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Close #2607. | {
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"Updating to pyarrow-4.0.1 didn't fix the issue",
"Here is an exemple dataset with 2 of these compressed JSON files: https://huggingface.co/datasets/thomwolf/github-python",
"Hi @thomwolf, thanks for reporting.\r\n\r\nIt seems this might be due to the fact that the JSON Dataset builder uses `pyarrow.json` (`paj... | 1,625,657,793,000 | 1,626,774,619,000 | 1,625,760,521,000 | MEMBER | null | ## Describe the bug
Using streaming to iterate on local gzip compressed JSON files raise a file not exist error
## Steps to reproduce the bug
```python
from datasets import load_dataset
streamed_dataset = load_dataset('json', split='train', data_files=data_files, streaming=True)
next(iter(streamed_dataset))
```
## Actual results
```
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-27a664e29784> in <module>
----> 1 next(iter(streamed_dataset))
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
336
337 def __iter__(self):
--> 338 for key, example in self._iter():
339 if self.features:
340 # we encode the example for ClassLabel feature types for example
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in _iter(self)
333 else:
334 ex_iterable = self._ex_iterable
--> 335 yield from ex_iterable
336
337 def __iter__(self):
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in __iter__(self)
76
77 def __iter__(self):
---> 78 for key, example in self.generate_examples_fn(**self.kwargs):
79 yield key, example
80
~/Documents/GitHub/datasets/src/datasets/iterable_dataset.py in wrapper(**kwargs)
282 def wrapper(**kwargs):
283 python_formatter = PythonFormatter()
--> 284 for key, table in generate_tables_fn(**kwargs):
285 batch = python_formatter.format_batch(table)
286 for i, example in enumerate(_batch_to_examples(batch)):
~/Documents/GitHub/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files, original_files)
85 file,
86 read_options=self.config.pa_read_options,
---> 87 parse_options=self.config.pa_parse_options,
88 )
89 except pa.ArrowInvalid as err:
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/_json.pyx in pyarrow._json._get_reader()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_input_stream()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.get_native_file()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile.__cinit__()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/io.pxi in pyarrow.lib.OSFile._open_readable()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
FileNotFoundError: [Errno 2] Failed to open local file 'gzip://file-000000000000.json::/Users/thomwolf/github-dataset/file-000000000000.json.gz'. Detail: [errno 2] No such file or directory
```
## Environment info
- `datasets` version: 1.9.1.dev0
- Platform: Darwin-19.6.0-x86_64-i386-64bit
- Python version: 3.7.7
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"#take"
] | 1,625,655,364,000 | 1,626,129,271,000 | 1,626,129,271,000 | CONTRIBUTOR | null | **Is your feature request related to a problem? Please describe.**
While training the model on sentence split the task in English we require to evaluate the trained model on `Exact Match`, `SARI` and `BLEU` score
like this
While training we require metrics which can give all the output
Currently, we don't have an exact match for text normalized data
**Describe the solution you'd like**
A custom metrics for wiki_split that can calculate these three values and provide it in the form of a single dictionary
For exact match, we can refer to [this](https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py)
**Describe alternatives you've considered**
Two metrics are already present one more can be added for an exact match then we can run all three metrics in training script
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```python
aiohttp.client_exceptions.ClientOSError: [Errno 104] Connection reset by peer
```
This error must trigger a retry instead of directly crashing
Therefore I extended the error type that triggers the retry to be the base aiohttp error type: `ClientError`
In particular both `ClientOSError` and `ServerDisconnectedError` inherit from `ClientError`. | {
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"Hi !\r\nIf we want something more general, we could either\r\n1. delete the extracted files after the arrow data generation automatically, or \r\n2. delete each extracted file during the arrow generation right after it has been closed.\r\n\r\nSolution 2 is better to save disk space during the arrow generation. Is ... | 1,625,644,576,000 | 1,626,685,698,000 | 1,626,685,698,000 | MEMBER | null | I'm loading a dataset constituted of 44 GB of compressed JSON files.
When loading the dataset with the JSON script, extracting the files create about 200 GB of uncompressed files before creating the 180GB of arrow cache tables
Having a simple way to delete the extracted files after usage (or even better, to stream extraction/delete) would be nice to avoid disk cluter.
I can maybe tackle this one in the JSON script unless you want a more general solution. | {
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https://api.github.com/repos/huggingface/datasets/issues/2602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2602/comments | https://api.github.com/repos/huggingface/datasets/issues/2602/events | https://github.com/huggingface/datasets/pull/2602 | 938,555,712 | MDExOlB1bGxSZXF1ZXN0Njg0OTE5MjMy | 2,602 | Remove import of transformers | {
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Related to huggingface/transformers#12549 and #502. | {
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https://api.github.com/repos/huggingface/datasets/issues/2601 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2601/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2601/comments | https://api.github.com/repos/huggingface/datasets/issues/2601/events | https://github.com/huggingface/datasets/pull/2601 | 938,096,396 | MDExOlB1bGxSZXF1ZXN0Njg0NTQyNjY5 | 2,601 | Fix `filter` with multiprocessing in case all samples are discarded | {
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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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] | closed | false | null | [] | null | [] | 1,625,590,405,000 | 1,625,662,231,000 | 1,625,662,231,000 | CONTRIBUTOR | null | ## Describe the bug
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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https://api.github.com/repos/huggingface/datasets/issues/2598 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2598/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2598/comments | https://api.github.com/repos/huggingface/datasets/issues/2598/events | https://github.com/huggingface/datasets/issues/2598 | 937,930,632 | MDU6SXNzdWU5Mzc5MzA2MzI= | 2,598 | Unable to download omp dataset | {
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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 datas... | 1,625,580,052,000 | 1,625,662,595,000 | 1,625,662,595,000 | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2597 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2597/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2597/comments | https://api.github.com/repos/huggingface/datasets/issues/2597/events | https://github.com/huggingface/datasets/pull/2597 | 937,917,770 | MDExOlB1bGxSZXF1ZXN0Njg0Mzk0MDIz | 2,597 | Remove redundant prepare_module | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2596/comments | https://api.github.com/repos/huggingface/datasets/issues/2596/events | https://github.com/huggingface/datasets/issues/2596 | 937,598,914 | MDU6SXNzdWU5Mzc1OTg5MTQ= | 2,596 | Transformer Class on dataset | {
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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... | 1,625,556,435,000 | 1,625,732,525,000 | null | NONE | null | Just wondering if you have intenttion to create
TransformerClass :
dataset --> dataset
and make determnistic transformation (ie not fit).
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2595/comments | https://api.github.com/repos/huggingface/datasets/issues/2595/events | https://github.com/huggingface/datasets/issues/2595 | 937,483,120 | MDU6SXNzdWU5Mzc0ODMxMjA= | 2,595 | ModuleNotFoundError: No module named 'datasets.tasks' while importing common voice datasets | {
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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. "
] | 1,625,541,655,000 | 1,625,551,189,000 | 1,625,551,189,000 | NONE | null | Error traceback:
---------------------------------------------------------------------------
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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https://api.github.com/repos/huggingface/datasets/issues/2593 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2593/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2593/comments | https://api.github.com/repos/huggingface/datasets/issues/2593/events | https://github.com/huggingface/datasets/pull/2593 | 937,242,137 | MDExOlB1bGxSZXF1ZXN0NjgzODMwMjcy | 2,593 | Support pandas 1.3.0 read_csv | {
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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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https://api.github.com/repos/huggingface/datasets/issues/2592 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2592/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2592/comments | https://api.github.com/repos/huggingface/datasets/issues/2592/events | https://github.com/huggingface/datasets/pull/2592 | 937,060,559 | MDExOlB1bGxSZXF1ZXN0NjgzNjc2MjA4 | 2,592 | Add c4.noclean infos | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2591/comments | https://api.github.com/repos/huggingface/datasets/issues/2591/events | https://github.com/huggingface/datasets/issues/2591 | 936,957,975 | MDU6SXNzdWU5MzY5NTc5NzU= | 2,591 | Cached dataset overflowing disk space | {
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"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 store... | 1,625,481,799,000 | 1,626,685,699,000 | 1,626,685,699,000 | CONTRIBUTOR | null | I'm training a Swedish Wav2vec2 model on a Linux GPU and having issues that the huggingface cached dataset folder is completely filling up my disk space (I'm training on a dataset of around 500 gb).
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 ... | 1,625,473,165,000 | 1,626,099,130,000 | 1,625,733,615,000 | MEMBER | null | 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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} | [] | 1,625,471,186,000 | 1,626,099,011,000 | 1,625,591,370,000 | MEMBER | null | 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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Our CI test suite should be exhaustive and test all the library functionalities. | {
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} | [] | 1,625,467,340,000 | 1,626,099,070,000 | 1,625,663,931,000 | MEMBER | null | 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 ... | 1,625,413,189,000 | 1,625,663,931,000 | 1,625,663,931,000 | NONE | null | ## 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
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- 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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https://api.github.com/repos/huggingface/datasets/issues/2583 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2583/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2583/comments | https://api.github.com/repos/huggingface/datasets/issues/2583/events | https://github.com/huggingface/datasets/issues/2583 | 936,034,976 | MDU6SXNzdWU5MzYwMzQ5NzY= | 2,583 | Error iteration over IterableDataset using Torch DataLoader | {
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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... | 1,625,255,758,000 | 1,626,771,885,000 | 1,625,528,903,000 | NONE | null | ## 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2582/comments | https://api.github.com/repos/huggingface/datasets/issues/2582/events | https://github.com/huggingface/datasets/pull/2582 | 935,859,104 | MDExOlB1bGxSZXF1ZXN0NjgyNzAzNzg3 | 2,582 | Add skip and take | {
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"@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... | 1,625,238,619,000 | 1,625,501,200,000 | 1,625,501,199,000 | MEMBER | null | 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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} | [] | 1,625,233,327,000 | 1,626,099,226,000 | 1,626,083,571,000 | CONTRIBUTOR | null | Hey,
I think it makes sense to perform search_batch threaded, so ES can perform search in parallel.
Cheers! | {
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https://api.github.com/repos/huggingface/datasets/issues/2580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2580/comments | https://api.github.com/repos/huggingface/datasets/issues/2580/events | https://github.com/huggingface/datasets/pull/2580 | 935,767,421 | MDExOlB1bGxSZXF1ZXN0NjgyNjI2MTkz | 2,580 | Fix Counter import | {
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cc: @abhishekkrthakur @thomwolf | {
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"> 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... | 1,625,170,954,000 | 1,628,693,184,000 | 1,625,482,227,000 | MEMBER | null | Close #2572.
cc: @thomwolf | {
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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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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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- link to README
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"Hi @suzyahyah, thanks for reporting.\r\n\r\nThe message you get is indeed not an error message, but a warning coming from Hugging Face `transformers`. The complete warning message is:\r\n```\r\nSome weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.wei... | 1,624,992,923,000 | 1,625,123,339,000 | 1,625,038,549,000 | NONE | null | When applying bertscore out of the box,
```Some weights of the model checkpoint at roberta-large were not used when initializing RobertaModel: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.weight']```
Following the typical usage from https://huggingface.co/docs/datasets/loading_metrics.html
```
from datasets import load_metric
metric = load_metric('bertscore')
# Example of typical usage
for batch in dataset:
inputs, references = batch
predictions = model(inputs)
metric.add_batch(predictions=predictions, references=references)
score = metric.compute(lang="en")
#score = metric.compute(model_type="roberta-large") # gives the same error
```
I am concerned about this because my usage shouldn't require any further fine-tuning and most people would expect to use BertScore out of the box? I realised the huggingface code is a wrapper around https://github.com/Tiiiger/bert_score, but I think this repo is anyway relying on the model code and weights from huggingface repo....
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.0-1041-aws-x86_64-with-glibc2.27
- Python version: 3.9.5
- PyArrow version: 3.0.0
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} | [] | closed | false | null | [] | null | [] | 1,624,987,164,000 | 1,625,132,014,000 | 1,625,132,013,000 | MEMBER | null | ### 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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} | [] | closed | false | null | [] | null | [] | 1,624,987,081,000 | 1,625,132,543,000 | 1,625,132,529,000 | MEMBER | null | 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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"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 :)"
] | 1,624,960,921,000 | 1,625,495,186,000 | 1,625,495,186,000 | MEMBER | null | 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 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2561/comments | https://api.github.com/repos/huggingface/datasets/issues/2561/events | https://github.com/huggingface/datasets/issues/2561 | 932,321,725 | MDU6SXNzdWU5MzIzMjE3MjU= | 2,561 | Existing cache for local dataset builder file updates is ignored with `ignore_verifications=True` | {
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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 c... | 1,624,952,583,000 | 1,625,057,724,000 | null | MEMBER | null | ## Describe the bug
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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2560/comments | https://api.github.com/repos/huggingface/datasets/issues/2560/events | https://github.com/huggingface/datasets/pull/2560 | 932,143,634 | MDExOlB1bGxSZXF1ZXN0Njc5NTMyODk4 | 2,560 | fix Dataset.map when num_procs > num rows | {
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"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 ... | 1,624,933,451,000 | 1,624,978,818,000 | 1,624,978,411,000 | CONTRIBUTOR | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2559/comments | https://api.github.com/repos/huggingface/datasets/issues/2559/events | https://github.com/huggingface/datasets/issues/2559 | 931,849,724 | MDU6SXNzdWU5MzE4NDk3MjQ= | 2,559 | Memory usage consistently increases when processing a dataset with `.map` | {
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"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"
] | 1,624,905,118,000 | 1,624,956,180,000 | null | MEMBER | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2558 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2558/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2558/comments | https://api.github.com/repos/huggingface/datasets/issues/2558/events | https://github.com/huggingface/datasets/pull/2558 | 931,736,647 | MDExOlB1bGxSZXF1ZXN0Njc5MTg0Njk1 | 2,558 | Update: WebNLG - update checksums | {
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I also pinned a specific revision so that it doesn't happen again in the future.
Fix https://github.com/huggingface/datasets/issues/2553 | {
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https://api.github.com/repos/huggingface/datasets/issues/2557 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2557/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2557/comments | https://api.github.com/repos/huggingface/datasets/issues/2557/events | https://github.com/huggingface/datasets/pull/2557 | 931,633,823 | MDExOlB1bGxSZXF1ZXN0Njc5MDk4ODg3 | 2,557 | Fix `fever` keys | {
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I fixed it by taking into account the file index as well. | {
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https://api.github.com/repos/huggingface/datasets/issues/2556 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2556/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2556/comments | https://api.github.com/repos/huggingface/datasets/issues/2556/events | https://github.com/huggingface/datasets/issues/2556 | 931,595,872 | MDU6SXNzdWU5MzE1OTU4NzI= | 2,556 | Better DuplicateKeysError error to help the user debug the issue | {
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] | open | false | null | [] | null | [] | 1,624,888,257,000 | 1,624,888,257,000 | null | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2555/comments | https://api.github.com/repos/huggingface/datasets/issues/2555/events | https://github.com/huggingface/datasets/pull/2555 | 931,585,485 | MDExOlB1bGxSZXF1ZXN0Njc5MDU4ODM3 | 2,555 | Fix code_search_net keys | {
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"Fix #2552."
] | 1,624,887,623,000 | 1,630,571,083,000 | 1,624,889,435,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2554/comments | https://api.github.com/repos/huggingface/datasets/issues/2554/events | https://github.com/huggingface/datasets/issues/2554 | 931,453,855 | MDU6SXNzdWU5MzE0NTM4NTU= | 2,554 | Multilabel metrics not supported | {
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"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 enou... | 1,624,878,586,000 | 1,634,128,153,000 | 1,625,733,615,000 | NONE | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2553/comments | https://api.github.com/repos/huggingface/datasets/issues/2553/events | https://github.com/huggingface/datasets/issues/2553 | 931,365,926 | MDU6SXNzdWU5MzEzNjU5MjY= | 2,553 | load_dataset("web_nlg") NonMatchingChecksumError | {
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"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 !"
] | 1,624,872,406,000 | 1,624,901,019,000 | 1,624,900,996,000 | NONE | null | Hi! It seems the WebNLG dataset gives a NonMatchingChecksumError.
## 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2552/comments | https://api.github.com/repos/huggingface/datasets/issues/2552/events | https://github.com/huggingface/datasets/issues/2552 | 931,354,687 | MDU6SXNzdWU5MzEzNTQ2ODc= | 2,552 | Keys should be unique error on code_search_net | {
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"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... | 1,624,871,720,000 | 1,630,937,310,000 | 1,630,571,129,000 | MEMBER | null | ## 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2551/comments | https://api.github.com/repos/huggingface/datasets/issues/2551/events | https://github.com/huggingface/datasets/pull/2551 | 930,967,978 | MDExOlB1bGxSZXF1ZXN0Njc4NTQzMjg1 | 2,551 | Fix FileSystems documentation | {
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} | [] | closed | false | null | [] | null | [] | 1,624,810,722,000 | 1,624,885,795,000 | 1,624,885,794,000 | CONTRIBUTOR | null | ### 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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] | closed | false | null | [] | null | [] | 1,624,806,058,000 | 1,632,663,759,000 | 1,632,663,759,000 | CONTRIBUTOR | null | Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data
What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.
Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score` refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.
If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411
If this change is ok with you, I'll open a pull request.
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"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... | 1,624,595,543,000 | 1,624,655,277,000 | 1,624,655,276,000 | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2548 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2548/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2548/comments | https://api.github.com/repos/huggingface/datasets/issues/2548/events | https://github.com/huggingface/datasets/issues/2548 | 929,232,831 | MDU6SXNzdWU5MjkyMzI4MzE= | 2,548 | Field order issue in loading json | {
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"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."
] | 1,624,541,393,000 | 1,624,545,403,000 | 1,624,545,245,000 | NONE | null | ## 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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https://api.github.com/repos/huggingface/datasets/issues/2547 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2547/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2547/comments | https://api.github.com/repos/huggingface/datasets/issues/2547/events | https://github.com/huggingface/datasets/issues/2547 | 929,192,329 | MDU6SXNzdWU5MjkxOTIzMjk= | 2,547 | Dataset load_from_disk is too slow | {
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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 wi... | 1,624,538,744,000 | 1,624,632,998,000 | null | NONE | null | @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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https://api.github.com/repos/huggingface/datasets/issues/2546 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2546/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2546/comments | https://api.github.com/repos/huggingface/datasets/issues/2546/events | https://github.com/huggingface/datasets/pull/2546 | 929,091,689 | MDExOlB1bGxSZXF1ZXN0Njc2OTk2MjQ0 | 2,546 | Add license to the Cambridge English Write & Improve + LOCNESS dataset card | {
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I added it and I also filled a few other empty sections. | {
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https://api.github.com/repos/huggingface/datasets/issues/2545 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2545/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2545/comments | https://api.github.com/repos/huggingface/datasets/issues/2545/events | https://github.com/huggingface/datasets/pull/2545 | 929,016,580 | MDExOlB1bGxSZXF1ZXN0Njc2OTMxOTYw | 2,545 | Fix DuplicatedKeysError in drop dataset | {
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cc: @VictorSanh. | {
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https://api.github.com/repos/huggingface/datasets/issues/2544 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2544/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2544/comments | https://api.github.com/repos/huggingface/datasets/issues/2544/events | https://github.com/huggingface/datasets/pull/2544 | 928,900,827 | MDExOlB1bGxSZXF1ZXN0Njc2ODM1MjYz | 2,544 | Fix logging levels | {
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} | [] | closed | false | null | [] | null | [] | 1,624,516,896,000 | 1,624,628,419,000 | 1,624,628,419,000 | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2543 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2543/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2543/comments | https://api.github.com/repos/huggingface/datasets/issues/2543/events | https://github.com/huggingface/datasets/issues/2543 | 928,571,915 | MDU6SXNzdWU5Mjg1NzE5MTU= | 2,543 | switching some low-level log.info's to log.debug? | {
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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."
] | 1,624,476,415,000 | 1,624,628,419,000 | 1,624,628,419,000 | MEMBER | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2542 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2542/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2542/comments | https://api.github.com/repos/huggingface/datasets/issues/2542/events | https://github.com/huggingface/datasets/issues/2542 | 928,540,382 | MDU6SXNzdWU5Mjg1NDAzODI= | 2,542 | `datasets.keyhash.DuplicatedKeysError` for `drop` and `adversarial_qa/adversarialQA` | {
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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 wit... | 1,624,473,676,000 | 1,624,657,805,000 | 1,624,546,628,000 | MEMBER | null | ## 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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"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/discofus... | 1,624,472,698,000 | 1,624,890,891,000 | 1,624,890,890,000 | MEMBER | null | Updating the discofuse link: https://github.com/google-research-datasets/discofuse/commit/fd4b120cb3dd19a417e7f3b5432010b574b5eeee | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2540/comments | https://api.github.com/repos/huggingface/datasets/issues/2540/events | https://github.com/huggingface/datasets/pull/2540 | 928,433,892 | MDExOlB1bGxSZXF1ZXN0Njc2NDM5NTM1 | 2,540 | Remove task templates if required features are removed during `Dataset.map` | {
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} | [] | closed | false | null | [] | null | [] | 1,624,465,225,000 | 1,624,545,675,000 | 1,624,541,643,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2539/comments | https://api.github.com/repos/huggingface/datasets/issues/2539/events | https://github.com/huggingface/datasets/pull/2539 | 927,952,429 | MDExOlB1bGxSZXF1ZXN0Njc2MDI5MDY5 | 2,539 | remove wi_locness dataset due to licensing issues | {
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"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 tha... | 1,624,433,732,000 | 1,624,632,762,000 | 1,624,632,762,000 | CONTRIBUTOR | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2538/comments | https://api.github.com/repos/huggingface/datasets/issues/2538/events | https://github.com/huggingface/datasets/issues/2538 | 927,940,691 | MDU6SXNzdWU5Mjc5NDA2OTE= | 2,538 | Loading partial dataset when debugging | {
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"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[... | 1,624,432,792,000 | 1,627,567,833,000 | null | NONE | null | 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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"`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... | 1,624,382,903,000 | 1,625,070,663,000 | 1,625,070,658,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2536/comments | https://api.github.com/repos/huggingface/datasets/issues/2536/events | https://github.com/huggingface/datasets/issues/2536 | 927,338,639 | MDU6SXNzdWU5MjczMzg2Mzk= | 2,536 | Use `Audio` features for `AutomaticSpeechRecognition` task template | {
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"I'm just retaking and working on #2324. 😉 "
] | 1,624,374,441,000 | 1,624,375,011,000 | null | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2535/comments | https://api.github.com/repos/huggingface/datasets/issues/2535/events | https://github.com/huggingface/datasets/pull/2535 | 927,334,349 | MDExOlB1bGxSZXF1ZXN0Njc1NTA3MTAw | 2,535 | Improve Features docs | {
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- Add docstrings to Features methods | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2534/comments | https://api.github.com/repos/huggingface/datasets/issues/2534/events | https://github.com/huggingface/datasets/pull/2534 | 927,201,435 | MDExOlB1bGxSZXF1ZXN0Njc1MzkzODg0 | 2,534 | Sync with transformers disabling NOTSET | {
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"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`..."
] | 1,624,366,461,000 | 1,624,545,767,000 | 1,624,545,767,000 | MEMBER | null | Close #2528. | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2533/comments | https://api.github.com/repos/huggingface/datasets/issues/2533/events | https://github.com/huggingface/datasets/pull/2533 | 927,193,264 | MDExOlB1bGxSZXF1ZXN0Njc1Mzg2OTMw | 2,533 | Add task template for automatic speech recognition | {
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"@SBrandeis @lhoestq i've integrated your suggestions, so this is ready for another review :)",
"Merging if it's good for you @lewtun :)"
] | 1,624,365,902,000 | 1,624,464,886,000 | 1,624,463,817,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2532/comments | https://api.github.com/repos/huggingface/datasets/issues/2532/events | https://github.com/huggingface/datasets/issues/2532 | 927,063,196 | MDU6SXNzdWU5MjcwNjMxOTY= | 2,532 | Tokenizer's normalization preprocessor cause misalignment in return_offsets_mapping for tokenizer classification task | {
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"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"
] | 1,624,356,498,000 | 1,624,425,445,000 | 1,624,425,445,000 | CONTRIBUTOR | null | [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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2531/comments | https://api.github.com/repos/huggingface/datasets/issues/2531/events | https://github.com/huggingface/datasets/pull/2531 | 927,017,924 | MDExOlB1bGxSZXF1ZXN0Njc1MjM2MDYz | 2,531 | Fix dev version | {
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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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2530/comments | https://api.github.com/repos/huggingface/datasets/issues/2530/events | https://github.com/huggingface/datasets/pull/2530 | 927,013,773 | MDExOlB1bGxSZXF1ZXN0Njc1MjMyNDk0 | 2,530 | Fixed label parsing in the ProductReviews dataset | {
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"@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... | 1,624,353,165,000 | 1,624,366,520,000 | 1,624,366,360,000 | CONTRIBUTOR | null | Fixed issue with parsing dataset labels. | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2529/comments | https://api.github.com/repos/huggingface/datasets/issues/2529/events | https://github.com/huggingface/datasets/pull/2529 | 926,378,812 | MDExOlB1bGxSZXF1ZXN0Njc0NjkxNjA5 | 2,529 | Add summarization template | {
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"> 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 collecte... | 1,624,291,711,000 | 1,624,458,131,000 | 1,624,455,010,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2528/comments | https://api.github.com/repos/huggingface/datasets/issues/2528/events | https://github.com/huggingface/datasets/issues/2528 | 926,314,656 | MDU6SXNzdWU5MjYzMTQ2NTY= | 2,528 | Logging cannot be set to NOTSET similar to transformers | {
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"Hi @joshzwiebel, thanks for reporting. We are going to align with `transformers`."
] | 1,624,287,894,000 | 1,624,545,767,000 | 1,624,545,767,000 | NONE | null | ## 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2527/comments | https://api.github.com/repos/huggingface/datasets/issues/2527/events | https://github.com/huggingface/datasets/pull/2527 | 926,031,525 | MDExOlB1bGxSZXF1ZXN0Njc0MzkzNjQ5 | 2,527 | Replace bad `n>1M` size tag | {
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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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2526/comments | https://api.github.com/repos/huggingface/datasets/issues/2526/events | https://github.com/huggingface/datasets/issues/2526 | 925,929,228 | MDU6SXNzdWU5MjU5MjkyMjg= | 2,526 | Add COCO datasets | {
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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.... | 1,624,261,712,000 | 1,640,007,218,000 | null | CONTRIBUTOR | null | ## 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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Note: this affects only TESTS_REQUIRE so I guess only developers not end users. | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2524/comments | https://api.github.com/repos/huggingface/datasets/issues/2524/events | https://github.com/huggingface/datasets/pull/2524 | 925,610,934 | MDExOlB1bGxSZXF1ZXN0Njc0MDQzNzk1 | 2,524 | Raise FileNotFoundError in WindowsFileLock | {
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"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 ... | 1,624,199,111,000 | 1,624,874,182,000 | 1,624,870,059,000 | CONTRIBUTOR | null | Closes #2443 | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2523/comments | https://api.github.com/repos/huggingface/datasets/issues/2523/events | https://github.com/huggingface/datasets/issues/2523 | 925,421,008 | MDU6SXNzdWU5MjU0MjEwMDg= | 2,523 | Fr | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2522/comments | https://api.github.com/repos/huggingface/datasets/issues/2522/events | https://github.com/huggingface/datasets/issues/2522 | 925,334,379 | MDU6SXNzdWU5MjUzMzQzNzk= | 2,522 | Documentation Mistakes in Dataset: emotion | {
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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 ... | 1,624,086,537,000 | 1,643,109,239,000 | null | NONE | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2521/comments | https://api.github.com/repos/huggingface/datasets/issues/2521/events | https://github.com/huggingface/datasets/pull/2521 | 925,030,685 | MDExOlB1bGxSZXF1ZXN0NjczNTgxNzQ4 | 2,521 | Insert text classification template for Emotion dataset | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2520/comments | https://api.github.com/repos/huggingface/datasets/issues/2520/events | https://github.com/huggingface/datasets/issues/2520 | 925,015,004 | MDU6SXNzdWU5MjUwMTUwMDQ= | 2,520 | Datasets with tricky task templates | {
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] | open | false | null | [] | null | [] | 1,624,030,437,000 | 1,624,031,186,000 | null | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2519/comments | https://api.github.com/repos/huggingface/datasets/issues/2519/events | https://github.com/huggingface/datasets/pull/2519 | 924,903,240 | MDExOlB1bGxSZXF1ZXN0NjczNDcyMzYy | 2,519 | Improve performance of pandas arrow extractor | {
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"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 a... | 1,624,022,681,000 | 1,624,266,366,000 | 1,624,266,366,000 | MEMBER | null | While reviewing PR #2505, I noticed that pandas arrow extractor could be refactored to be faster. | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2518/comments | https://api.github.com/repos/huggingface/datasets/issues/2518/events | https://github.com/huggingface/datasets/pull/2518 | 924,654,100 | MDExOlB1bGxSZXF1ZXN0NjczMjU5Nzg1 | 2,518 | Add task templates for tydiqa and xquad | {
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"Just tested TydiQA and it works fine :)"
] | 1,624,003,594,000 | 1,624,028,477,000 | 1,624,027,833,000 | MEMBER | null | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2517/comments | https://api.github.com/repos/huggingface/datasets/issues/2517/events | https://github.com/huggingface/datasets/pull/2517 | 924,643,345 | MDExOlB1bGxSZXF1ZXN0NjczMjUwODk1 | 2,517 | Fix typo in MatthewsCorrelation class name | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2516/comments | https://api.github.com/repos/huggingface/datasets/issues/2516/events | https://github.com/huggingface/datasets/issues/2516 | 924,597,470 | MDU6SXNzdWU5MjQ1OTc0NzA= | 2,516 | datasets.map pickle issue resulting in invalid mapping function | {
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"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 ... | 1,623,998,846,000 | 1,624,456,069,000 | null | NONE | null | 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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https://api.github.com/repos/huggingface/datasets/issues/2515 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2515/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2515/comments | https://api.github.com/repos/huggingface/datasets/issues/2515/events | https://github.com/huggingface/datasets/pull/2515 | 924,435,447 | MDExOlB1bGxSZXF1ZXN0NjczMDc3NTIx | 2,515 | CRD3 dataset card | {
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} | [] | closed | false | null | [] | null | [] | 1,623,975,847,000 | 1,624,270,724,000 | 1,624,270,724,000 | CONTRIBUTOR | null | This PR adds additional information to the CRD3 dataset card. | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2514/comments | https://api.github.com/repos/huggingface/datasets/issues/2514/events | https://github.com/huggingface/datasets/issues/2514 | 924,417,172 | MDU6SXNzdWU5MjQ0MTcxNzI= | 2,514 | Can datasets remove duplicated rows? | {
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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 b... | 1,623,972,938,000 | 1,638,434,361,000 | null | NONE | null | **Is your feature request related to a problem? Please describe.**
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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2513/comments | https://api.github.com/repos/huggingface/datasets/issues/2513/events | https://github.com/huggingface/datasets/issues/2513 | 924,174,413 | MDU6SXNzdWU5MjQxNzQ0MTM= | 2,513 | Corelation should be Correlation | {
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"Hi @colbym-MM, thanks for reporting. We are fixing it."
] | 1,623,950,928,000 | 1,624,005,835,000 | 1,624,005,835,000 | NONE | null | https://github.com/huggingface/datasets/blob/0e87e1d053220e8ecddfa679bcd89a4c7bc5af62/metrics/matthews_correlation/matthews_correlation.py#L66 | {
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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2512/comments | https://api.github.com/repos/huggingface/datasets/issues/2512/events | https://github.com/huggingface/datasets/issues/2512 | 924,069,353 | MDU6SXNzdWU5MjQwNjkzNTM= | 2,512 | seqeval metric does not work with a recent version of sklearn: classification_report() got an unexpected keyword argument 'output_dict' | {
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"Sorry, I was using an old version of sequeval"
] | 1,623,944,162,000 | 1,623,944,767,000 | 1,623,944,767,000 | NONE | null | ## 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2511/comments | https://api.github.com/repos/huggingface/datasets/issues/2511/events | https://github.com/huggingface/datasets/issues/2511 | 923,762,133 | MDU6SXNzdWU5MjM3NjIxMzM= | 2,511 | Add C4 | {
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"Update on this: I'm computing the checksums of the data files. It will be available soon",
"Added in #2575 :)"
] | 1,623,925,864,000 | 1,625,488,618,000 | 1,625,488,617,000 | MEMBER | null | ## Adding a Dataset
- **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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2510/comments | https://api.github.com/repos/huggingface/datasets/issues/2510/events | https://github.com/huggingface/datasets/pull/2510 | 923,735,485 | MDExOlB1bGxSZXF1ZXN0NjcyNDY3MzY3 | 2,510 | 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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2509/comments | https://api.github.com/repos/huggingface/datasets/issues/2509/events | https://github.com/huggingface/datasets/pull/2509 | 922,846,035 | MDExOlB1bGxSZXF1ZXN0NjcxNjcyMzU5 | 2,509 | Fix fingerprint when moving cache dir | {
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"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 :)"
] | 1,623,861,909,000 | 1,624,287,904,000 | 1,624,287,903,000 | MEMBER | null | The fingerprint of a dataset changes if the cache directory is moved.
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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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/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2508/comments | https://api.github.com/repos/huggingface/datasets/issues/2508/events | https://github.com/huggingface/datasets/issues/2508 | 921,863,173 | MDU6SXNzdWU5MjE4NjMxNzM= | 2,508 | Load Image Classification Dataset from Local | {
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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... | 1,623,797,013,000 | 1,646,152,184,000 | 1,646,152,184,000 | NONE | null | **Is your feature request related to a problem? Please describe.**
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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