datasets/issues/4516/events | https://github.com/huggingface/datasets/pull/4516 | 1,273,825,640 | PR_kwDODunzps45ykYX | 4,516 | Fix hashing for python 3.9 | [] | closed | false | null | 2 | 2022-06-16T16:42:31Z | 2022-06-28T13:33:46Z | 2022-06-28T13:23:06Z | null | In python 3.9, pickle hashes the `glob_ids` dictionary in addition to the `globs` of a function.
Therefore the test at `tests/test_fingerprint.py::RecurseDumpTest::test_recurse_dump_for_function_with_shuffled_globals` is currently failing for python 3.9
To make hashing deterministic when the globals are not in the same order, we also need to make the order of `glob_ids` deterministic.
Right now we don't have a CI to test python 3.9 but we should definitely have one. For this PR in particular I ran the tests locally using python 3.9 and they're passing now.
Fix https://github.com/huggingface/datasets/issues/4506 | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"What do you think @albertvillanova ?"
] |
https://api.github.com/repos/huggingface/datasets/issues/1134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1134/comments | https://api.github.com/repos/huggingface/datasets/issues/1134/events | https://github.com/huggingface/datasets/pull/1134 | 757,317,651 | MDExOlB1bGxSZXF1ZXN0NTMyNzE0MjQ2 | 1,134 | adding xquad-r dataset | [] | closed | false | null | 0 | 2020-12-04T18:39:13Z | 2020-12-05T16:50:47Z | 2020-12-05T16:50:47Z | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2493 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2493/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2493/comments | https://api.github.com/repos/huggingface/datasets/issues/2493/events | https://github.com/huggingface/datasets/pull/2493 | 919,833,281 | MDExOlB1bGxSZXF1ZXN0NjY5MDc4OTcw | 2,493 | add tensorflow-macos support | [] | closed | false | null | 1 | 2021-06-13T16:20:08Z | 2021-06-15T08:53:06Z | 2021-06-15T08:53:06Z | null | ref - https://github.com/huggingface/datasets/issues/2068 | {
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"@albertvillanova done!"
] |
https://api.github.com/repos/huggingface/datasets/issues/1501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1501/comments | https://api.github.com/repos/huggingface/datasets/issues/1501/events | https://github.com/huggingface/datasets/pull/1501 | 763,517,647 | MDExOlB1bGxSZXF1ZXN0NTM3OTYzMDU5 | 1,501 | Adds XED dataset | [] | closed | false | null | 1 | 2020-12-12T09:47:00Z | 2020-12-14T21:20:59Z | 2020-12-14T21:20:59Z | null | {
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"Hi @lhoestq @yjernite, requesting you to review this for any changes needed. Thanks! :)"
] | |
https://api.github.com/repos/huggingface/datasets/issues/387 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/387/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/387/comments | https://api.github.com/repos/huggingface/datasets/issues/387/events | https://github.com/huggingface/datasets/issues/387 | 656,361,357 | MDU6SXNzdWU2NTYzNjEzNTc= | 387 | Conversion through to_pandas output numpy arrays for lists instead of python objects | [] | closed | false | null | 1 | 2020-07-14T06:24:01Z | 2020-07-17T11:37:00Z | 2020-07-17T11:37:00Z | null | In a related question, the conversion through to_pandas output numpy arrays for the lists instead of python objects.
Here is an example:
```python
>>> dataset._data.slice(key, 1).to_pandas().to_dict("list")
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [array([ 101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292,
1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938,
4267, 12223, 21811, 1117, 2554, 119, 102])], 'token_type_ids': [array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0])], 'attention_mask': [array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1])]}
>>> type(dataset._data.slice(key, 1).to_pandas().to_dict("list")['input_ids'][0])
<class 'numpy.ndarray'>
>>> dataset._data.slice(key, 1).to_pydict()
{'sentence1': ['Amrozi accused his brother , whom he called " the witness " , of deliberately distorting his evidence .'], 'sentence2': ['Referring to him as only " the witness " , Amrozi accused his brother of deliberately distorting his evidence .'], 'label': [1], 'idx': [0], 'input_ids': [[101, 7277, 2180, 5303, 4806, 1117, 1711, 117, 2292, 1119, 1270, 107, 1103, 7737, 107, 117, 1104, 9938, 4267, 12223, 21811, 1117, 2554, 119, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
``` | {
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"To convert from arrow type we have three options: to_numpy, to_pandas and to_pydict/to_pylist.\r\n\r\n- to_numpy and to_pandas return numpy arrays instead of lists but are very fast.\r\n- to_pydict/to_pylist can be 100x slower and become the bottleneck for reading data, but at least they return lists.\r\n\r\nMaybe... |
https://api.github.com/repos/huggingface/datasets/issues/1959 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1959/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1959/comments | https://api.github.com/repos/huggingface/datasets/issues/1959/events | https://github.com/huggingface/datasets/issues/1959 | 818,055,644 | MDU6SXNzdWU4MTgwNTU2NDQ= | 1,959 | Bug in skip_rows argument of load_dataset function ? | [] | closed | false | null | 1 | 2021-02-27T23:32:54Z | 2021-03-09T10:21:32Z | 2021-03-09T10:21:32Z | null | Hello everyone,
I'm quite new to Git so sorry in advance if I'm breaking some ground rules of issues posting... :/
I tried to use the load_dataset function, from Huggingface datasets library, on a csv file using the skip_rows argument described on Huggingface page to skip the first row containing column names
`test_dataset = load_dataset('csv', data_files=['test_wLabel.tsv'], delimiter='\t', column_names=["id", "sentence", "label"], skip_rows=1)`
But I got the following error message
`__init__() got an unexpected keyword argument 'skip_rows'`
Have I used the wrong argument ? Am I missing something or is this a bug ?
Thank you very much for your time,
Best regards,
Arthur | {
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"Hi,\r\n\r\ntry `skiprows` instead. This part is not properly documented in the docs it seems.\r\n\r\n@lhoestq I'll fix this as part of a bigger PR that fixes typos in the docs."
] |
https://api.github.com/repos/huggingface/datasets/issues/6044 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6044/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6044/comments | https://api.github.com/repos/huggingface/datasets/issues/6044/events | https://github.com/huggingface/datasets/pull/6044 | 1,808,057,906 | PR_kwDODunzps5Vr7jr | 6,044 | Rename "pattern" to "path" in YAML data_files configs | [] | closed | false | null | 10 | 2023-07-17T15:41:16Z | 2023-07-19T16:59:55Z | 2023-07-19T16:48:06Z | null | To make it easier to understand for users.
They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s>
Glob patterns are still supported though
| {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
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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] | closed | false | null | 3 | 2020-11-25T12:47:58Z | 2021-01-13T11:31:25Z | 2021-01-13T11:31:25Z | 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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"Good point!",
"Yes indeed. We can probably improve that by using lazy imports",
"#1690 added fast start-up of the library "
] |
https://api.github.com/repos/huggingface/datasets/issues/3931 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3931/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3931/comments | https://api.github.com/repos/huggingface/datasets/issues/3931/events | https://github.com/huggingface/datasets/pull/3931 | 1,170,097,208 | PR_kwDODunzps40fBjx | 3,931 | Add align_labels_with_mapping docs | [
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] | closed | false | null | 1 | 2022-03-15T19:24:57Z | 2022-03-18T16:28:31Z | 2022-03-18T16:24:33Z | null | This PR documents the `align_labels_with_mapping` function to ensure predicted labels are aligned with the dataset, or to assign a different mapping of labels to ids (requested by @mariosasko 🎉 ).
For this specific code sample, the current dataset has a `mixed` label that the original [dataset](https://huggingface.co/datasets/poem_sentiment#data-fields) didn't. Is there a way to remove this label so it is completely aligned with the original dataset mappings? Otherwise, I'll just leave it as it is. | {
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] |
https://api.github.com/repos/huggingface/datasets/issues/4540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4540/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4540/comments | https://api.github.com/repos/huggingface/datasets/issues/4540/events | https://github.com/huggingface/datasets/issues/4540 | 1,280,142,942 | I_kwDODunzps5MTW5e | 4,540 | Avoid splitting by` .py` for the file. | [
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] | closed | false | null | 4 | 2022-06-22T13:26:55Z | 2022-07-07T13:17:44Z | 2022-07-07T13:17:44Z | null | https://github.com/huggingface/datasets/blob/90b3a98065556fc66380cafd780af9b1814b9426/src/datasets/load.py#L272
Hello,
Thanks you for this library .
I was using it and I had one edge case. my home folder name ends with `.py` it is `/home/espoir.py` so anytime I am running the code to load a local module this code here it is failing because after splitting it is trying to save the code to my home directory.
Step to reproduce.
- If you have a home folder which ends with `.py`
- load a module with a local folder
`qa_dataset = load_dataset("src/data/build_qa_dataset.py")`
it is failed
A possible workaround would be to use pathlib at the mentioned line
` meta_path = Path(importable_local_file).parent.joinpath("metadata.json")` this can alivate the issue .
Let me what are your thought on this and I can try to fix it by A PR.
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"Hi @espoirMur, thanks for reporting.\r\n\r\nYou are right: that code line could be improved and made more generically valid.\r\n\r\nOn the other hand, I would suggest using `os.path.splitext` instead.\r\n\r\nAre you willing to open a PR? :)",
"I will have a look.. \r\n\r\nThis weekend .. ",
"@albertvillanova ... |
https://api.github.com/repos/huggingface/datasets/issues/2837 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2837/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2837/comments | https://api.github.com/repos/huggingface/datasets/issues/2837/events | https://github.com/huggingface/datasets/issues/2837 | 979,298,297 | MDU6SXNzdWU5NzkyOTgyOTc= | 2,837 | prepare_module issue when loading from read-only fs | [
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] | closed | false | null | 1 | 2021-08-25T15:21:26Z | 2021-10-05T17:58:22Z | 2021-10-05T17:58:22Z | null | ## Describe the bug
When we use prepare_module from a readonly file system, we create a FileLock using the `local_path`.
This path is not necessarily writable.
`lock_path = local_path + ".lock"`
## Steps to reproduce the bug
Run `load_dataset` on a readonly python loader file.
```python
ds = load_dataset(
python_loader, data_files={"train": train_path, "test": test_path}
)
```
where `python_loader` is a path to a file located in a readonly folder.
## Expected results
This should work I think?
## Actual results
```python
return load_dataset(
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 711, in load_dataset
module_path, hash, resolved_file_path = prepare_module(
File "/usr/local/lib/python3.8/dist-packages/datasets/load.py", line 465, in prepare_module
with FileLock(lock_path):
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 314, in __enter__
self.acquire()
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 263, in acquire
self._acquire()
File "/usr/local/lib/python3.8/dist-packages/datasets/utils/filelock.py", line 378, in _acquire
fd = os.open(self._lock_file, open_mode)
OSError: [Errno 30] Read-only file system: 'YOUR_FILE.py.lock'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.8
- PyArrow version: 3.0.0
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"Hello, I opened #2887 to fix this."
] |
https://api.github.com/repos/huggingface/datasets/issues/5523 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5523/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5523/comments | https://api.github.com/repos/huggingface/datasets/issues/5523/events | https://github.com/huggingface/datasets/issues/5523 | 1,580,193,015 | I_kwDODunzps5eL9T3 | 5,523 | Checking that split name is correct happens only after the data is downloaded | [
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] | open | false | null | 0 | 2023-02-10T19:13:03Z | 2023-02-10T19:14:50Z | null | null | ### Describe the bug
Verification of split names (=indexing data by split) happens after downloading the data. So when the split name is incorrect, users learn about that only after the data is fully downloaded, for large datasets it might take a lot of time.
### Steps to reproduce the bug
Load any dataset with random split name, for example:
```python
from datasets import load_dataset
load_dataset("mozilla-foundation/common_voice_11_0", "en", split="blabla")
```
and the download will start smoothly, despite there is no split named "blabla".
### Expected behavior
Raise error when split name is incorrect.
### Environment info
`datasets==2.9.1.dev0` | {
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https://api.github.com/repos/huggingface/datasets/issues/5084 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5084/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5084/comments | https://api.github.com/repos/huggingface/datasets/issues/5084/events | https://github.com/huggingface/datasets/pull/5084 | 1,400,016,229 | PR_kwDODunzps5AVXwm | 5,084 | IterableDataset formatting in numpy/torch/tf/jax | [] | closed | false | null | 3 | 2022-10-06T16:53:38Z | 2022-12-20T17:19:52Z | 2022-12-20T17:19:52Z | null | This code now returns a numpy array:
```python
from datasets import load_dataset
ds = load_dataset("imagenet-1k", split="train", streaming=True).with_format("np")
print(next(iter(ds))["image"])
```
It also works with "arrow", "pandas", "torch", "tf" and "jax"
### Implementation details:
I'm using the existing code to format an Arrow Table to the right output format for simplicity.
Therefore it's probbaly not the most optimized approach.
For example to output PyTorch tensors it does this for every example:
python data -> arrow table -> numpy extracted data -> pytorch formatted data
### Releasing this feature
Even though I consider this as a bug/inconsistency, this change is a breaking change.
And I'm sure some users were relying on the torch iterable dataset to return PIL Image and used data collators to convert to pytorch.
So I guess this is `datasets` 3.0 ?
### TODO
- [x] merge https://github.com/huggingface/datasets/pull/5072
- [ ] docs
- [ ] tests
Close https://github.com/huggingface/datasets/issues/5083 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5084). All of your documentation changes will be reflected on that endpoint.",
"Actually I'm not happy with this implementation. It always require the iterable dataset to have definite `features`, which removes a lot of flexibi... |
https://api.github.com/repos/huggingface/datasets/issues/4242 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4242/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4242/comments | https://api.github.com/repos/huggingface/datasets/issues/4242/events | https://github.com/huggingface/datasets/pull/4242 | 1,217,665,960 | PR_kwDODunzps425BYf | 4,242 | Update auth when mirroring datasets on the hub | [] | closed | false | null | 1 | 2022-04-27T17:22:31Z | 2022-04-27T17:37:04Z | 2022-04-27T17:30:42Z | null | We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3. | {
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https://api.github.com/repos/huggingface/datasets/issues/4753 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4753/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4753/comments | https://api.github.com/repos/huggingface/datasets/issues/4753/events | https://github.com/huggingface/datasets/pull/4753 | 1,319,571,745 | PR_kwDODunzps48Ll8G | 4,753 | Add `language_bcp47` tag | [] | closed | false | null | 1 | 2022-07-27T13:31:16Z | 2022-07-27T14:50:03Z | 2022-07-27T14:37:56Z | null | Following (internal) https://github.com/huggingface/moon-landing/pull/3509, we need to move the bcp47 tags to `language_bcp47` and keep the `language` tag for iso 639 1-2-3 codes. In particular I made sure that all the tags in `languages` are not longer than 3 characters. I moved the rest to `language_bcp47` and fixed some of them.
After this PR is merged I think we can simplify the language validation from the DatasetMetadata class (and keep it bare-bone just for the tagging app)
PS: the CI is failing because of missing content in dataset cards that are unrelated to this PR | {
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https://api.github.com/repos/huggingface/datasets/issues/6015 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6015/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6015/comments | https://api.github.com/repos/huggingface/datasets/issues/6015/events | https://github.com/huggingface/datasets/pull/6015 | 1,798,807,893 | PR_kwDODunzps5VMhgB | 6,015 | Add metadata ui screenshot in docs | [] | closed | false | null | 3 | 2023-07-11T12:16:29Z | 2023-07-11T16:07:28Z | 2023-07-11T15:56:46Z | null | null | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... |
https://api.github.com/repos/huggingface/datasets/issues/2263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2263/comments | https://api.github.com/repos/huggingface/datasets/issues/2263/events | https://github.com/huggingface/datasets/pull/2263 | 867,420,912 | MDExOlB1bGxSZXF1ZXN0NjIzMDk0NTcy | 2,263 | test data added, dataset_infos updated | [] | closed | false | null | 0 | 2021-04-26T08:27:18Z | 2021-04-29T09:30:21Z | 2021-04-29T09:30:20Z | null | Fixes #2262. Thanks for pointing out issue with dataset @jinmang2! | {
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https://api.github.com/repos/huggingface/datasets/issues/971 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/971/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/971/comments | https://api.github.com/repos/huggingface/datasets/issues/971/events | https://github.com/huggingface/datasets/pull/971 | 754,784,041 | MDExOlB1bGxSZXF1ZXN0NTMwNjIxOTQz | 971 | add piqa | [] | closed | false | null | 0 | 2020-12-01T22:47:04Z | 2020-12-02T09:58:02Z | 2020-12-02T09:58:01Z | null | Physical Interaction: Question Answering (commonsense)
https://yonatanbisk.com/piqa/ | {
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