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https://api.github.com/repos/huggingface/datasets/issues/4963 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4963/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4963/comments | https://api.github.com/repos/huggingface/datasets/issues/4963/events | https://github.com/huggingface/datasets/issues/4963 | 1,368,201,188 | I_kwDODunzps5RjRfk | 4,963 | Dataset without script does not support regular JSON data file | {
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"Hi @julien-c,\r\n\r\nOut of the box, we only support JSON lines (NDJSON) data files, but your data file is a regular JSON file. The reason is we use `pyarrow.json.read_json` and this only supports line-delimited JSON. "
] | 2022-09-09T18:45:33Z | 2022-09-20T15:40:07Z | 2022-09-20T15:40:07Z | MEMBER | null | null | null | ### Link
https://huggingface.co/datasets/julien-c/label-studio-my-dogs
### Description
<img width="1115" alt="image" src="https://user-images.githubusercontent.com/326577/189422048-7e9c390f-bea7-4521-a232-43f049ccbd1f.png">
### Owner
Yes | {
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https://api.github.com/repos/huggingface/datasets/issues/5492 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5492/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5492/comments | https://api.github.com/repos/huggingface/datasets/issues/5492/events | https://github.com/huggingface/datasets/issues/5492 | 1,566,604,216 | I_kwDODunzps5dYHu4 | 5,492 | Push_to_hub in a pull request | {
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"default": true... | closed | false | {
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"Assigned to myself and will get to it in the next week, but if someone finds this issue annoying and wants to submit a PR before I do, just ping me here and I'll reassign :). ",
"I would like to be assigned to this issue, @nateraw . #self-assign"
] | 2023-02-01T18:32:14Z | 2023-10-16T13:30:48Z | 2023-10-16T13:30:48Z | MEMBER | null | null | null | Right now `ds.push_to_hub()` can push a dataset on `main` or on a new branch with `branch=`, but there is no way to open a pull request. Even passing `branch=refs/pr/x` doesn't seem to work: it tries to create a branch with that name
cc @nateraw
It should be possible to tweak the use of `huggingface_hub` in `push_to_hub` to make it open a PR or push to an existing PR | {
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https://api.github.com/repos/huggingface/datasets/issues/5577 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5577/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5577/comments | https://api.github.com/repos/huggingface/datasets/issues/5577/events | https://github.com/huggingface/datasets/issues/5577 | 1,598,587,665 | I_kwDODunzps5fSIMR | 5,577 | Cannot load `the_pile_openwebtext2` | {
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"Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n"
] | 2023-02-24T13:01:48Z | 2023-02-24T14:01:09Z | 2023-02-24T14:01:09Z | NONE | null | null | null | ### Describe the bug
I met the same bug mentioned in #3053 which is never fixed. Because several `reddit_scores` are larger than `int8` even `int16`. https://huggingface.co/datasets/the_pile_openwebtext2/blob/main/the_pile_openwebtext2.py#L62
### Steps to reproduce the bug
```python3
from datasets import load_dataset
dataset = load_dataset("the_pile_openwebtext2")
```
### Expected behavior
load as normal.
### Environment info
- `datasets` version: 2.10.0
- Platform: Linux-5.4.143.bsk.7-amd64-x86_64-with-glibc2.31
- Python version: 3.9.2
- PyArrow version: 11.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/4625 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4625/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4625/comments | https://api.github.com/repos/huggingface/datasets/issues/4625/events | https://github.com/huggingface/datasets/pull/4625 | 1,293,163,744 | PR_kwDODunzps46zELz | 4,625 | Unpack `dl_manager.iter_files` to allow parallization | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Cool thanks ! Yup it sounds like the right solution.\r\n\r\nIt looks like `_generate_tables` needs to be updated as well to fix the CI"
] | 2022-07-04T13:16:58Z | 2022-07-05T11:11:54Z | 2022-07-05T11:00:48Z | CONTRIBUTOR | null | 0 | {
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} | Iterate over data files outside `dl_manager.iter_files` to allow parallelization in streaming mode.
(The issue reported [here](https://discuss.huggingface.co/t/dataset-only-have-n-shard-1-when-has-multiple-shards-in-repo/19887))
PS: Another option would be to override `FilesIterable.__getitem__` to make it indexable and check for that type in `_shard_kwargs` and `n_shards,` but IMO this solution adds too much unnecessary complexity. | {
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https://api.github.com/repos/huggingface/datasets/issues/6016 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6016/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6016/comments | https://api.github.com/repos/huggingface/datasets/issues/6016/events | https://github.com/huggingface/datasets/pull/6016 | 1,798,968,033 | PR_kwDODunzps5VNEvn | 6,016 | Dataset string representation enhancement | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.",
"It we could have something similar to Polars, that would be great.\r\n\r\nThis is what Polars outputs: \r\n* `__repr__`/`__str__` :\r\n```\r\nshape... | 2023-07-11T13:38:25Z | 2023-07-16T10:26:18Z | null | NONE | null | 0 | {
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not sure if this is the right way to go about it, I will wait for your feedback | {
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https://api.github.com/repos/huggingface/datasets/issues/2734 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2734/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2734/comments | https://api.github.com/repos/huggingface/datasets/issues/2734/events | https://github.com/huggingface/datasets/pull/2734 | 956,844,874 | MDExOlB1bGxSZXF1ZXN0NzAwMzc4NjI4 | 2,734 | Update BibTeX entry | {
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"@SBrandeis thanks for the review, I applied your suggested changes, but CI is failing now not sure about the error.",
"Many thanks @Nilanshrajput !\r\nThe failing tests on CI are not related to your changes, merging master on your branch should fix them :)\r\nIf you're interested in what causes the CI to fail,... | 2020-12-12T13:46:48Z | 2020-12-17T15:34:50Z | 2020-12-17T15:34:50Z | CONTRIBUTOR | null | 0 | {
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The NQ-Open task is currently being used to evaluate submissions to the EfficientQA competition, which is part of the NeurIPS 2020 competition track. | {
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https://api.github.com/repos/huggingface/datasets/issues/469 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/469/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/469/comments | https://api.github.com/repos/huggingface/datasets/issues/469/events | https://github.com/huggingface/datasets/issues/469 | 671,876,963 | MDU6SXNzdWU2NzE4NzY5NjM= | 469 | invalid data type 'str' at _convert_outputs in arrow_dataset.py | {
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} | [] | closed | false | null | [] | null | [
"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the... | 2020-08-03T07:48:29Z | 2023-07-20T15:54:17Z | 2023-07-20T15:54:17Z | NONE | null | null | null | I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label.
Ex: Data
Text , Label #Column Header
I'm facing an Network issue, 1
I forgot my password, 2
Error StackTrace:
File "C:\**\transformers\trainer.py", line 492, in train
for step, inputs in enumerate(epoch_iterator):
File "C:\**\tqdm\std.py", line 1104, in __iter__
for obj in iterable:
File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__
data = self._next_data()
File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__
output_all_columns=self._output_all_columns,
File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem
outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns
File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type 'str'
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https://api.github.com/repos/huggingface/datasets/issues/48 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/48/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/48/comments | https://api.github.com/repos/huggingface/datasets/issues/48/events | https://github.com/huggingface/datasets/pull/48 | 612,504,687 | MDExOlB1bGxSZXF1ZXN0NDEzNDM2MTgz | 48 | [Command Convert] remove tensorflow import | {
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https://api.github.com/repos/huggingface/datasets/issues/350 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/350/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/350/comments | https://api.github.com/repos/huggingface/datasets/issues/350/events | https://github.com/huggingface/datasets/pull/350 | 652,398,691 | MDExOlB1bGxSZXF1ZXN0NDQ1NDczODYz | 350 | add from_pandas and from_dict | {
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} | I added two new methods to the `Dataset` class:
- `from_pandas()` to create a dataset from a pandas dataframe
- `from_dict()` to create a dataset from a dictionary (keys = columns)
It uses the `pa.Table.from_pandas` and `pa.Table.from_pydict` funcitons to do so.
It is also possible to specify the features types via `features=...` if there are ambiguities (null/nan values), otherwise the arrow schema is infered from the data automatically by pyarrow.
One question that I have right now:
+ Should we also add a `save()` method that would write the dataset on the disk ? Right now if we create a `Dataset` using those two new methods, the data are kept in RAM. Then to reload it we can call the `from_file()` method. | {
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https://api.github.com/repos/huggingface/datasets/issues/4417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4417/comments | https://api.github.com/repos/huggingface/datasets/issues/4417/events | https://github.com/huggingface/datasets/issues/4417 | 1,251,933,091 | I_kwDODunzps5Knvuj | 4,417 | how to convert a dict generator into a huggingface dataset. | {
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... | null | [
"@albertvillanova @lhoestq , could you please help me on this issue. ",
"Hi ! As mentioned on the [forum](https://discuss.huggingface.co/t/how-to-wrap-a-generator-with-hf-dataset/18464), the simplest for now would be to define a [dataset script](https://huggingface.co/docs/datasets/dataset_script) which can conta... | 2022-05-29T16:28:27Z | 2022-09-16T14:44:19Z | 2022-09-16T14:44:19Z | NONE | null | null | null | ### Link
_No response_
### Description
Hey there, I have used seqio to get a well distributed mixture of samples from multiple dataset. However the resultant output from seqio is a python generator dict, which I cannot produce back into huggingface dataset.
The generator contains all the samples needed for training the model but I cannot convert it into a huggingface dataset.
The code looks like this:
```
for ex in seqio_data:
print(ex[“text”])
```
I need to convert the seqio_data (generator) into huggingface dataset.
the complete seqio code goes here:
```
import functools
import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils
TaskRegistry = seqio.TaskRegistry
def gen_dataset(split, shuffle=False, seed=None, column="text", dataset_params=None):
dataset = load_dataset(**dataset_params)
if shuffle:
if seed:
dataset = dataset.shuffle(seed=seed)
else:
dataset = dataset.shuffle()
while True:
for item in dataset[str(split)]:
yield item[column]
def dataset_fn(split, shuffle_files, seed=None, dataset_params=None):
return tf.data.Dataset.from_generator(
functools.partial(gen_dataset, split, shuffle_files, seed, dataset_params=dataset_params),
output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name)
)
@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
"""Assign the value from the dataset to target_key in key_map"""
return {**key_map, target_key: x}
dataset_name = 'oscar-corpus/OSCAR-2109'
subset= 'mr'
dataset_params = {"path": dataset_name, "language":subset, "use_auth_token":True}
dataset_shapes = None
TaskRegistry.add(
"oscar_marathi_corpus",
source=seqio.FunctionDataSource(
dataset_fn=functools.partial(dataset_fn, dataset_params=dataset_params),
splits=("train", "validation"),
caching_permitted=False,
num_input_examples=dataset_shapes,
),
preprocessors=[
functools.partial(
target_to_key, key_map={
"targets": None,
}, target_key="targets")],
output_features={"targets": seqio.Feature(vocabulary=seqio.PassThroughVocabulary, add_eos=False, dtype=tf.string, rank=0)},
metric_fns=[]
)
dataset = seqio.get_mixture_or_task("oscar_marathi_corpus").get_dataset(
sequence_length=None,
split="train",
shuffle=True,
num_epochs=1,
shard_info=seqio.ShardInfo(index=0, num_shards=10),
use_cached=False,
seed=42
)
for _, ex in zip(range(5), dataset):
print(ex['targets'].numpy().decode())
```
### Owner
_No response_ | {
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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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https://api.github.com/repos/huggingface/datasets/issues/6464 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6464/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6464/comments | https://api.github.com/repos/huggingface/datasets/issues/6464/events | https://github.com/huggingface/datasets/pull/6464 | 2,020,860,462 | PR_kwDODunzps5g5djo | 6,464 | Add concurrent loading of shards to datasets.load_from_disk | {
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"If we use multithreading no need to ask for `num_proc`. And maybe we the same numbers of threads as tqdm by default (IIRC it's `max(32, cpu_count() + 4)`) - you can even use `tqdm.contrib.concurrent.thread_map` directly to simplify the code\r\n\r\nAlso you can ignore the `IN_MEMORY_MAX_SIZE` config for this. This ... | 2023-12-01T13:13:53Z | 2023-12-07T12:47:02Z | null | NONE | null | 0 | {
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} | In some file systems (like luster), memory mapping arrow files takes time. This can be accelerated by performing the mmap in parallel on processes or threads.
- Threads seem to be faster than processes when gathering the list of tables from the workers (see https://github.com/huggingface/datasets/issues/2252).
- I'm not sure if using threads would respect the `IN_MEMORY_MAX_SIZE` config.
- I'm not sure if we need to expose num_proc from `BaseReader.read` to `DatasetBuilder.as_dataset`. Since ` DatasetBuilder.as_dataset` is used in many places beside `load_dataset`.
### Tests on luster file system (on a shared partial node):
Loading 1231 shards of ~2GBs.
The files were pre-loaded in another process before the script runs (couldn't get a fresh node).
```python
import logging
from time import perf_counter
import datasets
logger = datasets.logging.get_logger(__name__)
datasets.logging.set_verbosity_info()
logging.basicConfig(level=logging.DEBUG, format="%(message)s")
class catchtime:
# context to measure loading time: https://stackoverflow.com/questions/33987060/python-context-manager-that-measures-time
def __init__(self, debug_print="Time", logger=logger):
self.debug_print = debug_print
self.logger = logger
def __enter__(self):
self.start = perf_counter()
return self
def __exit__(self, type, value, traceback):
self.time = perf_counter() - self.start
readout = f"{self.debug_print}: {self.time:.3f} seconds"
self.logger.info(readout)
dataset_path=""
# warmup
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=16
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=16)
# num_proc=32
with catchtime("Loading in parallel", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=32)
# num_proc=1
with catchtime("Loading in conseq", logger=logger):
ds = datasets.load_from_disk(dataset_path,num_proc=1)
```
#### Run 1
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:28<00:00, 13.96shards/s]
Loading in parallel: 88.690 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:48<00:00, 11.31shards/s]
Loading in parallel: 109.339 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:06<00:00, 18.56shards/s]
Loading in parallel: 66.931 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:09<00:00, 3.98shards/s]
Loading in conseq: 309.792 seconds
```
#### Run 2
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:38<00:00, 12.53shards/s]
Loading in parallel: 98.831 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:01<00:00, 10.16shards/s]
Loading in parallel: 121.669 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:07<00:00, 18.18shards/s]
Loading in parallel: 68.192 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:19<00:00, 3.86shards/s]
Loading in conseq: 319.759 seconds
```
#### Run 3
```
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [01:36<00:00, 12.74shards/s]
Loading in parallel: 96.936 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 16 threads: 100%|██████████| 1231/1231 [02:00<00:00, 10.24shards/s]
Loading in parallel: 120.761 seconds
open file: .../dataset_dict.json
Loading the dataset from disk using 32 threads: 100%|██████████| 1231/1231 [01:08<00:00, 18.04shards/s]
Loading in parallel: 68.666 seconds
open file: .../dataset_dict.json
Loading the dataset from disk: 100%|██████████| 1231/1231 [05:35<00:00, 3.67shards/s]
Loading in conseq: 335.777 seconds
```
fix #2252
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https://api.github.com/repos/huggingface/datasets/issues/3580 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3580/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3580/comments | https://api.github.com/repos/huggingface/datasets/issues/3580/events | https://github.com/huggingface/datasets/issues/3580 | 1,104,663,242 | I_kwDODunzps5B19LK | 3,580 | Bug in wiki bio load | {
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"+1, here's the error I got: \r\n\r\n```\r\n>>> from datasets import load_dataset\r\n>>>\r\n>>> load_dataset(\"wiki_bio\")\r\nDownloading: 7.58kB [00:00, 4.42MB/s]\r\nDownloading: 2.71kB [00:00, 1.30MB/s]\r\nUsing custom data configuration default\r\nDownloading and preparing dataset wiki_bio/default (download: 318... | 2022-01-15T10:04:33Z | 2022-01-31T08:38:09Z | 2022-01-31T08:38:09Z | NONE | null | null | null |
wiki_bio is failing to load because of a failing drive link . Can someone fix this ?
a | {
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https://api.github.com/repos/huggingface/datasets/issues/2073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2073/comments | https://api.github.com/repos/huggingface/datasets/issues/2073/events | https://github.com/huggingface/datasets/pull/2073 | 834,192,501 | MDExOlB1bGxSZXF1ZXN0NTk1MDYyMzQ2 | 2,073 | Fixes check of TF_AVAILABLE and TORCH_AVAILABLE | {
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} | # What is this PR doing
This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068 | {
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https://api.github.com/repos/huggingface/datasets/issues/2574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2574/comments | https://api.github.com/repos/huggingface/datasets/issues/2574/events | https://github.com/huggingface/datasets/pull/2574 | 934,632,378 | MDExOlB1bGxSZXF1ZXN0NjgxNjczMzYy | 2,574 | Add streaming in load a dataset docs | {
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} | Mention dataset streaming on the "loading a dataset" page of the documentation | {
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https://api.github.com/repos/huggingface/datasets/issues/789 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/789/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/789/comments | https://api.github.com/repos/huggingface/datasets/issues/789/events | https://github.com/huggingface/datasets/pull/789 | 734,237,839 | MDExOlB1bGxSZXF1ZXN0NTEzODM1MzE0 | 789 | dataset(ncslgr): add initial loading script | {
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"Hi @AmitMY, sorry for leaving you hanging for a minute :) \r\n\r\nWe've developed a new pipeline for adding datasets with a few extra steps, including adding a dataset card. You can find the full process [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)\r\n\r\nWould you be up for addin... | 2020-11-02T06:50:10Z | 2020-12-01T13:41:37Z | 2020-12-01T13:41:36Z | CONTRIBUTOR | null | 0 | {
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} | Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html
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https://api.github.com/repos/huggingface/datasets/issues/4363 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4363/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4363/comments | https://api.github.com/repos/huggingface/datasets/issues/4363/events | https://github.com/huggingface/datasets/issues/4363 | 1,238,897,652 | I_kwDODunzps5J2BP0 | 4,363 | The dataset preview is not available for this split. | {
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"Hi! A dataset has to be streamable to work with the viewer. I did a quick test, and yours is, so this might be a bug in the viewer. cc @severo \r\n",
"Looking at it. The message is now:\r\n\r\n```\r\nMessage: cannot cache function '__shear_dense': no locator available for file '/src/services/worker/.venv/... | 2022-05-17T16:34:43Z | 2022-06-08T12:32:10Z | 2022-06-08T09:26:56Z | NONE | null | null | null | I have uploaded the corpus developed by our lab in the speech domain to huggingface [datasets](https://huggingface.co/datasets/Roh/ryanspeech). You can read about the companion paper accepted in interspeech 2021 [here](https://arxiv.org/abs/2106.08468). The dataset works fine but I can't make the dataset preview work. It gives me the following error that I don't understand. Can you help me to begin debugging it?
```
Status code: 400
Exception: AttributeError
Message: 'NoneType' object has no attribute 'split'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4306 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4306/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4306/comments | https://api.github.com/repos/huggingface/datasets/issues/4306/events | https://github.com/huggingface/datasets/issues/4306 | 1,231,137,204 | I_kwDODunzps5JYam0 | 4,306 | `load_dataset` does not work with certain filename. | {
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"Never mind. It is because of the caching of datasets..."
] | 2022-05-10T13:14:04Z | 2022-05-10T18:58:36Z | 2022-05-10T18:58:09Z | NONE | null | null | null | ## Describe the bug
This is a weird bug that took me some time to find out.
I have a JSON dataset that I want to load with `load_dataset` like this:
```
data_files = dict(train="train.json.zip", val="val.json.zip")
dataset = load_dataset("json", data_files=data_files, field="data")
```
## Expected results
No error.
## Actual results
The val file is loaded as expected, but the train file throws JSON decoding error:
```
╭──────────────────────────── Traceback (most recent call last) ────────────────────────────╮
│ <ipython-input-74-97947e92c100>:5 in <module> │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/load.py:1687 in │
│ load_dataset │
│ │
│ 1684 │ try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES │
│ 1685 │ │
│ 1686 │ # Download and prepare data │
│ ❱ 1687 │ builder_instance.download_and_prepare( │
│ 1688 │ │ download_config=download_config, │
│ 1689 │ │ download_mode=download_mode, │
│ 1690 │ │ ignore_verifications=ignore_verifications, │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:605 in │
│ download_and_prepare │
│ │
│ 602 │ │ │ │ │ │ except ConnectionError: │
│ 603 │ │ │ │ │ │ │ logger.warning("HF google storage unreachable. Downloa │
│ 604 │ │ │ │ │ if not downloaded_from_gcs: │
│ ❱ 605 │ │ │ │ │ │ self._download_and_prepare( │
│ 606 │ │ │ │ │ │ │ dl_manager=dl_manager, verify_infos=verify_infos, **do │
│ 607 │ │ │ │ │ │ ) │
│ 608 │ │ │ │ │ # Sync info │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:694 in │
│ _download_and_prepare │
│ │
│ 691 │ │ │ │
│ 692 │ │ │ try: │
│ 693 │ │ │ │ # Prepare split will record examples associated to the split │
│ ❱ 694 │ │ │ │ self._prepare_split(split_generator, **prepare_split_kwargs) │
│ 695 │ │ │ except OSError as e: │
│ 696 │ │ │ │ raise OSError( │
│ 697 │ │ │ │ │ "Cannot find data file. " │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/builder.py:1151 in │
│ _prepare_split │
│ │
│ 1148 │ │ │
│ 1149 │ │ generator = self._generate_tables(**split_generator.gen_kwargs) │
│ 1150 │ │ with ArrowWriter(features=self.info.features, path=fpath) as writer: │
│ ❱ 1151 │ │ │ for key, table in logging.tqdm( │
│ 1152 │ │ │ │ generator, unit=" tables", leave=False, disable=True # not loggin │
│ 1153 │ │ │ ): │
│ 1154 │ │ │ │ writer.write_table(table) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/notebook.py:257 in │
│ __iter__ │
│ │
│ 254 │ │
│ 255 │ def __iter__(self): │
│ 256 │ │ try: │
│ ❱ 257 │ │ │ for obj in super(tqdm_notebook, self).__iter__(): │
│ 258 │ │ │ │ # return super(tqdm...) will not catch exception │
│ 259 │ │ │ │ yield obj │
│ 260 │ │ # NB: except ... [ as ...] breaks IPython async KeyboardInterrupt │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/tqdm/std.py:1183 in │
│ __iter__ │
│ │
│ 1180 │ │ # If the bar is disabled, then just walk the iterable │
│ 1181 │ │ # (note: keep this check outside the loop for performance) │
│ 1182 │ │ if self.disable: │
│ ❱ 1183 │ │ │ for obj in iterable: │
│ 1184 │ │ │ │ yield obj │
│ 1185 │ │ │ return │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/site-packages/datasets/packaged_modules/j │
│ son/json.py:90 in _generate_tables │
│ │
│ 87 │ │ │ # If the file is one json object and if we need to look at the list of │
│ 88 │ │ │ if self.config.field is not None: │
│ 89 │ │ │ │ with open(file, encoding="utf-8") as f: │
│ ❱ 90 │ │ │ │ │ dataset = json.load(f) │
│ 91 │ │ │ │ │
│ 92 │ │ │ │ # We keep only the field we are interested in │
│ 93 │ │ │ │ dataset = dataset[self.config.field] │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:293 in load │
│ │
│ 290 │ To use a custom ``JSONDecoder`` subclass, specify it with the ``cls`` │
│ 291 │ kwarg; otherwise ``JSONDecoder`` is used. │
│ 292 │ """ │
│ ❱ 293 │ return loads(fp.read(), │
│ 294 │ │ cls=cls, object_hook=object_hook, │
│ 295 │ │ parse_float=parse_float, parse_int=parse_int, │
│ 296 │ │ parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/__init__.py:357 in loads │
│ │
│ 354 │ if (cls is None and object_hook is None and │
│ 355 │ │ │ parse_int is None and parse_float is None and │
│ 356 │ │ │ parse_constant is None and object_pairs_hook is None and not kw): │
│ ❱ 357 │ │ return _default_decoder.decode(s) │
│ 358 │ if cls is None: │
│ 359 │ │ cls = JSONDecoder │
│ 360 │ if object_hook is not None: │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:337 in decode │
│ │
│ 334 │ │ containing a JSON document). │
│ 335 │ │ │
│ 336 │ │ """ │
│ ❱ 337 │ │ obj, end = self.raw_decode(s, idx=_w(s, 0).end()) │
│ 338 │ │ end = _w(s, end).end() │
│ 339 │ │ if end != len(s): │
│ 340 │ │ │ raise JSONDecodeError("Extra data", s, end) │
│ │
│ /home/tiankang/software/anaconda3/lib/python3.8/json/decoder.py:353 in raw_decode │
│ │
│ 350 │ │ │
│ 351 │ │ """ │
│ 352 │ │ try: │
│ ❱ 353 │ │ │ obj, end = self.scan_once(s, idx) │
│ 354 │ │ except StopIteration as err: │
│ 355 │ │ │ raise JSONDecodeError("Expecting value", s, err.value) from None │
│ 356 │ │ return obj, end │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
JSONDecodeError: Unterminated string starting at: line 85 column 20 (char 60051)
```
However, when I rename the `train.json.zip` to other names (like `training.json.zip`, or even to `train.json`), everything works fine; when I unzip the file to `train.json`, it works as well.
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-4.4.0-131-generic-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2163 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2163/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2163/comments | https://api.github.com/repos/huggingface/datasets/issues/2163/events | https://github.com/huggingface/datasets/pull/2163 | 849,669,366 | MDExOlB1bGxSZXF1ZXN0NjA4Mzk0NDMz | 2,163 | Concat only unique fields in DatasetInfo.from_merge | {
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"Hi @mariosasko,\r\nJust came across this PR and I was wondering if we can use\r\n`description = \"\\n\\n\".join(OrderedDict.fromkeys([info.description for info in dataset_infos]))`\r\n\r\nThis will obviate the need for `unique` and is almost as fast as `set`. We could have used `dict` inplace of `OrderedDict` but ... | 2021-04-03T14:31:30Z | 2021-04-06T14:40:00Z | 2021-04-06T14:39:59Z | CONTRIBUTOR | null | 0 | {
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} | I thought someone from the community with less experience would be interested in fixing this issue, but that wasn't the case.
Fixes #2103 | {
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https://api.github.com/repos/huggingface/datasets/issues/2185 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2185/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2185/comments | https://api.github.com/repos/huggingface/datasets/issues/2185/events | https://github.com/huggingface/datasets/issues/2185 | 852,684,395 | MDU6SXNzdWU4NTI2ODQzOTU= | 2,185 | .map() and distributed training | {
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"Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.\r\n\r\nAlso, multiprocessing the map function seem... | 2021-04-07T18:22:14Z | 2021-10-23T07:11:15Z | 2021-04-09T15:38:31Z | MEMBER | null | null | null | Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=True,
)
```
I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split).
When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.
Everything so far was done by launching a **single process script**.
I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files.
I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.
**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.
- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case)
- I am using 1.5.0 version of datasets if that matters. | {
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https://api.github.com/repos/huggingface/datasets/issues/1514 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1514/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1514/comments | https://api.github.com/repos/huggingface/datasets/issues/1514/events | https://github.com/huggingface/datasets/issues/1514 | 764,017,148 | MDU6SXNzdWU3NjQwMTcxNDg= | 1,514 | how to get all the options of a property in datasets | {
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"In a dataset, labels correspond to the `ClassLabel` feature that has the `names` property that returns string represenation of the integer classes (or `num_classes` to get the number of different classes).",
"I think the `features` attribute of the dataset object is what you are looking for:\r\n```\r\n>>> datase... | 2020-12-12T16:24:08Z | 2022-05-25T16:27:29Z | 2022-05-25T16:27:29Z | CONTRIBUTOR | null | null | null | Hi
could you tell me how I can get all unique options of a property of dataset?
for instance in case of boolq, if the user wants to know which unique labels it has, is there a way to access unique labels without getting all training data lables and then forming a set i mean? thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/5574 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5574/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5574/comments | https://api.github.com/repos/huggingface/datasets/issues/5574/events | https://github.com/huggingface/datasets/issues/5574 | 1,598,104,691 | I_kwDODunzps5fQSRz | 5,574 | c4 dataset streaming fails with `FileNotFoundError` | {
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} | [] | closed | false | null | [] | null | [
"Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets ... | 2023-02-24T07:57:32Z | 2023-12-18T07:32:32Z | 2023-02-27T04:03:38Z | NONE | null | null | null | ### Describe the bug
Loading the `c4` dataset in streaming mode with `load_dataset("c4", "en", split="validation", streaming=True)` and then using it fails with a `FileNotFoundException`.
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("c4", "en", split="train", streaming=True)
next(iter(dataset))
```
causes a
```
FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-train.00000-of-01024.json.gz
```
I can download this file manually though e.g. by entering this URL in a browser.
There is an underlying HTTP 403 status code:
```
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/8ef8d75b0e045dec4aa5123a671b4564466b0707086a7ed1ba8721626dfffbc9?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-train.00000-of-01024.json.gz%3B+filename%3D%22c4-train.00000-of-01024.json.gz%22%3B&response-content-type=application/gzip&Expires=1677483770&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvOGVmOGQ3NWIwZTA0NWRlYzRhYTUxMjNhNjcxYjQ1NjQ0NjZiMDcwNzA4NmE3ZWQxYmE4NzIxNjI2ZGZmZmJjOT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzQ4Mzc3MH19fV19&Signature=yjL3UeY72cf2xpnvPvD68eAYOEe2qtaUJV55sB-jnPskBJEMwpMJcBZvg2~GqXZdM3O-GWV-Z3CI~d4u5VCb4YZ-HlmOjr3VBYkvox2EKiXnBIhjMecf2UVUPtxhTa9kBVlWjqu4qKzB9gKXZF2Cwpp5ctLzapEaT2nnqF84RAL-rsqMA3I~M8vWWfivQsbBK63hMfgZqqKMgdWM0iKMaItveDl0ufQ29azMFmsR7qd8V7sU2Z-F1fAeohS8HpN9OOnClW34yi~YJ2AbgZJJBXA~qsylfVA0Qp7Q~yX~q4P8JF1vmJ2BjkiSbGrj3bAXOGugpOVU5msI52DT88yMdA__&Key-Pair-Id=KVTP0A1DKRTAX')
```
### Expected behavior
This should retrieve the first example from the C4 validation set. This worked a few days ago but stopped working now.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.31
- Python version: 3.9.16
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
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https://api.github.com/repos/huggingface/datasets/issues/1718 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1718/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1718/comments | https://api.github.com/repos/huggingface/datasets/issues/1718/events | https://github.com/huggingface/datasets/issues/1718 | 783,474,753 | MDU6SXNzdWU3ODM0NzQ3NTM= | 1,718 | Possible cache miss in datasets | {
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"Thanks for reporting !\r\nI was able to reproduce thanks to your code and find the origin of the bug.\r\nThe cache was not reusing the same file because one object was not deterministic. It comes from a conversion from `set` to `list` in the `datasets.arrrow_dataset.transmit_format` function, where the resulting l... | 2021-01-11T15:37:31Z | 2022-06-29T14:54:42Z | 2021-01-26T02:47:59Z | NONE | null | null | null | Hi,
I am using the datasets package and even though I run the same data processing functions, datasets always recomputes the function instead of using cache.
I have attached an example script that for me reproduces the problem.
In the attached example the second map function always recomputes instead of loading from cache.
Is this a bug or am I doing something wrong?
Is there a way for fix this and avoid all the recomputation?
Thanks
Edit:
transformers==3.5.1
datasets==1.2.0
```
from datasets import load_dataset
from transformers import AutoTokenizer
datasets = load_dataset('wikitext', 'wikitext-103-raw-v1')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)
column_names = datasets["train"].column_names
text_column_name = "text" if "text" in column_names else column_names[0]
def tokenize_function(examples):
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=60,
remove_columns=[text_column_name],
load_from_cache_file=True,
)
max_seq_length = tokenizer.model_max_length
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {
k: sum(examples[k], []) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
total_length = (total_length // max_seq_length) * max_seq_length
# Split by chunks of max_len.
result = {
k: [t[i: i + max_seq_length]
for i in range(0, total_length, max_seq_length)]
for k, t in concatenated_examples.items()
}
return result
tokenized_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=60,
load_from_cache_file=True,
)
print(tokenized_datasets)
print('finished')
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/738 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/738/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/738/comments | https://api.github.com/repos/huggingface/datasets/issues/738/events | https://github.com/huggingface/datasets/pull/738 | 723,033,923 | MDExOlB1bGxSZXF1ZXN0NTA0NjkxNjM4 | 738 | Replace seqeval code with original classification_report for simplicity | {
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} | [] | closed | false | null | [] | null | [
"Hello,\r\n\r\nI ran https://github.com/huggingface/transformers/blob/master/examples/token-classification/run.sh\r\n\r\nAnd received this error:\r\n```\r\n100%|██████████| 407/407 [21:37<00:00, 3.44s/it]Traceback (most recent call last):\r\n File \"run_ner.py\", line 445, in <module>\r\n main()\r\n File \"ru... | 2020-10-16T08:51:45Z | 2021-01-21T16:07:15Z | 2020-10-19T10:31:12Z | CONTRIBUTOR | null | 0 | {
"diff_url": "https://github.com/huggingface/datasets/pull/738.diff",
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"merged_at": "2020-10-19T10:31:11Z",
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"url": "https://api.github.com/repos/huggingface/datasets/pulls/738"
} | Recently, the original seqeval has enabled us to get per type scores and overall scores as a dictionary.
This PR replaces the current code with the original function(`classification_report`) to simplify it.
Also, the original code has been updated to fix #352.
- Related issue: https://github.com/chakki-works/seqeval/pull/38
```python
from datasets import load_metric
metric = load_metric("seqeval")
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
metric.compute(predictions=y_pred, references=y_true)
# Output: {'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1636 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1636/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1636/comments | https://api.github.com/repos/huggingface/datasets/issues/1636/events | https://github.com/huggingface/datasets/issues/1636 | 774,574,378 | MDU6SXNzdWU3NzQ1NzQzNzg= | 1,636 | winogrande cannot be dowloaded | {
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"I have same issue for other datasets (`myanmar_news` in my case).\r\n\r\nA version of `datasets` runs correctly on my local machine (**without GPU**) which looking for the dataset at \r\n```\r\nhttps://raw.githubusercontent.com/huggingface/datasets/master/datasets/myanmar_news/myanmar_news.py\r\n```\r\n\r\nMeanwhi... | 2020-12-24T22:28:22Z | 2022-10-05T12:35:44Z | 2022-10-05T12:35:44Z | NONE | null | null | null | Hi,
I am getting this error when trying to run the codes on the cloud. Thank you for any suggestion and help on this @lhoestq
```
File "./finetune_trainer.py", line 318, in <module>
main()
File "./finetune_trainer.py", line 148, in main
for task in data_args.tasks]
File "./finetune_trainer.py", line 148, in <listcomp>
for task in data_args.tasks]
File "/workdir/seq2seq/data/tasks.py", line 65, in get_dataset
dataset = self.load_dataset(split=split)
File "/workdir/seq2seq/data/tasks.py", line 466, in load_dataset
return datasets.load_dataset('winogrande', 'winogrande_l', split=split)
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/winogrande/winogrande.py
yo/0 I1224 14:17:46.419031 31226 main shadow.py:122 > Traceback (most recent call last):
File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 260, in <module>
main()
File "/usr/local/lib/python3.6/dist-packages/torch/distributed/launch.py", line 256, in main
cmd=cmd)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/4501 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4501/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4501/comments | https://api.github.com/repos/huggingface/datasets/issues/4501/events | https://github.com/huggingface/datasets/pull/4501 | 1,272,300,646 | PR_kwDODunzps45th2M | 4,501 | Corrected broken links in doc | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-06-15T14:12:17Z | 2022-06-15T15:11:05Z | 2022-06-15T15:00:56Z | MEMBER | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2697 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2697/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2697/comments | https://api.github.com/repos/huggingface/datasets/issues/2697/events | https://github.com/huggingface/datasets/pull/2697 | 950,021,623 | MDExOlB1bGxSZXF1ZXN0Njk0NjMyODg0 | 2,697 | Fix import on Colab | {
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"@lhoestq @albertvillanova - It might be a good idea to have a patch release after this gets merged (presumably tomorrow morning when you're around). The Colab issue linked to this PR is a pretty big blocker. "
] | 2021-07-21T19:03:38Z | 2021-07-22T07:09:08Z | 2021-07-22T07:09:07Z | CONTRIBUTOR | null | 0 | {
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} | Fix #2695, fix #2700. | {
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https://api.github.com/repos/huggingface/datasets/issues/2431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2431/comments | https://api.github.com/repos/huggingface/datasets/issues/2431/events | https://github.com/huggingface/datasets/issues/2431 | 907,413,691 | MDU6SXNzdWU5MDc0MTM2OTE= | 2,431 | DuplicatedKeysError when trying to load adversarial_qa | {
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] | closed | false | null | [] | null | [
"Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`"
] | 2021-05-31T12:11:19Z | 2021-06-01T08:54:03Z | 2021-06-01T08:52:11Z | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual results
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
>
>
>During handling of the above exception, another exception occurred:
>
>DuplicatedKeysError Traceback (most recent call last)
>
>/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
> 347 for hash, key in self.hkey_record:
> 348 if hash in tmp_record:
>--> 349 raise DuplicatedKeysError(key)
> 350 else:
> 351 tmp_record.add(hash)
>
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2600 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2600/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2600/comments | https://api.github.com/repos/huggingface/datasets/issues/2600/events | https://github.com/huggingface/datasets/issues/2600 | 938,086,745 | MDU6SXNzdWU5MzgwODY3NDU= | 2,600 | Crash when using multiprocessing (`num_proc` > 1) on `filter` and all samples are discarded | {
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] | closed | false | null | [] | null | [] | 2021-07-06T16:53:25Z | 2021-07-07T12:50:31Z | 2021-07-07T12:50:31Z | CONTRIBUTOR | null | null | 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/6484 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6484/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6484/comments | https://api.github.com/repos/huggingface/datasets/issues/6484/events | https://github.com/huggingface/datasets/issues/6484 | 2,033,333,294 | I_kwDODunzps55MjQu | 6,484 | [Feature Request] Dataset versioning | {
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"Hello @kenfus, this is meant to be possible to do yes. Let me ping @lhoestq or @mariosasko from the `datasets` team (`huggingface_hub` is only the underlying library to download files from the Hub but here it looks more like a `datasets` problem). ",
"Hi! https://github.com/huggingface/datasets/pull/6459 will fi... | 2023-12-08T16:01:35Z | 2023-12-11T19:13:46Z | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
I am working on a project, where I would like to test different preprocessing methods for my ML-data. Thus, I would like to work a lot with revisions and compare them. Currently, I was not able to make it work with the revision keyword because it was not redownloading the data, it was reading in some cached data, until I put `download_mode="force_redownload"`, even though the reversion was different.
Of course, I may have done something wrong or missed a setting somewhere!
**Describe the solution you'd like**
The solution would allow me to easily work with revisions:
- create a new dataset (by combining things, different preprocessing, ..) and give it a new revision (v.1.2.3), maybe like this:
`dataset_audio.push_to_hub('kenfus/xy', revision='v1.0.2')`
- then, get the current revision as follows:
```
dataset = load_dataset(
'kenfus/xy', revision='v1.0.2',
)
```
this downloads the new version and does not load in a different revision and all future map, filter, .. operations are done on this dataset and not loaded from cache produced from a different revision.
- if I rerun the run, the caching should be smart enough in every step to not reuse a mapping operation on a different revision.
**Describe alternatives you've considered**
I created my own caching, putting `download_mode="force_redownload"` and `load_from_cache_file=False,` everywhere.
**Additional context**
Thanks a lot for your great work! Creating NLP datasets and training a model with them is really easy and straightforward with huggingface.
This is the data loading in my script:
```
## CREATE PATHS
prepared_dataset_path = os.path.join(
DATA_FOLDER, str(DATA_VERSION), "prepared_dataset"
)
os.makedirs(os.path.join(DATA_FOLDER, str(DATA_VERSION)), exist_ok=True)
## LOAD DATASET
if os.path.exists(prepared_dataset_path):
print("Loading prepared dataset from disk...")
dataset_prepared = load_from_disk(prepared_dataset_path)
else:
print("Loading dataset from HuggingFace Datasets...")
dataset = load_dataset(
PATH_TO_DATASET, revision=DATA_VERSION, download_mode="force_redownload"
)
print("Preparing dataset...")
dataset_prepared = dataset.map(
prepare_dataset,
remove_columns=["audio", "transcription"],
num_proc=os.cpu_count(),
load_from_cache_file=False,
)
dataset_prepared.save_to_disk(prepared_dataset_path)
del dataset
if CHECK_DATASET:
## CHECK DATASET
dataset_prepared = dataset_prepared.map(
check_dimensions, num_proc=os.cpu_count(), load_from_cache_file=False
)
dataset_filtered = dataset_prepared.filter(
lambda example: not example["incorrect_dimension"],
load_from_cache_file=False,
)
for example in dataset_prepared.filter(
lambda example: example["incorrect_dimension"], load_from_cache_file=False
):
print(example["path"])
print(
f"Number of examples with incorrect dimension: {len(dataset_prepared) - len(dataset_filtered)}"
)
print("Number of examples train: ", len(dataset_filtered["train"]))
print("Number of examples test: ", len(dataset_filtered["test"]))
```
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https://api.github.com/repos/huggingface/datasets/issues/4152 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4152/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4152/comments | https://api.github.com/repos/huggingface/datasets/issues/4152/events | https://github.com/huggingface/datasets/issues/4152 | 1,202,034,115 | I_kwDODunzps5HpZXD | 4,152 | ArrayND error in pyarrow 5 | {
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} | [] | closed | false | null | [] | null | [
"Where do we bump the required pyarrow version? Any inputs on how I fix this issue? ",
"We need to bump it in `setup.py` as well as update some CI job to use pyarrow 6 instead of 5 in `.circleci/config.yaml` and `.github/workflows/benchmarks.yaml`"
] | 2022-04-12T15:41:40Z | 2022-05-04T09:29:46Z | 2022-05-04T09:29:46Z | MEMBER | null | null | null | As found in https://github.com/huggingface/datasets/pull/3903, The ArrayND features fail on pyarrow 5:
```python
import pyarrow as pa
from datasets import Array2D
from datasets.table import cast_array_to_feature
arr = pa.array([[[0]]])
feature_type = Array2D(shape=(1, 1), dtype="int64")
cast_array_to_feature(arr, feature_type)
```
raises
```python
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-8-04610f9fa78c> in <module>
----> 1 cast_array_to_feature(pa.array([[[0]]]), Array2D(shape=(1, 1), dtype="int32"))
~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1673 else:
-> 1674 return func(array, *args, **kwargs)
1675
1676 return wrapper
~/Desktop/hf/datasets/src/datasets/table.py in cast_array_to_feature(array, feature, allow_number_to_str)
1806 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1807 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1808 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1809 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
1810
~/Desktop/hf/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1672 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1673 else:
-> 1674 return func(array, *args, **kwargs)
1675
1676 return wrapper
~/Desktop/hf/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_number_to_str)
1705 array = array.storage
1706 if isinstance(pa_type, pa.ExtensionType):
-> 1707 return pa_type.wrap_array(array)
1708 elif pa.types.is_struct(array.type):
1709 if pa.types.is_struct(pa_type) and (
AttributeError: 'Array2DExtensionType' object has no attribute 'wrap_array'
```
The thing is that `cast_array_to_feature` is called when writing an Arrow file, so creating an Arrow dataset using any ArrayND type currently fails.
`wrap_array` has been added in pyarrow 6, so we can either bump the required pyarrow version or fix this for pyarrow 5 | {
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https://api.github.com/repos/huggingface/datasets/issues/2355 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2355/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2355/comments | https://api.github.com/repos/huggingface/datasets/issues/2355/events | https://github.com/huggingface/datasets/pull/2355 | 890,484,408 | MDExOlB1bGxSZXF1ZXN0NjQzNDk5NTIz | 2,355 | normalized TOCs and titles in data cards | {
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} | [] | closed | false | null | [] | null | [
"Oh right! I'd be in favor of still having the same TOC across the board, we can either leave it as is or add a `[More Info Needed]` `Contributions` Section wherever it's currently missing, wdyt?",
"(I thought those were programmatically updated based on git history :D )",
"Merging for now to avoid conflict sin... | 2021-05-12T20:59:59Z | 2021-05-14T13:23:12Z | 2021-05-14T13:23:12Z | MEMBER | null | 0 | {
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} | I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content
This PR normalizes all of them to the newer version | {
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https://api.github.com/repos/huggingface/datasets/issues/1733 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1733/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1733/comments | https://api.github.com/repos/huggingface/datasets/issues/1733/events | https://github.com/huggingface/datasets/issues/1733 | 784,903,002 | MDU6SXNzdWU3ODQ5MDMwMDI= | 1,733 | connection issue with glue, what is the data url for glue? | {
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"Hello @juliahane, which config of GLUE causes you trouble?\r\nThe URLs are defined in the dataset script source code: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py"
] | 2021-01-13T08:37:40Z | 2021-08-04T18:13:55Z | 2021-08-04T18:13:55Z | NONE | null | null | null | Hi
my codes sometimes fails due to connection issue with glue, could you tell me how I can have the URL datasets library is trying to read GLUE from to test the machines I am working on if there is an issue on my side or not
thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/407 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/407/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/407/comments | https://api.github.com/repos/huggingface/datasets/issues/407/events | https://github.com/huggingface/datasets/issues/407 | 658,672,736 | MDU6SXNzdWU2NTg2NzI3MzY= | 407 | MissingBeamOptions for Wikipedia 20200501.en | {
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"gists... | null | [
"Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ",
"Works for me! Thanks.",
"I found the same issue with almost any language other than English. (For English, it works). Will someone... | 2020-07-16T23:48:03Z | 2021-01-12T11:41:16Z | 2020-07-17T14:24:28Z | CONTRIBUTOR | null | null | null | There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):
```
nlp.load_dataset('wikipedia', "20200501.en", split='train')
```
And now, having pulled master, I get:
```
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, total: 34.06 GiB) to /home/hltcoe/mgordon/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/76b0b2747b679bb0ee7a1621e50e5a6378477add0c662668a324a5bc07d516dd...
Traceback (most recent call last):
File "scripts/download.py", line 11, in <module>
fire.Fire(download_pretrain)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 138, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 468, in _Fire
target=component.__name__)
File "/home/hltcoe/mgordon/.conda/envs/huggingface/lib/python3.6/site-packages/fire/core.py", line 672, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "scripts/download.py", line 6, in download_pretrain
nlp.load_dataset('wikipedia', "20200501.en", split='train')
File "/exp/mgordon/nlp/src/nlp/load.py", line 534, in load_dataset
save_infos=save_infos,
File "/exp/mgordon/nlp/src/nlp/builder.py", line 460, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/exp/mgordon/nlp/src/nlp/builder.py", line 870, in _download_and_prepare
"\n\t`{}`".format(usage_example)
nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, S
park, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.en', beam_runner='DirectRunner')`
``` | {
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"Fixed by:\r\n- #4735"
] | 2022-07-22T07:06:51Z | 2022-07-22T09:08:02Z | 2022-07-22T09:05:35Z | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
Loading Rouge metric gives error after latest rouge-score==0.0.7 release.
Downgrading rougemetric==0.0.4 works fine.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
## Expected results
A clear and concise description of the expected results.
from rouge_score import rouge_scorer, scoring
should run
## Actual results
Specify the actual results or traceback.
File "/root/.cache/huggingface/modules/datasets_modules/metrics/rouge/0ffdb60f436bdb8884d5e4d608d53dbe108e82dac4f494a66f80ef3f647c104f/rouge.py", line 21, in <module>
from rouge_score import rouge_scorer, scoring
ImportError: cannot import name 'rouge_scorer' from 'rouge_score' (unknown location)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Linux
- Python version:3.9
- PyArrow version:
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https://api.github.com/repos/huggingface/datasets/issues/4569 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4569/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4569/comments | https://api.github.com/repos/huggingface/datasets/issues/4569/events | https://github.com/huggingface/datasets/issues/4569 | 1,284,833,694 | I_kwDODunzps5MlQGe | 4,569 | Dataset Viewer issue for sst2 | {
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"Hi @lewtun, thanks for reporting.\r\n\r\nI have checked locally and refreshed the preview and it seems working smooth now:\r\n```python\r\nIn [8]: ds\r\nOut[8]: \r\nDatasetDict({\r\n train: Dataset({\r\n features: ['idx', 'sentence', 'label'],\r\n num_rows: 67349\r\n })\r\n validation: Datas... | 2022-06-26T07:32:54Z | 2022-06-27T06:37:48Z | 2022-06-27T06:37:48Z | MEMBER | null | null | null | ### Link
https://huggingface.co/datasets/sst2
### Description
Not sure what is causing this, however it seems that `load_dataset("sst2")` also hangs (even though it downloads the files without problem):
```
Status code: 400
Exception: Exception
Message: Give up after 5 attempts with ConnectionError
```
### Owner
No | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @khushmeeet, thanks for your contribution.\r\n\r\nBut wouldn't it be better to add this dataset to the Hub? \r\n- https://huggingface.co/docs/datasets/share\r\n- https://huggingface.co/docs/datasets/dataset_script",
"Hi @albertv... | 2022-07-09T04:46:07Z | 2022-07-15T13:30:32Z | 2022-07-15T13:15:42Z | CONTRIBUTOR | null | 0 | {
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} | This PR is for adding dataset, related to issue #4504.
We are adding Stanford dog breed dataset. It is a multi class image classification dataset.
Details can be found here - http://vision.stanford.edu/aditya86/ImageNetDogs/
Tests on dummy data is failing currently, which I am looking into. | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-03-23T14:51:48Z | 2022-03-23T18:24:52Z | 2022-03-23T18:19:27Z | CONTRIBUTOR | null | 0 | {
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} | Closes the file handler of the PIL image object in `Image.decode_example` to avoid the `Too many open files` error.
To pass [the image equality checks](https://app.circleci.com/pipelines/github/huggingface/datasets/10774/workflows/d56670e6-16bb-4c64-b601-a152c5acf5ed/jobs/65825) in CI, `Image.decode_example` calls `image.load()` regardless of how the image object is created (not only for the `PIL.Image.open(local_path)` case). This is needed because `load()` sets the `readonly` attribute of a `PIL.Image` object to 0 (it's 1 after `PIL.Image.open(file_like)`), and in the older PIL versions (only fixed on main), that attribute is considered in `PIL.Image.__eq__`. More info can be found here: https://github.com/python-pillow/Pillow/issues/5926.
Fix #3985
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"@lhoestq @albertvillanova Iwas trying to fix the failing tests in circleCI but is there a test elasticsearch instance somewhere? If not, can I launch a docker container to have one?"
] | 2021-10-07T13:28:35Z | 2022-07-06T15:19:48Z | null | CONTRIBUTOR | null | 0 | {
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- adding optional credentials for remote elasticsearch server
- enabling sample update in index
- upgrade the elasticsearch 7.10.1 python client
- adding ElasticsearchBulider to instantiate a dataset from an index and a filtering query | {
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https://api.github.com/repos/huggingface/datasets/issues/5268 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5268/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5268/comments | https://api.github.com/repos/huggingface/datasets/issues/5268/events | https://github.com/huggingface/datasets/pull/5268 | 1,455,633,978 | PR_kwDODunzps5DPIsp | 5,268 | Sharded save_to_disk + multiprocessing | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Added both num_shards and max_shard_size in push_to_hub/save_to_disk. Will take care of updating the tests later",
"It's ready for a final review @mariosasko and @albertvillanova, let me know what you think :)",
"Took your commen... | 2022-11-18T18:50:01Z | 2022-12-14T18:25:52Z | 2022-12-14T18:22:58Z | MEMBER | null | 0 | {
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} | Added `num_shards=` and `num_proc=` to `save_to_disk()`
EDIT: also added `max_shard_size=` to `save_to_disk()`, and also `num_shards=` to `push_to_hub`
I also:
- deprecated the fs parameter in favor of storage_options (for consistency with the rest of the lib) in save_to_disk and load_from_disk
- always embed the image/audio data in arrow when doing `save_to_disk`
- added a tqdm bar in `save_to_disk`
- Use the MockFileSystem in tests for `save_to_disk` and `load_from_disk`
- removed the unused integration tests with S3, since we can now test with `mockfs` instead of `s3fs`
TODO:
- [x] implem save_to_disk for dataset dict
- [x] save_to_disk for dataset dict tests
- [x] deprecate fs in dataset dict load_from_disk as well
- [x] update docs
Close #5263
Close https://github.com/huggingface/datasets/issues/4196
Close https://github.com/huggingface/datasets/issues/4351 | {
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https://api.github.com/repos/huggingface/datasets/issues/4083 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4083/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4083/comments | https://api.github.com/repos/huggingface/datasets/issues/4083/events | https://github.com/huggingface/datasets/pull/4083 | 1,190,025,878 | PR_kwDODunzps41gEbu | 4,083 | Add SacreBLEU Metric Card | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-04-01T16:24:56Z | 2022-04-12T20:45:00Z | 2022-04-12T20:38:40Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4322/comments | https://api.github.com/repos/huggingface/datasets/issues/4322/events | https://github.com/huggingface/datasets/pull/4322 | 1,233,596,947 | PR_kwDODunzps43s1wy | 4,322 | Added stratify option to train_test_split function. | {
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"> Nice thank you ! This will be super useful :)\r\n> \r\n> Could you also add some tests in test_arrow_dataset.py and add an example of usage in the `Example:` section of the `train_test_split` docstring ?\r\n\r\nI will try to do it, is there any documentation for adding test cases? I have never done it before.",
... | 2022-05-12T08:00:31Z | 2022-11-22T14:53:55Z | 2022-05-25T20:43:51Z | CONTRIBUTOR | null | 0 | {
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} | This PR adds `stratify` option to `train_test_split` method. I took reference from scikit-learn's `StratifiedShuffleSplit` class for implementing stratified split and integrated the changes as were suggested by @lhoestq.
It fixes #3452.
@lhoestq Please review and let me know, if any changes are required.
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https://api.github.com/repos/huggingface/datasets/issues/1743 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1743/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1743/comments | https://api.github.com/repos/huggingface/datasets/issues/1743/events | https://github.com/huggingface/datasets/issues/1743 | 787,631,412 | MDU6SXNzdWU3ODc2MzE0MTI= | 1,743 | Issue while Creating Custom Metric | {
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"Currently it's only possible to define the features for the two columns `references` and `predictions`.\r\nThe data for these columns can then be passed to `metric.add_batch` and `metric.compute`.\r\nInstead of defining more columns `text`, `offset_mapping` and `ground` you must include them in either references a... | 2021-01-17T07:01:14Z | 2022-06-01T15:49:34Z | 2022-06-01T15:49:34Z | CONTRIBUTOR | null | null | null | Hi Team,
I am trying to create a custom metric for my training as follows, where f1 is my own metric:
```python
def _info(self):
# TODO: Specifies the datasets.MetricInfo object
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features = datasets.Features({'predictions':datasets.Sequence(datasets.Value("int32")), "references": datasets.Sequence(datasets.Value("int32")),"offset_mapping":datasets.Sequence(datasets.Value("int32")),'text':datasets.Sequence(datasets.Value('string')),"ground":datasets.Sequence(datasets.Value("int32")),}),
# Homepage of the metric for documentation
homepage="http://metric.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
reference_urls=["http://path.to.reference.url/new_metric"]
)
def _compute(self,predictions,references,text,offset_mapping,spans):
pred_spans = []
for i,preds in enumerate(predictions):
current_preds = []
for j,token_preds in enumerate(preds):
if (preds>0.5):
current_preds+=list(range(offset_mapping[i][j][0],offset_mapping[i][j][1]))
pred_spans.append(current_spans)
return {
"Token Wise F1": f1_score(references,predictions,labels=[0,1]),
"Offset Wise F1": np.mean([f1(preds,gold) for preds,fold in zip(pred_spans,ground)])
}
```
I believe this is not correct. But that's not the issue I am facing right now. I get this error :
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-144-ed7349b50821> in <module>()
----> 1 new_metric.compute(predictions=inputs["labels"],references=inputs["labels"], text=inputs["text"], offset_mapping=inputs["offset_mapping"],ground=inputs["ground"] )
2 frames
/usr/local/lib/python3.6/dist-packages/datasets/features.py in encode_batch(self, batch)
802 encoded_batch = {}
803 if set(batch) != set(self):
--> 804 print(batch)
805 print(self)
806 raise ValueError("Column mismatch between batch {} and features {}".format(set(batch), set(self)))
ValueError: Column mismatch between batch {'references', 'predictions'} and features {'ground', 'predictions', 'offset_mapping', 'text', 'references'}
```
On checking the features.py file, I see the call is made from add_batch() in metrics.py which only takes in predictions and references.
How do I make my custom metric work? Will it work with a trainer even if I am able to make this metric work?
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/6476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6476/comments | https://api.github.com/repos/huggingface/datasets/issues/6476/events | https://github.com/huggingface/datasets/issues/6476 | 2,028,018,596 | I_kwDODunzps544Ruk | 6,476 | CI on windows is broken: PermissionError | {
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```
FAILED tests/test_load.py::test_loading_from_the_datasets_hub - NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\\Users\\RUNNER~1\\AppData\\Local\\Temp\\tmpfcnps56i\\hf-internal-testing___dataset_with_script\\default\\0.0.0\\c240e2be3370bdbd\\dataset_with_script-train.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/303/comments | https://api.github.com/repos/huggingface/datasets/issues/303/events | https://github.com/huggingface/datasets/pull/303 | 643,912,464 | MDExOlB1bGxSZXF1ZXN0NDM4NjI3Nzcw | 303 | allow to move files across file systems | {
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} | Users are allowed to use the `cache_dir` that they want.
Therefore it can happen that we try to move files across filesystems.
We were using `os.rename` that doesn't allow that, so I changed some of them to `shutil.move`.
This should fix #301 | {
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https://api.github.com/repos/huggingface/datasets/issues/1617 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1617/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1617/comments | https://api.github.com/repos/huggingface/datasets/issues/1617/events | https://github.com/huggingface/datasets/pull/1617 | 772,084,764 | MDExOlB1bGxSZXF1ZXN0NTQzNDE5MTM5 | 1,617 | cifar10 initial commit | {
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"Yee a Computer Vision dataset!",
"Yep, the first one ! Thank @czabo "
] | 2020-12-21T11:18:50Z | 2020-12-22T10:18:05Z | 2020-12-22T10:11:28Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5334/comments | https://api.github.com/repos/huggingface/datasets/issues/5334/events | https://github.com/huggingface/datasets/pull/5334 | 1,477,421,927 | PR_kwDODunzps5EY9zN | 5,334 | Clean up docstrings | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks ! Let us know if we can help :)\r\n\r\nSmall pref for having multiple PRs",
"Awesome, thanks! Sorry this one is a little big, I'll open some smaller ones next :)"
] | 2022-12-05T20:56:08Z | 2022-12-09T01:44:25Z | 2022-12-09T01:41:44Z | MEMBER | null | 0 | {
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} | As raised by @polinaeterna in #5324, some of the docstrings are a bit of a mess because it has both Markdown and Sphinx syntax. This PR fixes the docstring for `DatasetBuilder`.
I'll start working on cleaning up the rest of the docstrings and removing the old Sphinx syntax (let me know if you prefer one big PR with all the cleaned changes or multiple smaller ones)! 🧼 | {
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https://api.github.com/repos/huggingface/datasets/issues/5945 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5945/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5945/comments | https://api.github.com/repos/huggingface/datasets/issues/5945/events | https://github.com/huggingface/datasets/issues/5945 | 1,754,084,577 | I_kwDODunzps5ojTTh | 5,945 | Failing to upload dataset to the hub | {
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"Hi ! Feel free to re-run your code later, it will resume automatically where you left",
"Tried many times in the last 2 weeks, problem remains.",
"Alternatively you can save your dataset in parquet files locally and upload them to the hub manually\r\n\r\n```python\r\nfrom tqdm import tqdm\r\nnum_shards = 60\r\... | 2023-06-13T05:46:46Z | 2023-07-24T11:56:40Z | 2023-07-24T11:56:40Z | NONE | null | null | null | ### Describe the bug
Trying to upload a dataset of hundreds of thousands of audio samples (the total volume is not very large, 60 gb) to the hub with push_to_hub, it doesn't work.
From time to time one piece of the data (parquet) gets pushed and then I get RemoteDisconnected even though my internet is stable.
Please help.
I'm trying to upload the dataset for almost a week.
Thanks
### Steps to reproduce the bug
not relevant
### Expected behavior
Be able to upload thedataset
### Environment info
python: 3.9 | {
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https://api.github.com/repos/huggingface/datasets/issues/5895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5895/comments | https://api.github.com/repos/huggingface/datasets/issues/5895/events | https://github.com/huggingface/datasets/issues/5895 | 1,725,467,252 | I_kwDODunzps5m2Ip0 | 5,895 | The dir name and split strings are confused when loading ArmelR/stack-exchange-instruction dataset | {
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"Thanks for reporting, @DongHande.\r\n\r\nI think the issue is caused by the metadata in the dataset card: in the header of the `README.md`, they state that the dataset has 4 splits (\"finetune\", \"reward\", \"rl\", \"evaluation\"). \r\n```yaml\r\n splits:\r\n - name: finetune\r\n num_bytes: 6674567576\r\... | 2023-05-25T09:39:06Z | 2023-05-29T02:32:12Z | 2023-05-29T02:32:12Z | NONE | null | null | null | ### Describe the bug
When I load the ArmelR/stack-exchange-instruction dataset, I encounter a bug that may be raised by confusing the dir name string and the split string about the dataset.
When I use the script "datasets.load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)", it fails. But it succeeds when I add the "streaming = True" parameter.
The website of the dataset is https://huggingface.co/datasets/ArmelR/stack-exchange-instruction/ .
The traceback logs are as below:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/load.py", line 1797, in load_dataset
builder_instance.download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 985, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/builder.py", line 1706, in _prepare_split
split_info = self.info.splits[split_generator.name]
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/splits.py", line 530, in __getitem__
instructions = make_file_instructions(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 112, in make_file_instructions
name2filenames = {
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/arrow_reader.py", line 113, in <dictcomp>
info.name: filenames_for_dataset_split(
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 70, in filenames_for_dataset_split
prefix = filename_prefix_for_split(dataset_name, split)
File "/home/xxx/miniconda3/envs/code/lib/python3.9/site-packages/datasets/naming.py", line 54, in filename_prefix_for_split
if os.path.basename(name) != name:
File "/home/xxx/miniconda3/envs/code/lib/python3.9/posixpath.py", line 142, in basename
p = os.fspath(p)
TypeError: expected str, bytes or os.PathLike object, not NoneType
### Steps to reproduce the bug
1. import datasets library function: ```from datasets import load_dataset```
2. load dataset: ```ds=load_dataset('ArmelR/stack-exchange-instruction', data_dir="data/finetune", split="train", use_auth_token=True)```
### Expected behavior
The dataset can be loaded successfully without the streaming setting.
### Environment info
Linux,
python=3.9
datasets=2.12.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/4607 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4607/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4607/comments | https://api.github.com/repos/huggingface/datasets/issues/4607/events | https://github.com/huggingface/datasets/pull/4607 | 1,290,171,941 | PR_kwDODunzps46pLnd | 4,607 | Align more metadata with other repo types (models,spaces) | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"I just set a default value (None) for the deprecated licenses and languages fields, which should fix most of the CI failures.\r\n\r\nNote that the CI should still be red because you edited many dataset cards and they're still missing... | 2022-06-30T13:52:12Z | 2022-07-01T12:00:37Z | 2022-07-01T11:49:14Z | MEMBER | null | 0 | {
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} | see also associated PR on the `datasets-tagging` Space: https://huggingface.co/spaces/huggingface/datasets-tagging/discussions/2 (to merge after this one is merged) | {
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} | Fixes #2459
Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq. | {
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https://api.github.com/repos/huggingface/datasets/issues/2086 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2086/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2086/comments | https://api.github.com/repos/huggingface/datasets/issues/2086/events | https://github.com/huggingface/datasets/pull/2086 | 836,249,587 | MDExOlB1bGxSZXF1ZXN0NTk2Nzg0Mjcz | 2,086 | change user permissions to -rw-r--r-- | {
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"I tried this with `ade_corpus_v2` dataset. `ade_corpus_v2-train.arrow` (downloaded dataset) and `cache-25d41a4d3c2d8a25.arrow` (ran a mapping function on the dataset) both had file permission with octal value of `0644`. "
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"Sure, let's include this in today's release.",
"Cool ! The windows CI should be fixed on master now, feel free to merge :)"
] | 2022-01-28T13:06:29Z | 2022-01-28T15:35:58Z | 2022-01-28T15:35:58Z | CONTRIBUTOR | null | 0 | {
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} | My initial idea was to avoid the `flatten_indices` call and reorder a new column instead, but in the end I decided to follow `concatenate_datasets` and use `flatten_indices` to avoid padding when `dataset._indices.num_rows != dataset._data.num_rows`.
Fix #3599 | {
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"I think @thomwolf may also be working on this one as part of the Babi benchmark in #945 "
] | 2020-12-03T12:53:20Z | 2020-12-03T17:31:59Z | 2020-12-03T17:31:58Z | MEMBER | null | 0 | {
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"#self-assign"
] | 2021-11-16T22:44:08Z | 2021-12-06T09:49:45Z | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** VoxLingua107
- **Description:** VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
- **Paper:** https://arxiv.org/abs/2011.12998
- **Data:** http://bark.phon.ioc.ee/voxlingua107/
- **Motivation:** Nice audio classification dataset
cc @anton-l
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/6441 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6441/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6441/comments | https://api.github.com/repos/huggingface/datasets/issues/6441/events | https://github.com/huggingface/datasets/issues/6441 | 2,004,985,857 | I_kwDODunzps53gagB | 6,441 | Trouble Loading a Gated Dataset For User with Granted Permission | {
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"> Also when they try to click the url link for the dataset they get a 404 error.\r\n\r\nThis seems to be a Hub error then (cc @SBrandeis)",
"Could you report this to https://discuss.huggingface.co/c/hub/23, providing the URL of the dataset, or at least if the dataset is public or private?",
"Thanks for the rep... | 2023-11-21T19:24:36Z | 2023-12-13T08:27:16Z | 2023-12-13T08:27:16Z | NONE | null | null | null | ### Describe the bug
I have granted permissions to several users to access a gated huggingface dataset. The users accepted the invite and when trying to load the dataset using their access token they get
`FileNotFoundError: Couldn't find a dataset script at .....` . Also when they try to click the url link for the dataset they get a 404 error.
### Steps to reproduce the bug
1. Grant access to gated dataset for specific users
2. Users accept invitation
3. Users login to hugging face hub using cli login
4. Users run load_dataset
### Expected behavior
Dataset is loaded normally for users who were granted access to the gated dataset.
### Environment info
datasets==2.15.0
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"This same error will occur using `ds = datasets.load_dataset('json', data_files=['test.jsonl'])`",
"@cccntu I want to make a quick fix for this, but I am struggling to find where the json dataset builder is. Do you know?",
"@vvvm23 I think you mean think:\r\n```python\r\nds = datasets.load_dataset('json', data... | 2022-08-19T12:59:17Z | 2023-03-12T11:25:49Z | null | CONTRIBUTOR | null | null | null | **Is your feature request related to a problem? Please describe.**
```
from pathlib import Path
from datasets import Dataset
ds = Dataset.read_json(Path('data.json'))
```
causes an error
```
AttributeError: 'PosixPath' object has no attribute 'decode'
```
**Describe the solution you'd like**
It should be able to accept PosixPath and read the json from inside. | {
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"Yes following the Tensorflow Datasets convention, objects with type `Sequence of a Dict` are actually stored as a `dictionary of lists`.\r\nSee the [documentation](https://huggingface.co/docs/datasets/features.html?highlight=features) for more details",
"Thanks.\r\nThis is a bit (very) confusing, but I guess if ... | 2020-11-25T16:07:46Z | 2020-11-25T17:30:39Z | 2020-11-25T17:30:39Z | CONTRIBUTOR | null | null | null | I might misunderstand something, but I expect that if I define:
```python
"top": datasets.features.Sequence({
"middle": datasets.features.Sequence({
"bottom": datasets.Value("int32")
})
})
```
And I then create an example:
```python
yield 1, {
"top": [{
"middle": [
{"bottom": 1},
{"bottom": 2}
]
}]
}
```
I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```
and expect to be able to access `data[0]["top"][0]["middle"][0]`.
That is not the case. Here is `data[0]` as JSON:
```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```
Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
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- #5731 | {
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https://api.github.com/repos/huggingface/datasets/issues/3664 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3664/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3664/comments | https://api.github.com/repos/huggingface/datasets/issues/3664/events | https://github.com/huggingface/datasets/pull/3664 | 1,121,233,301 | PR_kwDODunzps4x7mg_ | 3,664 | [WIP] Return local paths to Common Voice | {
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"Cool thanks for giving it a try @anton-l ! \r\n\r\nWould be very much in favor of having \"real\" paths to the audio files again for non-streaming use cases. At the same time it would be nice to make the audio data loading script as understandable as possible so that the community can easily add audio datasets in ... | 2022-02-01T21:48:27Z | 2022-02-22T09:14:06Z | 2022-02-22T09:14:06Z | MEMBER | null | 0 | {
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} | Fixes https://github.com/huggingface/datasets/issues/3663
This is a proposed way of returning the old local file-based generator while keeping the new streaming generator intact.
TODO:
- [ ] brainstorm a bit more on https://github.com/huggingface/datasets/issues/3663 to see if we can do better
- [ ] refactor the heck out of this PR to avoid completely copying the logic between the two generators | {
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https://api.github.com/repos/huggingface/datasets/issues/6186 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6186/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6186/comments | https://api.github.com/repos/huggingface/datasets/issues/6186/events | https://github.com/huggingface/datasets/issues/6186 | 1,869,431,457 | I_kwDODunzps5vbUKh | 6,186 | Feature request: add code example of multi-GPU processing | {
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"That'd be a great idea! @mariosasko or @lhoestq, would it be possible to fix the code snippet or do you have another suggested way for doing this?",
"Indeed `if __name__ == \"__main__\"` is important in this case.\r\n\r\nNot sure about the imbalanced GPU usage though, but maybe you can try using the `torch.cuda.... | 2023-08-28T10:00:59Z | 2023-11-22T15:42:20Z | 2023-11-22T15:42:20Z | CONTRIBUTOR | null | null | null | ### Feature request
Would be great to add a code example of how to do multi-GPU processing with 🤗 Datasets in the documentation. cc @stevhliu
Currently the docs has a small [section](https://huggingface.co/docs/datasets/v2.3.2/en/process#map) on this saying "your big GPU call goes here", however it didn't work for me out-of-the-box.
Let's say you have a PyTorch model that can do translation, and you have multiple GPUs. In that case, you'd like to duplicate the model on each GPU, each processing (translating) a chunk of the data in parallel.
Here's how I tried to do that:
```
from datasets import load_dataset
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from multiprocess import set_start_method
import torch
import os
dataset = load_dataset("mlfoundations/datacomp_small")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
# put model on each available GPU
# also, should I do it like this or use nn.DataParallel?
model.to("cuda:0")
model.to("cuda:1")
set_start_method("spawn")
def translate_captions(batch, rank):
os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())
texts = batch["text"]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(model.device)
translated_tokens = model.generate(
**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["eng_Latn"], max_length=30
)
translated_texts = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
batch["translated_text"] = translated_texts
return batch
updated_dataset = dataset.map(translate_captions, with_rank=True, num_proc=2, batched=True, batch_size=256)
```
I've personally tried running this script on a machine with 2 A100 GPUs.
## Error 1
Running the code snippet above from the terminal (python script.py) resulted in the following error:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 125, in _main
prepare(preparation_data)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 289, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 96, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/niels/python_projects/datacomp/datasets_multi_gpu.py", line 16, in <module>
set_start_method("spawn")
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 247, in set_start_method
raise RuntimeError('context has already been set')
RuntimeError: context has already been set
```
## Error 2
Then, based on [this Stackoverflow answer](https://stackoverflow.com/a/71616344/7762882), I put the `set_start_method("spawn")` section in a try: catch block. This resulted in the following error:
```
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/dataset_dict.py", line 817, in <dictcomp>
k: dataset.map(
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 2926, in map
with Pool(nb_of_missing_shards, initargs=initargs, initializer=initializer) as pool:
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 119, in Pool
return Pool(processes, initializer, initargs, maxtasksperchild,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 215, in __init__
self._repopulate_pool()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 306, in _repopulate_pool
return self._repopulate_pool_static(self._ctx, self.Process,
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/pool.py", line 329, in _repopulate_pool_static
w.start()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/process.py", line 121, in start
self._popen = self._Popen(self)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/context.py", line 288, in _Popen
return Popen(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 32, in __init__
super().__init__(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_fork.py", line 19, in __init__
self._launch(process_obj)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/popen_spawn_posix.py", line 42, in _launch
prep_data = spawn.get_preparation_data(process_obj._name)
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 154, in get_preparation_data
_check_not_importing_main()
File "/home/niels/anaconda3/envs/datacomp/lib/python3.10/site-packages/multiprocess/spawn.py", line 134, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
```
So then I put the last line under a `if __name__ == '__main__':` block. Then the code snippet seemed to work, but it seemed that it's only leveraging a single GPU (based on monitoring `nvidia-smi`):
```
Mon Aug 28 12:19:24 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 515.65.01 Driver Version: 515.65.01 CUDA Version: 11.7 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A100-SXM... On | 00000000:01:00.0 Off | 0 |
| N/A 55C P0 76W / 275W | 8747MiB / 81920MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA A100-SXM... On | 00000000:47:00.0 Off | 0 |
| N/A 67C P0 274W / 275W | 59835MiB / 81920MiB | 100% Default |
| | | Disabled |
```
Both GPUs should have equal GPU usage, but I've always noticed that the last GPU has way more usage than the other ones. This made me think that `os.environ["CUDA_VISIBLE_DEVICES"] = str(rank % torch.cuda.device_count())` might not work inside a Python script, especially if done after importing PyTorch?
### Motivation
Would be great to clarify how to do multi-GPU data processing.
### Your contribution
If my code snippet can be fixed, I can contribute it to the docs :) | {
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} | I have noticed that after implementing `load_dataset_builder` (#2500), there is a redundant call to `prepare_module`. | {
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https://api.github.com/repos/huggingface/datasets/issues/6090 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6090/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6090/comments | https://api.github.com/repos/huggingface/datasets/issues/6090/events | https://github.com/huggingface/datasets/issues/6090 | 1,825,865,043 | I_kwDODunzps5s1H1T | 6,090 | FilesIterable skips all the files after a hidden file | {
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"Thanks for reporting. We've merged a PR with a fix."
] | 2023-07-28T07:25:57Z | 2023-07-28T10:51:14Z | 2023-07-28T10:50:11Z | NONE | null | null | null | ### Describe the bug
When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file.
The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`.
### Steps to reproduce the bug
https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8-
### Expected behavior
The script should print all the files except the hidden one.
### Environment info
- `datasets` version: 2.14.1
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.6
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/3154 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3154/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3154/comments | https://api.github.com/repos/huggingface/datasets/issues/3154/events | https://github.com/huggingface/datasets/issues/3154 | 1,034,361,806 | I_kwDODunzps49pxvO | 3,154 | Sacrebleu unexpected behaviour/requirement for data format | {
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"Hi @BramVanroy!\r\n\r\nGood question. This project relies on PyArrow (tables) to store data too big to fit in RAM. In the case of metrics, this means that the number of predictions and references has to match to form a table.\r\n\r\nThat's why your example throws an error even though it matches the schema:\r\n```p... | 2021-10-24T08:55:33Z | 2021-10-31T09:08:32Z | 2021-10-31T09:08:31Z | CONTRIBUTOR | null | null | null | ## Describe the bug
When comparing with the original `sacrebleu` implementation, the `datasets` implementation does some strange things that I do not quite understand. This issue was triggered when I was trying to implement TER and found the datasets implementation of BLEU [here](https://github.com/huggingface/datasets/pull/3153).
In the below snippet, the original sacrebleu snippet works just fine whereas the datasets implementation throws an error.
## Steps to reproduce the bug
```python
import sacrebleu
import datasets
refs = [
['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'],
['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.'],
]
hyps = ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.']
expected_bleu = 48.530827
ds_bleu = datasets.load_metric("sacrebleu")
bleu_score_sb = sacrebleu.corpus_bleu(hyps, refs).score
print(bleu_score_sb, expected_bleu)
# works: 48.5308...
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
print(bleu_score_ds, expected_bleu)
# ValueError: Predictions and/or references don't match the expected format.
```
This seems to be related to how datasets forces the features format here:
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L94-L99
and then manipulates the references during the compute stage here
https://github.com/huggingface/datasets/blob/87c71b9c29a40958973004910f97e4892559dfed/metrics/sacrebleu/sacrebleu.py#L119-L122
I do not quite understand why that is required since sacrebleu handles argument parsing quite well [by itself](https://github.com/mjpost/sacrebleu/blob/2787185dd0f8d224c72ee5a831d163c2ac711a47/sacrebleu/metrics/base.py#L229).
## Actual results
Traceback (most recent call last):
File "C:\Users\bramv\AppData\Roaming\JetBrains\PyCharm2020.3\scratches\scratch_23.py", line 23, in <module>
bleu_score_ds = ds_bleu.compute(predictions=hyps, references=refs)["score"]
File "C:\dev\python\datasets\src\datasets\metric.py", line 392, in compute
self.add_batch(predictions=predictions, references=references)
File "C:\dev\python\datasets\src\datasets\metric.py", line 439, in add_batch
raise ValueError(
ValueError: Predictions and/or references don't match the expected format.
Expected format: {'predictions': Value(dtype='string', id='sequence'), 'references': Sequence(feature=Value(dtype='string', id='sequence'), length=-1, id='references')},
Input predictions: ['The dog bit the man.', "It wasn't surprising.", 'The man had just bitten him.'],
Input references: [['The dog bit the man.', 'It was not unexpected.', 'The man bit him first.'], ['The dog had bit the man.', 'No one was surprised.', 'The man had bitten the dog.']]
## Environment info
- `datasets` version: 1.14.1.dev0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2699 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2699/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2699/comments | https://api.github.com/repos/huggingface/datasets/issues/2699/events | https://github.com/huggingface/datasets/issues/2699 | 950,221,226 | MDU6SXNzdWU5NTAyMjEyMjY= | 2,699 | cannot combine splits merging and streaming? | {
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"Hi ! That's missing indeed. We'll try to implement this for the next version :)\r\n\r\nI guess we just need to implement #2564 first, and then we should be able to add support for splits combinations"
] | 2021-07-22T01:13:25Z | 2021-07-22T08:27:47Z | null | NONE | null | null | null | this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits | {
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https://api.github.com/repos/huggingface/datasets/issues/5602 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5602/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5602/comments | https://api.github.com/repos/huggingface/datasets/issues/5602/events | https://github.com/huggingface/datasets/pull/5602 | 1,607,054,110 | PR_kwDODunzps5LJGfa | 5,602 | Return dict structure if columns are lists - to_tf_dataset | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.",
"This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was... | 2023-03-02T15:51:12Z | 2023-04-12T15:54:53Z | null | CONTRIBUTOR | null | 0 | {
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} | This PR introduces new logic to `to_tf_dataset` affecting the returned data structure, enabling a dictionary structure to be returned, even if only one feature column is selected.
If the passed in `columns` or `label_cols` to `to_tf_dataset` are a list, they are returned as a dictionary, respectively. If they are a string, the tensor is returned.
An outline of the behaviour:
```
dataset,to_tf_dataset(columns=["col_1"], label_cols="col_2")
# ({'col_1': col_1}, col_2}
dataset,to_tf_dataset(columns="col1", label_cols="col_2")
# (col1, col2)
dataset,to_tf_dataset(columns="col1")
# col1
dataset,to_tf_dataset(columns=["col_1"], labels=["col_2"])
# ({'col1': tensor}, {'col2': tensor}}
dataset,to_tf_dataset(columns="col_1", labels=["col_2"])
# (col1, {'col2': tensor}}
```
## Motivation
Currently, when calling `to_tf_dataset`, the returned dataset will always return a tuple structure if a single feature column is used. This can cause issues when calling `model.fit` on models which train without labels e.g. [TFVitMAEForPreTraining](https://github.com/huggingface/transformers/blob/b6f47b539377ac1fd845c7adb4ccaa5eb514e126/src/transformers/models/vit_mae/modeling_vit_mae.py#L849). Specifically, [this line](https://github.com/huggingface/transformers/blob/d9e28d91a8b2d09b51a33155d3a03ad9fcfcbd1f/src/transformers/modeling_tf_utils.py#L1521) where it's assumed the input `x` is a dictionary if there is no label.
## Example
Previous behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec=TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)>
```
New behaviour
```python
In [1]: import tensorflow as tf
...: from datasets import load_dataset
...:
...:
...: def transform(batch):
...: def _transform_img(img):
...: img = img.convert("RGB")
...: img = tf.keras.utils.img_to_array(img)
...: img = tf.image.resize(img, (224, 224))
...: img /= 255.0
...: img = tf.transpose(img, perm=[2, 0, 1])
...: return img
...: batch['pixel_values'] = [_transform_img(pil_img) for pil_img in batch['img']]
...: return batch
...:
...:
...: def collate_fn(examples):
...: pixel_values = tf.stack([example["pixel_values"] for example in examples])
...: return {"pixel_values": pixel_values}
...:
...:
...: dataset = load_dataset('cifar10')['train']
...: dataset = dataset.with_transform(transform)
...: dataset.to_tf_dataset(batch_size=8, columns=['pixel_values'], collate_fn=collate_fn)
Out[1]: <PrefetchDataset element_spec={'pixel_values': TensorSpec(shape=(None, 3, 224, 224), dtype=tf.float32, name=None)}>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/3311 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3311/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3311/comments | https://api.github.com/repos/huggingface/datasets/issues/3311/events | https://github.com/huggingface/datasets/issues/3311 | 1,060,387,957 | I_kwDODunzps4_NDx1 | 3,311 | Add WebSRC | {
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] | open | false | null | [] | null | [] | 2021-11-22T16:58:33Z | 2021-11-22T16:58:33Z | null | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** WebSRC
- **Description:** WebSRC is a novel Web-based Structural Reading Comprehension dataset. It consists of 0.44M question-answer pairs, which are collected from 6.5K web pages with corresponding HTML source code, screenshots and metadata.
- **Paper:** https://arxiv.org/abs/2101.09465
- **Data:** https://x-lance.github.io/WebSRC/dashboard.html#
- **Motivation:** Currently adding MarkupLM to HuggingFace Transformers, which achieves SOTA on this dataset.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/5986 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5986/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5986/comments | https://api.github.com/repos/huggingface/datasets/issues/5986/events | https://github.com/huggingface/datasets/pull/5986 | 1,772,233,111 | PR_kwDODunzps5TygOZ | 5,986 | Make IterableDataset.from_spark more efficient | {
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"@lhoestq would you be able to review this please and also approve the workflow?",
"Sounds good to me :) feel free to run `make style` to apply code formatting",
"_The documentation is not available anymore as the PR was closed or merged._",
"cool ! I think we can merge once all comments have been addressed",... | 2023-06-23T22:18:20Z | 2023-07-07T10:05:58Z | 2023-07-07T09:56:09Z | CONTRIBUTOR | null | 0 | {
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} | Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating. | {
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https://api.github.com/repos/huggingface/datasets/issues/258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/258/comments | https://api.github.com/repos/huggingface/datasets/issues/258/events | https://github.com/huggingface/datasets/issues/258 | 635,859,525 | MDU6SXNzdWU2MzU4NTk1MjU= | 258 | Why is dataset after tokenization far more larger than the orginal one ? | {
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"Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of... | 2020-06-10T01:27:07Z | 2020-06-10T12:46:34Z | 2020-06-10T12:46:34Z | CONTRIBUTOR | null | null | null | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M wikipedia-train.arrow
47511M tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? | {
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https://api.github.com/repos/huggingface/datasets/issues/2870 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2870/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2870/comments | https://api.github.com/repos/huggingface/datasets/issues/2870/events | https://github.com/huggingface/datasets/pull/2870 | 988,276,859 | MDExOlB1bGxSZXF1ZXN0NzI3MjI4Njk5 | 2,870 | Fix three typos in two files for documentation | {
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} | Changed "bacth_size" to "batch_size" (2x)
Changed "intsructions" to "instructions" | {
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https://api.github.com/repos/huggingface/datasets/issues/4393 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4393/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4393/comments | https://api.github.com/repos/huggingface/datasets/issues/4393/events | https://github.com/huggingface/datasets/pull/4393 | 1,244,876,662 | PR_kwDODunzps44RxWN | 4,393 | Update CI deprecated legacy image | {
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-23T09:35:42Z | 2022-05-23T10:08:28Z | 2022-05-23T09:59:55Z | MEMBER | null | 0 | {
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} | Now our CI still uses a deprecated legacy image:
> You’re using a [deprecated Docker convenience image.](https://discuss.circleci.com/t/legacy-convenience-image-deprecation/41034) Upgrade to a next-gen Docker convenience image.
This PR updates to next-generation convenience image.
Related to:
- #2955 | {
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https://api.github.com/repos/huggingface/datasets/issues/3349 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3349/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3349/comments | https://api.github.com/repos/huggingface/datasets/issues/3349/events | https://github.com/huggingface/datasets/pull/3349 | 1,067,853,601 | PR_kwDODunzps4vOF-s | 3,349 | raise exception instead of using assertions. | {
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"@mariosasko - Thanks for the review & suggestions. Updated as per the suggestions. ",
"@mariosasko - Hello, Are there any additional changes required from my end??. Wondering if this PR can be merged or still pending on additional steps.",
"@mariosasko - The approved changes in the PR now has conflicts with th... | 2021-12-01T01:37:51Z | 2021-12-20T16:07:27Z | 2021-12-20T16:07:27Z | CONTRIBUTOR | null | 0 | {
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} | fix for the remaining files https://github.com/huggingface/datasets/issues/3171 | {
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https://api.github.com/repos/huggingface/datasets/issues/2746 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2746/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2746/comments | https://api.github.com/repos/huggingface/datasets/issues/2746/events | https://github.com/huggingface/datasets/issues/2746 | 958,551,619 | MDU6SXNzdWU5NTg1NTE2MTk= | 2,746 | Cannot load `few-nerd` dataset | {
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"Hi @Mehrad0711,\r\n\r\nI'm afraid there is no \"canonical\" Hugging Face dataset named \"few-nerd\".\r\n\r\nThere are 2 kinds of datasets hosted at the Hugging Face Hub:\r\n- canonical datasets (their identifier contains no slash \"/\"): we, the Hugging Face team, supervise their implementation and we make sure th... | 2021-08-02T22:18:57Z | 2021-11-16T08:51:34Z | 2021-08-03T19:45:43Z | NONE | null | null | null | ## Describe the bug
Cannot load `few-nerd` dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```
## Actual results
Executing above code will give the following error:
```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
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https://api.github.com/repos/huggingface/datasets/issues/1236 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1236/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1236/comments | https://api.github.com/repos/huggingface/datasets/issues/1236/events | https://github.com/huggingface/datasets/pull/1236 | 758,263,012 | MDExOlB1bGxSZXF1ZXN0NTMzNDYzOTg2 | 1,236 | Opus finlex dataset of language pair Finnish and Swedish | {
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} | [] | closed | false | null | [] | null | [] | 2020-12-07T07:53:57Z | 2020-12-08T13:30:33Z | 2020-12-08T13:30:33Z | CONTRIBUTOR | null | 0 | {
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} | Added Opus_finlex dataset of language pair Finnish and Swedish
More info : http://opus.nlpl.eu/Finlex.php | {
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https://api.github.com/repos/huggingface/datasets/issues/6056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6056/comments | https://api.github.com/repos/huggingface/datasets/issues/6056/events | https://github.com/huggingface/datasets/pull/6056 | 1,815,086,963 | PR_kwDODunzps5WD4RY | 6,056 | Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is const... | 2023-07-21T03:13:21Z | 2023-08-17T08:26:53Z | null | NONE | null | 0 | {
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} | Context: issue #5990
In order to implement the checkpointing, I introduce a metadata folder that keeps one yaml file for each set that one is uploading. This yaml keeps track of what shards have already been uploaded, and which one the idx of the latest one was. Using this information I am then able to easily get the push_to_hub function to retrieve on demand past history of uploads and continue mapping and uploading from where it was left off. | {
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https://api.github.com/repos/huggingface/datasets/issues/5659 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5659/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5659/comments | https://api.github.com/repos/huggingface/datasets/issues/5659/events | https://github.com/huggingface/datasets/issues/5659 | 1,635,447,540 | I_kwDODunzps5hevL0 | 5,659 | [Audio] Soundfile/libsndfile requirements too stringent for decoding mp3 files | {
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"cc @polinaeterna @lhoestq ",
"@sanchit-gandhi can you please also post the logs of `pip install soundfile==0.12.1`? To check what wheel is being installed or if it's being built from source (I think it's the latter case). \r\nRequired `libsndfile` binary **should** be bundeled with `soundfile` wheel but I assume... | 2023-03-22T10:07:33Z | 2023-11-10T15:42:32Z | 2023-04-07T08:51:28Z | CONTRIBUTOR | null | null | null | ### Describe the bug
I'm encountering several issues trying to load mp3 audio files using `datasets` on a TPU v4.
The PR https://github.com/huggingface/datasets/pull/5573 updated the audio loading logic to rely solely on the `soundfile`/`libsndfile` libraries for loading audio samples, regardless of their file type.
The installation guide suggests that `libsndfile` is bundled in when `soundfile` is pip installed:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/docs/source/installation.md?plain=1#L70-L71
However, just pip installing `soundfile==0.12.1` throws an error that `libsndfile` is missing:
```
pip install soundfile==0.12.1
```
Then:
```python
>>> soundfile
>>> soundfile.__libsndfile_version__
```
<details>
<summary> Traceback (most recent call last): </summary>
```
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 161, in <module>
import _soundfile_data # ImportError if this doesn't exist
ModuleNotFoundError: No module named '_soundfile_data'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 170, in <module>
raise OSError('sndfile library not found using ctypes.util.find_library')
OSError: sndfile library not found using ctypes.util.find_library
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/soundfile.py", line 192, in <module>
_snd = _ffi.dlopen(_explicit_libname)
OSError: cannot load library 'libsndfile.so': libsndfile.so: cannot open shared object file: No such file or directory
```
</details>
Thus, I've followed the official instructions for installing the `soundfile` package from https://github.com/bastibe/python-soundfile#installation, which states that `libsndfile` needs to be installed separately as:
```
pip install --upgrade soundfile
sudo apt install libsndfile1
```
We can now import `soundfile`:
```python
>>> import soundfile
>>> soundfile.__version__
'0.12.1'
>>> soundfile.__libsndfile_version__
'1.0.28'
```
We see that we have `soundfile==0.12.1`, which matches the `datasets[audio]` package constraints:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/setup.py#L144-L147
But we have `libsndfile==1.0.28`, which is too low for decoding mp3 files:
https://github.com/huggingface/datasets/blob/e1af108015e43f9df8734a1faeeaeb9eafce3971/src/datasets/config.py#L136-L138
Updating/upgrading the `libsndfile` doesn't change this:
```
sudo apt-get update
sudo apt-get upgrade
```
Is there any other suggestion for how to get a compatible `libsndfile` version? Currently, the version bundled with Ubuntu `apt-get` is too low for decoding mp3 files.
Maybe we could add this under `setup.py` such that we install the correct `libsndfile` version when we do `pip install datasets[audio]`? IMO this would help circumvent such version issues.
### Steps to reproduce the bug
Environment described above. Loading mp3 files:
```python
from datasets import load_dataset
common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
print(next(iter(common_voice_es)))
```
```python
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[4], line 2
1 common_voice_es = load_dataset("common_voice", "es", split="validation", streaming=True)
----> 2 print(next(iter(common_voice_es)))
File ~/datasets/src/datasets/iterable_dataset.py:941, in IterableDataset.__iter__(self)
937 for key, example in ex_iterable:
938 if self.features:
939 # `IterableDataset` automatically fills missing columns with None.
940 # This is done with `_apply_feature_types_on_example`.
--> 941 yield _apply_feature_types_on_example(
942 example, self.features, token_per_repo_id=self._token_per_repo_id
943 )
944 else:
945 yield example
File ~/datasets/src/datasets/iterable_dataset.py:700, in _apply_feature_types_on_example(example, features, token_per_repo_id)
698 encoded_example = features.encode_example(example)
699 # Decode example for Audio feature, e.g.
--> 700 decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
701 return decoded_example
File ~/datasets/src/datasets/features/features.py:1864, in Features.decode_example(self, example, token_per_repo_id)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
-> 1864 return {
1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1865, in <dictcomp>(.0)
1850 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1851 """Decode example with custom feature decoding.
1852
1853 Args:
(...)
1861 `dict[str, Any]`
1862 """
1864 return {
-> 1865 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1866 if self._column_requires_decoding[column_name]
1867 else value
1868 for column_name, (feature, value) in zip_dict(
1869 {key: value for key, value in self.items() if key in example}, example
1870 )
1871 }
File ~/datasets/src/datasets/features/features.py:1308, in decode_nested_example(schema, obj, token_per_repo_id)
1305 elif isinstance(schema, (Audio, Image)):
1306 # we pass the token to read and decode files from private repositories in streaming mode
1307 if obj is not None and schema.decode:
-> 1308 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
1309 return obj
File ~/datasets/src/datasets/features/audio.py:167, in Audio.decode_example(self, value, token_per_repo_id)
162 raise RuntimeError(
163 "Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
164 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
165 )
166 elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
--> 167 raise RuntimeError(
168 "Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
169 'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '
170 )
172 if file is None:
173 token_per_repo_id = token_per_repo_id or {}
RuntimeError: Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`.
```
### Expected behavior
Load mp3 files!
### Environment info
- `datasets` version: 2.10.2.dev0
- Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.13.1
- PyArrow version: 11.0.0
- Pandas version: 1.5.3
- Soundfile version: 0.12.1
- Libsndfile version: 1.0.28 | {
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"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-26T12:46:07Z | 2023-07-27T16:15:11Z | 2023-07-27T16:14:40Z | MEMBER | null | 0 | {
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fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84
and making sure https://github.com/huggingface/datasets/issues/2918 works as well | {
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"This should be fixed now!\r\n\r\ncc @srush "
] | 2021-04-20T14:19:51Z | 2021-04-20T15:02:45Z | 2021-04-20T15:02:45Z | NONE | null | null | null | Link to datasets viwer (https://huggingface.co/datasets/viewer/) on Quick Tour page (https://huggingface.co/docs/datasets/quicktour.html) returns "502 Bad Gateway"
The same error with https://huggingface.co/datasets/viewer/?dataset=glue&config=mrpc | {
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https://api.github.com/repos/huggingface/datasets/issues/4243 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/4243/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/4243/comments | https://api.github.com/repos/huggingface/datasets/issues/4243/events | https://github.com/huggingface/datasets/pull/4243 | 1,217,689,909 | PR_kwDODunzps425Gkn | 4,243 | WIP: Initial shades loading script and readme | {
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"Thanks for your contribution, @shayne-longpre.\r\n\r\nAre you still interested in adding this dataset? As we are transferring the dataset scripts from this GitHub repo, we would recommend you to add this to the Hugging Face Hub: https://huggingface.co/datasets"
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https://api.github.com/repos/huggingface/datasets/issues/647 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/647/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/647/comments | https://api.github.com/repos/huggingface/datasets/issues/647/events | https://github.com/huggingface/datasets/issues/647 | 704,734,764 | MDU6SXNzdWU3MDQ3MzQ3NjQ= | 647 | Cannot download dataset_info.json | {
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"Thanks for reporting !\r\nWe should add support for servers without internet connection indeed\r\nI'll do that early next week",
"Thanks, @lhoestq !\r\nPlease let me know when it is available. ",
"Right now the recommended way is to create the dataset on a server with internet connection and then to save it an... | 2020-09-19T01:35:15Z | 2020-09-21T08:28:42Z | 2020-09-21T08:28:42Z | NONE | null | null | null | I am running my job on a cloud server where does not provide for connections from the standard compute nodes to outside resources. Hence, when I use `dataset.load_dataset()` to load data, I got an error like this:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/text/default-53ee3045f07ba8ca/0.0.0/dataset_info.json
```
I tried to open this link manually, but I cannot access this file. How can I download this file and pass it through `dataset.load_dataset()` manually?
Versions:
Python version 3.7.3
PyTorch version 1.6.0
TensorFlow version 2.3.0
datasets version: 1.0.1
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https://api.github.com/repos/huggingface/datasets/issues/2865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2865/comments | https://api.github.com/repos/huggingface/datasets/issues/2865/events | https://github.com/huggingface/datasets/pull/2865 | 986,460,698 | MDExOlB1bGxSZXF1ZXN0NzI1NjY1ODgx | 2,865 | Add MultiEURLEX dataset | {
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"Hi @lhoestq, we have this new cool multilingual dataset coming at EMNLP 2021. It would be really nice if we could have it in Hugging Face asap. Thanks! ",
"Hi @lhoestq, I adopted most of your suggestions:\r\n\r\n- Dummy data files reduced, including the 2 smallest documents per subset JSONL.\r\n- README was upda... | 2021-09-02T09:42:24Z | 2021-09-10T11:50:06Z | 2021-09-10T11:50:06Z | CONTRIBUTOR | null | 0 | {
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} | **Add new MultiEURLEX Dataset**
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource). Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels); this is multi-label classification task (given the text, predict multiple labels). | {
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https://api.github.com/repos/huggingface/datasets/issues/1342 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1342/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1342/comments | https://api.github.com/repos/huggingface/datasets/issues/1342/events | https://github.com/huggingface/datasets/pull/1342 | 759,794,121 | MDExOlB1bGxSZXF1ZXN0NTM0NzM1MzAw | 1,342 | [yaml] Fix metadata according to pre-specified scheme | {
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https://api.github.com/repos/huggingface/datasets/issues/5880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5880/comments | https://api.github.com/repos/huggingface/datasets/issues/5880/events | https://github.com/huggingface/datasets/issues/5880 | 1,719,090,101 | I_kwDODunzps5mdzu1 | 5,880 | load_dataset from s3 file system through streaming can't not iterate data | {
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"This sounds related to #5281.\r\n\r\nCan you try passing `storage_options=s3_client.storage_options` instead passing it to `use_auth_token=` ?",
"I tried `storage_options` before, but it doesn't work, I checked our source code and I found that we even didn't pass this parameter to the following process. if I use... | 2023-05-22T07:40:27Z | 2023-05-26T12:52:08Z | null | CONTRIBUTOR | null | null | null | ### Describe the bug
I have a JSON file in my s3 file system(minio), I can use load_dataset to get the file link, but I can't iterate it
<img width="816" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/cc0778d3-36f3-45b5-ac68-4e7c664c2ed0">
<img width="1144" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/76872af3-8b3c-42ff-9f55-528c920a7af1">
we can change 4 lines to fix this bug, you can check whether it is ok for us.
<img width="941" alt="image" src="https://github.com/huggingface/datasets/assets/59083384/5a22155a-ece7-496c-8506-047e5c235cd3">
### Steps to reproduce the bug
1. storage a file in you s3 file system
2. use load_dataset to read it through streaming
3. iterate it
### Expected behavior
can iterate it successfully
### Environment info
- `datasets` version: 2.12.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1
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https://api.github.com/repos/huggingface/datasets/issues/5498 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5498/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5498/comments | https://api.github.com/repos/huggingface/datasets/issues/5498/events | https://github.com/huggingface/datasets/issues/5498 | 1,568,190,529 | I_kwDODunzps5deLBB | 5,498 | TypeError: 'bool' object is not iterable when filtering a datasets.arrow_dataset.Dataset | {
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"Hi! Instead of a single boolean, your filter function should return an iterable (of booleans) in the batched mode like so:\r\n```python\r\ntrain_dataset = train_dataset.filter(\r\n function=lambda batch: [image is not None for image in batch[\"image\"]], \r\n batched=True,\r\n batc... | 2023-02-02T14:46:49Z | 2023-10-08T06:12:47Z | 2023-02-04T17:19:36Z | NONE | null | null | null | ### Describe the bug
Hi,
Thanks for the amazing work on the library!
**Describe the bug**
I think I might have noticed a small bug in the filter method.
Having loaded a dataset using `load_dataset`, when I try to filter out empty entries with `batched=True`, I get a TypeError.
### Steps to reproduce the bug
```
train_dataset = train_dataset.filter(
function=lambda example: example["image"] is not None,
batched=True,
batch_size=10)
```
Error message:
```
File .../lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(*args, **kwargs)
476 validate_fingerprint(kwargs[fingerprint_name])
478 # Call actual function
--> 480 out = func(self, *args, **kwargs)
...
-> 5666 indices_array = [i for i, to_keep in zip(indices, mask) if to_keep]
5667 if indices_mapping is not None:
5668 indices_array = pa.array(indices_array, type=pa.uint64())
TypeError: 'bool' object is not iterable
```
**Removing batched=True allows to bypass the issue.**
### Expected behavior
According to the doc, "[batch_size corresponds to the] number of examples per batch provided to function if batched = True", so we shouldn't need to remove the batchd=True arg?
source: https://huggingface.co/docs/datasets/v2.9.0/en/package_reference/main_classes#datasets.Dataset.filter
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31
- Python version: 3.9.10
- PyArrow version: 10.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/968 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/968/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/968/comments | https://api.github.com/repos/huggingface/datasets/issues/968/events | https://github.com/huggingface/datasets/pull/968 | 754,659,015 | MDExOlB1bGxSZXF1ZXN0NTMwNTIwMjEz | 968 | ADD Afrikaans NER | {
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"One trick if you want to add other datasets: consider running these commands each time you want to add a new dataset\r\n```\r\ngit checkout master\r\ngit fetch upstream\r\ngit rebase upstream/master\r\ngit checkout -b add-<my_dataset_name>\r\n```"
] | 2020-12-01T19:23:03Z | 2020-12-02T09:41:28Z | 2020-12-02T09:41:28Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/2910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2910/comments | https://api.github.com/repos/huggingface/datasets/issues/2910/events | https://github.com/huggingface/datasets/pull/2910 | 996,149,632 | PR_kwDODunzps4rvL9N | 2,910 | feat: 🎸 pass additional arguments to get private configs + info | {
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"Included in https://github.com/huggingface/datasets/pull/2906"
] | 2021-09-14T15:24:19Z | 2021-09-15T16:19:09Z | 2021-09-15T16:19:06Z | CONTRIBUTOR | null | 0 | {
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or infos of private datasets on the hub | {
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https://api.github.com/repos/huggingface/datasets/issues/208 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/208/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/208/comments | https://api.github.com/repos/huggingface/datasets/issues/208/events | https://github.com/huggingface/datasets/pull/208 | 626,398,519 | MDExOlB1bGxSZXF1ZXN0NDI0Mzk0ODIx | 208 | [Dummy data] insert config name instead of config | {
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} | Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself.
Also, @lhoestq fixed small import bug introduced by beam command I think. | {
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"Hi! Currently, we compute the final index as `index % len(dset)`. I agree this behavior is somewhat unexpected and that it would be more appropriate to raise an error instead (this is what `df.iloc` in Pandas does, for instance).\r\n\r\n@albertvillanova @lhoestq wdyt?",
"I agree. I think `index % len(dset)` was ... | 2022-02-11T15:20:01Z | 2022-02-14T19:19:21Z | 2022-02-14T19:19:21Z | NONE | null | null | null | ## Describe the bug
The `.select` method will not throw when sending `indices` bigger than the dataset length; `indices` will be wrapped instead. This behavior is not documented anywhere, and is not intuitive.
## Steps to reproduce the bug
```python
from datasets import Dataset
ds = Dataset.from_dict({"text": ["d", "e", "f"], "label": [4, 5, 6]})
res1 = ds.select([1, 2, 3])['text']
res2 = ds.select([1000])['text']
```
## Expected results
Both results should throw an `Error`.
## Actual results
`res1` will give `['e', 'f', 'd']`
`res2` will give `['e']`
## Environment info
Bug found from this environment:
- `datasets` version: 1.16.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.7
- PyArrow version: 6.0.1
It was also replicated on `master`.
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https://api.github.com/repos/huggingface/datasets/issues/473 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/473/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/473/comments | https://api.github.com/repos/huggingface/datasets/issues/473/events | https://github.com/huggingface/datasets/pull/473 | 672,007,247 | MDExOlB1bGxSZXF1ZXN0NDYyMTIwNzU4 | 473 | add DoQA dataset (ACL 2020) | {
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} | add DoQA dataset (ACL 2020) http://ixa.eus/node/12931 | {
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https://api.github.com/repos/huggingface/datasets/issues/6019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6019/comments | https://api.github.com/repos/huggingface/datasets/issues/6019/events | https://github.com/huggingface/datasets/pull/6019 | 1,799,532,822 | PR_kwDODunzps5VPAlD | 6,019 | Improve logging | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea... | 2023-07-11T18:30:23Z | 2023-07-12T19:34:14Z | 2023-07-12T17:19:28Z | CONTRIBUTOR | null | 0 | {
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} | Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO
(Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them)
Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444 | {
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https://api.github.com/repos/huggingface/datasets/issues/1732 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1732/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1732/comments | https://api.github.com/repos/huggingface/datasets/issues/1732/events | https://github.com/huggingface/datasets/pull/1732 | 784,874,490 | MDExOlB1bGxSZXF1ZXN0NTUzOTkzNTAx | 1,732 | [GEM Dataset] Added TurkCorpus, an evaluation dataset for sentence simplification. | {
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"Thank you for the feedback! I updated the code. "
] | 2021-01-13T07:50:19Z | 2021-01-14T10:19:41Z | 2021-01-14T10:19:41Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/5491 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5491/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5491/comments | https://api.github.com/repos/huggingface/datasets/issues/5491/events | https://github.com/huggingface/datasets/pull/5491 | 1,566,235,012 | PR_kwDODunzps5JA9OD | 5,491 | [MINOR] Typo | {
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https://api.github.com/repos/huggingface/datasets/issues/6164 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6164/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6164/comments | https://api.github.com/repos/huggingface/datasets/issues/6164/events | https://github.com/huggingface/datasets/pull/6164 | 1,859,560,007 | PR_kwDODunzps5YZZAJ | 6,164 | Fix: Missing a MetadataConfigs init when the repo has a `datasets_info.json` but no README | {
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} | When I try to push to an arrow repo (can provide the link on Slack), it uploads the files but fails to update the metadata, with
```
File "app.py", line 123, in add_new_eval
eval_results[level].push_to_hub(my_repo, token=TOKEN, split=SPLIT)
File "blabla_my_env_path/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5501, in push_to_hub
if not metadata_configs:
UnboundLocalError: local variable 'metadata_configs' referenced before assignment
```
This fixes it. | {
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https://api.github.com/repos/huggingface/datasets/issues/3761 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/3761/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/3761/comments | https://api.github.com/repos/huggingface/datasets/issues/3761/events | https://github.com/huggingface/datasets/issues/3761 | 1,144,830,702 | I_kwDODunzps5EPLru | 3,761 | Know your data for HF hub | {
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"Hi @Muhtasham you should take a look at https://huggingface.co/blog/data-measurements-tool and accompanying demo app at https://huggingface.co/spaces/huggingface/data-measurements-tool\r\n\r\nWe would be interested in your feedback. cc @meg-huggingface @sashavor @yjernite "
] | 2022-02-19T19:48:47Z | 2022-02-21T14:15:23Z | 2022-02-21T14:15:23Z | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
Would be great to see be able to understand datasets with the goal of improving data quality, and helping mitigate fairness and bias issues.
**Describe the solution you'd like**
Something like https://knowyourdata.withgoogle.com/ for HF hub | {
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https://api.github.com/repos/huggingface/datasets/issues/1516 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1516/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1516/comments | https://api.github.com/repos/huggingface/datasets/issues/1516/events | https://github.com/huggingface/datasets/pull/1516 | 764,032,327 | MDExOlB1bGxSZXF1ZXN0NTM4MjkzOTMw | 1,516 | adding wrbsc | {
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"@lhoestq thanks for the comments! Should be fixed in the latest commit, I assume the CI errors are unrelated. ",
"merging since the CI is fixed on master"
] | 2020-12-12T16:38:40Z | 2020-12-18T09:41:33Z | 2020-12-18T09:41:33Z | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/486 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/486/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/486/comments | https://api.github.com/repos/huggingface/datasets/issues/486/events | https://github.com/huggingface/datasets/issues/486 | 675,649,034 | MDU6SXNzdWU2NzU2NDkwMzQ= | 486 | Bookcorpus data contains pretokenized text | {
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"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do... | 2020-08-09T06:53:24Z | 2022-10-04T17:44:33Z | 2022-10-04T17:44:33Z | CONTRIBUTOR | null | null | null | It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end quotes, respectively.
On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575 | {
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https://api.github.com/repos/huggingface/datasets/issues/5379 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5379/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5379/comments | https://api.github.com/repos/huggingface/datasets/issues/5379/events | https://github.com/huggingface/datasets/pull/5379 | 1,504,010,639 | PR_kwDODunzps5F1r2k | 5,379 | feat: depth estimation dataset guide. | {
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"_The documentation is not available anymore as the PR was closed or merged._",
"Thanks for the changes, looks good to me!",
"@stevhliu I have pushed some quality improvements both in terms of code and content. Would you be able to re-review? ",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0... | 2022-12-20T05:32:11Z | 2023-01-13T12:30:31Z | 2023-01-13T12:23:34Z | MEMBER | null | 0 | {
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} | This PR adds a guide for prepping datasets for depth estimation.
PR to add documentation images is up here: https://huggingface.co/datasets/huggingface/documentation-images/discussions/22 | {
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