Datasets:

selfishark

/

hf-issues-dataset-with-comments

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I_kwDODunzps5HMmSV

Unable to view the dataset and loading the same dataset throws the error - ArrowInvalid: Exceeded maximum rows

closed

NONE

## Dataset viewer issue - -ArrowInvalid: Exceeded maximum rows **Link:** *https://huggingface.co/datasets/Pavithree/explainLikeImFive* *This is the subset of original eli5 dataset https://huggingface.co/datasets/vblagoje/lfqa. I just filtered the data samples which belongs to one particular subreddit thread. However, the dataset preview for train split returns the below mentioned error: Status code: 400 Exception: ArrowInvalid Message: Exceeded maximum rows When I try to load the same dataset it returns ArrowInvalid: Exceeded maximum rows error* Am I the one who added this dataset ? Yes

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https://github.com/huggingface/datasets/pull/4106

PR_kwDODunzps41uPpa

Support huggingface_hub 0.5

closed

MEMBER

Following https://github.com/huggingface/datasets/issues/4105 `huggingface_hub` deprecated some parameters in `HfApi` in 0.5. This PR updates all the calls to HfApi to remove all the deprecations, <s>and I set the `hugginface_hub` requirement to `>=0.5.0`</s> cc @adrinjalali @LysandreJik

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I_kwDODunzps5HL4cf

push to hub fails with huggingface-hub 0.5.0

closed

NONE

## Describe the bug `ds.push_to_hub` is failing when updating a dataset in the form "org_id/repo_id" ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("rubrix/news_test") ds.push_to_hub("<your-user>/news_test", token="<your-token>") ``` ## Expected results The dataset is successfully uploaded ## Actual results An error validation is raised: ```bash if repo_id and (name or organization): > raise ValueError( "Only pass `repo_id` and leave deprecated `name` and " "`organization` to be None." E ValueError: Only pass `repo_id` and leave deprecated `name` and `organization` to be None. ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.1 - `huggingface-hub`: 0.5 - Platform: macOS - Python version: 3.8.12 - PyArrow version: 6.0.0 cc @adrinjalali

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I_kwDODunzps5HLBuG

Add time series data - stock market

open

NONE

## Adding a Time Series Dataset - **Name:** 2min ticker data for stock market - **Description:** 8 stocks' data collected for 1month post ukraine-russia war. 4 NSE stocks and 4 NASDAQ stocks. Along with technical indicators (additional features) as shown in below image - **Data:** Collected by myself from investing.com - **Motivation:** Test applicability of transformer based model on stock market / time series problem 

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PR_kwDODunzps41s3T4

Add the `GSM8K` dataset

closed

CONTRIBUTOR

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PR_kwDODunzps41roGx

[hub] Fix `api.create_repo` call?

closed

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I_kwDODunzps5HIdOk

How can I download only the train and test split for full numbers using load_dataset()?

open

NONE

How can I download only the train and test split for full numbers using load_dataset()? I do not need the extra split and it will take 40 mins just to download in Colab. I have very short time in hand. Please help.

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PR_kwDODunzps41q4ce

Improve RedCaps dataset card

closed

CONTRIBUTOR

This PR modifies the RedCaps card to: * fix the formatting of the Point of Contact fields on the Hub * speed up the image fetching logic (aligns it with the [img2dataset](https://github.com/rom1504/img2dataset) tool) and make it more robust (return None if **any** exception is thrown)

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I_kwDODunzps5HH5uI

UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)

closed

NONE

## Describe the bug Error "UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128)" is thrown when downloading dataset. ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("nielsr/XFUN", "xfun.ja") ``` ## Expected results Dataset should be downloaded without exceptions ## Actual results Stack trace (for the second-time execution): Downloading and preparing dataset xfun/xfun.ja to /root/.cache/huggingface/datasets/nielsr___xfun/xfun.ja/0.0.0/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477... Downloading data files: 100% 2/2 [00:00<00:00, 88.48it/s] Extracting data files: 100% 2/2 [00:00<00:00, 79.60it/s] UnicodeDecodeErrorTraceback (most recent call last) <ipython-input-31-79c26bd1109c> in <module> 1 from datasets import load_dataset 2 ----> 3 datasets = load_dataset("nielsr/XFUN", "xfun.ja") /usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 ) 605 --> 606 # By default, return all splits 607 if split is None: 608 split = {s: s for s in self.info.splits} /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 692 Args: 693 split: `datasets.Split` which subset of the data to read. --> 694 695 Returns: 696 `Dataset` /usr/local/lib/python3.6/dist-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) /usr/local/lib/python3.6/dist-packages/tqdm/notebook.py in __iter__(self) 252 if not self.disable: 253 self.display(check_delay=False) --> 254 255 def __iter__(self): 256 try: /usr/local/lib/python3.6/dist-packages/tqdm/std.py in __iter__(self) 1183 for obj in iterable: 1184 yield obj -> 1185 return 1186 1187 mininterval = self.mininterval ~/.cache/huggingface/modules/datasets_modules/datasets/nielsr--XFUN/e06e948b673d1be9a390a83c05c10e49438bf03dd85ae9a4fe06f8747a724477/XFUN.py in _generate_examples(self, filepaths) 140 logger.info("Generating examples from = %s", filepath) 141 with open(filepath[0], "r") as f: --> 142 data = json.load(f) 143 144 for doc in data["documents"]: /usr/lib/python3.6/json/__init__.py in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw) 294 295 """ --> 296 return loads(fp.read(), 297 cls=cls, object_hook=object_hook, 298 parse_float=parse_float, parse_int=parse_int, /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe5 in position 213: ordinal not in range(128) ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 (but reproduced with many previous versions) - Platform: Docker: Linux da5b74136d6b 5.3.0-1031-azure #32~18.04.1-Ubuntu SMP Mon Jun 22 15:27:23 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux ; Base docker image is : huggingface/transformers-pytorch-cpu - Python version: 3.6.9 - PyArrow version: 6.0.1

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PR_kwDODunzps41qXjo

Proposing WikiSplit metric card

closed

CONTRIBUTOR

Pinging @lhoestq to ensure that my distinction between the dataset and the metric are clear :sweat_smile:

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PR_kwDODunzps41qPEu

Updating FrugalScore metric card

closed

CONTRIBUTOR

removing duplicate paragraph

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I_kwDODunzps5HHkGt

Add support for streaming Zarr stores for hosted datasets

closed

NONE

**Is your feature request related to a problem? Please describe.** Lots of geospatial data is stored in the Zarr format. This format works well for n-dimensional data and coordinates, and can have good compression. Unfortunately, HF datasets doesn't support streaming in data in Zarr format as far as I can tell. Zarr stores are designed to be easily streamed in from cloud storage, especially with xarray and fsspec. Since geospatial data tends to be very large, and on the order of TBs of data or 10's of TBs of data for a single dataset, it can be difficult to store the dataset locally for users. Just adding Zarr stores with HF git doesn't work well (see https://github.com/huggingface/datasets/issues/3823) as Zarr splits the data into lots of small chunks for fast loading, and that doesn't work well with git. I've somewhat gotten around that issue by tarring each Zarr store and uploading them as a single file, which seems to be working (see https://huggingface.co/datasets/openclimatefix/gfs-reforecast for example data files, although the script isn't written yet). This does mean that streaming doesn't quite work though. On the other hand, in https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv we stream in a Zarr store from a public GCP bucket quite easily. **Describe the solution you'd like** A way to upload Zarr stores for hosted datasets so that we can stream it with xarray and fsspec. **Describe alternatives you've considered** Tarring each Zarr store individually and just extracting them in the dataset script -> Downside this is a lot of data that probably doesn't fit locally for a lot of potential users. Pre-prepare examples in a format like Parquet -> Would use a lot more storage, and a lot less flexibility, in the eumetsat_uk_hrv, we use the one Zarr store for multiple different configurations.

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PR_kwDODunzps41oIFI

fix typo in rename_column error message

closed

CONTRIBUTOR

I feel bad submitting such a tiny change as a PR but it confused me today 😄

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I_kwDODunzps5HFKGO

Helo Mayfrends

closed

NONE

## Adding a Dataset - **Name:** *name of the dataset* - **Description:** *short description of the dataset (or link to social media or blog post)* - **Paper:** *link to the dataset paper if available* - **Data:** *link to the Github repository or current dataset location* - **Motivation:** *what are some good reasons to have this dataset* Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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I_kwDODunzps5HFHWZ

elena-soare/crawled-ecommerce: missing dataset

closed

NONE

elena-soare/crawled-ecommerce **Link:** *link to the dataset viewer page* *short description of the issue* Am I the one who added this dataset ? Yes-No

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PR_kwDODunzps41n40R

Fix dataset `amazon_us_reviews` metadata - 4/4/2022

closed

CONTRIBUTOR

Fixes #4048 by running `dataset-cli test` to reprocess data and regenerate metadata. Additionally I've updated the README to include up-to-date counts for the subsets.

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I_kwDODunzps5HDNcv

Build a Dataset One Example at a Time Without Loading All Data Into Memory

closed

NONE

**Is your feature request related to a problem? Please describe.** I have a very large dataset stored on disk in a custom format. I have some custom code that reads one data example at a time and yields it in the form of a dictionary. I want to construct a `Dataset` with all examples, and then save it to disk. I later want to load the saved `Dataset` and use it like any other HuggingFace dataset, get splits, wrap it in a PyTorch `DataLoader`, etc. **Crucially, I do not ever want to materialize all the data in memory while building the dataset.** **Describe the solution you'd like** I would like to be able to do something like the following. Notice how each example is read and then immediately added to the dataset. We do not store all the data in memory when constructing the `Dataset`. If it helps, I will know the schema of my dataset before hand. ``` # Initialize an empty Dataset, possibly from a known schema. dataset = Dataset() # Read in examples one by one using a custom data streamer. for example_dict in custom_example_dict_streamer("/path/to/raw/data"): # Add this example to the dict but do not store it in memory. dataset.add_item(example_dict) # Save the final dataset to disk as an Arrow-backed dataset. dataset.save_to_disk("/path/to/dataset") ... # I'd like to be able to later `load_from_disk` and use the loaded Dataset # just like any other memory-mapped pyarrow-backed HuggingFace dataset... loaded_dataset = Dataset.load_from_disk("/path/to/dataset") loaded_dataset.set_format(type="torch", columnns=["foo", "bar", "baz"]) dataloader = torch.utils.data.DataLoader(loaded_dataset, batch_size=16) ... ``` **Describe alternatives you've considered** I initially tried to read all the data into memory, construct a Pandas DataFrame and then call `Dataset.from_pandas`. This would not work as it requires storing all the data in memory. It seems that there is an `add_item` method already -- I tried to implement something like the desired API written above, but I've not been able to initialize an empty `Dataset` (this seems to require several layers of constructing `datasets.table.Table` which requires constructing a `pyarrow.lib.Table`, etc). I also considered writing my data to multiple sharded CSV files or JSON files and then using `from_csv` or `from_json`. I'd prefer not to do this because (1) I'd prefer to avoid the intermediate step of creating these temp CSV/JSON files and (2) I'm not sure if `from_csv` and `from_json` use memory-mapping. Do you have any suggestions on how I'd be able to achieve this use case? Does something already exist to support this? Thank you very much in advance!

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PR_kwDODunzps41mEs5

Avoid writing empty license files

closed

MEMBER

This PR avoids the creation of empty `LICENSE` files.

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PR_kwDODunzps41l7yd

Create metric card for Frugal Score

closed

CONTRIBUTOR

Proposing metric card for Frugal Score. @albertvillanova or @lhoestq -- there are certain aspects that I'm not 100% sure on (such as how exactly the distillation between BertScore and FrugalScore is done) -- so if you find that something isn't clear, please let me know!

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PR_kwDODunzps41l4yE

Remove unused legacy Beam utils

closed

MEMBER

This PR removes unused legacy custom `WriteToParquet`, once official Apache Beam includes the patch since version 2.22.0: - Patch PR: https://github.com/apache/beam/pull/11699 - Issue: https://issues.apache.org/jira/browse/BEAM-10022 In relation with: - #204

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PR_kwDODunzps41lnfO

Fix BeamWriter output Parquet file

closed

MEMBER

Since now, the `BeamWriter` saved a Parquet file with a simplified schema, where each field value was serialized to JSON. That resulted in Parquet files larger than Arrow files. This PR: - writes Parquet file preserving original schema and without serialization, thus avoiding serialization overhead and resulting in a smaller output file size. - fixes `parquet_to_arrow` function

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I_kwDODunzps5HAuo-

Dataset viewer issue for McGill-NLP/feedbackQA

closed

NONE

## Dataset viewer issue for '*McGill-NLP/feedbackQA*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/McGill-NLP/feedbackQA)* *short description of the issue* The dataset can be loaded correctly with `load_dataset` but the preview doesn't work. Error message: ``` Status code: 400 Exception: Status400Error Message: Not found. Maybe the cache is missing, or maybe the dataset does not exist. ``` Am I the one who added this dataset ? Yes

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I_kwDODunzps5G93Ch

datasets.set_progress_bar_enabled(False) not working in datasets v2

closed

NONE

## Describe the bug datasets.set_progress_bar_enabled(False) not working in datasets v2 ## Steps to reproduce the bug ```python datasets.set_progress_bar_enabled(False) ``` ## Expected results datasets not using any progress bar ## Actual results AttributeError: module 'datasets' has no attribute 'set_progress_bar_enabled ## Environment info datasets version 2

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I_kwDODunzps5G7uF_

Errors in `Train with Datasets` Tensorflow code section on Huggingface.co

closed

NONE

## Describe the bug Hi ### Error 1 Running the Tensforlow code on [Huggingface](https://huggingface.co/docs/datasets/use_dataset) gives a TypeError: __init__() got an unexpected keyword argument 'return_tensors' ### Error 2 `DataCollatorWithPadding` isn't imported ## Steps to reproduce the bug ```python import tensorflow as tf from datasets import load_dataset from transformers import AutoTokenizer dataset = load_dataset('glue', 'mrpc', split='train') tokenizer = AutoTokenizer.from_pretrained('bert-base-cased') dataset = dataset.map(lambda e: tokenizer(e['sentence1'], truncation=True, padding='max_length'), batched=True) data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf") train_dataset = dataset["train"].to_tf_dataset( columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'], shuffle=True, batch_size=16, collate_fn=data_collator, ) ``` This is the same code on Huggingface.co ## Actual results TypeError: __init__() got an unexpected keyword argument 'return_tensors' ## Environment info - `datasets` version: 2.0.0 - Platform: Windows-10-10.0.19044-SP0 - Python version: 3.9.7 - PyArrow version: 6.0.0 - Pandas version: 1.4.1 >

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PR_kwDODunzps41gEbu

Add SacreBLEU Metric Card

closed

CONTRIBUTOR

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PR_kwDODunzps41f3fb

Add chrF(++) Metric Card

closed

CONTRIBUTOR

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PR_kwDODunzps41fsxW

Close parquet writer properly in `push_to_hub`

closed

MEMBER

We don’t call writer.close(), which causes https://github.com/huggingface/datasets/issues/4077. It can happen that we upload the file before the writer is garbage collected and writes the footer. I fixed this by explicitly closing the parquet writer. Close https://github.com/huggingface/datasets/issues/4077.

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I_kwDODunzps5G6OHg

NonMatchingChecksumError for downloading conll2012_ontonotesv5 dataset

closed

CONTRIBUTOR

## Steps to reproduce the bug ```python datasets.load_dataset("conll2012_ontonotesv5", "english_v12") ``` ## Actual results ``` Downloading builder script: 32.2kB [00:00, 9.72MB/s] Downloading metadata: 20.0kB [00:00, 10.4MB/s] Downloading and preparing dataset conll2012_ontonotesv5/english_v12 (download: 174.83 MiB, generated: 204.29 MiB, post-processed: Unknown size , total: 379.12 MiB) to ... Traceback (most recent call last): [315/390] File "/home/yisiang/lgtn/conll2012/run.py", line 86, in <module> train() File "/home/yisiang/lgtn/conll2012/run.py", line 65, in train trainer.fit(model, datamodule=dm) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 740, in fit self._call_and_handle_interrupt( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 685, in _call_and_handle_inte rrupt return trainer_fn(*args, **kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 777, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1131, in _run self._data_connector.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 154, in pre pare_data self.trainer.datamodule.prepare_data() File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py", line 474, in wrapped_fn fn(*args, **kwargs) File "/home/yisiang/lgtn/_abstract_task/data.py", line 43, in prepare_data raw_dsets = datasets.load_dataset(**load_dataset_kwargs) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/load.py", line 1687, in load_dataset builder_instance.download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 605, in download_and_prepare self._download_and_prepare( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 1104, in _download_and_prepare super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/builder.py", line 676, in _download_and_prepare verify_checksums( File "/home/yisiang/miniconda3/envs/ai/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/zmycy7t9h9-1.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0

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https://github.com/huggingface/datasets/pull/4079

PR_kwDODunzps41eYRC

Increase max retries for GitHub datasets

closed

MEMBER

As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub datasets, as previously done for GitHub metrics: - #4063 Note that this is a temporary solution, while we decide when and how to load GitHub datasets from the Hub: - #4059 Fix #2048 Related to: - #4051 - #3210 - #2787 - #2075 - #2036 CC: @lhoestq

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PR_kwDODunzps41eWnl

Fix GithubMetricModuleFactory instantiation with None download_config

closed

MEMBER

Recent PR: - #4063 introduced a potential bug if `GithubMetricModuleFactory` is instantiated with None `download_config`. This PR add instantiation tests and fix that potential issue. CC: @lhoestq

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I_kwDODunzps5G5dXB

ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.

closed

CONTRIBUTOR

## Describe the bug When uploading a relatively large image dataset of > 1GB, reloading doesn't work for me, even though pushing to the hub went just fine. Basically, I do: ``` from datasets import load_dataset dataset = load_dataset("imagefolder", data_files="path_to_my_files") dataset.push_to_hub("dataset_name") # works fine, no errors reloaded_dataset = load_dataset("dataset_name") ``` and it returns: ``` /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file. ``` I created a Colab notebook to reproduce my error: https://colab.research.google.com/drive/141LJCcM2XyqprPY83nIQ-Zk3BbxWeahq?usp=sharing

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https://github.com/huggingface/datasets/pull/4076

PR_kwDODunzps41a1n2

Add ROUGE Metric Card

closed

CONTRIBUTOR

Add ROUGE metric card. I've left the 'Values from popular papers' section empty for the time being because I don't know the summarization literature very well and am therefore not sure which paper(s) to pull from (note that the original rouge paper does not seem to present specific values, just correlations with human judgements). Any suggestions on which paper(s) to pull from would be helpful! :)

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I_kwDODunzps5G1n5S

Add CCAgT dataset

open

NONE

## Adding a Dataset - **Name:** CCAgT dataset: Images of Cervical Cells with AgNOR Stain Technique - **Description:** The dataset contains 2540 images (1600x1200 where each pixel is 0.111μm×0.111μm) from three different slides, having at least one nucleus per image. These images are from fields belonging to a sample cervical slide, colored with silver-stained, a method known as Argyrophilic Nucleolar Organizer Regions (AgNOR). - **Paper:** https://doi.org/10.1109/cbms49503.2020.00110 - **Data:** https://arquivos.ufsc.br/d/373be2177a33426a9e6c/ or https://drive.google.com/drive/u/4/folders/1TBpYCv6S1ydASLauSzcsvO7Wc5O-WUw0 - **Motivation:** This is a unique dataset (because of the stain), for a major health problem, cervical cancer, with real data. Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi, this is a public version of the dataset that I have been working on, soon we will have another version of this dataset. But until this new version goes out, I thought I would add this dataset here, if it makes sense for the repository. You can assign the task to me if possible

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I_kwDODunzps5G1kt2

Error in google/xtreme_s dataset card

closed

NONE

**Link:** https://huggingface.co/datasets/google/xtreme_s Not a big deal but Hungarian is considered an Eastern European language, together with Serbian, Slovak, Slovenian (all correctly categorized; Slovenia is mostly to the West of Hungary, by the way).

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PR_kwDODunzps41adPA

Create a metric card for Competition MATH

closed

CONTRIBUTOR

Proposing metric card for Competition MATH

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PR_kwDODunzps41aIUG

Add installation instructions to image_process doc

closed

CONTRIBUTOR

This PR adds the installation instructions for the Image feature to the image process doc.

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I_kwDODunzps5GySZj

Loading issue for xuyeliu/notebookCDG dataset

closed

NONE

## Dataset viewer issue for '*xuyeliu/notebookCDG*' **Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/xuyeliu/notebookCDG)* *Couldn't load the xuyeliu/notebookCDG with provided scripts: * ``` from datasets import load_dataset dataset = load_dataset("xuyeliu/notebookCDG/dataset_notebook.pkl") ``` I get an error message as follows: FileNotFoundError: Couldn't find a dataset script at /home/code_documentation/code/xuyeliu/notebookCDG/notebookCDG.py or any data file in the same directory. Couldn't find 'xuyeliu/notebookCDG' on the Hugging Face Hub either: FileNotFoundError: Unable to resolve any data file that matches ['**train*'] in dataset repository xuyeliu/notebookCDG 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'] Am I the one who added this dataset ? No

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PR_kwDODunzps41VMYq

Create metric card for seqeval

closed

CONTRIBUTOR

Proposing metric card for seqeval. Not sure which values to report for Popular papers though.

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PR_kwDODunzps41VIMJ

Add support for metadata files to `imagefolder`

closed

CONTRIBUTOR

This PR adds support for metadata files to `imagefolder` to add an ability to specify image fields other than `image` and `label`, which are inferred from the directory structure in the loaded dataset. To be parsed as an image metadata file, a file should be named `"info.csv"` and should have the following structure: ``` image_id,some_col1_name,some_col2_name rel/path/to/image1.jpg,image1_col1_value,image1_col2_value rel/path/to/image2.jpg,image2_col1_value,image2_col2_value ... ``` This is how the resolution works: ``` - path/to/imagefolder/directory - info.csv - 10.jpg # referenced as 10.jpg in "info.csv" - Cat - 0.jpg # referenced as Cat/0.jpg in "info.csv" - 1.jpg # referenced as Cat/1.jpg in "info.csv" - Dog - 0.jpg # referenced as Dog/0.jpg in "info.csv" - 1.jpg # referenced as Dog/1.jpg in "info.csv" ``` Open questions: 1. IMO it makes more sense to store image metadata as JSON Lines than CSV. CSV is sufficient for textual metadata but not the best for representing bounding boxes, for instance. Also, JSON Lines is more strict, which is good in this case (CSV supports various delimiters, the header line is optional, etc., so it's easier to enforce rules on JSON Lines that it's on CSV) 2. A better name for the `image_id` column, which contains image identifiers? Maybe `image_file` or `image_filename`? 3. WDYT about making `with_metadata=True` the default behavior if the loaded repo/directory contains an `info.csv` file? An example repository: https://huggingface.co/datasets/mariosasko/PetImages. Can be loaded by installing `datasets` from the PR branch and running `load_dataset("mariosasko/PetImages", with_metadata=True)`. cc: @abhishekkrthakur (this PR should address https://huggingface.slack.com/archives/C02JB9L6JKF/p1645450017434029?thread_ts=1645157416.389499&cid=C02JB9L6JKF) TODOs: - [x] Test - [x] Metadata file nesting ``` - path/to/imagefolder/directory - info.csv - 10.jpg - Cat - info.csv # should have higher precedence in this directory than the top-level info.csv, but we choose the first "eligible" metadata file currently - 0.jpg - 1.jpg ```

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PR_kwDODunzps41VC0I

Improve out of bounds error message

closed

MEMBER

In 1.18.4 with https://github.com/huggingface/datasets/pull/3719 we introduced an error message for users using `select` with out of bounds indices. The message ended up being confusing for some users because it mentioned negative indices, which is not the main use case. I replaced it with a message that is very similar to the one you get with you try to access a list with an out-of-range index.

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PR_kwDODunzps41U7qc

Update datasets task tags to align tags with models

closed

MEMBER

**Requires https://github.com/huggingface/datasets/pull/4066 to be merged first** Following https://github.com/huggingface/datasets/pull/4066 we need to update many dataset tags to use the new ones. This PR takes case of this and is quite big - feel free to review only certain tags if you don't want to spend too much time on it. Note that the CI will never be green for this PR, because many dataset cards have missing tags or sections, and fixing them is out of scope of this PR (the CI on master will be green anyway)

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PR_kwDODunzps41U63x

Tasks alignment with models

closed

MEMBER

I updated our `tasks.json` file with the new task taxonomy that is aligned with models. The rule that defines a task is the following: **Two tasks are different if and only if the steps of their pipelines** are different, i.e. if they can’t reasonably be implemented using the same coherent code (level of granularity/complexity of the code to be defined - ideally I’d like to say “HF user’s level”) - this is the same definition in `transformers` I will update the tags of all the datasets in this repository [in another PR](https://github.com/huggingface/datasets/pull/4067) for readability. Main changes: - conditional-text-generation is split between summarization, translation, text-generation and text2text-generation - speech-processing is split into automatic-speech-recognition, audio-classification, etc. - structure-prediction is renamed token-classification - abstractive-qa now belongs to text2text-generation Here is just a simplified YAML dump of `tasks.json`: ```yaml audio-classification: - keyword-spotting - speaker-identification - speaker-intent-classification - emotion-recognition - speaker-language-identification audio-to-audio: [] automatic-speech-recognition: [] conversational: - dialogue-generation feature-extraction: [] fill-mask: - slot-filling - masked-language-modeling image-classification: - multi-label-image-classification - multi-class-image-classification image-segmentation: - instance-segmentation - semantic-segmentation - panoptic-segmentation image-to-text: - image-captioning multiple-choice: - multiple-choice-qa - multiple-choice-coreference-resolution object-detection: - face-detection - vehicle-detection question-answering: - extractive-qa - open-domain-qa - closed-domain-qa sentence-similarity: [] tabular-classification: [] tabular-to-text: - rdf-to-text summarization: - news-articles-summarization - news-articles-headline-generation table-to-text: [] table-question-answering: [] text-classification: - acceptability-classification - entity-linking-classification - fact-checking - intent-classification - multi-class-classification - multi-label-classification - natural-language-inference - semantic-similarity-classification - sentiment-classification - topic-classification - semantic-similarity-scoring - sentiment-scoring - sentiment-analysis - hate-speech-detection - text-scoring text-generation: - dialogue-modeling - language-modeling text-retrieval: - document-retrieval - utterance-retrieval - entity-linking-retrieval - fact-checking-retrieval text-to-image: [] text-to-tabular: - relation-extraction - semantic-role-labeling text-to-speech: [] text2text-generation: - text-simplification - explanation-generation - abstractive-qa - open-domain-abstractive-qa - closed-domain-qa - open-book-qa - closed-book-qa time-series-forecasting: - univariate-time-series-forecasting - multivariate-time-series-forecasting token-classification: - named-entity-recognition - part-of-speech-tagging - parsing - lemmatization - word-sense-disambiguation - coreference-resolution translation: [] visual-question-answering: [] voice-activity-detection: [] zero-shot-classification: [] zero-shot-image-classification: [] reinforcement-learning: [] other: [] ``` Feel free to comment and give suggestions, especially if you think we can also align this list with other projects cc @julien-c @osanseviero @severo @lewtun @yjernite @albertvillanova @mariosasko @polinaeterna

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PR_kwDODunzps41U5rq

Create metric card for METEOR

closed

CONTRIBUTOR

Proposing a metric card for METEOR

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PR_kwDODunzps41UqXS

Contributing MedMCQA dataset

closed

CONTRIBUTOR

Adding MedMCQA dataset ( https://paperswithcode.com/dataset/medmcqa ) **Name**: MedMCQA **Description**: MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. The dataset contains questions about the following topics: Anesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine (FM), Obstetrics and Gynecology (O&G), Medicine, Microbiology, Ophthalmology, Orthopedics Pathology, Pediatrics, Pharmacology, Physiology, Psychiatry, Radiology Skin, Preventive & Social Medicine (PSM), and Surgery **Code**: https://github.com/medmcqa/medmcqa All files are at place : **a dataset script** : medmcqa.py **a dataset card with tags and information** : README.md. **a metadata file** : dataset_infos.json **a dummy-data file** : Please help to generate this file, I was facing ` raise JSONDecodeError("Extra data", s, end)` error

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PR_kwDODunzps41UiDm

Increase max retries for GitHub metrics

closed

MEMBER

As GitHub recurrently raises connectivity issues, this PR increases the number of max retries to request GitHub metrics. Related to: - #3134 Also related to: - #4059

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I_kwDODunzps5Gtfhs

Loading mozilla-foundation/common_voice_7_0 dataset failed

closed

NONE

## Describe the bug I wanted to load `mozilla-foundation/common_voice_7_0` dataset with `fi` language and `test` split from datasets on Colab/Kaggle notebook, but I am getting an error `JSONDecodeError: [Errno Expecting value] Not Found: 0` while loading it. The bug seems to affect other languages and splits too than just the `fi` and `test` split. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token="YOUR TOKEN") ``` ## Expected results load `mozilla-foundation/common_voice_7_0` dataset succesfully ## Actual results ``` JSONDecodeError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 909 try: --> 910 return complexjson.loads(self.text, **kwargs) 911 except JSONDecodeError as e: /opt/conda/lib/python3.7/site-packages/simplejson/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, use_decimal, **kw) 524 and not use_decimal and not kw): --> 525 return _default_decoder.decode(s) 526 if cls is None: /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in decode(self, s, _w, _PY3) 369 s = str(s, self.encoding) --> 370 obj, end = self.raw_decode(s) 371 end = _w(s, end).end() /opt/conda/lib/python3.7/site-packages/simplejson/decoder.py in raw_decode(self, s, idx, _w, _PY3) 399 idx += 3 --> 400 return self.scan_once(s, idx=_w(s, idx).end()) JSONDecodeError: Expecting value: line 1 column 1 (char 0) During handling of the above exception, another exception occurred: JSONDecodeError Traceback (most recent call last) /tmp/ipykernel_358/370980805.py in <module> 1 # load Common Voice 7.0 dataset from Huggingface with Finnish "test" split ----> 2 test_dataset = load_dataset("mozilla-foundation/common_voice_7_0", "fi", split="test", use_auth_token=True) /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1690 ignore_verifications=ignore_verifications, 1691 try_from_hf_gcs=try_from_hf_gcs, -> 1692 use_auth_token=use_auth_token, 1693 ) 1694 /opt/conda/lib/python3.7/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 604 if not downloaded_from_gcs: 605 self._download_and_prepare( --> 606 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 607 ) 608 # Sync info /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1102 1103 def _download_and_prepare(self, dl_manager, verify_infos): -> 1104 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1105 1106 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /opt/conda/lib/python3.7/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 670 split_dict = SplitDict(dataset_name=self.name) 671 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 672 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 673 674 # Checksums verification ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _split_generators(self, dl_manager) 151 152 self._log_download(self.config.name, bundle_version, hf_auth_token) --> 153 archive = dl_manager.download(self._get_bundle_url(self.config.name, bundle_url_template)) 154 155 if self.config.version < datasets.Version("5.0.0"): ~/.cache/huggingface/modules/datasets_modules/datasets/mozilla-foundation--common_voice_7_0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/common_voice_7_0.py in _get_bundle_url(self, locale, url_template) 130 path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'") 131 use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024 --> 132 response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json() 133 return response["url"] 134 /opt/conda/lib/python3.7/site-packages/requests/models.py in json(self, **kwargs) 915 raise RequestsJSONDecodeError(e.message) 916 else: --> 917 raise RequestsJSONDecodeError(e.msg, e.doc, e.pos) 918 919 @property JSONDecodeError: [Errno Expecting value] Not Found: 0 ``` ## Environment info - `datasets` version: 2.0.0 - Platform: Linux-5.10.90+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 5.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5GtcMP

Loading cnn_dailymail dataset failed

closed

NONE

## Describe the bug I wanted to load cnn_dailymail dataset from huggingface datasets on jupyter lab, but I am getting an error ` NotADirectoryError:[Errno20] Not a directory ` while loading it. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('cnn_dailymail', '3.0.0') ``` ## Expected results load `cnn_dailymail` dataset succesfully ## Actual results failed to load and get error > NotADirectoryError: [Errno 20] Not a directory ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` 1.8.0: - Platform: Ubuntu-20.04 - Python version: 3.9.10 - PyArrow version: 3.0.0

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PR_kwDODunzps41Tbmg

Deprecate canonical Multilingual Librispeech

closed

CONTRIBUTOR

Deprecate canonical Multilingual Librispeech in favor of [the community one](https://huggingface.co/datasets/facebook/multilingual_librispeech) which supports streaming. However, there is a problem regarding new ASR template schema: since it's changed, I guess all community datasets that use this template do not work with new version of the library, including MLS. Should we somehow notify users about that or is it possible to change this line ourselves? For MLS specifically, I cannot change the code directly as I'm not the member of the Facebook org. Hm, and the code should be change after the release, no?

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PR_kwDODunzps41TC-o

Load GitHub datasets from Hub

open

MEMBER

We have recurrently had connection errors when requesting GitHub because sometimes the site is not available. This PR requests the Hub instead, once all GitHub datasets are mirrored on the Hub. Fix #2048 Related to: - #4051 - #3210 - #2787 - #2075 - #2036

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PR_kwDODunzps41RPhl

Updated annotations for nli_tr dataset

closed

CONTRIBUTOR

This PR adds annotation tags for `nli_tr` dataset so that the dataset can be searchable wrt. relevant query parameters. The annotations in this PR are based on the existing annotations of `snli` and `multi_nli` datasets as `nli_tr` is a machine-generated extension of those datasets. This PR is intended only for updating the annotation labels but a followup PR will focus on updating the missing sections in the `README.md` as well. Thanks for all your time to review it.

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I_kwDODunzps5GqGjR

`load_dataset` consumes too much memory

open

NONE

## Description `load_dataset` consumes more and more memory until it's killed, even though it's made with a generator. I'm adding a loading script for a new dataset, made up of ~15s audio coming from a tar file. Tried setting `DEFAULT_WRITER_BATCH_SIZE = 1` as per the discussion in #741 but the problem persists. ## Steps to reproduce the bug Here's my implementation of `_generate_examples`: ```python class MyDatasetBuilder(datasets.GeneratorBasedBuilder): DEFAULT_WRITER_BATCH_SIZE = 1 ... def _split_generators(self, dl_manager): archive_path = dl_manager.download(_DL_URLS[self.config.name]) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "audio_tarfile_path": archive_path["audio_tarfile"] }, ), ] def _generate_examples(self, audio_tarfile_path): key = 0 with tarfile.open(audio_tarfile_path, mode="r|") as audio_tarfile: for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 ``` I then try to load via `ds = load_dataset('./datasets/my_new_dataset', writer_batch_size=1)`, and memory usage grows until all 8GB of my machine are taken and process is killed (`Killed`). Also tried an untarred version of this using `os.walk` but the same happened. I created a script to confirm that one can safely go through such a generator, which runs just fine with memory <500MB at all times. ```python import tarfile def generate_examples(): audio_tarfile = tarfile.open("audios.tar", mode="r|") key = 0 for audio_tarinfo in audio_tarfile: audio_name = audio_tarinfo.name audio_file_obj = audio_tarfile.extractfile(audio_tarinfo) yield key, {"audio": {"path": audio_name, "bytes": audio_file_obj.read()}} key += 1 if __name__ == "__main__": examples = generate_examples() for example in examples: pass ``` ## Expected results Memory consumption should be similar to the non-huggingface script. ## Actual results Process is killed after consuming too much memory. ## Environment info - `datasets` version: 2.0.1.dev0 - Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-debian-10.12 - Python version: 3.7.12 - PyArrow version: 6.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5GpAq_

Unexpected behavior of _TempDirWithCustomCleanup

open

NONE

## Describe the bug This is not 100% a bug in `datasets`, but behavior that surprised me and I think this could be made more robust on the `datasets`side. When using `datasets.disable_caching()`, cache files are written to a temporary directory. This directory should be based on the environment variable TMPDIR. I want to set TMPDIR at runtime using os.ENVIRON["TMPDIR"] = something, but depending on other imported modules this can fail to take effect. ## Steps to reproduce the bug `_TempDirWithCustomCleanup` relies on `tempfile` to generate a path to a temporary directory. However, `tempfile` generates the path only once. This can be a problem when trying to set TMPDIR at runtime whenever other code imports `tempfile` first and does something unexpected. For example (after too much trial and error) I found out that a different part of the code base I work with defines a class `PatchedDataCollatorForLanguageModeling(transformers.DataCollatorForLanguageModeling)` based on a `transformers` class. This import is enough to trigger `tempfile` to generate `tempfile` to generate a temporary path and leading to the wrong path being cached in `tempfile.tempdir`. ## Suggestion: I could file this also as bug with `transformers`, but I think fixing this on the datasets would be much more robust: Datasets could recompute the temporary path once (technically possible via `tempfile._get_default_tempdir` or resetting the global variable `tempfile.tmpdir` to None) before setting its own global `_TEMP_DIR_FOR_TEMP_CACHE_FILES`.

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PR_kwDODunzps41PGF1

[DO NOT MERGE] Test doc-builder

closed

MEMBER

This is a test PR to ensure the changes in https://github.com/huggingface/doc-builder/pull/164 don't break anything in `datasets`

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PR_kwDODunzps41Nwjz

Support float data types in pearsonr/spearmanr metrics

closed

MEMBER

Fix #4053.

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I_kwDODunzps5Gmgqa

Modify datatype from `int32` to `float` for pearsonr, spearmanr.

closed

NONE

**Is your feature request related to a problem? Please describe.** - Now [Pearsonr](https://github.com/huggingface/datasets/blob/master/metrics/pearsonr/pearsonr.py) and [Spearmanr](https://github.com/huggingface/datasets/blob/master/metrics/spearmanr/spearmanr.py) both get input data as 'int32'. **Describe the solution you'd like** - Considering that those metrics are widely used for the STS task(labels are in 'float' data type), it would be better to modify datatype from 'int32' to 'float' for getting exact values of similarity.

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I_kwDODunzps5GmT3p

metric = metric_cls( TypeError: 'NoneType' object is not callable

closed

NONE

Hi, friend. I meet a problem. When I run the code: `metric = load_metric('glue', 'rte')` There is a problem raising: `metric = metric_cls( TypeError: 'NoneType' object is not callable ` I don't know why. Thanks for your help!

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I_kwDODunzps5GmIMz

ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py

closed

NONE

Hi, I meet a problem. When I run the code: `dataset = load_dataset('glue','sst2')` There is a issue raising: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/glue/glue.py I don't know why; it is ok when I use Google Chrome to view this url. Thanks for your help!

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PR_kwDODunzps41NAMF

Add RVL-CDIP dataset

closed

CONTRIBUTOR

Resolves #2762 Dataset Request : Add RVL-CDIP dataset [#2762](https://github.com/huggingface/datasets/issues/2762) This PR adds the RVL-CDIP dataset. The dataset contains Google Drive link for download and wasn't getting downloaded automatically, so I have provided manual_download_instructions. - I have added the dummy_data.zip as well. Needed inputs on how I can run the real data and the dummy data tests for datasets with manual download ? Inputs and suggestions for improvement are welcome. Thank you.

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PR_kwDODunzps41LSjv

Create metric card for the Code Eval metric

closed

CONTRIBUTOR

Creating initial Code Eval metric card

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I_kwDODunzps5Gj2yg

Split size error on `amazon_us_reviews` / `PC_v1_00` dataset

closed

CONTRIBUTOR

## Describe the bug When downloading this subset as of 3-28-2022 you will encounter a split size error after the dataset is extracted. The extracted dataset has roughly ~6m rows while the split expects <1m. Upon digging a little deeper, I downloaded the raw files from `https://s3.amazonaws.com/amazon-reviews-pds/tsv/amazon_reviews_us_PC_v1_00.tsv.gz` and extracted them. A line count via `wc -l` confirms the ~6m number that we see and the data looks valid at a glance (I did not check for duplicate rows). My guess is this file has either been updated in place or there is a bug in the dataset metadata. Happy to submit a PR and fix this up if turns out to be a metadata issue but wanted to get some other :eyes: on it first. ## Steps to reproduce the bug ```python load_dataset('amazon_us_reviews', 'PC_v1_00') ``` ## Expected results Dataset is downloaded and extracted successfully. ## Actual results An split size exception is thrown. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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I_kwDODunzps5GjzC1

Dataset.unique(column: str) -> ArrowNotImplementedError

closed

NONE

## Describe the bug I'm trying to use `unique()` function, but it fails ## Steps to reproduce the bug 1. Get dataset 2. Call `unique` 3. Error # Sample code to reproduce the bug ```python !pip show datasets from datasets import load_dataset dataset = load_dataset('wikiann', 'en') dataset['train'].column_names dataset['train'].unique(dataset['train'].column_names[0]) ``` ## Expected results It would be nice to actually see unique items ## Actual results Error: ```python --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) [<ipython-input-10-5e0de07ed42c>](https://s0qyv2vjaji-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab-20220324-060046-RC00_436956229#) in <module>() 6 7 dataset['train'].column_names ----> 8 dataset['train'].unique(dataset['train'].column_names[0]) 5 frames /usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Function unique has no kernel matching input types (array[list<item: string>]) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 2.0.0 - Platform: Google Collab - Python version: 3.7.13 - PyArrow version: 6.0.1

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PR_kwDODunzps41K6_H

Create metric card for XNLI

closed

CONTRIBUTOR

Proposing a metric card for XNLI

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PR_kwDODunzps41KtfV

Fix CLI dummy data generation

closed

MEMBER

PR: - #3868 broke the CLI dummy data generation. Fix #4044.

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I_kwDODunzps5GjTO-

CLI dummy data generation is broken

closed

MEMBER

## Describe the bug We get a TypeError when running CLI dummy data generation: ```shell datasets-cli dummy_data datasets/<your-dataset-folder> --auto_generate ``` gives: ``` File ".../huggingface/datasets/src/datasets/commands/dummy_data.py", line 361, in _autogenerate_dummy_data dataset_builder._prepare_split(split_generator) TypeError: _prepare_split() missing 1 required positional argument: 'check_duplicate_keys' ```

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PR_kwDODunzps41Kl0b

Create metric card for CUAD

closed

CONTRIBUTOR

Proposing a CUAD metric card

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I_kwDODunzps5GjGhH

Standardize metric ranges

open

CONTRIBUTOR

Several common metrics, like `exact_match` and `f1`, sometimes range from 0-1, and sometimes from 0-100. As discussed with @lhoestq , we think it makes more sense to report them from 0-1, which would entail changing the code of metrics such as [CUAD](https://github.com/huggingface/datasets/blob/master/metrics/cuad/cuad.py). @emibaylor and I will add other metrics here that we see reporting scores from 0-100 instead of 0-1.

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I_kwDODunzps5GjEtl

Add support for IIIF in datasets

open

CONTRIBUTOR

This is a feature request for support for IIIF in `datasets`. Apologies for the long issue. I have also used a different format to the usual feature request since I think that makes more sense but happy to use the standard template if preferred. ## What is [IIIF](https://iiif.io/)? IIIF (International Image Interoperability Framework) > is a set of open standards for delivering high-quality, attributed digital objects online at scale. It’s also an international community developing and implementing the IIIF APIs. IIIF is backed by a consortium of leading cultural institutions. The tl;dr is that IIIF provides various specifications for implementing useful functionality for: - Institutions to make available images for various use cases - Users to have a consistent way of interacting/requesting these images - For developers to have a common standard for developing tools for working with IIIF images that will work across all institutions that implement a particular IIIF standard (for example the image viewer for the BNF can also work for the Library of Congress if they both use IIIF). Some institutions that various levels of support IIF include: The British Library, Internet Archive, Library of Congress, Wikidata. There are also many smaller institutions that have IIIF support. An incomplete list can be found here: https://iiif.io/guides/finding_resources/ ## IIIF APIs IIIF consists of a number of APIs which could be integrated with datasets. I think the most obvious candidate for inclusion would be the [Image API](https://iiif.io/api/image/3.0/) ### IIIF Image API The Image API https://iiif.io/api/image/3.0/ is likely the most suitable first candidate for integration with datasets. The Image API offers a consistent protocol for requesting images via a URL: ```{scheme}://{server}{/prefix}/{identifier}/{region}/{size}/{rotation}/{quality}.{format}``` A concrete example of this: ```https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/full/0/default.jpg``` As you can see the scheme offers a number of options that can be specified in the URL, for example, size. Using the example URL we return:  We can change the size to request a size of 250 by 250, this is done by changing the size from `full` to `250,250` i.e. switching the URL to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/250,250/0/default.jpg`  We can also request the image with max width 250, max height 250 whilst maintaining the aspect ratio using `!w,h`. i.e. change the url to `https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg`  A full overview of the options for size can be found here: https://iiif.io/api/image/3.0/#42-size ## Why would/could this be useful for datasets? There are a few reasons why support for the IIIF Image API could be useful. Broadly the ability to have more control over how an image is returned from a server is useful for many ML workflows: - images can be requested in the right size, this prevents having to download/stream large images when the actual desired size is much smaller - can select a subset of an image: it is possible to select a sub-region of an image, this could be useful for example when you already have a bounding box for a subset of an image and then want to use this subset of an image for another task. For example, https://github.com/Living-with-machines/nnanno uses IIIF to request parts of a newspaper image that have been detected as 'photograph', 'illustration' etc for downstream use. - options for quality, rotation, the format can all be encoded in the URL request. These may become particularly useful when pre-training models on large image datasets where the cost of downloading images with 1600 pixel width when you actually want 240 has a larger impact. ## What could this look like in datasets? I think there are various ways in which support for IIIF could potentially be included in `datasets`. These suggestions aren't fully fleshed out but hopefully, give a sense of possible approaches that match existing `datasets` methods in their approach. ### Use through datasets scripts Loading images via URL is already supported. There are a few possible 'extras' that could be included when using IIIF. One option is to leverage the IIIF protocol in datasets scripts, i.e. the dataset script can expose the IIIF options via the dataset script: ```python ds = load_dataset("iiif_dataset", image_size="250,250", fmt="jpg") ``` This is already possible. The approach to parsing the IIIF URLs would be left to the person creating the dataset script. ### Support through dataset scripts (with some datasets support) This is similar to the above but `datasets` would offer some way of saying this is a iiif URL and then expose the options associated with IIIF images automatically. i.e. if you did something like: ```python features = {"label": ClassLabel(names=['dog','cat']), "url": datasets.IIIFURL()} ``` inside your loading script, you would automatically have exposed `size`, `fmt` etc. options when loading the dataset. ### Other possible integrations Some other possible pseudocode ways that a user could interact with IIIF URLs: The ability to cast to an `IIIFImage` feature type: ``` ds.cast_column('url', IIIFImage, download=False) ``` The ability to specify some options associated with IIIF urls. ``` ds = ds.set_iiif_options(column='url', size="250,250") ``` I think all of these would rely on having an `IIIFImage` feature type - this would be a little bit of a Frankenstein between a `string` and `datasets.Image`. I think most of the actual image behaviour would be exactly the same as `datasets.Image`, the difference would be that the underlying URL could be modified in various ways. ## prerequisite requirements There are a few pre-requisites that I can anticipate. This doesn't cover a full implementation of IIIF support which would have different requirements depending on the approach taken to implementing IIIF. Some of these features would be useful independently of adding IIIF support: ### support for handling failed images loaded via a URL (or a specific IIIFImage feature). Working with images via web requests will inevitably return the odd failed request. If these images are then requests and don't return it would be useful to have a `None` returned instead of an error. For example, when using `push_to_hub` `datasets` will try and include the image but currently fails with bad URLs. ```python from datasets import Dataset import datasets urls = ['https://stacks.stanford.edu/image/iiif/hg676jb4964%2F0380_796-44/full/!250,250/0/default.jpg']*3 urls.append("badurl.com/image.jpg") data = {"url":urls} ds = Dataset.from_dict(data) ds = ds.cast_column('url', datasets.Image()) ds[3]['url'] ``` returns a `FileNotFoundError`, for streaming large datasets of images using their URLs it could be useful to have `None` returned instead. This has implications for the actual training loop i.e. you now need to somehow skip those examples because of this it might not be desirable to support this. ### Caching support Since IIIF requests images via a URL it would be great to have a way of not requesting the images multiple times. This is tracked in https://github.com/huggingface/datasets/issues/3142 and I think this would also be very desirable to have here particularly as one of the primary use cases of IIIF may be to do unsupervised pre-training on large datasets of IIIF URLs. ### Support for Parsing IIIF URLs This gets closer to the actual implementation. Here the requirement would be some way for `datasets` to parse a URL that the users specify is an IIIF URL. An example of a Python library that does this: https://github.com/Princeton-CDH/piffle. I also have a rough version that uses `dataclasses` which I can share. ## Why it might not be worthwhile/suitable for datasets There are some reasons that this might not be worth implementing: - currently, IIIF is mainly used by cultural heritage organizations (museums, archives etc.) The adoption of IIIF in this sector has been growing but it's possible that adoption won't be extended to other industries which may also be a source of image data for training ML models. - It may end up being better to leave this to the user. It would for example be possible for someone to write map functions to change an IIIF URL to the correct size etc. Adding direct support for IIIF in datasets may potentially not be worth the trouble. - The impact of different approaches to doing image scaling can impact the downstream model's performance, see: https://twitter.com/wightmanr/status/1479528581466243073?s=20. Since different IIIF image servers may implement different approaches to resizing images this could have a downstream impact on model performance. think this is something that could be flagged to the end-user in the documentation. This probably also falls into general "gotchas" that probably aren't the `datasets` libraries' role to protect users from. Some of the requirements outlined above would be useful for images anyway. These could be implemented prior to a final decision about whether IIIF support could/should be added to datasets. ## Suggested next steps: I realise this is a long and slightly open-ended issue. I am happy to clarify/answer questions on IIIF and possible integrations. If the prerequisite requirements seem worth exploring/are better explored in their own issues let me know and I can open new issues for those.

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I_kwDODunzps5Gi6H1

Calling existing metrics from other metrics

open

CONTRIBUTOR

There are several cases of metrics calling other metrics, e.g. [Wiki Split](https://huggingface.co/metrics/wiki_split) which calls [BLEU](https://huggingface.co/metrics/bleu) and [SARI](https://huggingface.co/metrics/sari). These are all currently re-implemented each time (often with external code). A potentially more efficient and centralized way of doing things would maybe to have a single implementation and calling that implementation. E.g. @lhoestq 's proposal: ``` def _compute(...): bleu = load_metric("bleu", cache_dir=self.cache_dir, seed=self.seed) output = bleu._compute(...) ``` Something to keep in mind for the big metric reorg!

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PR_kwDODunzps41KFIf

Support streaming xcopa dataset

closed

MEMBER

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PR_kwDODunzps41JKUG

[DO NOT MERGE] Test doc-builder with skipped installation feature

closed

MEMBER

This PR is just for testing that we can build PR docs with changes made on the [`skip-install-for-real`](https://github.com/huggingface/doc-builder/tree/skip-install-for-real) branch of `doc-builder`.

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I_kwDODunzps5GhVom

Error while building documentation

closed

MEMBER

## Describe the bug 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. ```

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PR_kwDODunzps41I854

Fix building of documentation

closed

MEMBER

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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PR_kwDODunzps41Iwb2

Add zero_division argument to precision and recall metrics

closed

MEMBER

Fix #4025.

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PR_kwDODunzps41IpN1

Fix null checksum in xcopa dataset

closed

MEMBER

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PR_kwDODunzps41Ie6w

Fix checksum error in cats_vs_dogs dataset

closed

MEMBER

Recent PR updated the metadata JSON file of cats_vs_dogs dataset: - #3878 However, that new JSON file contains a None checksum. This PR fixes it. Fix #4032.

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I_kwDODunzps5GfPpx

can't download cats_vs_dogs dataset

closed

NONE