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Related to huggingface/transformers#12549 and #502. | {
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Also I moved the check for `num_proc` larger than dataset size added in #2566 up so that multiprocessing is not used with one process. | {
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If `filter` is applied to a dataset using multiprocessing (`num_proc` > 1) and all sharded datasets are empty afterwards (due to all samples being discarded), the program crashes.
## Steps to reproduce the bug
```python
from datasets import Dataset
data = Dataset.from_dict({'id': [0,1]})
data.filter(lambda x: False, num_proc=2)
```
## Expected results
An empty table should be returned without crashing.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 185, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2143, in filter
return self.map(
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1738, in map
result = concatenate_datasets(transformed_shards)
File "/home/user/venv/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3267, in concatenate_datasets
table = concat_tables(tables_to_concat, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 853, in concat_tables
return ConcatenationTable.from_tables(tables, axis=axis)
File "/home/user/venv/lib/python3.8/site-packages/datasets/table.py", line 713, in from_tables
blocks = to_blocks(tables[0])
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.12.11-300.fc34.x86_64-x86_64-with-glibc2.2.5
- Python version: 3.8.10
- PyArrow version: 3.0.0
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"Hi @erikadistefano , thanks for reporting the issue.\r\n\r\nI have created a Pull Request that should fix it. \r\n\r\nOnce merged into master, feel free to update your installed `datasets` library (either by installing it from our GitHub master branch or waiting until our next release) to be able to load omp dataset."
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} | MDU6SXNzdWU5Mzc5MzA2MzI= | null | 2021-07-06T14:00:52Z | https://api.github.com/repos/huggingface/datasets/issues/2598/comments | ## Describe the bug
The omp dataset cannot be downloaded because of a DuplicatedKeysError
## Steps to reproduce the bug
from datasets import load_dataset
omp = load_dataset('omp', 'posts_labeled')
print(omp)
## Expected results
This code should download the omp dataset and print the dictionary
## Actual results
Downloading and preparing dataset omp/posts_labeled (download: 1.27 MiB, generated: 13.31 MiB, post-processed: Unknown size, total: 14.58 MiB) to /home/erika_distefano/.cache/huggingface/datasets/omp/posts_labeled/1.1.0/2fe5b067be3bff1d4588d5b0cbb9b5b22ae1b9d5b026a8ff572cd389f862735b...
0 examples [00:00, ? examples/s]2021-07-06 09:43:55.868815: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.11.0
Traceback (most recent call last):
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 990, in _prepare_split
writer.write(example, key)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 338, in write
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "hf_datasets.py", line 32, in <module>
omp = load_dataset('omp', 'posts_labeled')
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/erika_distefano/.local/lib/python3.6/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 3326
Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.8.0
- Platform: Ubuntu 18.04.4 LTS
- Python version: 3.6.9
- PyArrow version: 3.0.0
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} | 2021-07-06T13:47:45Z | https://api.github.com/repos/huggingface/datasets/issues/2597/comments | I have noticed that after implementing `load_dataset_builder` (#2500), there is a redundant call to `prepare_module`. | {
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937,598,914 | https://api.github.com/repos/huggingface/datasets/issues/2596 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2596/events | [
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"Hi ! Do you have an example in mind that shows how this could be useful ?",
"Example:\n\nMerge 2 datasets into one datasets\n\nLabel extraction from dataset\n\ndataset(text, label)\n —> dataset(text, newlabel)\n\nTextCleaning.\n\n\nFor image dataset, \nTransformation are easier (ie linear algebra).\n\n\n\n\n\n\n> On Jul 6, 2021, at 17:39, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> Hi ! Do you have an example in mind that shows how this could be useful ?\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n",
"There are already a few transformations that you can apply on a dataset using methods like `dataset.map()`.\r\nYou can find examples in the documentation here:\r\nhttps://huggingface.co/docs/datasets/processing.html\r\n\r\nYou can merge two datasets with `concatenate_datasets()` or do label extraction with `dataset.map()` for example",
"Ok, sure.\n\nThanks for pointing on functional part.\nMy question is more\n“Philosophical”/Design perspective.\n\nThere are 2 perspetive:\n Add transformation methods to \n Dataset Class\n\n\n OR Create a Transformer Class\n which operates on Dataset Class.\n\nT(Dataset) —> Dataset\n\ndatasetnew = MyTransform.transform(dataset)\ndatasetNew.save(path)\n\n\nWhat would be the difficulty\nof implementing a Transformer Class\noperating at dataset level ?\n\n\nthanks\n\n\n\n\n\n\n\n\n\n> On Jul 6, 2021, at 22:00, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> There are already a few transformations that you can apply on a dataset using methods like dataset.map().\n> You can find examples in the documentation here:\n> https://huggingface.co/docs/datasets/processing.html\n> \n> You can merge two datasets with concatenate_datasets() or do label extraction with dataset.map() for example\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n",
"I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\r\n\r\nI guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)",
"Thanks for reply.\n\nWhat would be the constraints\nto have\nDataset —> Dataset consistency ?\n\nMain issue would be\nlarger than memory dataset and\nserialization on disk.\n\nTechnically,\none still process at atomic level\nand try to wrap the full results\ninto Dataset…. (!)\n\nWhat would you think ?\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 16:51, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> I can imagine that this would be a useful API to implement processing pipelines as transforms. They could be used to perform higher level transforms compared to the atomic transforms allowed by methods like map, filter, etc.\n> \n> I guess if you find any transform that could be useful for text dataset processing, image dataset processing etc. we could definitely start having such transforms :)\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n",
"We can be pretty flexible and not impose any constraints for transforms.\r\n\r\nMoreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like `map` work in a batched fashion to not fill up your RAM. So this shouldn't be an issue",
"Ok thanks.\n\nBut, Dataset has various flavors.\nIn current design of Dataset,\n how the serialization on disk is done (?)\n\n\nThe main issue is serialization \nof newdataset= Transform(Dataset)\n (ie thats why am referring to Out Of memory dataset…):\n\n Should be part of Transform or part of dataset ?\n\n\n\n\nMaybe, not, since the output is aimed to feed model in memory (?)\n\n\n\n\n\n\n\n\n> On Jul 7, 2021, at 18:04, Quentin Lhoest ***@***.***> wrote:\n> \n> \n> We can be pretty flexible and not impose any constraints for transforms.\n> \n> Moreover, this library is designed to support datasets bigger than memory. The datasets are loaded from the disk via memory mapping, without filling up RAM. Even processing functions like map work in a batched fashion to not fill up your RAM. So this shouldn't be an issue\n> \n> —\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub, or unsubscribe.\n",
"I'm not sure I understand, could you elaborate a bit more please ?\r\n\r\nEach dataset is a wrapper of a PyArrow Table that contains all the data. The table is loaded from an arrow file on the disk.\r\nWe have an ArrowWriter and ArrowReader class to write/read arrow tables on disk or in in-memory buffers."
] | Transformer Class on dataset | {
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} | MDU6SXNzdWU5Mzc1OTg5MTQ= | null | 2021-07-06T07:27:15Z | https://api.github.com/repos/huggingface/datasets/issues/2596/comments | Just wondering if you have intenttion to create
TransformerClass :
dataset --> dataset
and make determnistic transformation (ie not fit).
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937,483,120 | https://api.github.com/repos/huggingface/datasets/issues/2595 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2595/events | [
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"Hi @profsatwinder.\r\n\r\nIt looks like you are using an old version of `datasets`. Please update it with `pip install -U datasets` and indicate if the problem persists.",
"@albertvillanova Thanks for the information. I updated it to 1.9.0 and the issue is resolved. Thanks again. "
] | ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets | {
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---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-8-a7b592d3bca0> in <module>()
1 from datasets import load_dataset, load_metric
2
----> 3 common_voice_train = load_dataset("common_voice", "pa-IN", split="train+validation")
4 common_voice_test = load_dataset("common_voice", "pa-IN", split="test")
9 frames
/root/.cache/huggingface/modules/datasets_modules/datasets/common_voice/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b/common_voice.py in <module>()
19
20 import datasets
---> 21 from datasets.tasks import AutomaticSpeechRecognition
22
23
ModuleNotFoundError: No module named 'datasets.tasks' | {
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The csv reader raises an error:
```python
/usr/local/lib/python3.7/dist-packages/pandas/io/parsers/readers.py in _refine_defaults_read(dialect, delimiter, delim_whitespace, engine, sep, error_bad_lines, warn_bad_lines, on_bad_lines, names, prefix, defaults)
1304
1305 if names is not lib.no_default and prefix is not lib.no_default:
-> 1306 raise ValueError("Specified named and prefix; you can only specify one.")
1307
1308 kwds["names"] = None if names is lib.no_default else names
ValueError: Specified named and prefix; you can only specify one.
``` | {
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936,957,975 | https://api.github.com/repos/huggingface/datasets/issues/2591 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2591/events | [] | null | 2021-07-19T09:08:19Z | [] | https://github.com/huggingface/datasets/issues/2591 | CONTRIBUTOR | completed | null | null | [
"Hi! I'm transferring this issue over to `datasets`",
"I'm using the datasets concatenate dataset to combine the datasets and then train.\r\ntrain_dataset = concatenate_datasets([dataset1, dataset2, common_voice_train])\r\n\r\n",
"Hi @BirgerMoell.\r\n\r\nYou have several options:\r\n- to set caching to be stored on a different path location, other than the default one (`~/.cache/huggingface/datasets`):\r\n - either setting the environment variable `HF_DATASETS_CACHE` with the path to the new cache location\r\n - or by passing it with the parameter `cache_dir` when loading each of the datasets: `dataset = load_dataset(..., cache_dir=your_new_location)`\r\n\r\n You can get all the information in the docs: https://huggingface.co/docs/datasets/loading_datasets.html#cache-directory\r\n- I wouldn't recommend disabling caching, because current implementation generates cache files anyway, although in a temporary directory and they are deleted when the session closes. See details here: https://huggingface.co/docs/datasets/processing.html#enable-or-disable-caching\r\n- You could alternatively load the datasets in streaming mode. This is a new feature which allows loading the datasets without downloading the entire files. More information here: https://huggingface.co/docs/datasets/dataset_streaming.html",
"Hi @BirgerMoell,\r\n\r\nWe are planning to add a new feature to datasets, which could be interesting in your case: Add the option to delete temporary files (decompressed files) from the cache directory (see: #2481, #2604).\r\n\r\nWe will ping you once this feature is implemented, so that the size of your cache directory will be considerably reduced."
] | Cached dataset overflowing disk space | {
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The cache folder is 500gb (and now my disk space is full).
Is there a way to toggle caching or set the caching to be stored on a different device (I have another drive with 4 tb that could hold the caching files).
This might not technically be a bug, but I was unsure and I felt that the bug was the closest one.
Traceback (most recent call last):
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/multiprocess/pool.py", line 121, in worker
result = (True, func(*args, **kwds))
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 186, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/fingerprint.py", line 397, in wrapper
out = func(self, *args, **kwargs)
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1983, in _map_single
writer.finalize()
File "/home/birger/miniconda3/envs/wav2vec2/lib/python3.7/site-packages/datasets/arrow_writer.py", line 418, in finalize
self.pa_writer.close()
File "pyarrow/ipc.pxi", line 402, in pyarrow.lib._CRecordBatchWriter.close
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
OSError: [Errno 28] Error writing bytes to file. Detail: [errno 28] No space left on device
"""
The above exception was the direct cause of the following exception:
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"Hi ! Thanks for the fix :)\r\n\r\nIf I understand correctly, `OptionalSequence` doesn't have an associated arrow type that we know in advance unlike the other feature types, because it depends on the type of the examples.\r\n\r\nFor example, I tested this and it raises an error:\r\n```python\r\nimport datasets as ds\r\nimport pyarrow as pa\r\n\r\nfeatures = ds.Features({\"a\": ds.features.OptionalSequence(ds.Value(\"int32\"))})\r\nbatch = {\"a\": [[0]]}\r\n\r\nwriter = ds.ArrowWriter(features=features, stream=pa.BufferOutputStream())\r\nwriter.write_batch(batch)\r\n# ArrowInvalid: Could not convert [0] with type list: tried to convert to int\r\n```\r\nThis error happens because `features.type` is `StructType(struct<a: int32>)`.\r\n\r\nAnother way to add support for multilabel would be to have several configurations for these metrics. By default it would set the features without sequences, and for the multi label configuration it would use features with sequences. Let me know what you think",
"Hi @lhoestq, thanks for your feedback :)\r\n\r\nDefinitely, your suggested approach is simpler. I am going to refactor all my PR unless we could envision some other use cases where an OptionalSequence might be convenient, but for now I can't think of any...",
"@albertvillanova @lhoestq I couldnt find the related docs in F1 card: https://huggingface.co/spaces/evaluate-metric/f1\r\n\r\nHow do I perform multilabel F1 evaluation using evaluate package?",
"I was going to transfer your question to the `evaluate` GitHub repository, but I saw you have already done it (and even opened a PR):\r\n- https://github.com/huggingface/evaluate/issues/219\r\n- https://github.com/huggingface/evaluate/pull/221\r\n\r\nThanks, @fcakyon. ",
"Sorry to bomb you on multiple channels :sweat_smile: @albertvillanova, I have solved my problems, and opened a PR so that others also don't get confused :+1: "
] | Support multilabel metrics | {
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} | 2021-07-05T08:19:25Z | https://api.github.com/repos/huggingface/datasets/issues/2589/comments | Currently, multilabel metrics are not supported because `predictions` and `references` are defined as `Value("int32")`.
This PR creates a new feature type `OptionalSequence` which can act as either `Value("int32")` or `Sequence(Value("int32"))`, depending on the data passed.
Close #2554. | {
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} | 2021-07-05T07:46:26Z | https://api.github.com/repos/huggingface/datasets/issues/2588/comments | Remove environment variable fixture `env_max_in_memory_dataset_size`. This fixture does not work because env variable is read in datasets.config when first loading datasets, and it is never reread during tests. | {
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936,771,339 | https://api.github.com/repos/huggingface/datasets/issues/2587 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2587/events | [] | null | 2021-07-05T09:04:38Z | [] | https://github.com/huggingface/datasets/pull/2587 | MEMBER | null | false | null | [] | Add aiohttp to tests extras require | {
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} | 2021-07-05T07:14:01Z | https://api.github.com/repos/huggingface/datasets/issues/2587/comments | Currently, none of the streaming tests are runned within our CI test suite, because the streaming tests require aiohttp and this is missing from our tests extras require dependencies.
Our CI test suite should be exhaustive and test all the library functionalities. | {
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} | 2021-07-05T06:42:20Z | https://api.github.com/repos/huggingface/datasets/issues/2586/comments | Fix misalignment between:
- the answer text and
- the answer_start within the context
by keeping original leading blank spaces in the context.
Fix #2585. | {
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"Hi @mmajurski, thanks for reporting this issue.\r\n\r\nIndeed this misalignment arises because the source dataset context field contains leading blank spaces (and these are counted within the answer_start), while our datasets loading script removes these leading blank spaces.\r\n\r\nI'm going to fix our script so that all leading blank spaces in the source dataset are kept, and there is no misalignment between the answer text and the answer_start within the context.",
"If you are going to be altering the data cleaning from the source Squad dataset, here is one thing to consider.\r\nThere are occasional double spaces separating words which it might be nice to get rid of. \r\n\r\nEither way, thank you."
] | sqaud_v2 dataset contains misalignment between the answer text and the context value at the answer index | {
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} | MDU6SXNzdWU5MzY0ODQ0MTk= | null | 2021-07-04T15:39:49Z | https://api.github.com/repos/huggingface/datasets/issues/2585/comments | ## Describe the bug
The built in huggingface squad_v2 dataset that you can access via datasets.load_dataset contains mis-alignment between the answers['text'] and the characters in the context at the location specified by answers['answer_start'].
For example:
id = '56d1f453e7d4791d009025bd'
answers = {'text': ['Pure Land'], 'answer_start': [146]}
However the actual text in context at location 146 is 'ure Land,'
Which is an off-by-one error from the correct answer.
## Steps to reproduce the bug
```python
import datasets
def check_context_answer_alignment(example):
for a_idx in range(len(example['answers']['text'])):
# check raw dataset for answer consistency between context and answer
answer_text = example['answers']['text'][a_idx]
a_st_idx = example['answers']['answer_start'][a_idx]
a_end_idx = a_st_idx + len(example['answers']['text'][a_idx])
answer_text_from_context = example['context'][a_st_idx:a_end_idx]
if answer_text != answer_text_from_context:
#print(example['id'])
return False
return True
dataset = datasets.load_dataset('squad_v2', split='train', keep_in_memory=True)
start_len = len(dataset)
dataset = dataset.filter(check_context_answer_alignment,
num_proc=1,
keep_in_memory=True)
end_len = len(dataset)
print('{} instances contain mis-alignment between the answer text and answer index.'.format(start_len - end_len))
```
## Expected results
This code should result in 0 rows being filtered out from the dataset.
## Actual results
This filter command results in 258 rows being flagged as containing a discrepancy between the text contained within answers['text'] and the text in example['context'] at the answers['answer_start'] location.
This code will reproduce the problem and produce the following count:
"258 instances contain mis-alignment between the answer text and answer index."
## Environment info
Steps to rebuilt the Conda environment:
```
# create a virtual environment to stuff all these packages into
conda create -n round8 python=3.8 -y
# activate the virtual environment
conda activate round8
# install pytorch (best done through conda to handle cuda dependencies)
conda install pytorch torchvision torchtext cudatoolkit=11.1 -c pytorch-lts -c nvidia
pip install jsonpickle transformers datasets matplotlib
```
OS: Ubuntu 20.04
Python 3.8
Result of `conda env export`:
```
name: round8
channels:
- pytorch-lts
- nvidia
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=4.5=1_gnu
- blas=1.0=mkl
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- ca-certificates=2021.5.25=h06a4308_1
- certifi=2021.5.30=py38h06a4308_0
- cffi=1.14.5=py38h261ae71_0
- chardet=4.0.0=py38h06a4308_1003
- cryptography=3.4.7=py38hd23ed53_0
- cudatoolkit=11.1.74=h6bb024c_0
- ffmpeg=4.2.2=h20bf706_0
- freetype=2.10.4=h5ab3b9f_0
- gmp=6.2.1=h2531618_2
- gnutls=3.6.15=he1e5248_0
- idna=2.10=pyhd3eb1b0_0
- intel-openmp=2021.2.0=h06a4308_610
- jpeg=9b=h024ee3a_2
- lame=3.100=h7b6447c_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.35.1=h7274673_9
- libffi=3.3=he6710b0_2
- libgcc-ng=9.3.0=h5101ec6_17
- libgomp=9.3.0=h5101ec6_17
- libidn2=2.3.1=h27cfd23_0
- libopus=1.3.1=h7b6447c_0
- libpng=1.6.37=hbc83047_0
- libstdcxx-ng=9.3.0=hd4cf53a_17
- libtasn1=4.16.0=h27cfd23_0
- libtiff=4.2.0=h85742a9_0
- libunistring=0.9.10=h27cfd23_0
- libuv=1.40.0=h7b6447c_0
- libvpx=1.7.0=h439df22_0
- libwebp-base=1.2.0=h27cfd23_0
- lz4-c=1.9.3=h2531618_0
- mkl=2021.2.0=h06a4308_296
- mkl-service=2.3.0=py38h27cfd23_1
- mkl_fft=1.3.0=py38h42c9631_2
- mkl_random=1.2.1=py38ha9443f7_2
- ncurses=6.2=he6710b0_1
- nettle=3.7.3=hbbd107a_1
- ninja=1.10.2=hff7bd54_1
- numpy=1.20.2=py38h2d18471_0
- numpy-base=1.20.2=py38hfae3a4d_0
- olefile=0.46=py_0
- openh264=2.1.0=hd408876_0
- openssl=1.1.1k=h27cfd23_0
- pillow=8.2.0=py38he98fc37_0
- pip=21.1.2=py38h06a4308_0
- pycparser=2.20=py_2
- pyopenssl=20.0.1=pyhd3eb1b0_1
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.10=h12debd9_8
- pytorch=1.8.1=py3.8_cuda11.1_cudnn8.0.5_0
- readline=8.1=h27cfd23_0
- requests=2.25.1=pyhd3eb1b0_0
- setuptools=52.0.0=py38h06a4308_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.35.4=hdfb4753_0
- tk=8.6.10=hbc83047_0
- torchtext=0.9.1=py38
- torchvision=0.9.1=py38_cu111
- typing_extensions=3.7.4.3=pyha847dfd_0
- urllib3=1.26.4=pyhd3eb1b0_0
- wheel=0.36.2=pyhd3eb1b0_0
- x264=1!157.20191217=h7b6447c_0
- xz=5.2.5=h7b6447c_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.9=haebb681_0
- pip:
- click==8.0.1
- cycler==0.10.0
- datasets==1.8.0
- dill==0.3.4
- filelock==3.0.12
- fsspec==2021.6.0
- huggingface-hub==0.0.8
- joblib==1.0.1
- jsonpickle==2.0.0
- kiwisolver==1.3.1
- matplotlib==3.4.2
- multiprocess==0.70.12.2
- packaging==20.9
- pandas==1.2.4
- pyarrow==3.0.0
- pyparsing==2.4.7
- python-dateutil==2.8.1
- pytz==2021.1
- regex==2021.4.4
- sacremoses==0.0.45
- tokenizers==0.10.3
- tqdm==4.49.0
- transformers==4.6.1
- xxhash==2.0.2
prefix: /home/mmajurski/anaconda3/envs/round8
```
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} | 2021-07-02T20:26:22Z | https://api.github.com/repos/huggingface/datasets/issues/2584/comments | The dataset's author asked me to put this codalab link into the dataset's README. | {
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"Hi ! This is because you first need to format the dataset for pytorch:\r\n\r\n```python\r\n>>> import torch\r\n>>> from datasets import load_dataset\r\n>>> dataset = load_dataset('oscar', \"unshuffled_deduplicated_en\", split='train', streaming=True)\r\n>>> torch_iterable_dataset = dataset.with_format(\"torch\")\r\n>>> assert isinstance(torch_iterable_dataset, torch.utils.data.IterableDataset)\r\n>>> dataloader = torch.utils.data.DataLoader(torch_iterable_dataset, batch_size=4)\r\n>>> next(iter(dataloader))\r\n{'id': tensor([0, 1, 2, 3]), 'text': ['Mtendere Village was inspired...]}\r\n```\r\n\r\nThis is because the pytorch dataloader expects a subclass of `torch.utils.data.IterableDataset`. Since you can't pass an arbitrary iterable to a pytorch dataloader, you first need to build an object that inherits from `torch.utils.data.IterableDataset` using `with_format(\"torch\")` for example.\r\n",
"Thank you for that and the example! \r\n\r\nWhat you said makes total sense; I just somehow missed that and assumed HF IterableDataset was a subclass of Torch IterableDataset. "
] | Error iteration over IterableDataset using Torch DataLoader | {
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} | MDU6SXNzdWU5MzYwMzQ5NzY= | null | 2021-07-02T19:55:58Z | https://api.github.com/repos/huggingface/datasets/issues/2583/comments | ## Describe the bug
I have an IterableDataset (created using streaming=True) and I am trying to create batches using Torch DataLoader class by passing this IterableDataset to it. This throws error which is pasted below. I can do the same by using Torch IterableDataset. One thing I noticed is that in the former case when I look at the dataloader.sampler class I get torch.utils.data.sampler.SequentialSampler while the latter one gives torch.utils.data.dataloader._InfiniteConstantSampler.
I am not sure if this is how it is meant to be used, but that's what seemed reasonable to me.
## Steps to reproduce the bug
1. Does not work.
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
>>> dataloader = torch.utils.data.DataLoader(dataset, batch_size=4)
>>> dataloader.sampler
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
>>> for batch in dataloader:
... print(batch)
```
2. Works.
```python
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
class CustomIterableDataset(IterableDataset):
'Characterizes a dataset for PyTorch'
def __init__(self, data):
'Initialization'
self.data = data
def __iter__(self):
return iter(self.data)
data = list(range(12))
dataset = CustomIterableDataset(data)
dataloader = DataLoader(dataset, batch_size=4)
print("dataloader: ", dataloader.sampler)
for batch in dataloader:
print(batch)
```
## Expected results
To get batches of data with the batch size as 4. Output from the latter one (2) though Datasource is different here so actual data is different.
dataloader: <torch.utils.data.dataloader._InfiniteConstantSampler object at 0x7f1cc29e2c50>
tensor([0, 1, 2, 3])
tensor([4, 5, 6, 7])
tensor([ 8, 9, 10, 11])
## Actual results
<torch.utils.data.sampler.SequentialSampler object at 0x7f245a510208>
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
data = self._next_data()
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 474, in _next_data
index = self._next_index() # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
return next(self._sampler_iter) # may raise StopIteration
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
for idx in self.sampler:
File "/data/leshekha/lib/HFDatasets/lib/python3.6/site-packages/torch/utils/data/sampler.py", line 67, in __iter__
return iter(range(len(self.data_source)))
TypeError: object of type 'IterableDataset' has no len()
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: '1.8.1.dev0'
- Platform: Linux
- Python version: Python 3.6.8
- PyArrow version: '3.0.0'
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935,859,104 | https://api.github.com/repos/huggingface/datasets/issues/2582 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2582/events | [] | null | 2021-07-05T16:06:40Z | [] | https://github.com/huggingface/datasets/pull/2582 | MEMBER | null | false | null | [
"@lhoestq looks good. I tried with https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed and it worked nicely. I would add more unit tests for edge cases. What happens if the n is larger than the total number of samples? Just to make sure these cases are handled properly. ",
"Yup I'll add the tests thanks ;)\r\n\r\nMoreover, I just noticed something in your wiki snippets code. FYI you're using `++passage_counter ` at https://huggingface.co/datasets/vblagoje/wikipedia_snippets_streamed/blob/main/wikipedia_snippets_streamed.py#L102 but in python this doesn't increment the value @vblagoje ",
"Thanks @lhoestq - not easy to convert after 10+ years of Java"
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} | 2021-07-02T15:10:19Z | https://api.github.com/repos/huggingface/datasets/issues/2582/comments | As discussed in https://github.com/huggingface/datasets/pull/2375#discussion_r657084544 I added the `IterableDataset.skip` and `IterableDataset.take` methods that allows to do basic splitting of iterable datasets.
You can create new dataset with the first `n` examples using `IterableDataset.take()`, or you can get a dataset with the rest of the examples by skipping the first `n` examples with `IterableDataset.skip()`
One implementation detail:
Using `take` (or `skip`) prevents future dataset shuffling from shuffling the dataset shards, otherwise the taken examples could come from other shards. In this case it only uses the shuffle buffer.
I would have loved to allow the shards of the taken examples to be shuffled anyway, but since we don't know in advance the length of each shard we don't know what shards to take or skip.
I think this is ok though since users can shuffle before doing take or skip. I mentioned this in the documentation
cc @vblagoje @lewtun | {
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} | 2021-07-02T13:42:07Z | https://api.github.com/repos/huggingface/datasets/issues/2581/comments | Hey,
I think it makes sense to perform search_batch threaded, so ES can perform search in parallel.
Cheers! | {
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} | 2021-07-02T13:21:48Z | https://api.github.com/repos/huggingface/datasets/issues/2580/comments | Import from `collections` instead of `typing`. | {
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935,486,894 | https://api.github.com/repos/huggingface/datasets/issues/2579 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2579/events | [] | null | 2021-07-02T07:33:44Z | [] | https://github.com/huggingface/datasets/pull/2579 | MEMBER | null | false | null | [] | Fix BibTeX entry | {
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cc: @abhishekkrthakur @thomwolf | {
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935,187,497 | https://api.github.com/repos/huggingface/datasets/issues/2578 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2578/events | [] | null | 2021-08-11T14:46:24Z | [] | https://github.com/huggingface/datasets/pull/2578 | MEMBER | null | false | null | [
"> What if people want to run some tests without having zstandard ?\r\n> Usually what we do is add a decorator @require_zstandard for example\r\n\r\n@lhoestq I think I'm missing something here...\r\n\r\nTests are a *development* tool (to ensure we deliver a good quality lib), not something we offer to the end users of the lib. Users of the lib just `pip install datasets` and no tests are delivered with the lib (`tests` directory is outside the `src` code dir). \r\n\r\nOn the contrary, developers (contributors) of the lib do need to be able to run tests (TDD). And because of that, they are required to install datasets differently: `pip install -e .[dev]`, so that all required developing (and testing) dependencies are properly installed (included `zstandard`).\r\n\r\nApart from `zsatandard`, there are many other dev/test required dependencies for running tests, and we do not have a `@require_toto` for each and every of these dependencies in our tests: \r\n- `pytest` and `absl-py` (they are not dependencies in install_requires, but only in TEST_REQUIRE extras_require), \r\n- `boto3` (in test_filesystem.py), \r\n- `seqeval` (in test_metric_common.py), \r\n- `bs4` (used by eli5 and tested in test_hf_gcp.py)\r\n- ...\r\n\r\nSo IMHO, to run tests you should previously install datasets with dev or tests dependencies: either `pip install -e .[dev]` or `pip install -e .[tests]` (the latter to be used in CI testing-only part of the development cycle). And the tests should be written accordingly, assuming all tests dependencies are installed.",
"Hi !\r\nI was saying that because the other dependencies you mentioned are only required for _some_ tests. While here zstd is required for _all_ tests since it's imported in the conftest.py\r\nFeel free to keep it as it is right now, or maybe move the fixture to test_file_utils.py to allow users without zstd to run tests for their builders, dataset card etc. without issues",
"Thank you ! I think we can merge now",
"@lhoestq does this mean that the pile could have streaming support in the future? Afaik streaming doesnt support zstandard compressed type",
"> @lhoestq does this mean that the pile could have streaming support in the future? Afaik streaming doesnt support zstandard compressed type\r\n\r\njust for reference, i tried to stream one of the `.zst` files from [the pile](https://the-eye.eu/public/AI/pile/) using\r\n\r\n```python\r\ndata_files = [\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\"]\r\nstreamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n```\r\n\r\nand got the following error:\r\n\r\n```\r\nUsing custom data configuration default-4e71acadc389c254\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\n/tmp/ipykernel_1187680/10848115.py in <module>\r\n 1 data_files = [\"https://the-eye.eu/public/AI/pile/train/00.jsonl.zst\"]\r\n 2 \r\n----> 3 streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)\r\n 4 \r\n\r\n~/miniconda3/envs/hf/lib/python3.8/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, script_version, use_auth_token, task, streaming, **config_kwargs)\r\n 835 # this extends the open and os.path.join functions for data streaming\r\n 836 extend_module_for_streaming(builder_instance.__module__, use_auth_token=use_auth_token)\r\n--> 837 return builder_instance.as_streaming_dataset(\r\n 838 split=split,\r\n 839 use_auth_token=use_auth_token,\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/builder.py in as_streaming_dataset(self, split, base_path, use_auth_token)\r\n 922 data_dir=self.config.data_dir,\r\n 923 )\r\n--> 924 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}\r\n 925 # By default, return all splits\r\n 926 if split is None:\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py in _split_generators(self, dl_manager)\r\n 50 if not self.config.data_files:\r\n 51 raise ValueError(f\"At least one data file must be specified, but got data_files={self.config.data_files}\")\r\n---> 52 data_files = dl_manager.download_and_extract(self.config.data_files)\r\n 53 if isinstance(data_files, (str, list, tuple)):\r\n 54 files = data_files\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in download_and_extract(self, url_or_urls)\r\n 140 \r\n 141 def download_and_extract(self, url_or_urls):\r\n--> 142 return self.extract(self.download(url_or_urls))\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in extract(self, path_or_paths)\r\n 115 \r\n 116 def extract(self, path_or_paths):\r\n--> 117 urlpaths = map_nested(self._extract, path_or_paths, map_tuple=True)\r\n 118 return urlpaths\r\n 119 \r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)\r\n 202 num_proc = 1\r\n 203 if num_proc <= 1 or len(iterable) <= num_proc:\r\n--> 204 mapped = [\r\n 205 _single_map_nested((function, obj, types, None, True))\r\n 206 for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in <listcomp>(.0)\r\n 203 if num_proc <= 1 or len(iterable) <= num_proc:\r\n 204 mapped = [\r\n--> 205 _single_map_nested((function, obj, types, None, True))\r\n 206 for obj in utils.tqdm(iterable, disable=disable_tqdm)\r\n 207 ]\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/py_utils.py in _single_map_nested(args)\r\n 141 # Singleton first to spare some computation\r\n 142 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 143 return function(data_struct)\r\n 144 \r\n 145 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _extract(self, urlpath)\r\n 119 \r\n 120 def _extract(self, urlpath):\r\n--> 121 protocol = self._get_extraction_protocol(urlpath)\r\n 122 if protocol is None:\r\n 123 # no extraction\r\n\r\n~/miniconda3/envs/hf/lib/python3.8/site-packages/datasets/utils/streaming_download_manager.py in _get_extraction_protocol(self, urlpath)\r\n 137 elif path.endswith(\".zip\"):\r\n 138 return \"zip\"\r\n--> 139 raise NotImplementedError(f\"Extraction protocol for file at {urlpath} is not implemented yet\")\r\n 140 \r\n 141 def download_and_extract(self, url_or_urls):\r\n\r\nNotImplementedError: Extraction protocol for file at https://the-eye.eu/public/AI/pile/train/00.jsonl.zst is not implemented yet\r\n```\r\n\r\ni'm not sure whether @Shashi456 is referring to a fundamental limitation with \"streaming\" zstandard compression files or simply that we need to support the protocol in the streaming api of `datasets`\r\n\r\n",
"@lewtun our streaming mode patches the Python `open` function. I could have a look tomorrow if it is easily implementable for this case.",
"@lewtun, I have tested and yes, it is easily implementable. I've created a draft Pull Request with an implementation proposal: #2786.",
"thanks a lot @albertvillanova - now i can stream the pile :)"
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cc: @thomwolf | {
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} | 2021-07-01T15:51:25Z | https://api.github.com/repos/huggingface/datasets/issues/2576/comments | AllenAI is now hosting the processed C4 and mC4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them !
In this PR I added the mC4 dataset builder. It supports 108 languages
You can load it with
```python
from datasets import load_dataset
en_mc4 = load_dataset("mc4", "en")
fr_mc4 = load_dataset("mc4", "fr")
en_and_fr_mc4 = load_dataset("mc4", languages=["en", "fr"])
```
It also supports streaming, if you don't want to download hundreds of GB of data:
```python
en_mc4 = load_dataset("mc4", "en", streaming=True)
```
Regarding the dataset_infos.json, I will add them once I have them.
Also we can work on the dataset card at that will be at https://huggingface.co/datasets/mc4
For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections | {
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} | 2021-07-01T13:58:08Z | https://api.github.com/repos/huggingface/datasets/issues/2575/comments | The old code for the C4 dataset was to generate the C4 with Apache Beam, as in Tensorflow Datasets.
However AllenAI is now hosting the processed C4 dataset in this repo: https://huggingface.co/datasets/allenai/c4
Thanks a lot to them for their amazing work !
In this PR I changed the script to download and prepare the data directly from this repo.
It has 4 variants: en, en.noblocklist, en.noclean, realnewslike
You can load it with
```python
from datasets import load_dataset
c4 = load_dataset("c4", "en")
```
It also supports streaming, if you don't want to download hundreds of GB of data:
```python
c4 = load_dataset("c4", "en", streaming=True)
```
Regarding the dataset_infos.json, I haven't added the infos for en.noclean. I will add them once I have them.
Also we can work on the dataset card at https://huggingface.co/datasets/c4
For now I just added a link to https://huggingface.co/datasets/allenai/c4 as well as a few sections | {
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} | 2021-07-01T09:32:53Z | https://api.github.com/repos/huggingface/datasets/issues/2574/comments | Mention dataset streaming on the "loading a dataset" page of the documentation | {
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"This was actually a second error arising from a too small block-size in the json reader.\r\n\r\nFinding the right block size is difficult for the layman user"
] | Finding right block-size with JSON loading difficult for user | {
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} | MDU6SXNzdWU5MzQ1ODQ3NDU= | null | 2021-07-01T08:48:35Z | https://api.github.com/repos/huggingface/datasets/issues/2573/comments | As reported by @thomwolf, while loading a JSON Lines file with "json" loading script, he gets
> json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 383)
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] | https://github.com/huggingface/datasets/issues/2572 | MEMBER | completed | null | null | [
"I am trying to load a dataset using Hugging Face Datasets load_dataset method. I am getting the value error as show below. Can someone help with this? I am using Windows laptop and Google Colab notebook.\r\n\r\n```\r\n!pip install zstandard\r\nfrom datasets import load_dataset\r\n\r\nlds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\n\r\nWARNING:datasets.builder:Using custom data configuration default-a1d9e8eaedd958cd\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n[<ipython-input-12-5b4fdcb8e6d5>](https://localhost:8080/#) in <module>\r\n 6 )\r\n 7 \r\n----> 8 next(iter(law_dataset_streamed))\r\n\r\n17 frames\r\n[/usr/local/lib/python3.8/dist-packages/fsspec/core.py](https://localhost:8080/#) in get_compression(urlpath, compression)\r\n 485 compression = infer_compression(urlpath)\r\n 486 if compression is not None and compression not in compr:\r\n--> 487 raise ValueError(\"Compression type %s not supported\" % compression)\r\n 488 return compression\r\n 489 \r\n\r\nValueError: Compression type zstd not supported\r\n```",
"I just tried on google colab and this works:\r\n```python\r\n!pip install zstandard\r\n!pip install datasets\r\nfrom datasets import load_dataset\r\n\r\nlds = load_dataset(\r\n \"json\",\r\n data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n split=\"train\",\r\n streaming=True,\r\n)\r\nnext(iter(lds))\r\n```\r\n\r\nCan you check that you have a correct installation of `zstandard` ?",
"@lhoestq please note [this](https://github.com/huggingface/datasets/issues/2572#issuecomment-1363718916) is a duplicate of:\r\n- #5388",
"Oh thanks I missed that one !",
"> I just tried on google colab and this works:\r\n> \r\n> ```python\r\n> !pip install zstandard\r\n> !pip install datasets\r\n> from datasets import load_dataset\r\n> \r\n> lds = load_dataset(\r\n> \"json\",\r\n> data_files=\"https://the-eye.eu/public/AI/pile_preliminary_components/FreeLaw_Opinions.jsonl.zst\",\r\n> split=\"train\",\r\n> streaming=True,\r\n> )\r\n> next(iter(lds))\r\n> ```\r\n> \r\n> Can you check that you have a correct installation of `zstandard` ?\r\n\r\nI was downloading datasets first then was doing zstandard installation and that was causing the issue. This was highlighted by the Hugging Face staff and that helped. Now the issue is resolved. Thank you."
] | Support Zstandard compressed files | {
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"I think the failing test has nothing to do with my PR..."
] | Filter expected warning log from transformers | {
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933,402,521 | https://api.github.com/repos/huggingface/datasets/issues/2570 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2570/events | [] | null | 2021-06-30T15:31:01Z | [] | https://github.com/huggingface/datasets/pull/2570 | MEMBER | null | false | null | [] | Minor fix docs format for bertscore | {
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- link to README
- format of KWARGS_DESCRIPTION | {
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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.weight', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight']\r\n- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\n```\r\n\r\nIn this case, this behavior IS expected and you can safely ignore the warning message.\r\n\r\nThe reason is that you are just using RoBERTa to get the contextual embeddings of the input sentences/tokens, thus leaving away its head layer, whose weights are ignored.\r\n\r\nFeel free to reopen this issue if you need further explanations.",
"Hi @suzyahyah, I have created a Pull Request to filter out that warning message in this specific case, since the behavior is as expected and the warning message can only cause confusion for users (as in your case)."
] | Weights of model checkpoint not initialized for RobertaModel for Bertscore | {
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```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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} | 2021-06-29T17:19:24Z | https://api.github.com/repos/huggingface/datasets/issues/2568/comments | ### Add interleave_datasets for map-style datasets
Add support for map-style datasets (i.e. `Dataset` objects) in `interleave_datasets`.
It was only supporting iterable datasets (i.e. `IterableDataset` objects).
### Implementation details
It works by concatenating the datasets and then re-order the indices to make the new dataset.
### TODO
- [x] tests
- [x] docs
Close #2563 | {
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932,933,536 | https://api.github.com/repos/huggingface/datasets/issues/2567 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2567/events | [] | null | 2021-07-01T09:42:23Z | [] | https://github.com/huggingface/datasets/pull/2567 | MEMBER | null | false | null | [] | Add ASR task and new languages to resources | {
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} | 2021-06-29T17:18:01Z | https://api.github.com/repos/huggingface/datasets/issues/2567/comments | This PR adds a new `automatic-speech-recognition` task to the list of supported tasks in `tasks.json` and also includes a few new languages missing from `common_voice`.
Note: I used the [Papers with Code list](https://www.paperswithcode.com/area/speech/speech-recognition) as inspiration for the ASR subtasks | {
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## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
``` | {
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932,445,439 | https://api.github.com/repos/huggingface/datasets/issues/2565 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2565/events | [] | null | 2021-07-05T14:26:26Z | [] | https://github.com/huggingface/datasets/pull/2565 | MEMBER | null | false | null | [
"Wait until #2567 is merged so we can benefit from the tagger :)",
"thanks for the feedback @lhoestq! i've added the new language codes and this PR should be ready for a merge :)"
] | Inject templates for ASR datasets | {
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} | 2021-06-29T10:02:01Z | https://api.github.com/repos/huggingface/datasets/issues/2565/comments | This PR adds ASR templates for 5 of the most common speech datasets on the Hub, where "common" is defined by the number of models trained on them.
I also fixed a bunch of the tags in the READMEs 😎 | {
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"It is probably worth noting here that the [documentation](https://huggingface.co/docs/datasets/process#concatenate) is misleading (indicating that it does work for IterableDatasets):\r\n\r\n> You can also mix several datasets together by taking alternating examples from each one to create a new dataset. This is known as interleaving, and you can use it with [interleave_datasets()](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.interleave_datasets). **Both [interleave_datasets()](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.interleave_datasets) and [concatenate_datasets()](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.concatenate_datasets) will work with regular [Dataset](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.Dataset) and [IterableDataset](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/main_classes#datasets.IterableDataset) objects**. Refer to the [Stream](https://huggingface.co/docs/datasets/stream#interleave) section for an example of how it’s used. ",
"Thanks for the heads up, I'll fix that"
] | concatenate_datasets for iterable datasets | {
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} | MDU6SXNzdWU5MzIzODk2Mzk= | null | 2021-06-29T08:59:41Z | https://api.github.com/repos/huggingface/datasets/issues/2564/comments | Currently `concatenate_datasets` only works for map-style `Dataset`.
It would be nice to have it work for `IterableDataset` objects as well.
It would simply chain the iterables of the iterable datasets. | {
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Let's make it work for map-style `Dataset` objects as well.
It would work the same way: either alternate between the datasets in order or randomly given probabilities specified by the user. | {
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932,333,436 | https://api.github.com/repos/huggingface/datasets/issues/2562 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2562/events | [] | null | 2021-06-29T17:21:22Z | [] | https://github.com/huggingface/datasets/pull/2562 | MEMBER | null | false | null | [] | Minor fix in loading metrics docs | {
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932,321,725 | https://api.github.com/repos/huggingface/datasets/issues/2561 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2561/events | [
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"Hi ! I just tried to reproduce what you said:\r\n- create a local builder class\r\n- use `load_dataset`\r\n- update the builder class code\r\n- use `load_dataset` again (with or without `ignore_verifications=True`)\r\nAnd it creates a new cache, as expected.\r\n\r\nWhat modifications did you do to your builder's code ?",
"Hi @lhoestq. Thanks for your reply. I just did minor modifications for which it should not regenerate cache (for e.g. Adding a print statement). Overall, regardless of cache miss, there should be an explicit option to allow reuse of existing cache if author knows cache shouldn't be affected.",
"The cache is based on the hash of the dataset builder's code, so changing the code makes it recompute the cache.\r\n\r\nYou could still rename the cache directory of your previous computation to the new expected cache directory if you want to avoid having to recompute it and if you're sure that it would generate the exact same result.\r\n\r\nThe verifications are data integrity verifications: it checks the checksums of the downloaded files, as well as the size of the generated splits.",
"Hi @apsdehal,\r\n\r\nIf you decide to follow @lhoestq's suggestion to rename the cache directory of your previous computation to the new expected cache directory, you can do the following to get the name of the new expected cache directory once #2500 is merged:\r\n```python\r\nfrom datasets import load_dataset_builder\r\ndataset_builder = load_dataset_builder(\"path/to/your/dataset\")\r\nprint(dataset_builder.cache_dir)\r\n```\r\n\r\nThis way, you don't have to recompute the hash of the dataset script yourself each time you modify the script."
] | Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` | {
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If i have local file defining a dataset builder class and I load it using `load_dataset` functionality, the existing cache is ignored whenever the file is update even with `ignore_verifications=True`. This slows down debugging and cache generator for very large datasets.
## Steps to reproduce the bug
- Create a local dataset builder class
- load the local builder class file using `load_dataset` and let the cache build
- update the file's content
- The cache should rebuilt.
## Expected results
With `ignore_verifications=True`, `load_dataset` should pick up existing cache.
## Actual results
Creates new cache.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0
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932,143,634 | https://api.github.com/repos/huggingface/datasets/issues/2560 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2560/events | [] | null | 2021-06-29T15:00:18Z | [] | https://github.com/huggingface/datasets/pull/2560 | CONTRIBUTOR | null | false | null | [
"Hi ! Thanks for fixing this :)\r\n\r\nLooks like you have tons of changes due to code formatting.\r\nWe're using `black` for this, with a custom line length. To run our code formatting, you just need to run\r\n```\r\nmake style\r\n```\r\n\r\nThen for the windows error in the CI, I'm looking into it. It's probably just a file that isn't properly closed",
"CI is all green now ! Thanks :)\r\n\r\nThere are still many code formatting changes in your PR - probably due to the first commit you did.\r\nTo avoid conflicts with future PRs it would be nice to only have the changes related to the `num_proc` warning, and not have all those code formatting changes,\r\n\r\nCould you try remove those code formatting changes ?\r\n\r\nIf it's easier for you, you can make a new branch from `master` if needed",
"Thanks, @lhoestq! Apologies for the half-baked commits yesterday! I wasn’t able to step back in to resolve those CI issues until this morning.\r\n\r\nAlso, I’m surprised that `make style` isn’t resolving the formatting changes. I’m a bit stumped on that, so I’m going to re-apply on a new branch and open a PR as you suggested."
] | fix Dataset.map when num_procs > num rows | {
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} | 2021-06-29T02:24:11Z | https://api.github.com/repos/huggingface/datasets/issues/2560/comments | closes #2470
## Testing notes
To run updated tests:
```sh
pytest tests/test_arrow_dataset.py -k "BaseDatasetTest and test_map_multiprocessing" -s
```
With Python code (to view warning):
```python
from datasets import Dataset
dataset = Dataset.from_dict({"x": ["sample"]})
print(len(dataset))
dataset.map(lambda x: x, num_proc=10)
``` | {
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931,849,724 | https://api.github.com/repos/huggingface/datasets/issues/2559 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2559/events | [
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] | null | 2023-07-20T13:34:10Z | [] | https://github.com/huggingface/datasets/issues/2559 | CONTRIBUTOR | completed | null | null | [
"Hi ! Can you share the function you pass to `map` ?\r\nI know you mentioned it would be hard to share some code but this would really help to understand what happened",
"This is the same behavior as in #4883, so I'm closing this issue as a duplicate. "
] | Memory usage consistently increases when processing a dataset with `.map` | {
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} | MDU6SXNzdWU5MzE4NDk3MjQ= | null | 2021-06-28T18:31:58Z | https://api.github.com/repos/huggingface/datasets/issues/2559/comments | ## Describe the bug
I have a HF dataset with image paths stored in it and I am trying to load those image paths using `.map` with `num_proc=80`. I am noticing that the memory usage consistently keeps on increasing with time. I tried using `DEFAULT_WRITER_BATCH_SIZE=10` in the builder to decrease arrow writer's batch size but that doesn't seem to help.
## Steps to reproduce the bug
Providing code as it is would be hard. I can provide a MVP if that helps.
## Expected results
Memory usage should become consistent after some time following the launch of processing.
## Actual results
Memory usage keeps on increasing.
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-5.4.0-52-generic-x86_64-with-debian-bullseye-sid
- Python version: 3.7.7
- PyArrow version: 3.0.0 | {
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931,736,647 | 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/events | [] | null | 2021-06-28T17:23:17Z | [] | https://github.com/huggingface/datasets/pull/2558 | MEMBER | null | false | null | [] | Update: WebNLG - update checksums | {
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} | 2021-06-28T16:16:37Z | https://api.github.com/repos/huggingface/datasets/issues/2558/comments | 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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931,633,823 | https://api.github.com/repos/huggingface/datasets/issues/2557 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2557/events | [] | null | 2021-06-28T16:11:30Z | [] | https://github.com/huggingface/datasets/pull/2557 | MEMBER | null | false | null | [] | Fix `fever` keys | {
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I fixed it by taking into account the file index as well. | {
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"excuse me, my `datasets` version is `2.2.2`, but I also just see the error info like \r\n```\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```",
"Hi ! for which dataset do you have this error ?\r\n\r\nAlso note that this issue is just about improving the error message, which is not very friendly x)",
"@lhoestq I would like to take a hit at improving the error message. Will open a draft PR and will reach out to you for review\r\n",
"> DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\n\r\n@lhoestq when you mention 42th and 1337th in the above case , are these values the examples' \"id\" or are they the examples' index ? ",
"Hi ! Thanks @VijayKalmath :)\r\n\r\nIn the general case, examples don't have an \"id\" field, so I think it should correspond to the index",
"@lhoestq , I have opened a draft PR for this Issue. \r\n\r\nI wanted to check with you if there is a way to get `<path/to/the/dataset/script>` currently or do I need to add extra code to find that. \r\n\r\nIf I need to find the script , I can assume that the generator function will always be in `datasets/{dataset_name}/{dataset_name}.py`. ",
"Thanks !\r\n\r\n> I wanted to check with you if there is a way to get <path/to/the/dataset/script> currently or do I need to add extra code to find that.\r\n\r\nYou don't have access to this info inside the ArrowWriter unfortunately. This info is available in builder.py in the DatasetBuilder code that uses the ArrowWriter though, maybe a try-catch there can do the job"
] | Better DuplicateKeysError error to help the user debug the issue | {
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} | MDU6SXNzdWU5MzE1OTU4NzI= | null | 2021-06-28T13:50:57Z | https://api.github.com/repos/huggingface/datasets/issues/2556/comments | As mentioned in https://github.com/huggingface/datasets/issues/2552 it would be nice to improve the error message when a dataset fails to build because there are duplicate example keys.
The current one is
```python
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
and we could have something that guides the user to debugging the issue:
```python
DuplicateKeysError: both 42th and 1337th examples have the same keys `48`.
Please fix the dataset script at <path/to/the/dataset/script>
``` | {
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931,585,485 | https://api.github.com/repos/huggingface/datasets/issues/2555 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2555/events | [] | null | 2021-09-02T08:24:43Z | [] | https://github.com/huggingface/datasets/pull/2555 | MEMBER | null | false | null | [
"Fix #2552."
] | Fix code_search_net keys | {
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} | 2021-06-28T13:40:23Z | https://api.github.com/repos/huggingface/datasets/issues/2555/comments | There were duplicate keys in the `code_search_net` dataset, as reported in https://github.com/huggingface/datasets/issues/2552
I fixed the keys (it was an addition of the file and row indices, which was causing collisions)
Fix #2552. | {
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931,453,855 | https://api.github.com/repos/huggingface/datasets/issues/2554 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2554/events | [
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] | null | 2021-10-13T12:29:13Z | [] | https://github.com/huggingface/datasets/issues/2554 | NONE | completed | null | null | [
"Hi @GuillemGSubies, thanks for reporting.\r\n\r\nI have made a PR to fix this issue and allow metrics to be computed also for multilabel classification problems.",
"Looks nice, thank you very much! 🚀 ",
"Sorry for reopening but I just noticed that the `_compute` method for the F1 metric is still not good enough for multilabel problems:\r\n\r\nhttps://github.com/huggingface/datasets/blob/92a3ee549705aa0a107c9fa5caf463b3b3da2616/metrics/f1/f1.py#L115\r\n\r\nSomehow we should be able to change the parameter `average` at least",
"@GuillemGSubies, the parameter `average` passed to `_compute` is then passed to `f1_score`. This is right."
] | Multilabel metrics not supported | {
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} | MDU6SXNzdWU5MzE0NTM4NTU= | null | 2021-06-28T11:09:46Z | https://api.github.com/repos/huggingface/datasets/issues/2554/comments | When I try to use a metric like F1 macro I get the following error:
```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'list'
```
There is an explicit casting here:
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/src/datasets/features.py#L274
And looks like this is because here
https://github.com/huggingface/datasets/blob/fc79f61cbbcfa0e8c68b28c0a8257f17e768a075/metrics/f1/f1.py#L88
the features can only be integers, so we cannot use that F1 for multilabel. Instead, if I create the following F1 (ints replaced with sequence of ints), it will work:
```python
class F1(datasets.Metric):
def _info(self):
return datasets.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32")),
"references": datasets.Sequence(datasets.Value("int32")),
}
),
reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"],
)
def _compute(self, predictions, references, labels=None, pos_label=1, average="binary", sample_weight=None):
return {
"f1": f1_score(
references,
predictions,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
),
}
```
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931,365,926 | https://api.github.com/repos/huggingface/datasets/issues/2553 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2553/events | [
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] | https://github.com/huggingface/datasets/issues/2553 | NONE | completed | null | null | [
"Hi ! Thanks for reporting. This is due to the WebNLG repository that got updated today.\r\nI just pushed a fix at #2558 - this shouldn't happen anymore in the future.",
"This is fixed on `master` now :)\r\nWe'll do a new release soon !"
] | load_dataset("web_nlg") NonMatchingChecksumError | {
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## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('web_nlg', name="release_v3.0_en", split="dev")
```
Gives
```
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://gitlab.com/shimorina/webnlg-dataset/-/archive/master/webnlg-dataset-master.zip']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: macOS-11.3.1-x86_64-i386-64bit
- Python version: 3.9.4
- PyArrow version: 3.0.0
Also tested on Linux, with python 3.6.8 | {
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931,354,687 | https://api.github.com/repos/huggingface/datasets/issues/2552 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2552/events | [
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] | null | 2021-09-06T14:08:30Z | [] | https://github.com/huggingface/datasets/issues/2552 | MEMBER | completed | null | null | [
"Two questions:\r\n- with `datasets-cli env` we don't have any information on the dataset script version used. Should we give access to this somehow? Either as a note in the Error message or as an argument with the name of the dataset to `datasets-cli env`?\r\n- I don't really understand why the id is duplicated in the code of `code_search_net`, how can I debug this actually?",
"Thanks for reporting. There was indeed an issue with the keys. The key was the addition of the file id and row id, which resulted in collisions. I just opened a PR to fix this at https://github.com/huggingface/datasets/pull/2555\r\n\r\nTo help users debug this kind of errors we could try to show a message like this\r\n```python\r\nDuplicateKeysError: both 42th and 1337th examples have the same keys `48`.\r\nPlease fix the dataset script at <path/to/the/dataset/script>\r\n```\r\n\r\nThis way users who what to look for if they want to debug this issue. I opened an issue to track this: https://github.com/huggingface/datasets/issues/2556",
"and are we sure there are not a lot of datasets which are now broken with this change?",
"Thanks to the dummy data, we know for sure that most of them work as expected.\r\n`code_search_net` wasn't caught because the dummy data only have one dummy data file while the dataset script can actually load several of them using `os.listdir`. Let me take a look at all the other datasets that use `os.listdir` to see if the keys are alright",
"I found one issue on `fever` (PR here: https://github.com/huggingface/datasets/pull/2557)\r\nAll the other ones seem fine :)",
"Hi! Got same error when loading other dataset:\r\n```python3\r\nload_dataset('wikicorpus', 'raw_en')\r\n```\r\n\r\ntb:\r\n```pytb\r\n---------------------------------------------------------------------------\r\nDuplicatedKeysError Traceback (most recent call last)\r\n/opt/conda/lib/python3.8/site-packages/datasets/builder.py in _prepare_split(self, split_generator)\r\n 1109 example = self.info.features.encode_example(record)\r\n-> 1110 writer.write(example, key)\r\n 1111 finally:\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in write(self, example, key, writer_batch_size)\r\n 341 if self._check_duplicates:\r\n--> 342 self.check_duplicate_keys()\r\n 343 # Re-intializing to empty list for next batch\r\n\r\n/opt/conda/lib/python3.8/site-packages/datasets/arrow_writer.py in check_duplicate_keys(self)\r\n 352 if hash in tmp_record:\r\n--> 353 raise DuplicatedKeysError(key)\r\n 354 else:\r\n\r\nDuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 519\r\nKeys should be unique and deterministic in nature\r\n```\r\n\r\nVersion: datasets==1.11.0",
"Fixed by #2555.",
"The wikicorpus issue has been fixed by https://github.com/huggingface/datasets/pull/2844\r\n\r\nWe'll do a new release of `datasets` soon :)"
] | Keys should be unique error on code_search_net | {
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} | MDU6SXNzdWU5MzEzNTQ2ODc= | null | 2021-06-28T09:15:20Z | https://api.github.com/repos/huggingface/datasets/issues/2552/comments | ## Describe the bug
Loading `code_search_net` seems not possible at the moment.
## Steps to reproduce the bug
```python
>>> load_dataset('code_search_net')
Downloading: 8.50kB [00:00, 3.09MB/s]
Downloading: 19.1kB [00:00, 10.1MB/s]
No config specified, defaulting to: code_search_net/all
Downloading and preparing dataset code_search_net/all (download: 4.77 GiB, generated: 5.99 GiB, post-processed: Unknown size, total: 10.76 GiB) to /Users/thomwolf/.cache/huggingface/datasets/code_search_net/all/1.0.0/b3e8278faf5d67da1d06981efbeac3b76a2900693bd2239bbca7a4a3b0d6e52a...
Traceback (most recent call last):
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/builder.py", line 1067, in _prepare_split
writer.write(example, key)
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 343, in write
self.check_duplicate_keys()
File "/Users/thomwolf/Documents/GitHub/datasets/src/datasets/arrow_writer.py", line 354, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 48
Keys should be unique and deterministic in nature
```
## Environment info
- `datasets` version: 1.8.1.dev0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 2.0.0
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930,967,978 | https://api.github.com/repos/huggingface/datasets/issues/2551 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2551/events | [] | null | 2021-06-28T13:09:55Z | [] | https://github.com/huggingface/datasets/pull/2551 | CONTRIBUTOR | null | false | null | [] | Fix FileSystems documentation | {
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} | 2021-06-27T16:18:42Z | https://api.github.com/repos/huggingface/datasets/issues/2551/comments | ### What this fixes:
This PR resolves several issues I discovered in the documentation on the `datasets.filesystems` module ([this page](https://huggingface.co/docs/datasets/filesystems.html)).
### What were the issues?
When I originally tried implementing the code examples I faced several bugs attributed to:
- out of date [botocore](https://github.com/boto/botocore) call signatures
- capitalization errors in the `S3FileSystem` class name (written as `S3Filesystem` in one place)
- call signature errors for the `S3FileSystem` class constructor (uses parameter `sessions` instead of `session` in some places) (see [`s3fs`](https://s3fs.readthedocs.io/en/latest/api.html#s3fs.core.S3FileSystem) for where this constructor signature is defined)
### Testing/reviewing notes
Instructions for generating the documentation locally: [here](https://github.com/huggingface/datasets/tree/master/docs#generating-the-documentation). | {
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] | null | 2021-09-26T13:42:39Z | [] | https://github.com/huggingface/datasets/issues/2550 | CONTRIBUTOR | completed | null | null | [] | Allow for incremental cumulative metric updates in a distributed setup | {
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} | MDU6SXNzdWU5MzA5NTEyODc= | null | 2021-06-27T15:00:58Z | https://api.github.com/repos/huggingface/datasets/issues/2550/comments | 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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] | null | 2021-06-25T21:07:57Z | [] | https://github.com/huggingface/datasets/issues/2549 | NONE | completed | null | null | [
"Hi @nelson-liu,\r\n\r\nYou can pass the parameter `features` to `load_dataset`: https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset\r\n\r\nIf you look at the code of the MNLI script you referred in your question (https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py#L62-L77), you can see how the Features were originally specified. \r\n\r\nFeel free to use it as a template, customize it and pass it to `load_dataset` using the parameter `features`.",
"ah got it, thanks!"
] | Handling unlabeled datasets | {
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} | MDU6SXNzdWU5Mjk4MTkwOTM= | null | 2021-06-25T04:32:23Z | https://api.github.com/repos/huggingface/datasets/issues/2549/comments | Hi!
Is there a way for datasets to produce unlabeled instances (e.g., the `ClassLabel` can be nullable).
For example, I want to use the MNLI dataset reader ( https://github.com/huggingface/datasets/blob/master/datasets/multi_nli/multi_nli.py ) on a file that doesn't have the `gold_label` field. I tried setting `"label": data.get("gold_label")`, but got the following error:
```
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 953, in encode_example
return encode_nested_example(self, example)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in encode_nested_example
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 848, in <dictcomp>
k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 875, in encode_nested_example
return schema.encode_example(obj)
File "/home/nfliu/miniconda3/envs/debias/lib/python3.7/site-packages/datasets/features.py", line 653, in encode_example
if not -1 <= example_data < self.num_classes:
TypeError: '<=' not supported between instances of 'int' and 'NoneType'
```
What's the proper way to handle reading unlabeled datasets, especially for downstream usage with Transformers? | {
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] | null | 2021-06-24T14:36:43Z | [] | https://github.com/huggingface/datasets/issues/2548 | NONE | completed | null | null | [
"Hi @luyug, thanks for reporting.\r\n\r\nThe good news is that we fixed this issue only 9 days ago: #2507.\r\n\r\nThe patch is already in the master branch of our repository and it will be included in our next `datasets` release version 1.9.0.\r\n\r\nFeel free to reopen the issue if the problem persists."
] | Field order issue in loading json | {
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} | MDU6SXNzdWU5MjkyMzI4MzE= | null | 2021-06-24T13:29:53Z | https://api.github.com/repos/huggingface/datasets/issues/2548/comments | ## Describe the bug
The `load_dataset` function expects columns in alphabetical order when loading json files.
Similar bug was previously reported for csv in #623 and fixed in #684.
## Steps to reproduce the bug
For a json file `j.json`,
```
{"c":321, "a": 1, "b": 2}
```
Running the following,
```
f= datasets.Features({'a': Value('int32'), 'b': Value('int32'), 'c': Value('int32')})
json_data = datasets.load_dataset('json', data_files='j.json', features=f)
```
## Expected results
A successful load.
## Actual results
```
File "pyarrow/table.pxi", line 1409, in pyarrow.lib.Table.cast
ValueError: Target schema's field names are not matching the table's field names: ['c', 'a', 'b'], ['a', 'b', 'c']
```
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-957.1.3.el7.x86_64-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 3.0.0
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"Hi ! It looks like an issue with the virtual disk you are using.\r\n\r\nWe load datasets using memory mapping. In general it makes it possible to load very big files instantaneously since it doesn't have to read the file (it just assigns virtual memory to the file on disk).\r\nHowever there happens to be issues with virtual disks (for example on spot instances), for which memory mapping does a pass over the entire file, and this takes a while. We are discussing about this issue here: #2252 \r\n\r\nMemory mapping is something handled by the OS so we can't do much about it, though we're still trying to figure out what's causing this behavior exactly to see what we can do.",
"Okay, that's exactly my case, with spot instances... Therefore this isn't something we can change in any way to be able to load the dataset faster? I mean, what do you do internally at huggingface for being able to use spot instances with datasets efficiently?",
"There are no solutions yet unfortunately.\r\nWe're still trying to figure out a way to make the loading instantaneous on such disks, I'll keep you posted"
] | Dataset load_from_disk is too slow | {
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} | MDU6SXNzdWU5MjkxOTIzMjk= | null | 2021-06-24T12:45:44Z | https://api.github.com/repos/huggingface/datasets/issues/2547/comments | @lhoestq
## Describe the bug
It's not normal that I have to wait 7-8 hours for a dataset to be loaded from disk, as there are no preprocessing steps, it's only loading it with load_from_disk. I have 96 cpus, however only 1 is used for this, which is inefficient. Moreover, its usage is at 1%... This is happening in the context of a language model training, therefore I'm wasting 100$ each time I have to load the dataset from disk again (because the spot instance was stopped by aws and I need to relaunch it for example).
## Steps to reproduce the bug
Just get the oscar in spanish (around 150GGB) and try to first save in disk and then load the processed dataset. It's not dependent on the task you're doing, it just depends on the size of the text dataset.
## Expected results
I expect the dataset to be loaded in a normal time, by using the whole machine for loading it, I mean if you store the dataset in multiple files (.arrow) and then load it from multiple files, you can use multiprocessing for that and therefore don't waste so much time.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Ubuntu 18
- Python version: 3.8
I've seen you're planning to include a streaming mode for load_dataset, but that only saves the downloading and processing time, that's not being a problem for me, you cannot save the pure loading from disk time, therefore that's not a solution for my use case or for anyone who wants to use your library for training a language model. | {
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I added it and I also filled a few other empty sections. | {
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cc: @VictorSanh. | {
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} | 2021-06-24T06:41:36Z | https://api.github.com/repos/huggingface/datasets/issues/2544/comments | Sometimes default `datasets` logging can be too verbose. One approach could be reducing some logging levels, from info to debug, or from warning to info.
Close #2543.
cc: @stas00 | {
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"Hi @stas00, thanks for pointing out this issue with logging.\r\n\r\nI agree that `datasets` can sometimes be too verbose... I can create a PR and we could discuss there the choice of the log levels for different parts of the code."
] | switching some low-level log.info's to log.debug? | {
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} | MDU6SXNzdWU5Mjg1NzE5MTU= | null | 2021-06-23T19:26:55Z | https://api.github.com/repos/huggingface/datasets/issues/2543/comments | In https://github.com/huggingface/transformers/pull/12276 we are now changing the examples to have `datasets` on the same log level as `transformers`, so that one setting can do a consistent logging across all involved components.
The trouble is that now we get a ton of these:
```
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 acquired on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
06/23/2021 12:15:31 - INFO - datasets.arrow_writer - Done writing 50 examples in 12280 bytes /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.
06/23/2021 12:15:31 - INFO - datasets.arrow_dataset - Set __getitem__(key) output type to python objects for no columns (when key is int or slice) and don't output other (un-formatted) columns.
06/23/2021 12:15:31 - INFO - datasets.utils.filelock - Lock 139627640431136 released on /home/stas/.cache/huggingface/metrics/sacrebleu/default/default_experiment-1-0.arrow.lock
```
May I suggest that these can be `log.debug` as it's no informative to the user.
More examples: these are not informative - too much information:
```
06/23/2021 12:14:26 - INFO - datasets.load - Checking /home/stas/.cache/huggingface/datasets/downloads/459933f1fe47711fad2f6ff8110014ff189120b45ad159ef5b8e90ea43a174fa.e23e7d1259a8c6274a82a42a8936dd1b87225302c6dc9b7261beb3bc2daac640.py for additional imports.
06/23/2021 12:14:27 - INFO - datasets.builder - Constructing Dataset for split train, validation, test, from /home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
```
While these are:
```
06/23/2021 12:14:27 - INFO - datasets.info - Loading Dataset Infos from /home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt16/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
I also realize that `transformers` examples don't have do use `info` for `datasets` to let the default `warning` keep logging to less noisy.
But I think currently the log levels are slightly misused and skewed by 1 level. Many `warnings` will better be `info`s and most `info`s be `debug`.
e.g.:
```
06/23/2021 12:14:27 - WARNING - datasets.builder - Reusing dataset wmt16 (/home/stas/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/0d9fb3e814712c785176ad8cdb9f465fbe6479000ee6546725db30ad8a8b5f8a)
```
why is this a warning? it is informing me that the cache is used, there is nothing to be worried about. I'd have it as `info`.
Warnings are typically something that's bordering error or the first thing to check when things don't work as expected.
infrequent info is there to inform of the different stages or important events.
Everything else is debug.
At least the way I understand things.
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"very much related: https://github.com/huggingface/datasets/pull/2333",
"Hi @VictorSanh, thank you for reporting this issue with duplicated keys.\r\n\r\n- The issue with \"adversarial_qa\" was fixed 23 days ago: #2433. Current version of `datasets` (1.8.0) includes the patch.\r\n- I am investigating the issue with `drop`. I'll ping you to keep you informed.",
"Hi @VictorSanh, the issue is already fixed and merged into master branch and will be included in our next release version 1.9.0.",
"thank you!"
] | `datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA` | {
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} | MDU6SXNzdWU5Mjg1NDAzODI= | null | 2021-06-23T18:41:16Z | https://api.github.com/repos/huggingface/datasets/issues/2542/comments | ## Describe the bug
Failure to generate the datasets (`drop` and subset `adversarialQA` from `adversarial_qa`) because of duplicate keys.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("drop")
load_dataset("adversarial_qa", "adversarialQA")
```
## Expected results
The examples keys should be unique.
## Actual results
```bash
>>> load_dataset("drop")
Using custom data configuration default
Downloading and preparing dataset drop/default (download: 7.92 MiB, generated: 111.88 MiB, post-processed: Unknown size, total: 119.80 MiB) to /home/hf/.cache/huggingface/datasets/drop/default/0.1.0/7a94f1e2bb26c4b5c75f89857c06982967d7416e5af935a9374b9bccf5068026...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 992, in _prepare_split
num_examples, num_bytes = writer.finalize()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 409, in finalize
self.check_duplicate_keys()
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/arrow_writer.py", line 349, in check_duplicate_keys
raise DuplicatedKeysError(key)
datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 28553293-d719-441b-8f00-ce3dc6df5398
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1044-gcp-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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928,529,078 | https://api.github.com/repos/huggingface/datasets/issues/2541 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2541/events | [] | null | 2021-06-28T14:34:51Z | [] | https://github.com/huggingface/datasets/pull/2541 | CONTRIBUTOR | null | false | null | [
"The CI is failing because the dataset tags for `discofuse` are missing. I'm merging this PR since this is unrelated to this PR, but feel free to open another PR to add the tags here if you have some time:\r\n\r\nhttps://github.com/huggingface/datasets/blob/19408f9fab85c79b966085574cd2da3b90959179/datasets/discofuse/README.md#L1-L5\r\n\r\nThe missing tags are:\r\n```\r\n'annotations_creators', 'language_creators', 'licenses', 'multilinguality', 'pretty_name', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n```\r\nThanks again !"
] | update discofuse link cc @ekQ | {
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928,433,892 | https://api.github.com/repos/huggingface/datasets/issues/2540 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2540/events | [] | null | 2021-06-24T14:41:15Z | [] | https://github.com/huggingface/datasets/pull/2540 | MEMBER | null | false | null | [] | Remove task templates if required features are removed during `Dataset.map` | {
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} | 2021-06-23T16:20:25Z | https://api.github.com/repos/huggingface/datasets/issues/2540/comments | This PR fixes a bug reported by @craffel where removing a dataset's columns during `Dataset.map` triggered a `KeyError` because the `TextClassification` template tried to access the removed columns during `DatasetInfo.__post_init__`:
```python
from datasets import load_dataset
# `yelp_polarity` comes with a `TextClassification` template
ds = load_dataset("yelp_polarity", split="test")
ds
# Dataset({
# features: ['text', 'label'],
# num_rows: 38000
# })
# Triggers KeyError: 'label' - oh noes!
ds.map(lambda x: {"inputs": 0}, remove_columns=ds.column_names)
```
I wrote a unit test to make sure I could reproduce the error and then patched a fix. | {
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927,952,429 | https://api.github.com/repos/huggingface/datasets/issues/2539 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2539/events | [] | null | 2021-06-25T14:52:42Z | [] | https://github.com/huggingface/datasets/pull/2539 | CONTRIBUTOR | null | false | null | [
"Hi ! I'm sorry to hear that.\r\nThough we are not redistributing the dataset, we just provide a python script that downloads and process the dataset from its original source hosted at https://www.cl.cam.ac.uk\r\n\r\nTherefore I'm not sure what's the issue with licensing. What do you mean exactly ?",
"I think that the main issue is that the licesenses of the data are not made clear in the huggingface hub – other people wrongly assumed that the data was license-free, which resulted in commercial use, which is against the licenses.\r\nIs it possible to add the licenses from the original download to huggingface? that would help clear any confusion (licenses can be found here: https://www.cl.cam.ac.uk/research/nl/bea2019st/data/wi+locness_v2.1.bea19.tar.gz)",
"Thanks for the clarification @SimonHFL \r\nYou're completely right, we need to show the licenses.\r\nI just added them here: https://huggingface.co/datasets/wi_locness#licensing-information",
"Hi guys, I'm one of the authors of this dataset. \r\n\r\nTo clarify, we're happy for you to keep the data in the repo on 2 conditions:\r\n1. You don't host the data yourself.\r\n2. You make it clear that anyone who downloads the data via HuggingFace should read and abide by the license. \r\n\r\nI think you've now met these conditions, so we're all good, but I just wanted to make it clear in case there are any issues in the future. Thanks again to @aseifert for bringing this to our attention! :)",
"Thanks for your message @chrisjbryant :)\r\nI'm closing this PR then.\r\n\r\nAnd thanks for reporting @aseifert"
] | remove wi_locness dataset due to licensing issues | {
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} | 2021-06-23T07:35:32Z | https://api.github.com/repos/huggingface/datasets/issues/2539/comments | It was brought to my attention that this dataset's license is not only missing, but also prohibits redistribution. I contacted the original author to apologize for this oversight and asked if we could still use it, but unfortunately we can't and the author kindly asked to take down this dataset. | {
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927,940,691 | https://api.github.com/repos/huggingface/datasets/issues/2538 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2538/events | [] | null | 2023-04-19T11:05:38Z | [] | https://github.com/huggingface/datasets/issues/2538 | NONE | null | null | null | [
"Hi ! `load_dataset` downloads the full dataset once and caches it, so that subsequent calls to `load_dataset` just reloads the dataset from your disk.\r\nThen when you specify a `split` in `load_dataset`, it will just load the requested split from the disk. If your specified split is a sliced split (e.g. `\"train[:10]\"`), then it will load the 10 first rows of the train split that you have on disk.\r\n\r\nTherefore, as long as you don't delete your cache, all your calls to `load_dataset` will be very fast. Except the first call that downloads the dataset of course ^^",
"That’s a use case for the new streaming feature, no?",
"Hi @reachtarunhere.\r\n\r\nBesides the above insights provided by @lhoestq and @thomwolf, there is also a Dataset feature in progress (I plan to finish it this week): #2249, which will allow you, when calling `load_dataset`, to pass the option to download/preprocess/cache only some specific split(s), which will definitely speed up your workflow.\r\n\r\nIf this feature is interesting for you, I can ping you once it will be merged into the master branch.",
"Thanks all for responding.\r\n\r\nHey @albertvillanova \r\n\r\nThanks. Yes, I would be interested.\r\n\r\n@lhoestq I think even if a small split is specified it loads up the full dataset from the disk (please correct me if this is not the case). Because it does seem to be slow to me even on subsequent calls. There is no repeated downloading so it seems that the cache is working.\r\n\r\nI am not aware of the streaming feature @thomwolf mentioned. So I might need to read up on it.",
"@reshinthadithyan I use the .select function to have a fraction of indices.",
"If I want to create a dataset, containing only the 10 elements of a given dataset (slice it), how do I do that?",
"```python \r\nsmall_ds = ds.select(range(10))\r\n```",
"\r\n\r\n> ```python\r\n> small_ds = ds.select(range(10))\r\n> ```\r\n\r\nThanks, but this doesn't help me to save time during initial loading, right?",
"Indeed by default load_dataset would download and prepare everything as Arrow files. And passing `split=train[:10]` memory maps only the beginning of the full dataset that has been prepared on disk.\r\n\r\nIf you don't want to download everything, you can use streaming : \r\n```python \r\nids = load_dataset(..., streaming=True)\r\nfirst_samples = list(ids[\"train\"].take(10))\r\n```\r\n\r\nTo get a Dataset you can use \r\n```python \r\nds = Dataset.from_generator(ids.take(10).__iter__)\r\n```\r\n\r\nedit: fixed small bug",
"Thanks @lhoestq, but I don't think it is 100% accurate, as it doesn't keep the dataset structure exactly the same.\r\nTo load the full dataset, I do:\r\n```\r\ndata = load_dataset(\"json\", data_files=\"a.json\")\r\ntrain_data = data[\"train\"].shuffle()\r\n```\r\n\r\nBut when I am changing it as per your instructions: \r\n```\r\nids = load_dataset(\"json\", data_files=\"a.json\", streaming=True)\r\ndata = Dataset.from_generator(ids[\"train\"].take(1).__iter__)\r\ntrain_data = data[\"train\"].shuffle()\r\n```\r\nIt throws KeyError.\r\nI need a simple way, like you suggested, to have a subset of a Dataset, which exactly the same attributes.\r\n",
"Whoops I fixed my code sorry\r\n```diff\r\n- ds = Dataset.from_generator(ids[\"train\"].take(10).__iter__)\r\n+ ds = Dataset.from_generator(ids.take(10).__iter__)\r\n```\r\n\r\nin your case that means running\r\n```python\r\ntrain_data = data.shuffle()\r\n```\r\n\r\nwithout `[\"train\"]`"
] | Loading partial dataset when debugging | {
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} | MDU6SXNzdWU5Mjc5NDA2OTE= | null | 2021-06-23T07:19:52Z | https://api.github.com/repos/huggingface/datasets/issues/2538/comments | I am using PyTorch Lightning along with datasets (thanks for so many datasets already prepared and the great splits).
Every time I execute load_dataset for the imdb dataset it takes some time even if I specify a split involving very few samples. I guess this due to hashing as per the other issues.
Is there a way to only load part of the dataset on load_dataset? This would really speed up my workflow.
Something like a debug mode would really help. Thanks! | {
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927,472,659 | https://api.github.com/repos/huggingface/datasets/issues/2537 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2537/events | [] | null | 2021-06-30T16:31:03Z | [] | https://github.com/huggingface/datasets/pull/2537 | MEMBER | null | false | null | [
"`pyarrow` 1.0.0 doesn't support some types in parquet, we'll have to bump its minimum version.\r\n\r\nAlso I still need to add dummy data to test the parquet builder.",
"I had to bump the minimum pyarrow version to 3.0.0 to properly support parquet.\r\n\r\nEverything is ready for review now :)\r\nI reused pretty much the same tests we had for CSV",
"Done !\r\nNow we're still allowing pyarrow>=1.0.0, but when users want to use parquet features they're asked to update to pyarrow>=3.0.0"
] | Add Parquet loader + from_parquet and to_parquet | {
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} | 2021-06-22T17:28:23Z | https://api.github.com/repos/huggingface/datasets/issues/2537/comments | Continuation of #2247
I added a "parquet" dataset builder, as well as the methods `Dataset.from_parquet` and `Dataset.to_parquet`.
As usual, the data are converted to arrow in a batched way to avoid loading everything in memory. | {
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927,338,639 | https://api.github.com/repos/huggingface/datasets/issues/2536 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2536/events | [
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"I'm just retaking and working on #2324. 😉 ",
"Resolved via https://github.com/huggingface/datasets/pull/4006."
] | Use `Audio` features for `AutomaticSpeechRecognition` task template | {
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} | MDU6SXNzdWU5MjczMzg2Mzk= | null | 2021-06-22T15:07:21Z | https://api.github.com/repos/huggingface/datasets/issues/2536/comments | In #2533 we added a task template for speech recognition that relies on the file paths to the audio files. As pointed out by @SBrandeis this is brittle as it doesn't port easily across different OS'.
The solution is to use dedicated `Audio` features when casting the dataset. These features are not yet available in `datasets`, but should be included in the `AutomaticSpeechRecognition` template once they are. | {
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927,334,349 | https://api.github.com/repos/huggingface/datasets/issues/2535 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2535/events | [] | null | 2021-06-23T13:40:43Z | [] | https://github.com/huggingface/datasets/pull/2535 | MEMBER | null | false | null | [] | Improve Features docs | {
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} | 2021-06-22T15:03:27Z | https://api.github.com/repos/huggingface/datasets/issues/2535/comments | - Fix rendering and cross-references in Features docs
- Add docstrings to Features methods | {
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927,201,435 | https://api.github.com/repos/huggingface/datasets/issues/2534 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2534/events | [] | null | 2021-06-24T14:42:47Z | [] | https://github.com/huggingface/datasets/pull/2534 | MEMBER | null | false | null | [
"Nice thanks ! I think there are other places with\r\n```python\r\nnot_verbose = bool(logger.getEffectiveLevel() > WARNING)\r\n```\r\n\r\nCould you replace them as well ?",
"Sure @lhoestq! I was not sure if this change should only be circumscribed to `http_get`..."
] | Sync with transformers disabling NOTSET | {
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} | 2021-06-22T12:54:21Z | https://api.github.com/repos/huggingface/datasets/issues/2534/comments | Close #2528. | {
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927,193,264 | https://api.github.com/repos/huggingface/datasets/issues/2533 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2533/events | [] | null | 2021-06-23T16:14:46Z | [] | https://github.com/huggingface/datasets/pull/2533 | MEMBER | null | false | null | [
"@SBrandeis @lhoestq i've integrated your suggestions, so this is ready for another review :)",
"Merging if it's good for you @lewtun :)"
] | Add task template for automatic speech recognition | {
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} | 2021-06-22T12:45:02Z | https://api.github.com/repos/huggingface/datasets/issues/2533/comments | This PR adds a task template for automatic speech recognition. In this task, the input is a path to an audio file which the model consumes to produce a transcription.
Usage:
```python
from datasets import load_dataset
from datasets.tasks import AutomaticSpeechRecognition
ds = load_dataset("timit_asr", split="train[:10]")
# Dataset({
# features: ['file', 'text', 'phonetic_detail', 'word_detail', 'dialect_region', 'sentence_type', 'speaker_id', 'id'],
# num_rows: 10
# })
task = AutomaticSpeechRecognition(audio_file_column="file", transcription_column="text")
ds.prepare_for_task(task)
# Dataset({
# features: ['audio_file', 'transcription'],
# num_rows: 10
# })
``` | {
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927,063,196 | https://api.github.com/repos/huggingface/datasets/issues/2532 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2532/events | [
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] | null | 2021-06-23T05:17:25Z | [] | https://github.com/huggingface/datasets/issues/2532 | CONTRIBUTOR | completed | null | null | [
"Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?",
"> Hi @jerryIsHere, thanks for reporting the issue. But are you sure this is a bug in HuggingFace **Datasets**?\r\n\r\nOh, I am sorry\r\nI would reopen the post on huggingface/transformers"
] | Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task | {
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} | MDU6SXNzdWU5MjcwNjMxOTY= | null | 2021-06-22T10:08:18Z | https://api.github.com/repos/huggingface/datasets/issues/2532/comments | [This colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) implements a token classification input pipeline extending the logic from [this hugging example](https://huggingface.co/transformers/custom_datasets.html#tok-ner).
The pipeline works fine with most instance in different languages, but unfortunately, [the Japanese Kana ligature (a form of abbreviation? I don't know Japanese well)](https://en.wikipedia.org/wiki/Kana_ligature) break the alignment of `return_offsets_mapping`:
Without the try catch block, it riase `ValueError: NumPy boolean array indexing assignment cannot assign 88 input values to the 87 output values where the mask is true`, example shown here [(another colab notebook)](https://colab.research.google.com/drive/1MmOqf3ppzzdKKyMWkn0bJy6DqzOO0SSm?usp=sharing)
It is clear that the normalizer is the process that break the alignment, as it is observed that `tokenizer._tokenizer.normalizer.normalize_str('ヿ')` return 'コト'.
One workaround is to include `tokenizer._tokenizer.normalizer.normalize_str` before the tokenizer preprocessing pipeline, which is also provided in the [first colab notebook](https://colab.research.google.com/drive/151gKyo0YIwnlznrOHst23oYH_a3mAe3Z?usp=sharing) with the name `udposTestDatasetWorkaround`.
I guess similar logics should be included inside the tokenizer and the offsets_mapping generation process such that user don't need to include them in their code. But I don't understand the code of tokenizer well that I think I am not able to do this.
p.s.
**I am using my own dataset building script in the provided example, but the script should be equivalent to the changes made by this [update](https://github.com/huggingface/datasets/pull/2466)**
`get_dataset `is just a simple wrapping for `load_dataset`
and the `tokenizer` is just `XLMRobertaTokenizerFast.from_pretrained("xlm-roberta-large")` | {
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927,017,924 | https://api.github.com/repos/huggingface/datasets/issues/2531 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2531/events | [] | null | 2021-06-22T09:47:10Z | [] | https://github.com/huggingface/datasets/pull/2531 | MEMBER | null | false | null | [] | Fix dev version | {
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} | 2021-06-22T09:17:10Z | https://api.github.com/repos/huggingface/datasets/issues/2531/comments | The dev version that ends in `.dev0` should be greater than the current version.
However it happens that `1.8.0 > 1.8.0.dev0` for example.
Therefore we need to use `1.8.1.dev0` for example in this case.
I updated the dev version to use `1.8.1.dev0`, and I also added a comment in the setup.py in the release steps about this. | {
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927,013,773 | https://api.github.com/repos/huggingface/datasets/issues/2530 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2530/events | [] | null | 2021-06-22T12:55:20Z | [] | https://github.com/huggingface/datasets/pull/2530 | CONTRIBUTOR | null | false | null | [
"@lhoestq, can you please review this PR?\r\nWhat exactly is the problem in the test case? Should it matter?",
"Hi ! Thanks for fixing this :)\r\n\r\nThe CI fails for two reasons:\r\n- the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in the yaml tags:\r\n```yaml\r\npretty_name: Turkish Product Reviews\r\n```\r\n- The test that runs the turkish_product_reviews.py file on the dummy_data.zip data returned 0 examples. Indeed it looks like you changed dummy_data.zip file and now it is an empty zip file. I think you can fix that by reverting your change to the dummy_data.zip file",
"> Hi ! Thanks for fixing this :)\r\n> \r\n> The CI fails for two reasons:\r\n> \r\n> * the `pretty_name` tag is missing in yaml tags in ./datasets/turkish_product_reviews/README.md. You can fix that by adding this in the yaml tags:\r\n> \r\n> \r\n> ```yaml\r\n> pretty_name: Turkish Product Reviews\r\n> ```\r\n> \r\n> * The test that runs the turkish_product_reviews.py file on the dummy_data.zip data returned 0 examples. Indeed it looks like you changed dummy_data.zip file and now it is an empty zip file. I think you can fix that by reverting your change to the dummy_data.zip file\r\n\r\nMany thanks for the quick feedback.\r\nI made the relevant fixes but still got the error :(",
"> Thanks !\r\n> The CI was failing because of the dataset card that was missing some sections. I fixed that.\r\n> \r\n> It's all good now\r\n\r\nSuper. Thanks for the support."
] | Fixed label parsing in the ProductReviews dataset | {
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} | 2021-06-22T09:12:45Z | https://api.github.com/repos/huggingface/datasets/issues/2530/comments | Fixed issue with parsing dataset labels. | {
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926,378,812 | https://api.github.com/repos/huggingface/datasets/issues/2529 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2529/events | [] | null | 2021-06-23T14:22:11Z | [] | https://github.com/huggingface/datasets/pull/2529 | MEMBER | null | false | null | [
"> Nice thanks !\r\n> Could you just move the test outside of the BaseDatasetTest class please ? Otherwise it will unnecessarily be run twice.\r\n\r\nsure, on it! thanks for the explanations about the `self._to` method :)",
"@lhoestq i've moved all the task template tests outside of `BaseDatasetTest` and collected them in their dedicated test case. (at some point i'll revisit this so we can just use `pytest` natively, but the PR is already getting out-of-scope :))"
] | Add summarization template | {
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} | 2021-06-21T16:08:31Z | https://api.github.com/repos/huggingface/datasets/issues/2529/comments | This PR adds a task template for text summarization. As far as I can tell, we do not need to distinguish between "extractive" or "abstractive" summarization - both can be handled with this template.
Usage:
```python
from datasets import load_dataset
from datasets.tasks import Summarization
ds = load_dataset("xsum", split="train")
# Dataset({
# features: ['document', 'summary', 'id'],
# num_rows: 204045
# })
summarization = Summarization(text_column="document", summary_column="summary")
ds.prepare_for_task(summarization)
# Dataset({
# features: ['text', 'summary'],
# num_rows: 204045
# })
```
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926,314,656 | https://api.github.com/repos/huggingface/datasets/issues/2528 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2528/events | [
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"Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`."
] | Logging cannot be set to NOTSET similar to transformers | {
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} | MDU6SXNzdWU5MjYzMTQ2NTY= | null | 2021-06-21T15:04:54Z | https://api.github.com/repos/huggingface/datasets/issues/2528/comments | ## Describe the bug
In the transformers library you can set the verbosity level to logging.NOTSET to work around the usage of tqdm and IPywidgets, however in Datasets this is no longer possible. This is because transformers set the verbosity level of tqdm with [this](https://github.com/huggingface/transformers/blob/b53bc55ba9bb10d5ee279eab51a2f0acc5af2a6b/src/transformers/file_utils.py#L1449)
`disable=bool(logging.get_verbosity() == logging.NOTSET)`
and datasets accomplishes this like [so](https://github.com/huggingface/datasets/blob/83554e410e1ab8c6f705cfbb2df7953638ad3ac1/src/datasets/utils/file_utils.py#L493)
`not_verbose = bool(logger.getEffectiveLevel() > WARNING)`
## Steps to reproduce the bug
```python
import datasets
import logging
datasets.logging.get_verbosity = lambda : logging.NOTSET
datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
```
## Expected results
The code should download and load the dataset as normal without displaying progress bars
## Actual results
```ImportError Traceback (most recent call last)
<ipython-input-4-aec65c0509c6> in <module>
----> 1 datasets.load_dataset("patrickvonplaten/librispeech_asr_dummy")
~/venv/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, script_version, use_auth_token, task, **config_kwargs)
713 dataset=True,
714 return_resolved_file_path=True,
--> 715 use_auth_token=use_auth_token,
716 )
717 # Set the base path for downloads as the parent of the script location
~/venv/lib/python3.7/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, dynamic_modules_path, return_resolved_file_path, **download_kwargs)
350 file_path = hf_bucket_url(path, filename=name, dataset=False)
351 try:
--> 352 local_path = cached_path(file_path, download_config=download_config)
353 except FileNotFoundError:
354 raise FileNotFoundError(
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
289 use_etag=download_config.use_etag,
290 max_retries=download_config.max_retries,
--> 291 use_auth_token=download_config.use_auth_token,
292 )
293 elif os.path.exists(url_or_filename):
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
668 headers=headers,
669 cookies=cookies,
--> 670 max_retries=max_retries,
671 )
672
~/venv/lib/python3.7/site-packages/datasets/utils/file_utils.py in http_get(url, temp_file, proxies, resume_size, headers, cookies, timeout, max_retries)
493 initial=resume_size,
494 desc="Downloading",
--> 495 disable=not_verbose,
496 )
497 for chunk in response.iter_content(chunk_size=1024):
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in __init__(self, *args, **kwargs)
217 total = self.total * unit_scale if self.total else self.total
218 self.container = self.status_printer(
--> 219 self.fp, total, self.desc, self.ncols)
220 self.sp = self.display
221
~/venv/lib/python3.7/site-packages/tqdm/notebook.py in status_printer(_, total, desc, ncols)
95 if IProgress is None: # #187 #451 #558 #872
96 raise ImportError(
---> 97 "IProgress not found. Please update jupyter and ipywidgets."
98 " See https://ipywidgets.readthedocs.io/en/stable"
99 "/user_install.html")
ImportError: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-5.4.95-42.163.amzn2.x86_64-x86_64-with-debian-10.8
- Python version: 3.7.10
- PyArrow version: 3.0.0
I am running this code on Deepnote and which important to this issue **does not** support IPywidgets
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926,031,525 | https://api.github.com/repos/huggingface/datasets/issues/2527 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2527/events | [] | null | 2021-06-21T15:06:50Z | [] | https://github.com/huggingface/datasets/pull/2527 | MEMBER | null | false | null | [] | Replace bad `n>1M` size tag | {
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} | 2021-06-21T09:42:35Z | https://api.github.com/repos/huggingface/datasets/issues/2527/comments | Some datasets were still using the old `n>1M` tag which has been replaced with tags `1M<n<10M`, etc.
This resulted in unexpected results when searching for datasets bigger than 1M on the hub, since it was only showing the ones with the tag `n>1M`. | {
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925,929,228 | https://api.github.com/repos/huggingface/datasets/issues/2526 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2526/events | [
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"I'm currently adding it, the entire dataset is quite big around 30 GB so I add splits separately. You can take a look here https://huggingface.co/datasets/merve/coco",
"I talked to @lhoestq and it's best if I download this dataset through TensorFlow datasets instead, so I'll be implementing that one really soon.\r\n@NielsRogge ",
"I started adding COCO, will be done tomorrow EOD\r\nmy work so far https://github.com/merveenoyan/datasets (my fork)",
"Hi Merve @merveenoyan , thank you so much for your great contribution! May I ask about the current progress of your implementation? Cuz I see the pull request is still in progess here. Or can I just run the COCO scripts in your fork repo?",
"Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon. ",
"> Hello @yixuanren I had another prioritized project about to be merged, but I'll start continuing today will finish up soon.\r\n\r\nIt's really nice of you!! I see you've commited another version just now",
"@yixuanren we're working on it, will be available soon, thanks a lot for your patience",
"Hi @NielsRogge and @merveenoyan, did you find a way to load a dataset with COCO annotations to HF's hub?\r\nI have a panoptic segmentation dataset in COCO format and would like to share it with the community.\r\nThanks in advance :)",
"The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)",
"> The COCO format is not supported out of the box in the HF's hub - you'd need to reformat it to an [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) with metadata format, or write a [loading script](https://huggingface.co/docs/datasets/image_dataset#loading-script)\r\n\r\nHi @lhoestq , thank you for your quick reply.\r\nI've correctly created a metadata.jsonl file for a dataset with instance segmentation annotations [here](https://huggingface.co/datasets/lombardata/data_2017)\r\nbut do not understand how I can integrate panoptic annotations with the metadata format of ImageFolder datasets. The \"problem\" with panoptic annotations is that we have a folder with images, a json file with annotations and another folder with png annotations.\r\n\r\nI checked between all the datasets already published on HuggingFace and, the only one who has uploaded a correct panoptic dataset is @NielsRogge [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo). Indeed he accomplished to have three fields : \r\n1.image (image)\r\n2.label (image)\r\n3.segments_info (list)\r\nbut I not find the corresponding code that allows to upload a panoptic dataset from this 3 sources.\r\nCan you please share an example code?\r\nThanks !",
"Both were uploaded using `ds.push_to_hub()` :)\r\n\r\nYou can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n\r\n```python\r\nfrom datasets import Dataset, Image\r\n\r\nds = Dataset.from_dict(...)\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds.push_to_hub(...)\r\n```",
"> Both were uploaded using `ds.push_to_hub()` :)\r\n> \r\n> You can get a Dataset from a python dictionary using `ds = Dataset.from_dict(...)` and casts the paths to images to the `Image()` type using `ds = ds.cast_column(\"image\", Image())`.\r\n> \r\n> ```python\r\n> from datasets import Dataset, Image\r\n> \r\n> ds = Dataset.from_dict(...)\r\n> ds = ds.cast_column(\"image\", Image())\r\n> ds = ds.cast_column(\"label\", Image())\r\n> ds.push_to_hub(...)\r\n> ```\r\n\r\nThank you very much @lhoestq , I succesfully created a hf dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_21) with the two fields :\r\n1.image (image)\r\n2.label (image)\r\nfollowing your suggestions. Now still remain the problem of uploading **segments_info** information to the dataset.\r\nThere is a function that easily imports the _panoptic_coco_annotation.json_ file to a segment_info field?\r\nI think we must define a **list_of_segment**, i.e. a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nand then apply again the **cast_column** function [here](https://github.com/huggingface/datasets/blob/2.13.0/src/datasets/arrow_dataset.py#L2060) but with a list as a second argument, like : \r\n```python\r\nfrom datasets import Dataset, Image\r\nds = ds.cast_column(\"image\", Image())\r\nds = ds.cast_column(\"label\", Image())\r\nds = ds.cast_column(\"segments_info\", list)\r\n```\r\nbut I do not see how to transfer the information of the _panoptic_coco_annotation.json_ to a list of lists of this type : \r\n```python\r\n[ { \"area\": 214858, \"bbox\": [ 0, 0, 511, 760 ], \"category_id\": 0, \"id\": 7895160, \"iscrowd\": 0 }, { \"area\": 73067, \"bbox\": [ 98, 719, 413, 253 ], \"category_id\": 3, \"id\": 3289680, \"iscrowd\": 0 }, { \"area\": 832, \"bbox\": [ 53, 0, 101, 16 ], \"category_id\": 5, \"id\": 5273720, \"iscrowd\": 0 }, { \"area\": 70668, \"bbox\": [ 318, 60, 191, 392 ], \"category_id\": 8, \"id\": 15132390, \"iscrowd\": 0 }, { \"area\": 32696, \"bbox\": [ 0, 100, 78, 872 ], \"category_id\": 18, \"id\": 472063, \"iscrowd\": 0 }, { \"area\": 76045, \"bbox\": [ 42, 48, 264, 924 ], \"category_id\": 37, \"id\": 16713830, \"iscrowd\": 0 }, { \"area\": 27103, \"bbox\": [ 288, 482, 216, 306 ], \"category_id\": 47, \"id\": 16753408, \"iscrowd\": 0 } ]\r\n```\r\nlike @NielsRogge has done [here](https://huggingface.co/datasets/nielsr/coco-panoptic-val2017) and [here](https://huggingface.co/datasets/nielsr/ade20k-panoptic-demo).\r\nThank you again for your help and have a good day !",
"You can pass this data in .from_dict() - no need to cast anything for this column\r\n\r\n```python\r\nds = Dataset.from_dict({\r\n \"image\": [...],\r\n \"label\": [...],\r\n \"segments_info\": [...],\r\n)}\r\n```\r\n\r\nwhere `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.",
"> You can pass this data in .from_dict() - no need to cast anything for this column\r\n> \r\n> ```python\r\n> ds = Dataset.from_dict({\r\n> \"image\": [...],\r\n> \"label\": [...],\r\n> \"segments_info\": [...],\r\n> )}\r\n> ```\r\n> \r\n> where `segments_info` is the list of the segment_infos of all the examples in the dataset, and therefore is a list of lists of dicts.\r\n\r\nThank you for the quick reply @lhoestq , but then how to generate the `segments_info` list of lists of dicts starting from a _panoptic_coco_annotation.json_ file ?\r\n\r\n\r\n",
"You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere",
"> You read the JSON file and transform the data yourself. I don't think there's an automatic converter somewhere\r\n\r\nPerfect, I've done it and succesfully uploaded a new dataset [here](https://huggingface.co/datasets/lombardata/panoptic_2023_06_22), but I've (I hope) a last problem.\r\nThe dataset has currently 302 images and, when I upload it to the hub, only the first page of images is correctly uploaded.\r\nWhen I try to see the second/third/fourth page of items of my dataset, I can see that the fields **segments_info** and **image_name** are correctly uploaded, while the images are not (the \"null\" string is shown everywhere).\r\n\r\nI've checked the path of images that are not uploaded and they exists, is there a problem with the size of the dataset ?\r\nHow can I upload the whole dataset to the hub ?\r\nThank you again @lhoestq and have a good day !",
"Awesome ! Your dataset looks all good 🤗 \r\n\r\nThe `null` in the viewer is a bug on our side, let me investigate"
] | Add COCO datasets | {
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} | MDU6SXNzdWU5MjU5MjkyMjg= | null | 2021-06-21T07:48:32Z | https://api.github.com/repos/huggingface/datasets/issues/2526/comments | ## Adding a Dataset
- **Name:** COCO
- **Description:** COCO is a large-scale object detection, segmentation, and captioning dataset.
- **Paper + website:** https://cocodataset.org/#home
- **Data:** https://cocodataset.org/#download
- **Motivation:** It would be great to have COCO available in HuggingFace datasets, as we are moving beyond just text. COCO includes multi-modalities (images + text), as well as a huge amount of images annotated with objects, segmentation masks, keypoints etc., on which models like DETR (which I recently added to HuggingFace Transformers) are trained. Currently, one needs to download everything from the website and place it in a local folder, but it would be much easier if we can directly access it through the datasets API.
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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925,896,358 | https://api.github.com/repos/huggingface/datasets/issues/2525 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2525/events | [] | null | 2021-06-21T10:01:13Z | [] | https://github.com/huggingface/datasets/pull/2525 | CONTRIBUTOR | null | false | null | [] | Use scikit-learn package rather than sklearn in setup.py | {
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} | 2021-06-21T07:04:25Z | https://api.github.com/repos/huggingface/datasets/issues/2525/comments | The sklearn package is an historical thing and should probably not be used by anyone, see https://github.com/scikit-learn/scikit-learn/issues/8215#issuecomment-344679114 for some caveats.
Note: this affects only TESTS_REQUIRE so I guess only developers not end users. | {
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925,610,934 | https://api.github.com/repos/huggingface/datasets/issues/2524 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2524/events | [] | null | 2021-06-28T09:56:22Z | [] | https://github.com/huggingface/datasets/pull/2524 | COLLABORATOR | null | false | null | [
"Hi ! Could you clarify what it fixes exactly and give more details please ? Especially why this is related to the windows hanging error ?",
"This has already been merged, but I'll clarify the idea of this PR. Before this merge, FileLock was the only component affected by the max path limit on Windows (that came to my notice) because of its infinite loop that would suppress errors. So instead of suppressing the `FileNotFoundError` that is thrown by `os.open` if the file name is longer than the max allowed path length, this PR reraises it to notify the user."
] | Raise FileNotFoundError in WindowsFileLock | {
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} | 2021-06-20T14:25:11Z | https://api.github.com/repos/huggingface/datasets/issues/2524/comments | Closes #2443 | {
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925,421,008 | https://api.github.com/repos/huggingface/datasets/issues/2523 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2523/events | [] | null | 2021-06-19T18:48:23Z | [] | https://github.com/huggingface/datasets/issues/2523 | NONE | completed | null | null | [] | Fr | {
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925,334,379 | https://api.github.com/repos/huggingface/datasets/issues/2522 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2522/events | [
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"Hi,\r\n\r\nthis issue has been already reported in the dataset repo (https://github.com/dair-ai/emotion_dataset/issues/2), so this is a bug on their side.",
"The documentation has another bug in the dataset card [here](https://huggingface.co/datasets/emotion). \r\n\r\nIn the dataset summary **six** emotions are mentioned: *\"six basic emotions: anger, fear, joy, love, sadness, and surprise\"*, however, in the datafields section we have only **five**:\r\n```\r\nlabel: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4).\r\n```",
"@GDGauravDutta the dataset author replied in their issue: https://github.com/dair-ai/emotion_dataset/issues/2\r\n> The dataset released is a preprocessed six emotion variant released for educational and research purposes.\r\n\r\n@albertovilla the dataset card was fixed with 6 emotions."
] | Documentation Mistakes in Dataset: emotion | {
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} | MDU6SXNzdWU5MjUzMzQzNzk= | null | 2021-06-19T07:08:57Z | https://api.github.com/repos/huggingface/datasets/issues/2522/comments | As per documentation,
Dataset: emotion
Homepage: https://github.com/dair-ai/emotion_dataset
Dataset: https://github.com/huggingface/datasets/blob/master/datasets/emotion/emotion.py
Permalink: https://huggingface.co/datasets/viewer/?dataset=emotion
Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the paper.
But when we view the data, there are only 6 emotions, anger, fear, joy, sadness, surprise, and trust. | {
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925,030,685 | https://api.github.com/repos/huggingface/datasets/issues/2521 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2521/events | [] | null | 2021-06-21T09:22:31Z | [] | https://github.com/huggingface/datasets/pull/2521 | MEMBER | null | false | null | [] | Insert text classification template for Emotion dataset | {
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} | 2021-06-18T15:56:19Z | https://api.github.com/repos/huggingface/datasets/issues/2521/comments | This PR includes a template and updated `dataset_infos.json` for the `emotion` dataset. | {
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925,015,004 | https://api.github.com/repos/huggingface/datasets/issues/2520 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2520/events | [
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"The `task_templates` API is deprecated in favor of the `train-eval-index` YAML field, so I'm closing this issue."
] | Datasets with tricky task templates | {
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} | MDU6SXNzdWU5MjUwMTUwMDQ= | null | 2021-06-18T15:33:57Z | https://api.github.com/repos/huggingface/datasets/issues/2520/comments | I'm collecting a list of datasets here that don't follow the "standard" taxonomy and require further investigation to implement task templates for.
## Text classification
* [hatexplain](https://huggingface.co/datasets/hatexplain): ostensibly a form of text classification, but not in the standard `(text, target)` format and each sample appears to be tokenized.
* [muchocine](https://huggingface.co/datasets/muchocine): contains two candidate text columns (long-form and summary) which in principle requires two `TextClassification` templates which is not currently supported | {
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924,903,240 | https://api.github.com/repos/huggingface/datasets/issues/2519 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2519/events | [] | null | 2021-06-21T09:06:06Z | [] | https://github.com/huggingface/datasets/pull/2519 | MEMBER | null | false | null | [
"Looks like this change\r\n```\r\npa_table[pa_table.column_names[0]].to_pandas(types_mapper=pandas_types_mapper)\r\n```\r\ndoesn't return a Series with the correct type.\r\nThis is related to https://issues.apache.org/jira/browse/ARROW-9664\r\n\r\nSince the types_mapper isn't taken into account, the ArrayXD types are not converted to the correct pandas extension dtype",
"@lhoestq I think I found a workaround... 😉 ",
"For some reason the benchmarks are not run Oo",
"Anyway, merging.\r\nWe'll see on master how much speed ups we got"
] | Improve performance of pandas arrow extractor | {
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} | 2021-06-18T13:24:41Z | https://api.github.com/repos/huggingface/datasets/issues/2519/comments | While reviewing PR #2505, I noticed that pandas arrow extractor could be refactored to be faster. | {
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924,654,100 | https://api.github.com/repos/huggingface/datasets/issues/2518 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2518/events | [] | null | 2021-06-18T15:01:17Z | [] | https://github.com/huggingface/datasets/pull/2518 | MEMBER | null | false | null | [
"Just tested TydiQA and it works fine :)"
] | Add task templates for tydiqa and xquad | {
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} | 2021-06-18T08:06:34Z | https://api.github.com/repos/huggingface/datasets/issues/2518/comments | This PR adds question-answering templates to the remaining datasets that are linked to a model on the Hub.
Notes:
* I could not test the tydiqa implementation since I don't have enough disk space 😢 . But I am confident the template works :)
* there exist other datasets like `fquad` and `mlqa` which are candidates for question-answering templates, but some work is needed to handle the ordering of nested column described in #2434
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924,643,345 | https://api.github.com/repos/huggingface/datasets/issues/2517 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2517/events | [] | null | 2021-06-18T08:43:55Z | [] | https://github.com/huggingface/datasets/pull/2517 | MEMBER | null | false | null | [] | Fix typo in MatthewsCorrelation class name | {
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924,597,470 | https://api.github.com/repos/huggingface/datasets/issues/2516 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2516/events | [
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] | null | 2021-06-23T13:47:49Z | [] | https://github.com/huggingface/datasets/issues/2516 | NONE | null | null | null | [
"Hi ! `map` calls `__getstate__` using `dill` to hash your map function. This is used by the caching mechanism to recover previously computed results. That's why you don't see any `__setstate__` call.\r\n\r\nWhy do you change an attribute of your tokenizer when `__getstate__` is called ?",
"@lhoestq because if I try to pickle my custom tokenizer (it contains a pure python pretokenization step in an otherwise rust backed tokenizer) I get\r\n\r\n> Exception: Error while attempting to pickle Tokenizer: Custom PreTokenizer cannot be serialized\r\n\r\nSo I remove the Custom PreTokenizer in `__getstate__` and then restore it in `__setstate__` (since it doesn't contain any state). This is what my `__getstate__` / `__setstate__` looks like:\r\n\r\n def __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n out = self.__dict__.copy()\r\n logger.debug(\"Detaching pre_tokenizer\")\r\n out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n return out\r\n\r\n def __setstate__(self, d):\r\n \"\"\"\r\n Reinstates pre_tokenizer\r\n \"\"\"\r\n logger.debug(\"Reattaching pre_tokenizer\")\r\n self.__dict__ = d\r\n self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\nIf this is the case can you think of another way of avoiding my issue?",
"Actually, maybe I need to deep copy `self.__dict__`? That way `self` isn't modified. That was my intention and I thought it was working - I'll double-check after the weekend.",
"Doing a deep copy results in the warning:\r\n\r\n> 06/20/2021 16:02:15 - WARNING - datasets.fingerprint - Parameter 'function'=<function tokenize_function at 0x7f1e95f05d40> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n\r\n\r\n```\r\ndef __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n out = copy.deepcopy(self.__dict__)\r\n logger.debug(\"Detaching pre_tokenizer\")\r\n out['_tokenizer'].pre_tokenizer = tokenizers.pre_tokenizers.Sequence([]) \r\n return out\r\n```",
"Looks like there is still an object that is not pickable in your `tokenize_function` function.\r\n\r\nYou can test if an object can be pickled and hashed by using \r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nHasher.hash(my_object)\r\n```\r\n\r\nUnder the hood it pickles the object to compute its hash, so it calls `__getstate__` when applicable.",
"I figured it out, the problem is deep copy itself uses pickle (unless you implement `__deepcopy__`). So when I changed `__getstate__` it started throwing an error.\r\n\r\nI'm sure there's a better way of doing this, but in order to return the `__dict__` without the non-pikelable pre-tokeniser and without modifying self I removed the pre-tokenizers, did a deep copy and then re-generated it.\r\n\r\nIt does work - although I noticed Hasher doesn't call `__hash__` if the object being hashed implements it which I feel it should? If it did I could return a hash of the tokenizers.json file instead.\r\n\r\n```\r\n def __getstate__(self):\r\n \"\"\"\r\n Removes pre_tokenizer since it cannot be pickled\r\n \"\"\"\r\n logger.debug(\"Copy state dict\")\r\n self.backend_tokenizer.pre_tokenizer = tokenizers.pre_tokenizers.Sequence([])\r\n out = copy.deepcopy(self.__dict__) #self.__dict__.copy()\r\n self.backend_tokenizer.pre_tokenizer = self._pre_tokenizer()\r\n\r\n return out\r\n```\r\n",
"I'm glad you figured something out :)\r\n\r\nRegarding hashing: we're not using hashing for the same purpose as the python `__hash__` purpose (which is in general for dictionary lookups). For example it is allowed for python hashing to not return the same hash across sessions, while our hashing must return the same hashes across sessions for the caching to work properly."
] | datasets.map pickle issue resulting in invalid mapping function | {
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} | MDU6SXNzdWU5MjQ1OTc0NzA= | null | 2021-06-18T06:47:26Z | https://api.github.com/repos/huggingface/datasets/issues/2516/comments | I trained my own tokenizer, and I needed to use a python custom class. Because of this I have to detach the custom step before saving and reattach after restore. I did this using the standard pickle `__get_state__` / `__set_state__` mechanism. I think it's correct but it fails when I use it inside a function which is mapped to a dataset, i.e. in the manner of run_mlm.py and other huggingface scripts.
The following reproduces the issue - most likely I'm missing something
A simulated tokeniser which can be pickled
```
class CustomTokenizer:
def __init__(self):
self.state = "init"
def __getstate__(self):
print("__getstate__ called")
out = self.__dict__.copy()
self.state = "pickled"
return out
def __setstate__(self, d):
print("__setstate__ called")
self.__dict__ = d
self.state = "restored"
tokenizer = CustomTokenizer()
```
Test that it actually works - prints "__getstate__ called" and "__setstate__ called"
```
import pickle
serialized = pickle.dumps(tokenizer)
restored = pickle.loads(serialized)
assert restored.state == "restored"
```
Simulate a function that tokenises examples, when dataset.map is called, this function
```
def tokenize_function(examples):
assert tokenizer.state == "restored" # this shouldn't fail but it does
output = tokenizer(examples) # this will fail as tokenizer isn't really a tokenizer
return output
```
Use map to simulate tokenization
```
import glob
from datasets import load_dataset
assert tokenizer.state == "restored"
train_files = glob.glob('train*.csv')
validation_files = glob.glob('validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
)
```
What's happening is I can see that __getstate__ is called but not __setstate__, so the state of `tokenize_function` is invalid at the point that it's actually executed. This doesn't matter as far as I can see for the standard tokenizers as they don't use __getstate__ / __setstate__. I'm not sure if there's another hook I'm supposed to implement as well?
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-22-a2aef4f74aaa> in <module>
8 tokenized_datasets = datasets.map(
9 tokenize_function,
---> 10 batched=True,
11 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)
487 desc=desc,
488 )
--> 489 for k, dataset in self.items()
490 }
491 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
487 desc=desc,
488 )
--> 489 for k, dataset in self.items()
490 }
491 )
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/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)
1633 fn_kwargs=fn_kwargs,
1634 new_fingerprint=new_fingerprint,
-> 1635 desc=desc,
1636 )
1637 else:
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
184 }
185 # apply actual function
--> 186 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
187 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
188 # re-apply format to the output
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/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
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/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)
1961 indices,
1962 check_same_num_examples=len(input_dataset.list_indexes()) > 0,
-> 1963 offset=offset,
1964 )
1965 except NumExamplesMismatch:
~/.pyenv/versions/3.7.6/envs/xxx/lib/python3.7/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1853 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1854 processed_inputs = (
-> 1855 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1856 )
1857 if update_data is None:
<ipython-input-21-8ee4a8ba5b1b> in tokenize_function(examples)
1 def tokenize_function(examples):
----> 2 assert tokenizer.state == "restored"
3 tokenizer(examples)
4 return examples
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924,417,172 | https://api.github.com/repos/huggingface/datasets/issues/2514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2514/events | [
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"Hi ! For now this is probably the best option.\r\nWe might add a feature like this in the feature as well.\r\n\r\nDo you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\nOtherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases",
"Yes, I'd like to work on this feature once I'm done with #2500, but first I have to do some research, and see if the implementation wouldn't be too complex.\r\n\r\nIn the meantime, maybe [this lib](https://github.com/TomScheffers/pyarrow_ops) can help. However, note that this lib operates directly on pyarrow tables and relies only on `hash` to find duplicates (e.g. `-1` and `-2` have the same hash in Python 3, so this lib will treat them as duplicates), which doesn't make much sense.",
"> Hi ! For now this is probably the best option.\r\n> We might add a feature like this in the feature as well.\r\n> \r\n> Do you know any deduplication method that works on arbitrary big datasets without filling up RAM ?\r\n> Otherwise we can have do the deduplication in memory like pandas but I feel like this is going to be limiting for some cases\r\n\r\nGreat if this is can be done. Thanks!!\r\n\r\nNot sure if you are asking me. In any case I don't know of any unfortunately :( in practice if data is really large we normally do it with spark (only for info. I understand this is not useful in developing this library..)",
"Hello,\r\n\r\nI'm also interested in this feature.\r\nHas there been progress on this issue?\r\n\r\nCould we use a similar trick as above, but with a better hashing algorithm like SHA?\r\n\r\nWe could also use a [bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), should we care a lot about collision in this case?",
"For reference, we can get a solution fairly easily if we assume that we can hold in memory all unique values. \r\n\r\n```python\r\nfrom datasets import Dataset\r\nfrom itertools import cycle\r\nfrom functools import partial\r\n\r\nmemory = set()\r\ndef is_unique(elem:Any , column: str, memory: set) -> bool:\r\n if elem[column] in memory:\r\n return False\r\n else:\r\n memory.add(elem[column])\r\n return True\r\n\r\n# Example dataset\r\nds = Dataset.from_dict({\"col1\" : [sent for i, sent in zip(range(10), cycle([\"apple\", \"orange\", \"pear\"]))],\r\n \"col2\": [i % 5 for i in range(10)]})\r\n\r\n# Drop duplicates in `ds` on \"col1\"\r\nds2 = ds.filter(partial(is_unique, column=\"col1\", memory=memory))\r\n```\r\n\r\nOf course, we can improve the API so that we can introduce `Dataset.drop_duplicates`.\r\nFor the parallel version, we can use a shared memory set.",
"An approach that works assuming you can hold the all the unique document hashes in memory:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndef get_hash(example):\r\n \"\"\"Get hash of content field.\"\"\"\r\n return {\"hash\": hash(example[\"content\"])} # can use any hashing function here\r\n \r\ndef check_uniques(example, uniques):\r\n \"\"\"Check if current hash is still in set of unique hashes and remove if true.\"\"\"\r\n if example[\"hash\"] in uniques:\r\n uniques.remove(example[\"hash\"])\r\n return True\r\n else:\r\n return False\r\n\r\nds = load_dataset(\"some_dataset\")\r\nds = ds.map(get_hash)\r\nuniques = set(ds.unique(\"hash\"))\r\nds_filter = ds.filter(check_uniques, fn_kwargs={\"uniques\": uniques})\r\n```\r\nIf the `uniques` could be stored in arrow then no additional memory would used at all but I don't know if this is possible.\r\n",
"@lvwerra hey, could you tell me how reliable is this deduplication method. i am currently using the same deduplication strategy to deduplicate a large text corpus to pretrain LLMs ~ 11B to 20B. just needed to ensure if this strategy would be fine on large datasets for LLMs pretraining. ",
"Hi @StephennFernandes I'm also trying to pretrain an llm, and need to do deduplication for my dataset, \r\nwhich method you applied please?",
"Hey @Manel-Hik \n\nThe following is a simpler yet really effective deduplication code that i has used in the past. \n\ngiven that I have limited training corpus for the languages I wanted to train i relied on this code. https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned/blob/main/deduplicate.py\n\n\nfor more robust and stronger deduplication, refer to this huggingface repo, that's newly released: https://github.com/huggingface/datatrove\n\n\n\n",
"Thanks a lot Sure I will check it @StephennFernandes ",
"Hi, is there any updates? Thanks!",
"Update July 2024\r\n\r\nPyArrow now supports `first`/`last` aggregation which would allow us to implement this functionality. [Link](https://github.com/apache/arrow/issues/30950)\r\n\r\nso if we want to move in this direction we can :) Is that something we want to do? Would be happy to contribute."
] | Can datasets remove duplicated rows? | {
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i find myself more and more relying on datasets just to do all the preprocessing. One thing however, for removing duplicated rows, I couldn't find out how and am always converting datasets to pandas to do that..
**Describe the solution you'd like**
have a functionality of " remove duplicated rows"
**Describe alternatives you've considered**
convert dataset to pandas, remove duplicate, and convert back...
**Additional context**
no | {
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924,174,413 | https://api.github.com/repos/huggingface/datasets/issues/2513 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2513/events | [] | null | 2021-06-18T08:43:55Z | [
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"Hi @colbym-MM, thanks for reporting. We are fixing it."
] | Corelation should be Correlation | {
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924,069,353 | https://api.github.com/repos/huggingface/datasets/issues/2512 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2512/events | [
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"Sorry, I was using an old version of sequeval"
] | seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict' | {
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} | MDU6SXNzdWU5MjQwNjkzNTM= | null | 2021-06-17T15:36:02Z | https://api.github.com/repos/huggingface/datasets/issues/2512/comments | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset, load_metric
seqeval = load_metric("seqeval")
seqeval.compute(predictions=[['A']], references=[['A']])
```
## Expected results
The function computes a dict with metrics
## Actual results
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-69a57f5cf06f> in <module>
1 from datasets import load_dataset, load_metric
2 seqeval = load_metric("seqeval")
----> 3 seqeval.compute(predictions=[['A']], references=[['A']])
~/p3/lib/python3.7/site-packages/datasets/metric.py in compute(self, *args, **kwargs)
396 references = self.data["references"]
397 with temp_seed(self.seed):
--> 398 output = self._compute(predictions=predictions, references=references, **kwargs)
399
400 if self.buf_writer is not None:
~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/81eda1ff004361d4fa48754a446ec69bb7aa9cf4d14c7215f407d1475941c5ff/seqeval.py in _compute(self, predictions, references, suffix)
95
96 def _compute(self, predictions, references, suffix=False):
---> 97 report = classification_report(y_true=references, y_pred=predictions, suffix=suffix, output_dict=True)
98 report.pop("macro avg")
99 report.pop("weighted avg")
TypeError: classification_report() got an unexpected keyword argument 'output_dict'
```
## Environment info
sklearn=0.24
datasets=1.1.3
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923,762,133 | https://api.github.com/repos/huggingface/datasets/issues/2511 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2511/events | [
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"Update on this: I'm computing the checksums of the data files. It will be available soon",
"Added in #2575 :)"
] | Add C4 | {
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- **Name:** *C4*
- **Description:** *https://github.com/allenai/allennlp/discussions/5056*
- **Paper:** *https://arxiv.org/abs/1910.10683*
- **Data:** *https://huggingface.co/datasets/allenai/c4*
- **Motivation:** *Used a lot for pretraining*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Should fix https://github.com/huggingface/datasets/issues/1710 | {
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923,735,485 | https://api.github.com/repos/huggingface/datasets/issues/2510 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2510/events | [] | null | 2021-06-17T10:45:25Z | [] | https://github.com/huggingface/datasets/pull/2510 | MEMBER | null | false | null | [] | Add align_labels_with_mapping to DatasetDict | {
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In this PR I also added `DatasetDict.align_labels_with_mapping` | {
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922,846,035 | https://api.github.com/repos/huggingface/datasets/issues/2509 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2509/events | [] | null | 2021-06-21T15:05:04Z | [] | https://github.com/huggingface/datasets/pull/2509 | MEMBER | null | false | null | [
"Windows, why are you doing this to me ?",
"Thanks @lhoestq, I'm starting reviewing this PR.",
"Yea issues on windows are about long paths, not long filenames.\r\nWe can make sure the lock filenames are not too long, but not for the paths",
"Took your suggestions into account @albertvillanova :)"
] | Fix fingerprint when moving cache dir | {
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I fixed that by setting the fingerprint to be the hash of:
- the relative cache dir (dataset_name/version/config_id)
- the requested split
Close #2496
I had to fix an issue with the filelock filename that was too long (>255). It prevented the tests to run on my machine. I just added `hash_filename_if_too_long` in case this happens, to not get filenames longer than 255.
We usually have long filenames for filelocks because they are named after the path that is being locked. In case the path is a cache directory that has long directory names, then the filelock filename could en up being very long. | {
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921,863,173 | https://api.github.com/repos/huggingface/datasets/issues/2508 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2508/events | [
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"Hi ! Is this folder structure a standard, a bit like imagenet ?\r\nIn this case maybe we can consider having a dataset loader for cifar-like, imagenet-like, squad-like, conll-like etc. datasets ?\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nmy_custom_cifar = load_dataset(\"cifar_like\", data_dir=\"path/to/data/dir\")\r\n```\r\n\r\nLet me know what you think",
"Yep that would be sweet - closing for now as we found a workaround. ",
"@lhoestq I think we'll want a generic `image-folder` dataset (same as 'imagenet-like'). This is like `torchvision.datasets.ImageFolder`, and is something vision folks are used to seeing.",
"Opening this back up, since I'm planning on tackling this. Already posted a quick version of it on my account on the hub.\r\n\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset('nateraw/image-folder', data_files='PetImages/')\r\n```",
"Bumping this one following our recent discussion @mariosasko @nateraw :)"
] | Load Image Classification Dataset from Local | {
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Yes - we would like to load an image classification dataset with datasets without having to write a custom data loader.
**Describe the solution you'd like**
Given a folder structure with images of each class in each folder, the ability to load these folders into a HuggingFace dataset like "cifar10".
**Describe alternatives you've considered**
Implement ViT training outside of the HuggingFace Trainer and without datasets (we did this but prefer to stay on the main path)
Write custom data loader logic
**Additional context**
We're training ViT on custom dataset
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Close #2366. | {
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921,435,598 | https://api.github.com/repos/huggingface/datasets/issues/2506 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2506/events | [] | null | 2021-06-15T16:25:36Z | [] | https://github.com/huggingface/datasets/pull/2506 | CONTRIBUTOR | null | false | null | [] | Add course banner | {
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} | 2021-06-15T14:03:54Z | https://api.github.com/repos/huggingface/datasets/issues/2506/comments | This PR adds a course banner similar to the one you can now see in the [Transformers repo](https://github.com/huggingface/transformers) that links to the course. Let me know if placement seems right to you or not, I can move it just below the badges too. | {
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921,234,797 | https://api.github.com/repos/huggingface/datasets/issues/2505 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2505/events | [] | null | 2021-06-28T09:53:39Z | [] | https://github.com/huggingface/datasets/pull/2505 | MEMBER | null | false | null | [
"Looks like we have a nice speed up in some benchmarks. For example:\r\n- `read_formatted numpy 5000`: 4.584777 sec -> 0.487113 sec\r\n- `read_formatted torch 5000`: 4.565676 sec -> 1.289514 sec",
"Can we convert this draft to PR @lhoestq ?",
"Ready for review ! cc @vblagoje",
"@lhoestq I tried the branch and it works for me. Although performance trace now shows a speedup, the overall pre-training speed up is minimal. But that's on my plate to explore further. ",
"Thanks for investigating @vblagoje \r\n\r\n@albertvillanova , do you have any comments on this PR ? Otherwise I think we can merge it"
] | Make numpy arrow extractor faster | {
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} | 2021-06-15T10:11:32Z | https://api.github.com/repos/huggingface/datasets/issues/2505/comments | I changed the NumpyArrowExtractor to call directly to_numpy and see if it can lead to speed-ups as discussed in https://github.com/huggingface/datasets/issues/2498
This could make the numpy/torch/tf/jax formatting faster | {
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"Hi @arnaudstiegler, thanks for reporting. I'm investigating it.",
"@arnaudstiegler I have just checked that these mismatches are already present in the original dataset: https://github.com/megagonlabs/SubjQA\r\n\r\nWe are going to contact the dataset owners to report this.",
"I have:\r\n- opened an issue in their repo: https://github.com/megagonlabs/SubjQA/issues/3\r\n- written an email to all the paper authors",
"Please [see my response](https://github.com/megagonlabs/SubjQA/issues/3#issuecomment-905160010). There will be a fix in a couple of days."
] | SubjQA wrong boolean values in entries | {
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} | MDU6SXNzdWU5MjA2MzYxODY= | null | 2021-06-14T17:42:46Z | https://api.github.com/repos/huggingface/datasets/issues/2503/comments | ## Describe the bug
SubjQA seems to have a boolean that's consistently wrong.
It defines:
- question_subj_level: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).
- is_ques_subjective: A boolean subjectivity label derived from question_subj_level (i.e., scores below 4 are considered as subjective)
However, `is_ques_subjective` seems to have wrong values in the entire dataset.
For instance, in the example in the dataset card, we have:
- "question_subj_level": 2
- "is_ques_subjective": false
However, according to the description, the question should be subjective since the `question_subj_level` is below 4
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920,623,572 | https://api.github.com/repos/huggingface/datasets/issues/2502 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2502/events | [] | null | 2021-06-21T16:15:50Z | [] | https://github.com/huggingface/datasets/pull/2502 | MEMBER | null | false | null | [] | JAX integration | {
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} | 2021-06-14T17:24:23Z | https://api.github.com/repos/huggingface/datasets/issues/2502/comments | Hi !
I just added the "jax" formatting, as we already have for pytorch, tensorflow, numpy (and also pandas and arrow).
It does pretty much the same thing as the pytorch formatter except it creates jax.numpy.ndarray objects.
```python
from datasets import Dataset
d = Dataset.from_dict({"foo": [[0., 1., 2.]]})
d = d.with_format("jax")
d[0]
# {'foo': DeviceArray([0., 1., 2.], dtype=float32)}
```
A few details:
- The default integer precision for jax depends on the jax configuration `jax_enable_x64` (see [here](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), I took that into account. Unless `jax_enable_x64` is specified, it is int32 by default
- AFAIK it's not possible to do a full conversion from arrow data to jax data. We are doing arrow -> numpy -> jax but the numpy -> jax part doesn't do zero copy unfortutanely (see [here](https://github.com/google/jax/issues/4486))
- the env var for disabling JAX is `USE_JAX`. However I noticed that in `transformers` it is `USE_FLAX`. This is not an issue though IMO
I also updated `convert_to_python_objects` to allow users to pass jax.numpy.ndarray objects to build a dataset.
Since the `convert_to_python_objects` method became slow because it's the time when pytorch, tf (and now jax) are imported, I fixed it by checking the `sys.modules` to avoid unecessary import of pytorch, tf or jax.
Close #2495 | {
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