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Intermittent JSON parse error when streaming the Pile
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NONE
| null |
## Describe the bug
I have an intermittent error when streaming the Pile, where I get a JSON parse error which causes my program to crash.
This is intermittent - when I rerun the program with the same random seed it does not crash in the same way. The exact point this happens also varied - it happened to me 11B tokens and 4 days into a training run, and now just happened 2 minutes into one, but I can't reliably reproduce it.
I'm using a remote machine with 8 A6000 GPUs via runpod.io
## Expected results
I have a DataLoader which can iterate through the whole Pile
## Actual results
Stack trace:
```
Failed to read file 'zstd://12.jsonl::https://the-eye.eu/public/AI/pile/train/12.jsonl.zst' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Invalid value. in row 0
```
I'm currently using HuggingFace accelerate, which also gave me the following stack trace, but I've also experienced this problem intermittently when using DataParallel, so I don't think it's to do with parallelisation
```
Traceback (most recent call last):
File "ddp_script.py", line 1258, in <module>
main()
File "ddp_script.py", line 1143, in main
for c, batch in tqdm.tqdm(enumerate(data_iter)):
File "/opt/conda/lib/python3.7/site-packages/tqdm/std.py", line 1195, in __iter__
for obj in iterable:
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 503, in __iter__
next_batch, next_batch_info, next_skip = self._fetch_batches(main_iterator)
File "/opt/conda/lib/python3.7/site-packages/accelerate/data_loader.py", line 454, in _fetch_batches
broadcast_object_list(batch_info)
File "/opt/conda/lib/python3.7/site-packages/accelerate/utils/operations.py", line 333, in broadcast_object_list
torch.distributed.broadcast_object_list(object_list, src=from_process)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1900, in broadcast_object_list
object_list[i] = _tensor_to_object(obj_view, obj_size)
File "/opt/conda/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 1571, in _tensor_to_object
return _unpickler(io.BytesIO(buf)).load()
_pickle.UnpicklingError: invalid load key, '@'.
```
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(
cfg["dataset_name"], streaming=True, split="train")
dataset = dataset.remove_columns("meta")
dataset = dataset.map(tokenize_and_concatenate, batched=True)
dataset = dataset.with_format(type="torch")
train_data_loader = DataLoader(
dataset, batch_size=cfg["batch_size"], num_workers=3)
for batch in train_data_loader:
continue
```
`tokenize_and_concatenate` is a custom tokenization function I defined on the GPT-NeoX tokenizer to tokenize the text, separated by endoftext tokens, and reshape to have length batch_size, I don't think this is related to tokenization:
```
import numpy as np
import einops
import torch
def tokenize_and_concatenate(examples):
texts = examples["text"]
full_text = tokenizer.eos_token.join(texts)
div = 20
length = len(full_text) // div
text_list = [full_text[i * length: (i + 1) * length]
for i in range(div)]
tokens = tokenizer(text_list, return_tensors="np", padding=True)[
"input_ids"
].flatten()
tokens = tokens[tokens != tokenizer.pad_token_id]
n = len(tokens)
curr_batch_size = n // (seq_len - 1)
tokens = tokens[: (seq_len - 1) * curr_batch_size]
tokens = einops.rearrange(
tokens,
"(batch_size seq) -> batch_size seq",
batch_size=curr_batch_size,
seq=seq_len - 1,
)
prefix = np.ones((curr_batch_size, 1), dtype=np.int64) * \
tokenizer.bos_token_id
return {
"text": np.concatenate([prefix, tokens], axis=1)
}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-5.4.0-105-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.13
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
ZStandard data:
Version: 0.18.0
Summary: Zstandard bindings for Python
Home-page: https://github.com/indygreg/python-zstandard
Author: Gregory Szorc
Author-email: [email protected]
License: BSD
Location: /opt/conda/lib/python3.7/site-packages
Requires:
Required-by:
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added from_generator method to IterableDataset class.
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[] | null | 3 | 2022-09-30T22:14:05Z | 2022-10-05T12:51:48Z | 2022-10-05T12:10:48Z |
CONTRIBUTOR
| null |
Hello,
This resolves issues #4988.
I added a method `from_generator` to class `IterableDataset`.
I modified the `read` method of input stream generator to also return Iterable_dataset.
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Revert task removal in folder-based builders
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[] | null | 1 | 2022-09-30T14:50:03Z | 2022-10-03T12:23:35Z | 2022-10-03T12:21:31Z |
CONTRIBUTOR
| null |
Reverts the removal of `task_templates` in the folder-based builders. I also added the `AudioClassifaction` task for consistency.
This is needed to fix https://github.com/huggingface/transformers/issues/19177.
I think we should soon deprecate and remove the current task API (and investigate if it's possible to integrate the `train eval index` API), but we need to update the Transformers examples before that so we don't break them.
cc @NielsRogge
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CONTRIBUTOR
| null |
Even though we save the `format` state in `save_to_disk`, we don't restore it in `load_from_disk`. We should fix that.
Reported here: https://discuss.huggingface.co/t/save-to-disk-loses-formatting-information/23815
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Add `kwargs` to `Dataset.from_generator`
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CONTRIBUTOR
| null |
Add the `kwargs` param to `from_generator` to align it with the rest of the `from_` methods (this param allows passing custom `writer_batch_size` for instance).
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PR_kwDODunzps4_7KI2
| 5,048 |
Fix bug with labels of eurlex config of lex_glue dataset
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[] | null | 4 | 2022-09-30T09:47:12Z | 2022-09-30T16:30:25Z | 2022-09-30T16:21:41Z |
CONTRIBUTOR
| null |
Fix for a critical bug in the EURLEX dataset label list to make LexGLUE EURLEX results replicable.
In LexGLUE (Chalkidis et al., 2022), the following is mentioned w.r.t. EUR-LEX: _"It supports four different label granularities, comprising 21, 127, 567, 7390 EuroVoc concepts, respectively. We use the 100 most frequent concepts from level 2 [...]”._ The current label list has all 127 labels, which leads to different (lower) results, as communicated by users.
Thanks!
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PR_kwDODunzps4_64bS
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Fix cats_vs_dogs
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[] | null | 1 | 2022-09-30T08:47:29Z | 2022-09-30T10:23:22Z | 2022-09-30T09:34:28Z |
MEMBER
| null |
Reported in https://github.com/huggingface/datasets/pull/3878
I updated the number of examples
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I_kwDODunzps5S7qjn
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Audiofolder creates empty Dataset if files same level as metadata
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NONE
| null |
## Describe the bug
When audio files are at the same level as the metadata (`metadata.csv` or `metadata.jsonl` ), the `load_dataset` returns a `DatasetDict` with no rows but the correct columns.
https://github.com/huggingface/datasets/blob/1ea4d091b7a4b83a85b2eeb8df65115d39af3766/docs/source/audio_dataset.mdx?plain=1#L88
## Steps to reproduce the bug
`metadata.csv`:
```csv
file_name,duration,transcription
./2063_fe9936e7-62b2-4e62-a276-acbd344480ce_1.wav,10.768,hello
```
```python
>>> audio_dataset = load_dataset("audiofolder", data_dir="/audio-data/")
>>> audio_dataset
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
I've tried, with no success,:
- setting `split` to something else so I don't get a `DatasetDict`,
- removing the `./`,
- using `.jsonl`.
## Expected results
```
Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 1
})
```
## Actual results
```
DatasetDict({
train: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
validation: Dataset({
features: ['audio', 'duration', 'transcription'],
num_rows: 0
})
})
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.13.0-1025-aws-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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I_kwDODunzps5S7V05
| 5,045 |
Automatically revert to last successful commit to hub when a push_to_hub is interrupted
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[] | null | 5 | 2022-09-29T18:08:12Z | 2022-09-30T16:49:21Z | null |
NONE
| null |
**Is your feature request related to a problem? Please describe.**
I pushed a modification of a large dataset (remove a column) to the hub. The push was interrupted after some files were committed to the repo. This left the dataset to raise an error on load_dataset() (ValueError couldn’t cast … because column names don’t match). Only by specifying the previous (complete) commit as revision=commit_hash in load_data(), I was able to repair this and after a successful, complete push, the dataset loads without error again.
**Describe the solution you'd like**
Would it make sense to detect an incomplete push_to_hub() and automatically revert to the previous commit/revision?
**Describe alternatives you've considered**
Leave everything as is, the revision parameter in load_dataset() allows to manually fix this problem.
**Additional context**
Provide useful defaults
|
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I_kwDODunzps5S7K6c
| 5,044 |
integrate `load_from_disk` into `load_dataset`
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[] | null | 4 | 2022-09-29T17:37:12Z | 2022-09-30T16:59:19Z | null |
MEMBER
| null |
**Is your feature request related to a problem? Please describe.**
Is it possible to make `load_dataset` more universal similar to `from_pretrained` in `transformers` so that it can handle the hub, and the local path datasets of all supported types?
Currently one has to choose a different loader depending on how the dataset has been created.
e.g. this won't work:
```
$ git clone https://huggingface.co/datasets/severo/test-parquet
$ python -c 'from datasets import load_dataset; ds=load_dataset("test-parquet"); \
ds.save_to_disk("my_dataset"); load_dataset("my_dataset")'
[...]
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/stas/anaconda3/envs/py38-pt112/lib/python3.8/site-packages/datasets/table.py", line 1968, in cast_table_to_schema
raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nbecause column names don't match")
ValueError: Couldn't cast
_data_files: list<item: struct<filename: string>>
child 0, item: struct<filename: string>
child 0, filename: string
```
both times the dataset is being loaded from disk. Why does it fail the second time?
Why can't `save_to_disk` generate a dataset that can be immediately loaded by `load_dataset`?
e.g. the simplest hack would be to have `save_to_disk` add some flag to the saved dataset, that tells `load_dataset` to internally call `load_from_disk`. like having `save_to_disk` create a `load_me_with_load_from_disk.txt` file ;) and `load_dataset` will support that feature from saved datasets from new `datasets` versions. The old ones will still need to use `load_from_disk` explicitly. Unless the flag is not needed and one can immediately tell by looking at the saved dataset that it was saved via `save_to_disk` and thus use `load_from_disk` internally.
The use-case is defining a simple API where the user only ever needs to pass a `dataset_name_or_path` and it will always just work. Currently one needs to manually add additional switches telling the system whether to use one loading method or the other which works but it's not smooth.
Thank you!
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PR_kwDODunzps4_3uzy
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Fix `flatten_indices` with empty indices mapping
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[] | null | 1 | 2022-09-29T16:17:28Z | 2022-09-30T15:46:39Z | 2022-09-30T15:44:25Z |
CONTRIBUTOR
| null |
Fix #5038
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PR_kwDODunzps4_2eqa
| 5,042 |
Update swiss judgment prediction
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[] | null | 1 | 2022-09-29T12:10:02Z | 2022-09-30T07:14:00Z | 2022-09-29T14:32:02Z |
CONTRIBUTOR
| null |
I forgot to add the new citation.
|
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PR_kwDODunzps4_2WES
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Support streaming hendrycks_test dataset.
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[] | null | 1 | 2022-09-29T11:37:58Z | 2022-09-30T07:13:38Z | 2022-09-29T12:07:29Z |
MEMBER
| null |
This PR:
- supports streaming
- fixes the description section of the dataset card
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PR_kwDODunzps4_11O2
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Fix NonMatchingChecksumError in hendrycks_test dataset
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[] | null | 1 | 2022-09-29T09:37:43Z | 2022-09-29T10:06:22Z | 2022-09-29T10:04:19Z |
MEMBER
| null |
Update metadata JSON.
Fix #5039.
|
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I_kwDODunzps5S3xuj
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Hendrycks Checksum
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NONE
| null |
Hi,
The checksum for [hendrycks_test](https://huggingface.co/datasets/hendrycks_test) does not compare correctly, I guess it has been updated on the remote.
```
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://people.eecs.berkeley.edu/~hendrycks/data.tar']
```
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I_kwDODunzps5S1BaS
| 5,038 |
`Dataset.unique` showing wrong output after filtering
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[] | null | 2 | 2022-09-28T16:20:35Z | 2022-09-30T15:44:25Z | 2022-09-30T15:44:25Z |
CONTRIBUTOR
| null |
## Describe the bug
After filtering a dataset, and if no samples remain, `Dataset.unique` will return the unique values of the unfiltered dataset.
## Steps to reproduce the bug
```python
from datasets import Dataset
dataset = Dataset.from_dict({'id': [0]})
dataset = dataset.filter(lambda _: False)
print(dataset.unique('id'))
```
## Expected results
The above code should return an empty list since the dataset is empty.
## Actual results
```bash
[0]
```
## Environment info
- `datasets` version: 2.5.1
- Platform: Linux-5.18.19-100.fc35.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.14
- PyArrow version: 7.0.0
- Pandas version: 1.3.5
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PR_kwDODunzps4_xcp0
| 5,037 |
Improve CI performance speed of PackagedDatasetTest
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[] | null | 2 | 2022-09-28T12:08:16Z | 2022-09-30T16:05:42Z | 2022-09-30T16:03:24Z |
MEMBER
| null |
This PR improves PackagedDatasetTest CI performance speed. For Ubuntu (latest):
- Duration (without parallelism) before: 334.78s (5.58m)
- Duration (without parallelism) afterwards: 0.48s
The approach is passing a dummy `data_files` argument to load the builder, so that it avoids the slow inferring of it over the entire root directory of the repo.
## Total duration of PackagedDatasetTest
| | Before | Afterwards | Improvement
|---|---:|---:|---:|
| Linux | 334.78s | 0.48s | x700
| Windows | 513.02s | 1.09s | x500
## Durations by each individual sub-test
More accurate durations, running them on GitHub, for Linux (latest).
Before this PR, the total test time (without parallelism) for `tests/test_dataset_common.py::PackagedDatasetTest` is 334.78s (5.58m)
```
39.07s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_imagefolder
38.94s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_audiofolder
34.18s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_parquet
34.12s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_csv
34.00s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_pandas
34.00s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_text
33.86s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_json
10.39s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_audiofolder
6.50s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_audiofolder
6.46s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_imagefolder
6.40s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_imagefolder
5.77s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_csv
5.77s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_text
5.74s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_parquet
5.69s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_json
5.68s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_pandas
5.67s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_parquet
5.67s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_pandas
5.66s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_json
5.66s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_csv
5.55s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_configs_text
(42 durations < 0.005s hidden.)
```
With this PR: 0.48s
```
0.09s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_audiofolder
0.08s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_csv
0.08s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_imagefolder
0.06s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_json
0.05s call tests/test_dataset_common.py::PackagedDatasetTest::test_builder_class_audiofolder
0.05s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_parquet
0.04s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_pandas
0.03s call tests/test_dataset_common.py::PackagedDatasetTest::test_load_dataset_offline_text
(55 durations < 0.005s hidden.)
```
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Add oversampling strategy iterable datasets interleave
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[] | null | 1 | 2022-09-28T10:10:23Z | 2022-09-30T12:30:48Z | 2022-09-30T12:28:23Z |
CONTRIBUTOR
| null |
Hello everyone,
Following the issue #4893 and the PR #4831, I propose here an oversampling strategy for a `IterableDataset` list.
The `all_exhausted` strategy stops building the new dataset as soon as all samples in each dataset have been added at least once.
It follows roughly the same logic behind #4831, namely:
- if ``probabilities`` is `None` and the strategy is `all_exhausted`, it simply performs a round robin interleaving that stops when the longest dataset is out of samples. Here the new dataset length will be $maxLengthDataset*nbDataset$.
- if ``probabilities`` is not `None` and the strategy is `all_exhausted`, it keeps trace of the datasets which were out of samples but continues to add them to the new dataset, and stops as soons as every dataset runs out of samples at least once.
In order to be consistent and also to align with the `Dataset` behavior, please note that the behavior of the default strategy (`first_exhausted`) has been changed. Namely, it really stops when a dataset is out of samples whereas it used to stop when receiving the `StopIteration` error.
To give an example of the last note, with the following snippet:
```
>>> from tests.test_iterable_dataset import *
>>> d1 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [0, 1, 2]])), {}))
>>> d2 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [10, 11, 12, 13]])), {}))
>>> d3 = IterableDataset(ExamplesIterable((lambda: (yield from [(i, {"a": i}) for i in [20, 21, 22, 23, 24]])), {}))
>>> dataset = interleave_datasets([d1, d2, d3])
>>> [x["a"] for x in dataset]
```
The result here will then be `[10, 0, 11, 1, 2]` instead of `[10, 0, 11, 1, 2, 20, 12, 13]`.
I modified the behavior because I found it to be consistent with the under/oversampling approach and because it unified the undersampling and oversampling code, but I stay open to any suggestions.
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Fix typos in load docstrings and comments
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[] | null | 1 | 2022-09-28T08:05:07Z | 2022-09-28T17:28:40Z | 2022-09-28T17:26:15Z |
MEMBER
| null |
Minor fix of typos in load docstrings and comments
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Update README.md of yahoo_answers_topics dataset
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Remove redundant code from some dataset module factories
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[] | null | 1 | 2022-09-28T07:06:26Z | 2022-09-28T16:57:51Z | 2022-09-28T16:55:12Z |
MEMBER
| null |
This PR removes some redundant code introduced by mistake after a refactoring in:
- #4576
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new dataset type: single-label and multi-label video classification
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[] | null | 6 | 2022-09-27T19:40:11Z | 2022-11-02T19:10:13Z | null |
NONE
| null |
**Is your feature request related to a problem? Please describe.**
In my research, I am dealing with multi-modal (audio+text+frame sequence) video classification. It would be great if the datasets library supported generating multi-modal batches from a video dataset.
**Describe the solution you'd like**
Assume I have video files having single/multiple labels. I want to train a single/multi-label video classification model. I want datasets to support generating multi-modal batches (audio+frame sequence) from video files. Audio waveform and frame sequence can be extracted from each video clip then I can use any audio, image and video model from transformers library to extract features which will be fed into my model.
**Describe alternatives you've considered**
Currently, I am using https://github.com/facebookresearch/pytorchvideo dataloaders. There seems to be not much alternative.
**Additional context**
I am wiling to open a PR but don't know where to start.
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Support hfh 0.10 implicit auth
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[] | null | 4 | 2022-09-27T18:37:49Z | 2022-09-30T09:18:24Z | 2022-09-30T09:15:59Z |
MEMBER
| null |
In huggingface-hub 0.10 the `token` parameter is deprecated for dataset_info and list_repo_files in favor of use_auth_token.
Moreover if use_auth_token=None then the user's token is used implicitly.
I took those two changes into account
Close https://github.com/huggingface/datasets/issues/4990
TODO:
- [x] fix tests
We should wait hfh 0.10 to be relased first to make sure it works correctly before merging
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https://api.github.com/repos/huggingface/datasets/issues/5030
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PR_kwDODunzps4_tfBO
| 5,030 |
Fast dataset iter
|
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[] | null | 2 | 2022-09-27T16:44:51Z | 2022-09-29T15:50:44Z | 2022-09-29T15:48:17Z |
CONTRIBUTOR
| null |
Use `pa.Table.to_reader` to make iteration over examples/batches faster in `Dataset.{__iter__, map}`
TODO:
* [x] benchmarking (the only benchmark for now - iterating over (single) examples of `bookcorpus` (75 mil examples) in Colab is approx. 2.3x faster)
* [x] check if iterating over bigger chunks + slicing to fetch individual examples in `_iter` yields better performance
|
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PR_kwDODunzps4_r8-j
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Fix import in `ClassLabel` docstring example
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[] | null | 1 | 2022-09-27T11:35:29Z | 2022-09-27T14:03:24Z | 2022-09-27T12:27:50Z |
CONTRIBUTOR
| null |
This PR addresses a super-simple fix: adding a missing `import` to the `ClassLabel` docstring example, as it was formatted as `from datasets Features`, so it's been fixed to `from datasets import Features`.
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| 1,386,272,533 |
I_kwDODunzps5SoNcV
| 5,028 |
passing parameters to the method passed to Dataset.from_generator()
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[] | null | 1 | 2022-09-26T15:20:06Z | 2022-10-03T13:00:00Z | 2022-10-03T13:00:00Z |
NONE
| null |
Big thanks for providing dataset creation via a generator.
I want to ask whether there is any way that parameters can be passed to the method Dataset.from_generator() method, like as follows.
```
from datasets import Dataset
def gen(param1):
for idx in len(custom_dataset):
yield custom_dataset[idx] + param1
ds = Dataset.from_generator(gen(param1))
```
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PR_kwDODunzps4_nFUE
| 5,027 |
Fix typo in error message
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[] | null | 1 | 2022-09-26T14:10:09Z | 2022-09-27T12:28:03Z | 2022-09-27T12:26:02Z |
CONTRIBUTOR
| null | null |
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PR_kwDODunzps4_mz1w
| 5,026 |
patch CI_HUB_TOKEN_PATH with Path instead of str
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[] | null | 1 | 2022-09-26T13:19:01Z | 2022-09-26T14:30:55Z | 2022-09-26T14:28:45Z |
CONTRIBUTOR
| null |
Should fix the tests for `huggingface_hub==0.10.0rc0` prerelease (see [failed CI](https://github.com/huggingface/datasets/actions/runs/3127805250/jobs/5074879144)).
Related to [this thread](https://huggingface.slack.com/archives/C02V5EA0A95/p1664195165294559) (internal link).
Note: this should be a backward compatible fix (e.g. works also with previous versions of `huggingface_hub`)
I am not sure where to put the changes so feel free to cherry-pick the commit and close this one without merging.
cc @lhoestq
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I_kwDODunzps5SnNpn
| 5,025 |
Custom Json Dataset Throwing Error when batch is False
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[] | null | 2 | 2022-09-26T12:38:39Z | 2022-09-27T19:50:00Z | 2022-09-27T19:50:00Z |
NONE
| null |
## Describe the bug
A clear and concise description of what the bug is.
I tried to create my custom dataset using below code
```
from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from torchvision import transforms
from transformers import AutoProcessor
# we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes,
# based on the checkpoint we provide from the hub
from datasets import load_dataset
def prepare_examples(examples):
#Some preporcessing for each image and text as all my data saved in cloud
#For this reason I couldn't set the batch to True.
encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,
truncation=True, padding="max_length")
# encoding['pixel_values']=np.array(encoding['pixel_values'])
return encoding
dataset = load_dataset("json", data_files='issues.jsonl')
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
features = dataset["train"].features
column_names = dataset["train"].column_names
# we need to define custom features for `set_format` (used later on) to work properly
features = Features({
'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'labels': Sequence(feature=Value(dtype='int64')),
})
train_dataset = dataset["train"].map(
prepare_examples,
batched=False,
remove_columns=column_names,
features=features
)
```
It throws below error.
```
/opt/conda/lib/python3.7/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
172 storage = to_pyarrow_listarray(data, pa_type)
--> 173 return pa.ExtensionArray.from_storage(pa_type, storage)
174
/opt/conda/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.ExtensionArray.from_storage()
TypeError: Incompatible storage type list<item: list<item: list<item: list<item: float>>>> for extension type extension<arrow.py_extension_type<Array3DExtensionType>>
```
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
```
rom datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
from torchvision import transforms
from transformers import AutoProcessor
# we'll use the Auto API here - it will load LayoutLMv3Processor behind the scenes,
# based on the checkpoint we provide from the hub
from datasets import load_dataset
def prepare_examples(examples):
#Some preporcessing for each image and text as all my data saved in cloud
encoding = processor(img_as_tensor, words, boxes=boxes, word_labels=labels,
truncation=True, padding="max_length")
# encoding['pixel_values']=np.array(encoding['pixel_values'])
return encoding
dataset = load_dataset("json", data_files='issues.jsonl')
processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
features = dataset["train"].features
column_names = dataset["train"].column_names
# we need to define custom features for `set_format` (used later on) to work properly
features = Features({
'pixel_values': Array3D(dtype="float32", shape=(3, 224, 224)),
'input_ids': Sequence(feature=Value(dtype='int64')),
'attention_mask': Sequence(Value(dtype='int64')),
'bbox': Array2D(dtype="int64", shape=(512, 4)),
'labels': Sequence(feature=Value(dtype='int64')),
})
train_dataset = dataset["train"].map(
prepare_examples,
batched=False,
remove_columns=column_names,
features=features
)
## Expected results
A clear and concise description of the expected results.
Expected would be similar to all the otherdatasets with no error.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Unix
- Python version: 3.9
- PyArrow version: 9.0.0
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PR_kwDODunzps4_mZ3J
| 5,024 |
Fix string features of xcsr dataset
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[] | null | 1 | 2022-09-26T11:55:36Z | 2022-09-28T07:56:18Z | 2022-09-28T07:54:19Z |
MEMBER
| null |
This PR fixes string features of `xcsr` dataset to avoid character splitting.
Fix #5023.
CC: @yangxqiao, @yuchenlin
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I_kwDODunzps5Smt4Y
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Text strings are split into lists of characters in xcsr dataset
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MEMBER
| null |
## Describe the bug
Text strings are split into lists of characters.
Example for "X-CSQA-en":
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': ['T',
'h',
'e',
' ',
'd',
'e',
'n',
't',
'a',
'l',
' ',
'o',
'f',
'f',
'i',
'c',
'e',
' ',
'h',
'a',
'n',
'd',
'l',
'e',
'd',
' ',
'a',
' ',
'l',
'o',
't',
' ',
'o',
'f',
' ',
'p',
'a',
't',
'i',
'e',
'n',
't',
's',
' ',
'w',
'h',
'o',
' ',
'e',
'x',
'p',
'e',
'r',
'i',
'e',
'n',
'c',
'e',
'd',
' ',
't',
'r',
'a',
'u',
'm',
'a',
't',
'i',
'c',
' ',
'm',
'o',
'u',
't',
'h',
' ',
'i',
'n',
'j',
'u',
'r',
'y',
',',
' ',
'w',
'h',
'e',
'r',
'e',
' ',
'w',
'e',
'r',
'e',
' ',
't',
'h',
'e',
's',
'e',
' ',
'p',
'a',
't',
'i',
'e',
'n',
't',
's',
' ',
'c',
'o',
'm',
'i',
'n',
'g',
' ',
'f',
'r',
'o',
'm',
'?'],
'choices': [{'label': ['A'], 'text': ['t', 'o', 'w', 'n']},
{'label': ['B'], 'text': ['m', 'i', 'c', 'h', 'i', 'g', 'a', 'n']},
{'label': ['C'], 'text': ['h', 'o', 's', 'p', 'i', 't', 'a', 'l']},
{'label': ['D'], 'text': ['s', 'c', 'h', 'o', 'o', 'l', 's']},
{'label': ['E'],
'text': ['o',
'f',
'f',
'i',
'c',
'e',
' ',
'b',
'u',
'i',
'l',
'd',
'i',
'n',
'g']}]},
'answerKey': 'C'}
## Steps to reproduce the bug
```python
ds = load_dataset("datasets/xcsr", "X-CSQA-en", split="validation", streaming=True)
item = next(iter(ds))
item
```
## Expected results
```
{'id': 'd3845adc08414fda',
'lang': 'en',
'question': {'stem': 'The dental office handled a lot of patients who experienced traumatic mouth injury, where were these patients coming from?',
'choices': {'label': ['A', 'B', 'C', 'D', 'E'],
'text': ['town', 'michigan', 'hospital', 'schools', 'office building']}},
'answerKey': 'C'}
```
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Fix languages of X-CSQA configs in xcsr dataset
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[] | null | 4 | 2022-09-26T05:13:39Z | 2022-09-26T12:27:20Z | 2022-09-26T10:57:30Z |
MEMBER
| null |
Fix #5017.
CC: @yangxqiao, @yuchenlin
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[] | null | 3 | 2022-09-26T03:22:14Z | 2022-09-29T08:07:50Z | 2022-09-29T08:07:50Z |
NONE
| null |
## Describe the bug
Including the strings "test" or "train" anywhere in a filename causes `datasets` to infer the split and silently ignore all other files.
This behavior is documented for directory names but not filenames: https://huggingface.co/docs/datasets/image_dataset#imagefolder
## Steps to reproduce the bug
`metadata.jsonl`
```json
{"file_name": "photo of a cat.jpg", "text": "a photo of a cat"}
{"file_name": "photo of a dog.jpg", "text": "a photo of a dog"}
{"file_name": "photo of a train.jpg", "text": "a photo of a train"}
{"file_name": "photo of test tubes.jpg", "text": "a photo of test tubes"}
```
`bug.py`
```python
from datasets import load_dataset
dataset = load_dataset("dataset")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['image', 'text'],
# num_rows: 1
# })
# test: Dataset({
# features: ['image', 'text'],
# num_rows: 1
# })
# })
for split in dataset:
for n in dataset[split]:
print(n['text'])
# a photo of a train
# a photo of test tubes
```
## Expected results
One single dataset with all four images / a warning for unused files / documentation of this behavior
## Actual results
Only the images with "test" or "train" in the name are loaded
## Environment info
- `datasets` version: 2.5.1
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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Fix URLs of sbu_captions dataset
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[] | null | 1 | 2022-09-24T14:00:33Z | 2022-09-28T07:20:20Z | 2022-09-28T07:18:23Z |
CONTRIBUTOR
| null |
Forbidden
You don't have permission to access /~vicente/sbucaptions/sbu-captions-all.tar.gz on this server.
Additionally, a 403 Forbidden error was encountered while trying to use an ErrorDocument to handle the request.
Apache/2.4.6 (Red Hat Enterprise Linux) OpenSSL/1.0.2k-fips PHP/5.4.16 mod_fcgid/2.3.9 mod_wsgi/3.4 Python/2.7.5 mod_perl/2.0.11 Perl/v5.16.3 Server at [www.cs.virginia.edu](mailto:[email protected]) Port 443
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Update swiss judgment prediction
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[] | null | 4 | 2022-09-24T13:28:57Z | 2022-09-28T07:13:39Z | 2022-09-28T05:48:50Z |
CONTRIBUTOR
| null |
Hi,
I updated the dataset to include additional data made available recently. When I test it locally, it seems to work. However, I get the following error with the dummy data creation:
`Dummy data generation done but dummy data test failed since splits ['train', 'validation', 'test'] have 0 examples for config 'fr'`. Do you know why this could be the case?
Cheers,
Joel
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Create all YAML dataset_info
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[] | null | 2 | 2022-09-23T18:08:15Z | 2022-10-03T17:08:05Z | 2022-10-03T17:08:05Z |
MEMBER
| null |
Following https://github.com/huggingface/datasets/pull/4926
Creates all the `dataset_info` YAML fields in the dataset cards
The JSON are also updated using the simplified backward compatible format added in https://github.com/huggingface/datasets/pull/4926
Needs https://github.com/huggingface/datasets/pull/4926 to be merged first
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xcsr: X-CSQA simply uses english for all alleged non-english data
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NONE
| null |
## Describe the bug
All the alleged non-english subcollections for the X-CSQA task in the [xcsr benchmark dataset ](https://huggingface.co/datasets/xcsr) seem to be copies of the english subcollection, rather than translations. This is in contrast to the data description:
> we automatically translate the original CSQA and CODAH datasets, which only have English versions, to 15 other languages, forming development and test sets for studying X-CSR
## Steps to reproduce the bug
```python
# let's say you want to load the french X-CSQA subcollection
french = datasets.load_dataset("xcsr", "X-CSQA-fr")
# for good measure, let's load english too
english = datasets.load_dataset("xcsr", "X-CSQA-en")
# let's inspect
"".join(english['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
"".join(french['test'][0]['question']['stem'])
# output: 'The people wanted to stop the parade, so what did they set up to thwart it?'
# what? Why are they both in english?
# I've checked this for validation and train splits too, across many datapoints. It's all the same english dataset
# maybe i need to look better?
french['test'].unique('lang')
# output: ['en']
# no, it's all english
```
## Expected results
Accessing a subcollection in language X should return a subcollection containg samples in language X
## Actual results
Accessing a subcollection in language X returns a subcollection containing samples in English.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.5.1
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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Fix tar extraction vuln
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[] | null | 1 | 2022-09-23T14:22:21Z | 2022-09-29T12:42:26Z | 2022-09-29T12:40:28Z |
MEMBER
| null |
Fix for CVE-2007-4559
Description:
Directory traversal vulnerability in the (1) extract and (2) extractall functions in the tarfile
module in Python allows user-assisted remote attackers to overwrite arbitrary files via a .. (dot dot)
sequence in filenames in a TAR archive, a related issue to CVE-2001-1267.
I fixed it by using the solution proposed in https://stackoverflow.com/questions/10060069/safely-extract-zip-or-tar-using-python
It blocks extraction of files with an absolute path or double dots and symlinks.
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Transfer dataset scripts to Hub
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MEMBER
| null |
Before merging:
- #4974
TODO:
- [x] Create label: ["dataset contribution"](https://github.com/huggingface/datasets/pulls?q=label%3A%22dataset+contribution%22)
- [x] Create project: [Datasets: Transfer datasets to Hub](https://github.com/orgs/huggingface/projects/22/)
- [x] PRs:
- [x] Add dataset: we should recommend transfer all additions of datasets to the Hub, under the appropriate namespace; no more additions of datasets on GitHub
- [x] Update dataset: in general, we should merge bug fixes; enhancements should be considered on a case-by-case basis, depending on whether there is a more suitable namespace on the Hub
- [ ] Issues
Finally:
- [x] #4974
Let me know what you think! :hugs:
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I need to read the custom dataset in conll format
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I_kwDODunzps5SdUCj
| 5,013 |
would huggingface like publish cpp binding for datasets package ?
|
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[] | null | 5 | 2022-09-23T07:42:49Z | 2022-09-27T03:40:30Z | null |
NONE
| null |
HI:
I use cpp env libtorch, I like use hugggingface ,but huggingface not cpp binding, would you like publish cpp binding for it.
thanks
|
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| 1,382,851,096 |
I_kwDODunzps5SbKIY
| 5,012 |
Force JSON format regardless of file naming on S3
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[] | null | 1 | 2022-09-22T18:28:15Z | 2022-09-26T09:31:38Z | null |
NONE
| null |
I have a file on S3 created by Data Version Control, it looks like `s3://dvc/ac/badff5b134382a0f25248f1b45d7b2` but contains a json file. If I run
```python
dataset = load_dataset(
"json",
data_files='s3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
)
```
It gives me
```
InvalidSchema: No connection adapters were found for 's3://dvc/ac/badff5b134382a0f25248f1b45d7b2'
```
However, I cannot go ahead and change the names of the s3 file. Is there a way to "force" load a S3 url with certain decoder (JSON, CSV, etc.) regardless of s3 URL naming?
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| 1,382,609,587 |
I_kwDODunzps5SaPKz
| 5,011 |
Audio: `encode_example` fails with IndexError
|
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[] | null | 1 | 2022-09-22T15:07:27Z | 2022-09-23T09:05:18Z | 2022-09-23T09:05:18Z |
CONTRIBUTOR
| null |
## Describe the bug
Loading the dataset [earnings-22](https://huggingface.co/datasets/sanchit-gandhi/earnings22_split) from the Hub yields an Index Error. I created this dataset locally and then pushed to hub at the specified URL. Thus, I expect the dataset should work out-of-the-box! Indeed, the dataset viewer functions correctly, and there were no issues when I had the dataset locally.
Don't think it's a sound file bug as the version matches what worked previously.
Update: the bug appeared for me on a GPU, mysteriously on a TPU I can't repro and it downloads correctly...
## Steps to reproduce the bug
```python
from datasets import load_dataset
earnings22 = load_dataset("sanchit-gandhi/earnings22_split")
```
## Expected results
```
>>> earnings22
DatasetDict({
validation: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2650
})
train: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 52006
})
test: Dataset({
features: ['source_id', 'audio', 'segment_id', 'sentence', 'start_ts', 'end_ts', 'id'],
num_rows: 2735
})
})
```
## Actual results
```
Traceback (most recent call last):
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 2764, in _map_single
writer.write(example)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 451, in write
self.write_examples_on_file()
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 409, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 508, in write_batch
arrays.append(pa.array(typed_sequence))
File "pyarrow/array.pxi", line 231, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/arrow_writer.py", line 197, in __arrow_array__
out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/table.py", line 1795, in cast_array_to_feature
return feature.cast_storage(array)
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in cast_storage
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 190, in <listcomp>
storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])
File "/opt/conda/envs/hf/lib/python3.8/site-packages/datasets/features/audio.py", line 92, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/opt/conda/envs/hf/lib/python3.8/site-packages/soundfile.py", line 313, in write
channels = data.shape[1]
IndexError: tuple index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.19.0-21-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
Plus:
- SoundFile version: 0.10.3.post1
cc @lhoestq @polinaeterna
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PR_kwDODunzps4_bB3q
| 5,010 |
Add deprecation warning to multilingual_librispeech dataset card
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[] | null | 1 | 2022-09-22T11:41:59Z | 2022-09-23T12:04:37Z | 2022-09-23T12:02:45Z |
MEMBER
| null |
Besides the current deprecation warning in the script of `multilingual_librispeech`, this PR adds a deprecation warning to its dataset card as well.
The format of the deprecation warning is aligned with the one in the library documentation when docstrings contain the `<Deprecated/>` tag.
Related to:
- #4060
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I_kwDODunzps5SU1lT
| 5,009 |
Error loading StonyBrookNLP/tellmewhy dataset from hub even though local copy loads correctly
|
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[] | null | 8 | 2022-09-21T16:23:06Z | 2022-09-29T13:07:29Z | 2022-09-29T13:07:29Z |
NONE
| null |
## Describe the bug
I have added a new dataset with the identifier `StonyBrookNLP/tellmewhy` to the hub. When I load the individual files using my local copy using `dataset = datasets.load_dataset("json", data_files="data/train.jsonl")`, it loads the dataset correctly. However, when I try to load it from the hub, I get an error (pasted below). Additionally, `dataset = datasets.load_dataset("json", data_dir="data/")` throws the same error.
## Steps to reproduce the bug
```python
dataset = datasets.load_dataset('StonyBrookNLP/tellmewhy')
```
## Expected results
Successfully load the `StonyBrookNLP/tellmewhy` dataset.
## Actual results
```
Using custom data configuration StonyBrookNLP--tellmewhy-82712924092694ff
Downloading and preparing dataset json/StonyBrookNLP--tellmewhy to /home/yklal95/.cache/huggingface/datasets/StonyBrookNLP___json/StonyBrookNLP--tellmewhy-82712924092694ff/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253...
Downloading data files: 100%|██████████████████████████████| 3/3 [00:00<00:00, 957.46it/s]
Extracting data files: 100%|███████████████████████████████| 3/3 [00:00<00:00, 299.14it/s]
Traceback (most recent call last):
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 17, in <module>
main(args)
File "/home/yklal95/tmw-generalization/src/load_datasets.py", line 11, in main
dataset = datasets.load_dataset(args.dataset_name)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/load.py", line 1746, in load_dataset
builder_instance.download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 793, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/builder.py", line 1277, in _prepare_split
writer.write_table(table)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/arrow_writer.py", line 524, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 2005, in table_cast
return cast_table_to_schema(table, schema)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1969, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1681, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1822, in cast_array_to_feature
casted_values = _c(array.values, feature.feature)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1853, in cast_array_to_feature
return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1683, in wrapper
return func(array, *args, **kwargs)
File "/home/yklal95/anaconda3/envs/tmw-generalization/lib/python3.9/site-packages/datasets/table.py", line 1761, in array_cast
raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type int64 to null
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.15.0-121-generic-x86_64-with-glibc2.27
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.5.0
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Re-apply input columns change
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[] | null | 1 | 2022-09-21T15:09:01Z | 2022-09-22T13:57:36Z | 2022-09-22T13:55:23Z |
CONTRIBUTOR
| null |
Fixes the `filter` + `input_columns` combination, which is used in the `transformers` examples for instance.
Revert #5006 (which in turn reverts #4971)
Fix https://github.com/huggingface/datasets/issues/4858
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PR_kwDODunzps4_WvFQ
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Add some note about running the transformers ci before a release
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Revert input_columns change
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[] | null | 2 | 2022-09-21T13:49:20Z | 2022-09-21T14:14:33Z | 2022-09-21T14:11:57Z |
MEMBER
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Revert https://github.com/huggingface/datasets/pull/4971
Fix https://github.com/huggingface/datasets/issues/5005
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I_kwDODunzps5ST6uA
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Release 2.5.0 breaks transformers CI
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MEMBER
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## Describe the bug
As reported by @lhoestq:
> see https://app.circleci.com/pipelines/github/huggingface/transformers/47634/workflows/b491886b-e66e-4edb-af96-8b459e72aa25/jobs/564563
this is used here: [https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55[…]torch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py](https://github.com/huggingface/transformers/blob/3b19c0317b6909e2d7f11b5053895ac55250e7da/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py#L482-L488)
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PR_kwDODunzps4_WQck
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Remove license tag file and validation
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[] | null | 1 | 2022-09-21T12:35:14Z | 2022-09-22T11:47:41Z | 2022-09-22T11:45:46Z |
MEMBER
| null |
As requested, we are removing the validation of the licenses from `datasets` because this is done on the Hub.
Fix #4994.
Related to:
- #4926, which is removing all the validation from `datasets`
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PR_kwDODunzps4_Vdko
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Fix missing use_auth_token in streaming docstrings
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[] | null | 1 | 2022-09-21T09:27:03Z | 2022-09-21T16:24:01Z | 2022-09-21T16:20:59Z |
MEMBER
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This PRs fixes docstrings:
- adds the missing `use_auth_token` param
- updates syntax of param types
- adds params to docstrings without them
- fixes return/yield types
- fixes syntax
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I_kwDODunzps5SSh9a
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Dataset Viewer issue for loubnabnl/humaneval-x
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] | null | 2 | 2022-09-21T09:06:17Z | 2022-09-21T11:49:49Z | 2022-09-21T11:49:49Z |
NONE
| null |
### Link
https://huggingface.co/datasets/loubnabnl/humaneval-x/viewer/
### Description
The dataset has subsets but the viewer gets stuck in the default subset even when I select another one (the data loading of the subsets works fine)
### Owner
Yes
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PR_kwDODunzps4_TBWa
| 5,001 |
Support loading XML datasets
|
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[] | null | 3 | 2022-09-20T18:42:58Z | 2022-11-01T12:44:42Z | null |
MEMBER
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CC: @davanstrien
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I_kwDODunzps5SPLHW
| 5,000 |
Dataset Viewer issue for asapp/slue
|
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[] | null | 9 | 2022-09-20T16:45:45Z | 2022-09-27T07:04:03Z | 2022-09-21T07:24:07Z |
NONE
| null |
### Link
https://huggingface.co/datasets/asapp/slue/viewer/
### Description
Hi,
I wonder how to get the dataset viewer of our slue dataset to work.
Best,
Felix
### Owner
Yes
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PR_kwDODunzps4_SQxL
| 4,999 |
Add EmptyDatasetError
|
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[] | null | 1 | 2022-09-20T15:28:05Z | 2022-09-21T12:23:43Z | 2022-09-21T12:21:24Z |
MEMBER
| null |
examples:
from the hub:
```python
Traceback (most recent call last):
File "playground/ttest.py", line 3, in <module>
print(load_dataset("lhoestq/empty"))
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1686, in load_dataset
**config_kwargs,
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1458, in load_dataset_builder
data_files=data_files,
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1171, in dataset_module_factory
raise e1 from None
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1162, in dataset_module_factory
download_mode=download_mode,
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 760, in get_module
else get_data_patterns_in_dataset_repository(hfh_dataset_info, self.data_dir)
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/data_files.py", line 678, in get_data_patterns_in_dataset_repository
) from None
datasets.data_files.EmptyDatasetError: The dataset repository at 'lhoestq/empty' doesn't contain any data file.
```
from local directory:
```python
Traceback (most recent call last):
File "playground/ttest.py", line 3, in <module>
print(load_dataset("playground/empty"))
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1686, in load_dataset
**config_kwargs,
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1458, in load_dataset_builder
data_files=data_files,
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 1107, in dataset_module_factory
path, data_dir=data_dir, data_files=data_files, download_mode=download_mode
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/load.py", line 625, in get_module
else get_data_patterns_locally(base_path)
File "/Users/quentinlhoest/Desktop/hf/nlp/src/datasets/data_files.py", line 460, in get_data_patterns_locally
raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data file") from None
datasets.data_files.EmptyDatasetError: The directory at playground/empty doesn't contain any data file
```
Close https://github.com/huggingface/datasets/issues/4995
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PR_kwDODunzps4_Ryp3
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Don't add a tag on the Hub on release
|
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[] | null | 1 | 2022-09-20T13:54:57Z | 2022-09-20T14:11:46Z | 2022-09-20T14:08:54Z |
MEMBER
| null |
Datasets with no namespace on the Hub have tags to redirect to the version of datasets where they come from.
I’m about to remove them all because I think it looks bad/unexpected in the UI and it’s not actually useful
Therefore I'm also disabling tagging.
Note that the CI job will be completely removed in https://github.com/huggingface/datasets/pull/4974 anyway
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Add support for parsing JSON files in array form
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[] | null | 1 | 2022-09-20T13:31:26Z | 2022-09-20T15:42:40Z | 2022-09-20T15:40:06Z |
CONTRIBUTOR
| null |
Support parsing JSON files in the array form (top-level object is an array). For simplicity, `json.load` is used for decoding. This means the entire file is loaded into memory. If requested, we can optimize this by introducing a param similar to `lines` in [`pandas.read_json`](https://pandas.pydata.org/docs/reference/api/pandas.read_json.html), which, if set to `True`, would allow us to read in chunks.
Fixes https://github.com/huggingface/datasets/issues/4963
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I_kwDODunzps5SNyMJ
| 4,996 |
Dataset Viewer issue for Jean-Baptiste/wikiner_fr
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[] | null | 2 | 2022-09-20T12:32:07Z | 2022-09-27T12:35:44Z | 2022-09-27T12:35:44Z |
CONTRIBUTOR
| null |
### Link
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr
### Description
```
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
File "/src/services/worker/src/worker/utils.py", line 123, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/responses/first_rows.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 718, in __iter__
for key, example in self._iter():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 708, in _iter
yield from ex_iterable
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 112, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/tmp/modules-cache/datasets_modules/datasets/Jean-Baptiste--wikiner_fr/683a580ba6ec769d508f7dfc603a651667b0ed3817b1ae5bfd45f97cc024923f/wikiner_fr.py", line 165, in _generate_examples
dataset = Dataset.load_from_disk(filepath)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1210, in load_from_disk
with open(Path(dataset_path, config.DATASET_STATE_JSON_FILENAME).as_posix(), encoding="utf-8") as state_file:
FileNotFoundError: [Errno 2] No such file or directory: 'zip:/data/train::https:/huggingface.co/datasets/Jean-Baptiste/wikiner_fr/resolve/main/data.zip/state.json'
```
Is it an error with the dataset script, or the data itself, @huggingface/datasets?
https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr/tree/main
### Owner
No
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Get a specific Exception when the dataset has no data
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CONTRIBUTOR
| null |
In the dataset viewer on the Hub (https://huggingface.co/datasets/glue/viewer), we would like (https://github.com/huggingface/moon-landing/issues/3882) to show a specific message when the repository lacks any data files.
In that case, instead of showing a complex traceback, we want to show a call to action to help the user upload data.
To do that, it would be very helpful to know for sure that the repository is missing any (supported) data files.
It could be done by raising a custom exception, for example, `NoDataError`.
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delete the hardcoded license list in `datasets`
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MEMBER
| null |
> Feel free to delete the license list in `datasets` [...]
>
> Also FYI in #4926 I also removed all the validation steps anyway (language, license, types etc.)
_Originally posted by @lhoestq in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238401662_
> [...], in my opinion we can just delete this file from `datasets`, the validation is happening hub-side anyways now?
_Originally posted by @julien-c in https://github.com/huggingface/datasets/issues/4930#issuecomment-1238390659_
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fix: avoid casting tuples after Dataset.map
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CONTRIBUTOR
| null |
This PR updates features.py to avoid casting tuples to lists when reading the results of Dataset.map as suggested by @lhoestq [here](https://github.com/huggingface/datasets/issues/4676#issuecomment-1187371367) in https://github.com/huggingface/datasets/issues/4676.
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Support streaming iwslt2017 dataset
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MEMBER
| null |
Support streaming iwslt2017 dataset.
Once this PR is merged:
- [x] Remove old ".tgz" data files from the Hub.
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Fix missing tags in dataset cards
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[] | null | 1 | 2022-09-20T06:42:07Z | 2022-09-22T12:25:32Z | 2022-09-20T07:37:30Z |
MEMBER
| null |
Fix missing tags in dataset cards:
- aeslc
- empathetic_dialogues
- event2Mind
- gap
- iwslt2017
- newsgroup
- qa4mre
- scicite
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921
- #4931
- #4979
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CONTRIBUTOR
| null |
## Describe the bug
In the 2 lines listed below, a token is passed to `huggingface_hub` to get information from a dataset. If no token is provided, a "no-token" string is passed. What is the purpose of it ? If no real, I would prefer if the `None` value could be sent directly to be handle by `huggingface_hub`. I feel that here it is working because we assume the token will never be validated.
https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L753
https://github.com/huggingface/datasets/blob/5b23f58535f14cc4dd7649485bce1ccc836e7bca/src/datasets/load.py#L1121
## Expected results
Pass `token=None` to `huggingface_hub`.
## Actual results
`token="no-token"` is passed.
## Environment info
`huggingface_hub v0.10.0dev`
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I_kwDODunzps5SEMrp
| 4,989 |
Running add_column() seems to corrupt existing sequence-type column info
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[] | null | 1 | 2022-09-17T17:42:05Z | 2022-09-19T12:54:54Z | 2022-09-19T12:54:54Z |
NONE
| null |
I have a dataset that contains a column ("foo") that is a sequence type of length 4. So when I run .to_pandas() on it, the resulting dataframe correctly contains 4 columns - foo_0, foo_1, foo_2, foo_3. So the 1st row of the dataframe might look like:
ds = load_dataset(...)
df = ds.to_pandas()
df:
foo_0 | foo_1 | foo_2 | foo_3
0.0 | 1.0 | 2.0 | 3.0
If I run .add_column("new_col", data) on the dataset, and then .to_pandas() on the resulting new dataset, the resulting dataframe contains only 2 columns - foo, new_col. The values in column foo are lists of length 4, the 4 elements that should have been split into separate columns. Dataframe 1st row would be:
ds = load_dataset(...)
new_ds = ds.add_column("new_col", data)
df = new_ds.to_pandas()
df:
foo | new_col
[0.0, 1.0, 2.0, 3.0] | new_val
I've explored the 2 datasets in a debugger and haven't noticed any changes to any attributes related to the foo column, but I can't determine why the dataframes are so different.
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I_kwDODunzps5SBZFI
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Add `IterableDataset.from_generator` to the API
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CONTRIBUTOR
| null |
We've just added `Dataset.from_generator` to the API. It would also be cool to add `IterableDataset.from_generator` to support creating an iterable dataset from a generator.
cc @lhoestq
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| 4,987 |
Embed image/audio data in dl_and_prepare parquet
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MEMBER
| null |
Embed the bytes of the image or audio files in the Parquet files directly, instead of having a "path" that points to a local file.
Indeed Parquet files are often used to share data or to be used by workers that may not have access to the local files.
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PR_kwDODunzps4_GNSd
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[doc] Fix broken snippet that had too many quotes
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[] | null | 2 | 2022-09-16T12:41:07Z | 2022-09-16T22:12:21Z | 2022-09-16T17:32:14Z |
CONTRIBUTOR
| null |
Hello!
### Pull request overview
* Fix broken snippet in https://huggingface.co/docs/datasets/main/en/process that has too many quotes
### Details
The snippet in question can be found here: https://huggingface.co/docs/datasets/main/en/process#map
This screenshot shows the issue, there is a quote too many, causing the snippet to be colored incorrectly:
The change speaks for itself.
Thank you for the detailed documentation, by the way.
- Tom Aarsen
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Prefer split patterns from directories over split patterns from filenames
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CONTRIBUTOR
| null |
related to https://github.com/huggingface/datasets/issues/4895
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docs: ✏️ add links to the Datasets API
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[] | null | 2 | 2022-09-16T09:34:12Z | 2022-09-16T13:10:14Z | 2022-09-16T13:07:33Z |
CONTRIBUTOR
| null |
I added some links to the Datasets API in the docs. See https://github.com/huggingface/datasets-server/pull/566 for a companion PR in the datasets-server. The idea is to improve the discovery of the API through the docs.
I'm a bit shy about pasting a lot of links to the API in the docs, so it's minimal for now. I'm interested in ideas to integrate the API better in these docs without being too much. cc @lhoestq @julien-c @albertvillanova @stevhliu.
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I_kwDODunzps5R_wXG
| 4,983 |
How to convert torch.utils.data.Dataset to huggingface dataset?
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[] | null | 7 | 2022-09-16T09:15:10Z | 2022-09-20T11:23:43Z | 2022-09-20T11:23:43Z |
NONE
| null |
I look through the huggingface dataset docs, and it seems that there is no offical support function to convert `torch.utils.data.Dataset` to huggingface dataset. However, there is a way to convert huggingface dataset to `torch.utils.data.Dataset`, like below:
```python
from datasets import Dataset
data = [[1, 2],[3, 4]]
ds = Dataset.from_dict({"data": data})
ds = ds.with_format("torch")
ds[0]
ds[:2]
```
So is there something I miss, or there IS no function to convert `torch.utils.data.Dataset` to huggingface dataset. If so, is there any way to do this convert?
Thanks.
|
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| 1,375,604,693 |
I_kwDODunzps5R_g_V
| 4,982 |
Create dataset_infos.json with VALIDATION and TEST splits
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[] | null | 3 | 2022-09-16T08:21:19Z | 2022-09-28T07:59:39Z | 2022-09-28T07:59:39Z |
NONE
| null |
The problem is described in that [issue](https://github.com/huggingface/datasets/issues/4895#issuecomment-1247975569).
> When I try to create data_infos.json using datasets-cli test Peter.py --save_infos --all_configs I get an error:
> ValueError: Unknown split "test". Should be one of ['train'].
>
> The data_infos.json is created perfectly fine when I use only one split - datasets.Split.TRAIN
>
> You can find the code here: https://huggingface.co/datasets/sberbank-ai/Peter/tree/add_splits (add_splits branch)
I tried to clear the cache folder, than I got an another error. I run:
```
git clone https://huggingface.co/datasets/sberbank-ai/Peter
cd Peter
git checkout add_splits # switch to a add_splits branch
rm dataset_infos.json # remove local dataset_infos.json
rm -r ~/.cache/huggingface # remove cached dataset_infos.json
datasets-cli test Peter.py --save_infos --all_configs # trying to create new dataset_infos.json
```
The error message:
```
Using custom data configuration default
Testing builder 'default' (1/1)
Downloading and preparing dataset peter/default to /Users/kalinin/.cache/huggingface/datasets/peter/default/0.0.0/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d...
Downloading data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 5160.63it/s]
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]Traceback (most recent call last):
File "/usr/local/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.9/site-packages/datasets/commands/datasets_cli.py", line 39, in main
service.run()
File "/usr/local/lib/python3.9/site-packages/datasets/commands/test.py", line 137, in run
builder.download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 704, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 1227, in _download_and_prepare
super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 771, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/Users/kalinin/.cache/huggingface/modules/datasets_modules/datasets/Peter/ef579519e140d6a40df2555996f26165f04c47557d7373709c8d7e7b4fd7465d/Peter.py", line 23, in _split_generators
data_files = dl_manager.download_and_extract(_URLS)
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 431, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/usr/local/lib/python3.9/site-packages/datasets/download/download_manager.py", line 403, in extract
extracted_paths = map_nested(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 393, in map_nested
mapped = [
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 394, in <listcomp>
_single_map_nested((function, obj, types, None, True, None))
File "/usr/local/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 330, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 213, in cached_path
output_path = ExtractManager(cache_dir=download_config.cache_dir).extract(
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 46, in extract
self.extractor.extract(input_path, output_path, extractor_format)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/extract.py", line 263, in extract
with FileLock(lock_path):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 399, in __init__
max_filename_length = os.statvfs(os.path.dirname(lock_file)).f_namemax
FileNotFoundError: [Errno 2] No such file or directory: ''
Exception ignored in: <function BaseFileLock.__del__ at 0x11caeec10>
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 328, in __del__
self.release(force=True)
File "/usr/local/lib/python3.9/site-packages/datasets/utils/filelock.py", line 303, in release
with self._thread_lock:
AttributeError: 'UnixFileLock' object has no attribute '_thread_lock'
Extracting data files: 0%| | 0/4 [00:00<?, ?it/s]
```
Can you help me please?
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 9.0.0
- Pandas version: 1.2.4
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I_kwDODunzps5R9ii1
| 4,981 |
Can't create a dataset with `float16` features
|
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[] | null | 4 | 2022-09-15T21:03:24Z | 2022-09-26T09:34:50Z | null |
CONTRIBUTOR
| null |
## Describe the bug
I can't create a dataset with `float16` features.
I understand from the traceback that this is a `pyarrow` error, but I don't see anywhere in the `datasets` documentation about how to successfully do this. Is it actually supported? I've tried older versions of `pyarrow` as well with the same exact error.
The bug seems to arise from `datasets` casting the values to `double` and then `pyarrow` doesn't know how to convert those back to `float16`... does that sound right? Is there a way to bypass this since it's not necessary in the `numpy` and `torch` cases?
Thanks!
## Steps to reproduce the bug
All of the following raise the following error with the same exact (as far as I can tell) traceback:
```python
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
```python
from datasets import Dataset, Features, Value
Dataset.from_dict({"x": [0.0, 1.0, 2.0]}, features=Features(x=Value("float16")))
import numpy as np
Dataset.from_dict({"x": np.arange(3, dtype=np.float16)}, features=Features(x=Value("float16")))
import torch
Dataset.from_dict({"x": torch.arange(3).to(torch.float16)}, features=Features(x=Value("float16")))
```
## Expected results
A dataset with `float16` features is successfully created.
## Actual results
```python
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
Cell In [14], line 1
----> 1 Dataset.from_dict({"x": [1.0, 2.0, 3.0]}, features=Features(x=Value("float16")))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_dataset.py:870, in Dataset.from_dict(cls, mapping, features, info, split)
865 mapping = features.encode_batch(mapping)
866 mapping = {
867 col: OptimizedTypedSequence(data, type=features[col] if features is not None else None, col=col)
868 for col, data in mapping.items()
869 }
--> 870 pa_table = InMemoryTable.from_pydict(mapping=mapping)
871 if info.features is None:
872 info.features = Features({col: ts.get_inferred_type() for col, ts in mapping.items()})
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:750, in InMemoryTable.from_pydict(cls, *args, **kwargs)
734 @classmethod
735 def from_pydict(cls, *args, **kwargs):
736 """
737 Construct a Table from Arrow arrays or columns
738
(...)
748 :class:`datasets.table.Table`:
749 """
--> 750 return cls(pa.Table.from_pydict(*args, **kwargs))
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:3648, in pyarrow.lib.Table.from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/table.pxi:5174, in pyarrow.lib._from_pydict()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:343, in pyarrow.lib.asarray()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:231, in pyarrow.lib.array()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py:197, in TypedSequence.__arrow_array__(self, type)
192 # otherwise we can finally use the user's type
193 elif type is not None:
194 # We use cast_array_to_feature to support casting to custom types like Audio and Image
195 # Also, when trying type "string", we don't want to convert integers or floats to "string".
196 # We only do it if trying_type is False - since this is what the user asks for.
--> 197 out = cast_array_to_feature(out, type, allow_number_to_str=not self.trying_type)
198 return out
199 except (TypeError, pa.lib.ArrowInvalid) as e: # handle type errors and overflows
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1853, in cast_array_to_feature(array, feature, allow_number_to_str)
1851 return array_cast(array, get_nested_type(feature), allow_number_to_str=allow_number_to_str)
1852 elif not isinstance(feature, (Sequence, dict, list, tuple)):
-> 1853 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
1854 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1683, in _wrap_for_chunked_arrays.<locals>.wrapper(array, *args, **kwargs)
1681 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1682 else:
-> 1683 return func(array, *args, **kwargs)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/datasets/table.py:1762, in array_cast(array, pa_type, allow_number_to_str)
1760 if pa.types.is_null(pa_type) and not pa.types.is_null(array.type):
1761 raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
-> 1762 return array.cast(pa_type)
1763 raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{pa_type}")
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/array.pxi:919, in pyarrow.lib.Array.cast()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/compute.py:389, in cast(arr, target_type, safe, options)
387 else:
388 options = CastOptions.safe(target_type)
--> 389 return call_function("cast", [arr], options)
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:560, in pyarrow._compute.call_function()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/_compute.pyx:355, in pyarrow._compute.Function.call()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()
File ~/scratch/scratch-env-39/.venv/lib/python3.9/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.9.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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I_kwDODunzps5R8tJz
| 4,980 |
Make `pyarrow` optional
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[] | null | 3 | 2022-09-15T17:38:03Z | 2022-09-16T17:23:47Z | 2022-09-16T17:23:47Z |
NONE
| null |
**Is your feature request related to a problem? Please describe.**
Is `pyarrow` really needed for every dataset?
**Describe the solution you'd like**
It is made optional.
**Describe alternatives you've considered**
Likely, no.
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Fix missing tags in dataset cards
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[] | null | 1 | 2022-09-15T16:51:03Z | 2022-09-22T12:37:55Z | 2022-09-15T17:12:09Z |
MEMBER
| null |
Fix missing tags in dataset cards:
- amazon_us_reviews
- art
- discofuse
- indic_glue
- ubuntu_dialogs_corpus
This PR partially fixes the missing tags in dataset cards. Subsequent PRs will follow to complete this task.
Related to:
- #4833
- #4891
- #4896
- #4908
- #4921
- #4931
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Update IndicGLUE download links
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I_kwDODunzps5R1b1t
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Providing dataset size
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[] | null | 3 | 2022-09-14T13:09:27Z | 2022-09-15T16:03:58Z | null |
CONTRIBUTOR
| null |
**Is your feature request related to a problem? Please describe.**
Especially for big datasets like [LAION](https://huggingface.co/datasets/laion/laion2B-en/), it's hard to know exactly the downloaded size (because there are many files and you don't have their exact size when downloaded).
**Describe the solution you'd like**
Auto-populating the downloaded dataset size on the dataset page would be really useful, including that of each split (when there are some).
**Describe alternatives you've considered**
People should be adding this to dataset cards, but I don't think that is systematically the case :slightly_smiling_face:
**Additional context**
Mentioned to @lhoestq
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| 1,372,322,382 |
I_kwDODunzps5Ry_pO
| 4,976 |
Hope to adapt Python3.9 as soon as possible
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NONE
| null |
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.
**Additional context**
Add any other context about the feature request here.
|
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PR_kwDODunzps4-4NXX
| 4,975 |
Add `fn_kwargs` param to `IterableDataset.map`
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[] | null | 1 | 2022-09-13T16:19:05Z | 2022-09-13T16:47:47Z | 2022-09-13T16:45:34Z |
CONTRIBUTOR
| null |
Add the `fn_kwargs` parameter to `IterableDataset.map`.
("Resolves" https://discuss.huggingface.co/t/how-to-use-large-image-text-datasets-in-hugging-face-hub-without-downloading-for-free/22780/3)
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PR_kwDODunzps4-4Iri
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[GH->HF] Part 2: Remove all dataset scripts from github
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[] | null | 6 | 2022-09-13T16:01:12Z | 2022-10-03T17:09:39Z | 2022-10-03T17:07:32Z |
MEMBER
| null |
Now that all the datasets live on the Hub we can remove the /datasets directory that contains all the dataset scripts of this repository
- [x] Needs https://github.com/huggingface/datasets/pull/4973 to be merged first
- [x] and PR to be enabled on the Hub for non-namespaced datasets
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[GH->HF] Load datasets from the Hub
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[] | null | 2 | 2022-09-13T15:01:41Z | 2022-09-15T15:26:51Z | 2022-09-15T15:24:26Z |
MEMBER
| null |
Currently datasets with no namespace (e.g. squad, glue) are loaded from github.
In this PR I changed this logic to use the Hugging Face Hub instead.
This is the first step in removing all the dataset scripts in this repository
related to discussions in https://github.com/huggingface/datasets/pull/4059 (I should have continued from this PR actually)
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PR_kwDODunzps4-3VVF
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Fix map batched with torch output
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[] | null | 1 | 2022-09-13T13:16:34Z | 2022-09-20T09:42:02Z | 2022-09-20T09:39:33Z |
MEMBER
| null |
Reported in https://discuss.huggingface.co/t/typeerror-when-applying-map-after-set-format-type-torch/23067/2
Currently it fails if one uses batched `map` and the map function returns a torch tensor.
I fixed it for torch, tf, jax and pandas series.
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PR_kwDODunzps4-zk3g
| 4,971 |
Preserve non-`input_colums` in `Dataset.map` if `input_columns` are specified
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[] | null | 1 | 2022-09-12T18:08:24Z | 2022-09-13T13:51:08Z | 2022-09-13T13:48:45Z |
CONTRIBUTOR
| null |
Currently, if the `input_columns` list in `Dataset.map` is specified, the columns not in that list are dropped after the `map` transform.
This makes the behavior inconsistent with `IterableDataset.map`.
(It seems this issue was introduced by mistake in https://github.com/huggingface/datasets/pull/2246)
Fix https://github.com/huggingface/datasets/issues/4858
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PR_kwDODunzps4-wkY2
| 4,970 |
Support streaming nli_tr dataset
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[] | null | 1 | 2022-09-12T07:48:45Z | 2022-09-12T08:45:04Z | 2022-09-12T08:43:08Z |
MEMBER
| null |
Support streaming nli_tr dataset.
This PR removes legacy `codecs.open` and replaces it with `open` that supports passing encoding.
Fix #3186.
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PR_kwDODunzps4-wPOk
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Fix data URL and metadata of vivos dataset
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[] | null | 1 | 2022-09-12T06:12:34Z | 2022-09-12T07:16:15Z | 2022-09-12T07:14:19Z |
MEMBER
| null |
After contacting the authors of the VIVOS dataset to report that their data server is down, we have received a reply from Hieu-Thi Luong that their data is now hosted on Zenodo: https://doi.org/10.5281/zenodo.7068130
This PR updates their data URL and some metadata (homepage, citation and license).
Fix #4936.
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PR_kwDODunzps4-wKkw
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Support streaming compguesswhat dataset
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[] | null | 1 | 2022-09-12T05:42:24Z | 2022-09-12T08:00:06Z | 2022-09-12T07:58:06Z |
MEMBER
| null |
Support streaming `compguesswhat` dataset.
Fix #3191.
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PR_kwDODunzps4-vbS-
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Strip "/" in local dataset path to avoid empty dataset name error
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[Apple M1] MemoryError: Cannot allocate write+execute memory for ffi.callback()
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[] | null | 3 | 2022-09-10T15:55:49Z | 2022-11-18T23:45:02Z | null |
NONE
| null |
## Describe the bug
I'm trying to run `cast_column("audio", Audio())` on Apple M1 Pro, but it seems that it doesn't work.
## Steps to reproduce the bug
```python
import datasets
dataset = load_dataset("csv", data_files="./train.csv")["train"]
dataset = dataset.map(lambda x: {"audio": str(DATA_DIR / "audio" / x["audio"])})
dataset = dataset.cast_column("audio", Audio())
dataset[0]
```
## Expected results
```
{'audio': {'bytes': None,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav'},
'english_transcription': 'I would like to set up a joint account with my partner',
'intent_class': 11,
'lang_id': 4,
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
'transcription': 'I would like to set up a joint account with my partner'}
```
## Actual results
````---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
Input In [6], in <cell line: 1>()
----> 1 dataset[0]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2165, in Dataset.__getitem__(self, key)
2163 def __getitem__(self, key): # noqa: F811
2164 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2165 return self._getitem(
2166 key,
2167 )
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/arrow_dataset.py:2150, in Dataset._getitem(self, key, decoded, **kwargs)
2148 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
2149 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2150 formatted_output = format_table(
2151 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
2152 )
2153 return formatted_output
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
530 python_formatter = PythonFormatter(features=None)
531 if format_columns is None:
--> 532 return formatter(pa_table, query_type=query_type)
533 elif query_type == "column":
534 if key in format_columns:
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
280 if query_type == "row":
--> 281 return self.format_row(pa_table)
282 elif query_type == "column":
283 return self.format_column(pa_table)
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:312, in PythonFormatter.format_row(self, pa_table)
310 row = self.python_arrow_extractor().extract_row(pa_table)
311 if self.decoded:
--> 312 row = self.python_features_decoder.decode_row(row)
313 return row
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/formatting/formatting.py:221, in PythonFeaturesDecoder.decode_row(self, row)
220 def decode_row(self, row: dict) -> dict:
--> 221 return self.features.decode_example(row) if self.features else row
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1647, in Features.decode_example(self, example, token_per_repo_id)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
-> 1647 return {
1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1648, in <dictcomp>(.0)
1634 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):
1635 """Decode example with custom feature decoding.
1636
1637 Args:
(...)
1644 :obj:`dict[str, Any]`
1645 """
1647 return {
-> 1648 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
1649 if self._column_requires_decoding[column_name]
1650 else value
1651 for column_name, (feature, value) in zip_dict(
1652 {key: value for key, value in self.items() if key in example}, example
1653 )
1654 }
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/features.py:1260, in decode_nested_example(schema, obj, token_per_repo_id)
1257 # Object with special decoding:
1258 elif isinstance(schema, (Audio, Image)):
1259 # we pass the token to read and decode files from private repositories in streaming mode
-> 1260 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
1261 return obj
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:156, in Audio.decode_example(self, value, token_per_repo_id)
154 array, sampling_rate = self._decode_non_mp3_file_like(file)
155 else:
--> 156 array, sampling_rate = self._decode_non_mp3_path_like(path, token_per_repo_id=token_per_repo_id)
157 return {"path": path, "array": array, "sampling_rate": sampling_rate}
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/datasets/features/audio.py:257, in Audio._decode_non_mp3_path_like(self, path, format, token_per_repo_id)
254 use_auth_token = None
256 with xopen(path, "rb", use_auth_token=use_auth_token) as f:
--> 257 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono)
258 return array, sampling_rate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/util/decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
86 extra_args = len(args) - len(all_args)
87 if extra_args <= 0:
---> 88 return f(*args, **kwargs)
90 # extra_args > 0
91 args_msg = [
92 "{}={}".format(name, arg)
93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
94 ]
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:164, in load(path, sr, mono, offset, duration, dtype, res_type)
161 else:
162 # Otherwise try soundfile first, and then fall back if necessary
163 try:
--> 164 y, sr_native = __soundfile_load(path, offset, duration, dtype)
166 except RuntimeError as exc:
167 # If soundfile failed, try audioread instead
168 if isinstance(path, (str, pathlib.PurePath)):
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/librosa/core/audio.py:195, in __soundfile_load(path, offset, duration, dtype)
192 context = path
193 else:
194 # Otherwise, create the soundfile object
--> 195 context = sf.SoundFile(path)
197 with context as sf_desc:
198 sr_native = sf_desc.samplerate
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
626 self._mode = mode
627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
632 self.seek(0)
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1179, in SoundFile._open(self, file, mode_int, closefd)
1177 file_ptr = _snd.sf_open_fd(file, mode_int, self._info, closefd)
1178 elif _has_virtual_io_attrs(file, mode_int):
-> 1179 file_ptr = _snd.sf_open_virtual(self._init_virtual_io(file),
1180 mode_int, self._info, _ffi.NULL)
1181 else:
1182 raise TypeError("Invalid file: {0!r}".format(self.name))
File ~/miniconda3/envs/rodan/lib/python3.8/site-packages/soundfile.py:1197, in SoundFile._init_virtual_io(self, file)
1194 def _init_virtual_io(self, file):
1195 """Initialize callback functions for sf_open_virtual()."""
1196 @_ffi.callback("sf_vio_get_filelen")
-> 1197 def vio_get_filelen(user_data):
1198 curr = file.tell()
1199 file.seek(0, SEEK_END)
MemoryError: Cannot allocate write+execute memory for ffi.callback(). You might be running on a system that prevents this. For more information, see https://cffi.readthedocs.io/en/latest/using.html#callbacks
```
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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| 1,368,617,322 |
I_kwDODunzps5Rk3Fq
| 4,964 |
Column of arrays (2D+) are using unreasonably high memory
|
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open
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[] | null | 10 | 2022-09-10T13:07:22Z | 2022-09-22T18:29:22Z | null |
CONTRIBUTOR
| null |
## Describe the bug
When trying to store `Array2D, Array3D, etc` as column values in a dataset, accessing that column (or creating depending on how you create it, see code below) will cause more than 10 fold of memory usage.
## Steps to reproduce the bug
```python
from datasets import Dataset, Features, Array2D, Array3D
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data}, features=Features({column_name: Array3D(shape=array_shape, dtype="float64")}))
```
the code above will use about 10Gb of RAM while constructing the `dataset` object.
The code below will use roughly the same amount of memory (and time) when trying to actually access the data itself of that column.
```python
from datasets import Dataset
import numpy as np
column_name = "a"
array_shape = (64, 64, 3)
data = np.random.random((10000,) + array_shape)
dataset = Dataset.from_dict({column_name: data})
dataset[column_name]
```
## Expected results
Some memory overhead, but not like as it is now and certainly not an overhead of such runtime that is currently happening.
## Actual results
Enormous memory- and runtime overhead.
## Environment info
- `datasets` version: 2.3.2
- Platform: macOS-12.5.1-arm64-arm-64bit
- Python version: 3.8.13
- PyArrow version: 9.0.0
- Pandas version: 1.4.4
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I_kwDODunzps5RjRfk
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Dataset without script does not support regular JSON data file
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[] | null | 1 | 2022-09-09T18:45:33Z | 2022-09-20T15:40:07Z | 2022-09-20T15:40:07Z |
MEMBER
| 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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PR_kwDODunzps4-sh-o
| 4,962 |
Update setup.py
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[] | null | 2 | 2022-09-09T17:57:56Z | 2022-09-12T14:33:04Z | 2022-09-12T14:33:04Z |
NONE
| null |
exclude broken version of fsspec. See the [related issue](https://github.com/huggingface/datasets/issues/4961)
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I_kwDODunzps5Ri-qB
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fsspec 2022.8.2 breaks xopen in streaming mode
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[] | null | 6 | 2022-09-09T17:26:55Z | 2022-09-12T17:45:50Z | 2022-09-12T14:32:05Z |
NONE
| null |
## Describe the bug
When fsspec 2022.8.2 is installed in your environment, xopen will prematurely close files, making streaming mode inoperable.
## Steps to reproduce the bug
```python
import datasets
data = datasets.load_dataset('MLCommons/ml_spoken_words', 'id_wav', split='train', streaming=True)
```
## Expected results
Dataset should load as iterator.
## Actual results
```
[/usr/local/lib/python3.7/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1737 # Return iterable dataset in case of streaming
1738 if streaming:
-> 1739 return builder_instance.as_streaming_dataset(split=split)
1740
1741 # Some datasets are already processed on the HF google storage
[/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in as_streaming_dataset(self, split, base_path)
1023 )
1024 self._check_manual_download(dl_manager)
-> 1025 splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
1026 # By default, return all splits
1027 if split is None:
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _split_generators(self, dl_manager)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in <listcomp>(.0)
182 name=datasets.Split.TRAIN,
183 gen_kwargs={
--> 184 "audio_archives": [download_audio(split="train", lang=lang) for lang in self.config.languages],
185 "local_audio_archives_paths": [download_extract_audio(split="train", lang=lang) for lang in
186 self.config.languages] if not dl_manager.is_streaming else None,
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives(dl_manager, lang, format, split)
267 # for streaming case
268 def _download_audio_archives(dl_manager, lang, format, split):
--> 269 archives_paths = _download_audio_archives_paths(dl_manager, lang, format, split)
270 return [dl_manager.iter_archive(archive_path) for archive_path in archives_paths]
[~/.cache/huggingface/modules/datasets_modules/datasets/MLCommons--ml_spoken_words/321ea853cf0a05abb7a2d7efea900692a3d8622af65a2f3ce98adb7800a5d57b/ml_spoken_words.py](https://localhost:8080/#) in _download_audio_archives_paths(dl_manager, lang, format, split)
251 n_files_path = dl_manager.download(n_files_url)
252
--> 253 with open(n_files_path, "r", encoding="utf-8") as file:
254 n_files = int(file.read().strip()) # the file contains a number of archives
255
ValueError: I/O operation on closed file.
```
## Environment info
- `datasets` version: 2.4.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
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I_kwDODunzps5Rio9X
| 4,960 |
BioASQ AttributeError: 'BuilderConfig' object has no attribute 'schema'
|
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[] | null | 2 | 2022-09-09T16:06:43Z | 2022-09-13T08:51:03Z | null |
NONE
| null |
## Describe the bug
I am trying to load a dataset from drive and running into an error.
## Steps to reproduce the bug
```python
data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
```
## Actual results
`AttributeError: 'BuilderConfig' object has no attribute 'schema'`
<details>
```
Using custom data configuration default-a1ca3e05be5abf2f
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Input In [8], in <cell line: 2>()
1 data_dir = "/Users/dlituiev/repos/datasets/bioasq/BioASQ-training9b"
----> 2 bioasq_task_b = load_dataset("aps/bioasq_task_b", data_dir=data_dir)
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1723, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1720 ignore_verifications = ignore_verifications or save_infos
1722 # Create a dataset builder
-> 1723 builder_instance = load_dataset_builder(
1724 path=path,
1725 name=name,
1726 data_dir=data_dir,
1727 data_files=data_files,
1728 cache_dir=cache_dir,
1729 features=features,
1730 download_config=download_config,
1731 download_mode=download_mode,
1732 revision=revision,
1733 use_auth_token=use_auth_token,
1734 **config_kwargs,
1735 )
1737 # Return iterable dataset in case of streaming
1738 if streaming:
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/load.py:1526, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1523 raise ValueError(error_msg)
1525 # Instantiate the dataset builder
-> 1526 builder_instance: DatasetBuilder = builder_cls(
1527 cache_dir=cache_dir,
1528 config_name=config_name,
1529 data_dir=data_dir,
1530 data_files=data_files,
1531 hash=hash,
1532 features=features,
1533 use_auth_token=use_auth_token,
1534 **builder_kwargs,
1535 **config_kwargs,
1536 )
1538 return builder_instance
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:1154, in GeneratorBasedBuilder.__init__(self, writer_batch_size, *args, **kwargs)
1153 def __init__(self, *args, writer_batch_size=None, **kwargs):
-> 1154 super().__init__(*args, **kwargs)
1155 # Batch size used by the ArrowWriter
1156 # It defines the number of samples that are kept in memory before writing them
1157 # and also the length of the arrow chunks
1158 # None means that the ArrowWriter will use its default value
1159 self._writer_batch_size = writer_batch_size or self.DEFAULT_WRITER_BATCH_SIZE
File ~/opt/anaconda3/envs/spacy3/lib/python3.10/site-packages/datasets/builder.py:307, in DatasetBuilder.__init__(self, cache_dir, config_name, hash, base_path, info, features, use_auth_token, repo_id, data_files, data_dir, name, **config_kwargs)
305 if info is None:
306 info = self.get_exported_dataset_info()
--> 307 info.update(self._info())
308 info.builder_name = self.name
309 info.config_name = self.config.name
File ~/.cache/huggingface/modules/datasets_modules/datasets/aps--bioasq_task_b/3d54b1213f7e8001eef755af92877f9efa44161ee83c2a70d5d649defa95759e/bioasq_task_b.py:477, in BioasqTaskBDataset._info(self)
474 def _info(self):
475
476 # BioASQ Task B source schema
--> 477 if self.config.schema == "source":
478 features = datasets.Features(
479 {
480 "id": datasets.Value("string"),
(...)
504 }
505 )
506 # simplified schema for QA tasks
AttributeError: 'BuilderConfig' object has no attribute 'schema'
```
</details>
## Environment info
- `datasets` version: 2.4.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.4
- PyArrow version: 9.0.0
- Pandas version: 1.4.3
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| 1,367,924,429 |
PR_kwDODunzps4-rx6l
| 4,959 |
Fix data URLs of compguesswhat dataset
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[] | null | 1 | 2022-09-09T14:36:10Z | 2022-09-09T16:01:34Z | 2022-09-09T15:59:04Z |
MEMBER
| null |
After we informed the `compguesswhat` dataset authors about an error with their data URLs, they have updated them:
- https://github.com/CompGuessWhat/compguesswhat.github.io/issues/1
This PR updates their data URLs in our loading script.
Related to:
- #3191
|
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| 1,367,695,376 |
I_kwDODunzps5RhWAQ
| 4,958 |
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.4.0/datasets/jsonl/jsonl.py
|
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[] | null | 1 | 2022-09-09T11:29:55Z | 2022-09-09T11:38:44Z | 2022-09-09T11:38:44Z |
NONE
| null |
Hi,
When I use load_dataset from local jsonl files, below error happens, and I type the link into the browser prompting me `404: Not Found`. I download the other `.py` files using the same method and it works. It seems that the server is missing the appropriate file, or it is a problem with the code version.
```
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.0/datasets/jsonl/jsonl.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x2b08342004c0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
```
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PR_kwDODunzps4-nGIk
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Add `Dataset.from_generator`
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[] | null | 3 | 2022-09-08T15:08:25Z | 2022-09-16T14:46:35Z | 2022-09-16T14:44:18Z |
CONTRIBUTOR
| null |
Add `Dataset.from_generator` to the API to allow creating datasets from data larger than RAM. The implementation relies on a packaged module not exposed in `load_dataset` to tie this method with `datasets`' caching mechanism.
Closes https://github.com/huggingface/datasets/issues/4417
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Fix TF tests for 2.10
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[] | null | 1 | 2022-09-08T14:39:10Z | 2022-09-08T15:16:51Z | 2022-09-08T15:14:44Z |
MEMBER
| null |
Fixes #4953
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I_kwDODunzps5RcVbq
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Raise a more precise error when the URL is unreachable in streaming mode
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[] | null | 0 | 2022-09-08T13:52:37Z | 2022-09-08T13:53:36Z | null |
CONTRIBUTOR
| null |
See for example:
- https://github.com/huggingface/datasets/issues/3191
- https://github.com/huggingface/datasets/issues/3186
It would help provide clearer information on the Hub and help the dataset maintainer solve the issue by themselves quicker. Currently:
- https://huggingface.co/datasets/compguesswhat
<img width="1029" alt="Capture d’écran 2022-09-08 à 15 51 37" src="https://user-images.githubusercontent.com/1676121/189139946-6deffb91-f21b-4281-8825-a98026c69740.png">
- https://huggingface.co/datasets/nli_tr
<img width="1032" alt="Capture d’écran 2022-09-08 à 15 51 44" src="https://user-images.githubusercontent.com/1676121/189139963-d26490ed-ad23-48ea-9cfc-1ab9c4d08d0c.png">
cc @albertvillanova
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Pin TensorFlow temporarily
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[] | null | 1 | 2022-09-08T13:46:15Z | 2022-09-08T14:12:33Z | 2022-09-08T14:10:03Z |
MEMBER
| null |
Temporarily fix TensorFlow until a permanent solution is found.
Related to:
- #4953
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I_kwDODunzps5RcPIi
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CI test of TensorFlow is failing
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[] | null | 0 | 2022-09-08T13:39:29Z | 2022-09-08T15:14:45Z | 2022-09-08T15:14:45Z |
MEMBER
| null |
## Describe the bug
The following CI test fails: https://github.com/huggingface/datasets/runs/8246722693?check_suite_focus=true
```
FAILED tests/test_py_utils.py::TempSeedTest::test_tensorflow - AssertionError:
```
Details:
```
_________________________ TempSeedTest.test_tensorflow _________________________
[gw0] linux -- Python 3.7.13 /opt/hostedtoolcache/Python/3.7.13/x64/bin/python
self = <tests.test_py_utils.TempSeedTest testMethod=test_tensorflow>
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
def gen_random_output():
model = layers.Dense(2)
x = tf.random.uniform((1, 3))
return model(x).numpy()
with temp_seed(42, set_tensorflow=True):
out1 = gen_random_output()
with temp_seed(42, set_tensorflow=True):
out2 = gen_random_output()
out3 = gen_random_output()
> np.testing.assert_equal(out1, out2)
E AssertionError:
E Arrays are not equal
E
E Mismatched elements: 2 / 2 (100%)
E Max absolute difference: 0.84619296
E Max relative difference: 16.083529
E x: array([[-0.793581, 0.333286]], dtype=float32)
E y: array([[0.052612, 0.539708]], dtype=float32)
tests/test_py_utils.py:149: AssertionError
```
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