id
int64 599M
3.29B
| url
stringlengths 58
61
| html_url
stringlengths 46
51
| number
int64 1
7.72k
| title
stringlengths 1
290
| state
stringclasses 2
values | comments
int64 0
70
| created_at
timestamp[s]date 2020-04-14 10:18:02
2025-08-05 09:28:51
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-08-05 11:39:56
| closed_at
timestamp[s]date 2020-04-14 12:01:40
2025-08-01 05:15:45
⌀ | user_login
stringlengths 3
26
| labels
listlengths 0
4
| body
stringlengths 0
228k
⌀ | is_pull_request
bool 2
classes |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
969,728,545
|
https://api.github.com/repos/huggingface/datasets/issues/2794
|
https://github.com/huggingface/datasets/issues/2794
| 2,794
|
Warnings and documentation about pickling incorrect
|
open
| 0
| 2021-08-12T23:09:13
| 2021-08-12T23:09:31
| null |
mbforbes
|
[
"bug"
] |
## Describe the bug
I have a docs bug and a closely related docs enhancement suggestion!
### Bug
The warning and documentation say "either `dill` or `pickle`" for fingerprinting. But it seems that `dill`, which is installed by `datasets` by default, _must_ work, or else the fingerprinting fails.
Warning:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L262
Docs:
> For a transform to be hashable, it needs to be pickleable using dill or pickle.
> – [docs](https://huggingface.co/docs/datasets/processing.html#fingerprinting)
For my code, `pickle` works, but `dill` fails. The `dill` failure has already been reported in https://github.com/huggingface/datasets/issues/2643. However, the `dill` failure causes a hashing failure in the datasets library, without any backing off to `pickle`. This implies that it's not the case that either `dill` **or** `pickle` can work, but that `dill` must work if it is installed. I think this is more accurate wording, since it is installed and used by default:
https://github.com/huggingface/datasets/blob/c93525dc291346e54212567fa72d7d607befe937/setup.py#L83
... and the hashing will fail if it fails.
### Enhancement
I think it'd be very helpful to add to the documentation how to debug hashing failures. It took me a while to figure out how to diagnose this. There is a very nice two-liner by @lhoestq in https://github.com/huggingface/datasets/issues/2516#issuecomment-865173139:
```python
from datasets.fingerprint import Hasher
Hasher.hash(my_object)
```
I think add this to the docs will help future users quickly debug any hashing troubles of their own :-)
## Steps to reproduce the bug
`dill` but not `pickle` hashing failure in https://github.com/huggingface/datasets/issues/2643
## Expected results
If either `dill` or `pickle` can successfully hash, the hashing will succeed.
## Actual results
If `dill` or `pickle` cannot hash, the hashing fails.
## Environment info
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
| false
|
968,967,773
|
https://api.github.com/repos/huggingface/datasets/issues/2793
|
https://github.com/huggingface/datasets/pull/2793
| 2,793
|
Fix type hint for data_files
|
closed
| 0
| 2021-08-12T14:42:37
| 2021-08-12T15:35:29
| 2021-08-12T15:35:29
|
albertvillanova
|
[] |
Fix type hint for `data_files` in signatures and docstrings.
| true
|
968,650,274
|
https://api.github.com/repos/huggingface/datasets/issues/2792
|
https://github.com/huggingface/datasets/pull/2792
| 2,792
|
Update: GooAQ - add train/val/test splits
|
closed
| 2
| 2021-08-12T11:40:18
| 2021-08-27T15:58:45
| 2021-08-27T15:58:14
|
bhavitvyamalik
|
[] |
[GooAQ](https://github.com/allenai/gooaq) dataset was recently updated after splits were added for the same. This PR contains new updated GooAQ with train/val/test splits and updated README as well.
| true
|
968,360,314
|
https://api.github.com/repos/huggingface/datasets/issues/2791
|
https://github.com/huggingface/datasets/pull/2791
| 2,791
|
Fix typo in cnn_dailymail
|
closed
| 0
| 2021-08-12T08:38:42
| 2021-08-12T11:17:59
| 2021-08-12T11:17:59
|
omaralsayed
|
[] | null | true
|
967,772,181
|
https://api.github.com/repos/huggingface/datasets/issues/2790
|
https://github.com/huggingface/datasets/pull/2790
| 2,790
|
Fix typo in test_dataset_common
|
closed
| 0
| 2021-08-12T01:10:29
| 2021-08-12T11:31:29
| 2021-08-12T11:31:29
|
nateraw
|
[] | null | true
|
967,361,934
|
https://api.github.com/repos/huggingface/datasets/issues/2789
|
https://github.com/huggingface/datasets/pull/2789
| 2,789
|
Updated dataset description of DaNE
|
closed
| 1
| 2021-08-11T19:58:48
| 2021-08-12T16:10:59
| 2021-08-12T16:06:01
|
KennethEnevoldsen
|
[] | null | true
|
967,149,389
|
https://api.github.com/repos/huggingface/datasets/issues/2788
|
https://github.com/huggingface/datasets/issues/2788
| 2,788
|
How to sample every file in a list of files making up a split in a dataset when loading?
|
closed
| 1
| 2021-08-11T17:43:21
| 2023-07-25T17:40:50
| 2023-07-25T17:40:50
|
brijow
|
[] |
I am loading a dataset with multiple train, test, and validation files like this:
```
data_files_dict = {
"train": [train_file1, train_file2],
"test": [test_file1, test_file2],
"val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
"csv",
data_files=data_files_dict,
split=['train[:8]', 'test[:8]', 'val[:8]']
)
```
However, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.
I'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.
Is this type of splitting supported? If so, how can I do it?
| false
|
967,018,406
|
https://api.github.com/repos/huggingface/datasets/issues/2787
|
https://github.com/huggingface/datasets/issues/2787
| 2,787
|
ConnectionError: Couldn't reach https://raw.githubusercontent.com
|
closed
| 9
| 2021-08-11T16:19:01
| 2023-10-03T12:39:25
| 2021-08-18T15:09:18
|
jinec
|
[
"bug"
] |
Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
| false
|
966,282,934
|
https://api.github.com/repos/huggingface/datasets/issues/2786
|
https://github.com/huggingface/datasets/pull/2786
| 2,786
|
Support streaming compressed files
|
closed
| 0
| 2021-08-11T09:02:06
| 2021-08-17T05:28:39
| 2021-08-16T06:36:19
|
albertvillanova
|
[] |
Add support to stream compressed files (current options in fsspec):
- bz2
- lz4
- xz
- zstd
cc: @lewtun
| true
|
965,461,382
|
https://api.github.com/repos/huggingface/datasets/issues/2783
|
https://github.com/huggingface/datasets/pull/2783
| 2,783
|
Add KS task to SUPERB
|
closed
| 5
| 2021-08-10T22:14:07
| 2021-08-12T16:45:01
| 2021-08-11T20:19:17
|
anton-l
|
[] |
Add the KS (keyword spotting) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051).
- [s3prl instructions](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/README.md#ks-keyword-spotting)
- [s3prl implementation](https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/speech_commands/dataset.py)
- [TFDS implementation](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/audio/speech_commands.py)
Some notable quirks:
- The dataset is originally single-archive (train+val+test all in one), but the test set has a "canonical" distribution in a separate archive, which is also used here (see `_split_ks_files()`).
- The `_background_noise_`/`_silence_` audio files are much longer than others, so they require some sort of slicing for downstream training. I decided to leave the implementation of that up to the users, since TFDS and s3prl take different approaches (either slicing wavs deterministically, or subsampling randomly at runtime)
Related to #2619.
| true
|
964,858,439
|
https://api.github.com/repos/huggingface/datasets/issues/2782
|
https://github.com/huggingface/datasets/pull/2782
| 2,782
|
Fix renaming of corpus_bleu args
|
closed
| 0
| 2021-08-10T11:02:34
| 2021-08-10T11:16:07
| 2021-08-10T11:16:07
|
albertvillanova
|
[] |
Last `sacrebleu` release (v2.0.0) has renamed `sacrebleu.corpus_bleu` args from `(sys_stream, ref_streams)` to `(hipotheses, references)`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR passes the args without parameter names, so that it is valid for all versions of `sacrebleu`.
This is a partial hotfix of #2781.
Close #2781.
| true
|
964,805,351
|
https://api.github.com/repos/huggingface/datasets/issues/2781
|
https://github.com/huggingface/datasets/issues/2781
| 2,781
|
Latest v2.0.0 release of sacrebleu has broken some metrics
|
closed
| 0
| 2021-08-10T09:59:41
| 2021-08-10T11:16:07
| 2021-08-10T11:16:07
|
albertvillanova
|
[
"bug"
] |
## Describe the bug
After `sacrebleu` v2.0.0 release (see changes here: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15), some of `datasets` metrics are broken:
- Default tokenizer `sacrebleu.DEFAULT_TOKENIZER` no longer exists:
- #2739
- #2778
- Bleu tokenizers are no longer accessible with `sacrebleu.TOKENIZERS`:
- #2779
- `corpus_bleu` args have been renamed from `(sys_stream, ref_streams)` to `(hipotheses, references)`:
- #2782
| false
|
964,794,764
|
https://api.github.com/repos/huggingface/datasets/issues/2780
|
https://github.com/huggingface/datasets/pull/2780
| 2,780
|
VIVOS dataset for Vietnamese ASR
|
closed
| 0
| 2021-08-10T09:47:36
| 2021-08-12T11:09:30
| 2021-08-12T11:09:30
|
binh234
|
[] | null | true
|
964,775,085
|
https://api.github.com/repos/huggingface/datasets/issues/2779
|
https://github.com/huggingface/datasets/pull/2779
| 2,779
|
Fix sacrebleu tokenizers
|
closed
| 0
| 2021-08-10T09:24:27
| 2021-08-10T11:03:08
| 2021-08-10T10:57:54
|
albertvillanova
|
[] |
Last `sacrebleu` release (v2.0.0) has removed `sacrebleu.TOKENIZERS`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR makes a hot fix of the bug by using a private function in `sacrebleu`: `sacrebleu.metrics.bleu._get_tokenizer()`.
Eventually, this should be further fixed in order to use only public functions.
This is a partial hotfix of #2781.
| true
|
964,737,422
|
https://api.github.com/repos/huggingface/datasets/issues/2778
|
https://github.com/huggingface/datasets/pull/2778
| 2,778
|
Do not pass tokenize to sacrebleu
|
closed
| 0
| 2021-08-10T08:40:37
| 2021-08-10T10:03:37
| 2021-08-10T10:03:37
|
albertvillanova
|
[] |
Last `sacrebleu` release (v2.0.0) has removed `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR does not pass `tokenize` to `sacrebleu` (note that the user cannot pass it anyway) and `sacrebleu` will use its default, no matter where it is and how it is called.
Related to #2739.
This is a partial hotfix of #2781.
| true
|
964,696,380
|
https://api.github.com/repos/huggingface/datasets/issues/2777
|
https://github.com/huggingface/datasets/pull/2777
| 2,777
|
Use packaging to handle versions
|
closed
| 0
| 2021-08-10T07:51:39
| 2021-08-18T13:56:27
| 2021-08-18T13:56:27
|
albertvillanova
|
[] |
Use packaging module to handle/validate/check versions of Python packages.
Related to #2769.
| true
|
964,400,596
|
https://api.github.com/repos/huggingface/datasets/issues/2776
|
https://github.com/huggingface/datasets/issues/2776
| 2,776
|
document `config.HF_DATASETS_OFFLINE` and precedence
|
open
| 0
| 2021-08-09T21:23:17
| 2021-08-09T21:23:17
| null |
stas00
|
[
"enhancement"
] |
https://github.com/huggingface/datasets/pull/1976 implemented `HF_DATASETS_OFFLINE`, but:
1. `config.HF_DATASETS_OFFLINE` is not documented
2. the precedence is not documented (env, config)
I'm thinking it probably should be similar to what it says https://huggingface.co/docs/datasets/loading_datasets.html#from-the-huggingface-hub about `datasets.config.IN_MEMORY_MAX_SIZE`:
Quote:
> The default in 🤗 Datasets is to memory-map the dataset on disk unless you set datasets.config.IN_MEMORY_MAX_SIZE different from 0 bytes (default). In that case, the dataset will be copied in-memory if its size is smaller than datasets.config.IN_MEMORY_MAX_SIZE bytes, and memory-mapped otherwise. This behavior can be enabled by setting either the configuration option datasets.config.IN_MEMORY_MAX_SIZE (higher precedence) or the environment variable HF_DATASETS_IN_MEMORY_MAX_SIZE (lower precedence) to nonzero.
Context: trying to use `config.HF_DATASETS_OFFLINE` here:
https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/48
but are uncertain if it's safe, since it's not documented as a public API.
Thank you!
@lhoestq, @albertvillanova
| false
|
964,303,626
|
https://api.github.com/repos/huggingface/datasets/issues/2775
|
https://github.com/huggingface/datasets/issues/2775
| 2,775
|
`generate_random_fingerprint()` deterministic with 🤗Transformers' `set_seed()`
|
closed
| 3
| 2021-08-09T19:28:51
| 2024-01-26T15:05:36
| 2024-01-26T15:05:35
|
mbforbes
|
[
"bug"
] |
## Describe the bug
**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the "random" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.
Hi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L260-L265
However, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:
```text
Loading cached processed dataset at /home/xxx/.cache/huggingface/datasets/csv/default-xxx/0.0.0/xxx/cache-xxx.arrow
```
The path is exactly the same each run (e.g., last 26 runs).
This becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py)). The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.
I think that
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L248
... is actually consistent because randomness is being controlled in HuggingFace/Transformers for reproducibility. I've added a demo of this below.
## Steps to reproduce the bug
```python
# Contents of print_fingerprint.py
from transformers import set_seed
from datasets.fingerprint import generate_random_fingerprint
set_seed(42)
print(generate_random_fingerprint())
```
```bash
for i in {0..10}; do
python print_fingerprint.py
done
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
```
## Expected results
After the "random hash" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.
## Actual results
After the "random hash" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
| false
|
963,932,199
|
https://api.github.com/repos/huggingface/datasets/issues/2774
|
https://github.com/huggingface/datasets/pull/2774
| 2,774
|
Prevent .map from using multiprocessing when loading from cache
|
closed
| 6
| 2021-08-09T12:11:38
| 2021-09-09T10:20:28
| 2021-09-09T10:20:28
|
thomasw21
|
[] |
## Context
On our setup, we use different setup to train vs proprocessing datasets. Usually we are able to obtain a high number of cpus to preprocess, which allows us to use `num_proc` however we can't use as many during training phase. Currently if we use `num_proc={whatever the preprocessing value was}` we load from cache, but we get:
```
Traceback (most recent call last):
File "lib/python3.8/site-packages/multiprocess/pool.py", line 131, in worker
put((job, i, result))
File "lib/python3.8/site-packages/multiprocess/queues.py", line 371, in put
self._writer.send_bytes(obj)
File "lib/python3.8/site-packages/multiprocess/connection.py", line 203, in send_bytes
self._send_bytes(m[offset:offset + size])
File "lib/python3.8/site-packages/multiprocess/connection.py", line 414, in _send_bytes
self._send(header + buf)
File "lib/python3.8/site-packages/multiprocess/connection.py", line 371, in _send
n = write(self._handle, buf)
BrokenPipeError: [Errno 32] Broken pipe
```
Our current guess, is that we're spawning too many processes compared to the number of cpus available, and it's running OOM. Also we're loading this in DDP setting which means that for each gpu, I need to spawn a high number of processes to match the preprocessing fingerprint.
Instead what we suggest:
- Allow loading shard sequentially, sharing the same fingerprint as the multiprocessed one, in order to leverage multiprocessing when we actually generate the cache, and remove it when loading from cache.
## Current issues
~I'm having a hard time making fingerprints match. For some reason, the multiprocessing and the sequential version generate two different hash.~
**EDIT**: Turns out multiprocessing and sequential have different `transform` value for fingerprinting (check `fingerprint_transform`) when running `_map_single`:
- sequential : `datasets.arrow_dataset.Dataset._map_single`
- multiprocessing: `datasets.arrow_dataset._map_single`
This discrepancy is caused by multiprocessing pickling the transformer function, it doesn't seem to keep the `Dataset` hierarchy. I'm still unclear on why `func.__qual_name__` isn't handled correctly in multiprocessing. But replacing `__qualname__` by `__name__` fixes the issue.
## What was done
~We try to prevent the usage of multiprocessing when loading a dataset. Instead we load all cached shards sequentially.~
I couldn't find a nice way to obtain the cached_file_name and check they all exist before deciding to use the multiprocessing flow or not. Instead I expose an optional boolean `sequential` in `map` method.
## TODO
- [x] Check that the multiprocessed version and the sequential version output the same output
- [x] Check that sequential can load multiprocessed
- [x] Check that multiprocessed can load sequential
## Test
```python
from datasets import load_dataset
from multiprocessing import Pool
import random
def process(batch, rng):
length = len(batch["text"])
return {**batch, "processed_text": [f"PROCESSED {rng.random()}" for _ in range(length)]}
dataset = load_dataset("stas/openwebtext-10k", split="train")
print(dataset.column_names)
print(type(dataset))
rng = random.Random(42)
dataset1 = dataset.map(process, batched=True, batch_size=50, num_proc=4, fn_kwargs={"rng": rng})
# This one should be loaded from cache
rng = random.Random(42)
dataset2 = dataset.map(process, batched=True, batch_size=50, num_proc=4, fn_kwargs={"rng": rng}, sequential=True)
# Just to check that the random generator was correct
print(dataset1[-1]["processed_text"])
print(dataset2[-1]["processed_text"])
```
## Other solutions
I chose to load everything sequentially, but we can probably find a way to load shards in parallel using another number of workers (essentially this would be an argument not used for fingerprinting, allowing to allow `m` shards using `n` processes, which would be very useful when same dataset have to be loaded on two different setup, and we still want to leverage cache).
Also we can use a env variable similarly to `TOKENIZERS_PARALLELISM` as this seems generally setup related (though this changes slightly if we use multiprocessing).
cc @lhoestq (since I had asked you previously on `num_proc` being used for fingerprinting). Don't know if this is acceptable.
| true
|
963,730,497
|
https://api.github.com/repos/huggingface/datasets/issues/2773
|
https://github.com/huggingface/datasets/issues/2773
| 2,773
|
Remove dataset_infos.json
|
closed
| 1
| 2021-08-09T07:43:19
| 2024-05-04T14:52:10
| 2024-05-04T14:52:10
|
albertvillanova
|
[
"enhancement",
"generic discussion"
] |
**Is your feature request related to a problem? Please describe.**
As discussed, there are infos in the `dataset_infos.json` which are redundant and we could have them only in the README file.
Others could be migrated to the README, like: "dataset_size", "size_in_bytes", "download_size", "splits.split_name.[num_bytes, num_examples]",...
However, there are others that do not seem too meaningful in the README, like the checksums.
**Describe the solution you'd like**
Open a discussion to decide what to do with the `dataset_infos.json` files: which information to be migrated and/or which information to be kept.
cc: @julien-c @lhoestq
| false
|
963,348,834
|
https://api.github.com/repos/huggingface/datasets/issues/2772
|
https://github.com/huggingface/datasets/issues/2772
| 2,772
|
Remove returned feature constrain
|
open
| 0
| 2021-08-08T04:01:30
| 2021-08-08T08:48:01
| null |
PosoSAgapo
|
[
"enhancement"
] |
In the current version, the returned value of the map function has to be list or ndarray. However, this makes it unsuitable for many tasks. In NLP, many features are sparse like verb words, noun chunks, if we want to assign different values to different words, which will result in a large sparse matrix if we only score useful words like verb words.
Mostly, when using it on large scale, saving it as a whole takes a lot of disk storage and making it hard to read, the normal method is saving it in sparse form. However, the NumPy does not support sparse, therefore I have to use PyTorch or scipy to transform a matrix into special sparse form, which is not a form that can be transformed into list or ndarry. This violates the feature constraints of the map function.
I do appreciate the convenience of Datasets package, but I do not think the compulsory datatype constrain is necessary, in some cases, we just cannot transform it into a list or ndarray due to some reasons. Any way to fix this? Or what I can do to disable the compulsory datatype constrain?
| false
|
963,257,036
|
https://api.github.com/repos/huggingface/datasets/issues/2771
|
https://github.com/huggingface/datasets/pull/2771
| 2,771
|
[WIP][Common Voice 7] Add common voice 7.0
|
closed
| 2
| 2021-08-07T16:01:10
| 2021-12-06T23:24:02
| 2021-12-06T23:24:02
|
patrickvonplaten
|
[] |
This PR allows to load the new common voice dataset manually as explained when doing:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab")
```
=>
```
Please follow the manual download instructions:
You need to manually the dataset from `https://commonvoice.mozilla.org/en/datasets`.
Make sure you choose the version `Common Voice Corpus 7.0`.
Choose a language of your choice and find the corresponding language-id, *e.g.*, `Abkhaz` with language-id `ab`. The following language-ids are available:
['ab', 'ar', 'as', 'az', 'ba', 'bas', 'be', 'bg', 'br', 'ca', 'cnh', 'cs', 'cv', 'cy', 'de', 'dv', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy-NL', 'ga-IE', 'gl', 'gn', 'ha', 'hi', 'hsb', 'hu', 'hy-AM', 'ia', 'id', 'it', 'ja', 'ka', 'kab', 'kk', 'kmr', 'ky', 'lg', 'lt', 'lv', 'mn', 'mt', 'nl', 'or', 'pa-IN', 'pl', 'pt', 'rm-sursilv', 'rm-vallader', 'ro', 'ru', 'rw', 'sah', 'sk', 'sl', 'sr', 'sv-SE', 'ta', 'th', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'vot', 'zh-CN', 'zh-HK', 'zh-TW']
Next, you will have to enter your email address to download the dataset in the `tar.gz` format. Save the file under <path-to-file>.
The file should then be extracted with: ``tar -xvzf <path-to-file>`` which will extract a folder called ``cv-corpus-7.0-2021-07-21``.
The dataset can then be loaded with `datasets.load_dataset("common_voice", <language-id>, data_dir="<path-to-'cv-corpus-7.0-2021-07-21'-folder>", ignore_verifications=True).
```
Having followed those instructions one can then download the data as follows:
```python
from datasets import load_dataset
ds = load_dataset("./datasets/datasets/common_voice_7", "ab", data_dir="./cv-corpus-7.0-2021-07-21/", ignore_verifications=True)
```
## TODO
- [ ] Discuss naming. Is the name ok here "common_voice_7"? The dataset script differs only really in one point from `common_voice.py` in that all the metadata is different (more hours etc...) and that it has to use manual data dir for now
- [ ] Ideally we should get a bundled download link. For `common_voice.py` there is a bundled download link: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/{}.tar.gz` that allows one to directly download the data. However such a link is missing for Common Voice 7. I guess we should try to contact common voice about it and ask whether we could host the data or help otherwise somehow. See: https://github.com/common-voice/common-voice-bundler/issues/15 cc @yjernite
- [ ] I did not compute the dataset.json and it would mean that I'd have to download 76 datasets totalling around 1TB manually before running the checksum command. This just takes too much time. For now the user will have to add a `ignore_verifications=True` to download the data. This step would also be much easier if we could get a bundled link
- [ ] Add dummy data
| true
|
963,246,512
|
https://api.github.com/repos/huggingface/datasets/issues/2770
|
https://github.com/huggingface/datasets/pull/2770
| 2,770
|
Add support for fast tokenizer in BertScore
|
closed
| 0
| 2021-08-07T15:00:03
| 2021-08-09T12:34:43
| 2021-08-09T11:16:25
|
mariosasko
|
[] |
This PR adds support for a fast tokenizer in BertScore, which has been added recently to the lib.
Fixes #2765
| true
|
963,240,802
|
https://api.github.com/repos/huggingface/datasets/issues/2769
|
https://github.com/huggingface/datasets/pull/2769
| 2,769
|
Allow PyArrow from source
|
closed
| 0
| 2021-08-07T14:26:44
| 2021-08-09T15:38:39
| 2021-08-09T15:38:39
|
patrickvonplaten
|
[] |
When installing pyarrow from source the version is:
```python
>>> import pyarrow; pyarrow.__version__
'2.1.0.dev612'
```
-> however this breaks the install check at init of `datasets`. This PR makes sure that everything coming after the last `'.'` is removed.
| true
|
963,229,173
|
https://api.github.com/repos/huggingface/datasets/issues/2768
|
https://github.com/huggingface/datasets/issues/2768
| 2,768
|
`ArrowInvalid: Added column's length must match table's length.` after using `select`
|
closed
| 2
| 2021-08-07T13:17:29
| 2021-08-09T11:26:43
| 2021-08-09T11:26:43
|
lvwerra
|
[
"bug"
] |
## Describe the bug
I would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
ds = ds.add_column('ones', [1]*128)
```
## Expected results
I would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column.
## Actual results
Specify the actual results or traceback.
```python
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
/var/folders/l4/2905jygx4tx5jv8_kn03vxsw0000gn/T/ipykernel_6301/868709636.py in <module>
1 from datasets import load_dataset
2 ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
----> 3 ds = ds.add_column('ones', [0]*128)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint)
2965 column_table = InMemoryTable.from_pydict({name: column})
2966 # Concatenate tables horizontally
-> 2967 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)
2968 # Update features
2969 info = self.info.copy()
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis)
715 table_blocks = to_blocks(table)
716 blocks = _extend_blocks(blocks, table_blocks, axis=axis)
--> 717 return cls.from_blocks(blocks)
718
719 @property
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks)
663 return cls(table, blocks)
664 else:
--> 665 table = cls._concat_blocks_horizontally_and_vertically(blocks)
666 return cls(table, blocks)
667
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks)
623 if not tables:
624 continue
--> 625 pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)
626 pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)
627 return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis)
612 else:
613 for name, col in zip(table.column_names, table.columns):
--> 614 pa_table = pa_table.append_column(name, col)
615 return pa_table
616 else:
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 5.0.0
| false
|
963,002,120
|
https://api.github.com/repos/huggingface/datasets/issues/2767
|
https://github.com/huggingface/datasets/issues/2767
| 2,767
|
equal operation to perform unbatch for huggingface datasets
|
closed
| 5
| 2021-08-06T19:45:52
| 2022-03-07T13:58:00
| 2022-03-07T13:58:00
|
dorooddorood606
|
[
"bug"
] |
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| false
|
962,994,198
|
https://api.github.com/repos/huggingface/datasets/issues/2766
|
https://github.com/huggingface/datasets/pull/2766
| 2,766
|
fix typo (ShuffingConfig -> ShufflingConfig)
|
closed
| 0
| 2021-08-06T19:31:40
| 2021-08-10T14:17:03
| 2021-08-10T14:17:02
|
daleevans
|
[] |
pretty straightforward, it should be Shuffling instead of Shuffing
| true
|
962,861,395
|
https://api.github.com/repos/huggingface/datasets/issues/2765
|
https://github.com/huggingface/datasets/issues/2765
| 2,765
|
BERTScore Error
|
closed
| 1
| 2021-08-06T15:58:57
| 2021-08-09T11:16:25
| 2021-08-09T11:16:25
|
gagan3012
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
bert = load_metric('bertscore')
bert.compute(predictions=predictions, references=references,lang='en')
```
# Bug
`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Colab
- Python version:
- PyArrow version:
| false
|
962,554,799
|
https://api.github.com/repos/huggingface/datasets/issues/2764
|
https://github.com/huggingface/datasets/pull/2764
| 2,764
|
Add DER metric for SUPERB speaker diarization task
|
closed
| 1
| 2021-08-06T09:12:36
| 2023-07-11T09:35:23
| 2023-07-11T09:35:23
|
albertvillanova
|
[
"transfer-to-evaluate"
] | null | true
|
961,895,523
|
https://api.github.com/repos/huggingface/datasets/issues/2763
|
https://github.com/huggingface/datasets/issues/2763
| 2,763
|
English wikipedia datasets is not clean
|
closed
| 1
| 2021-08-05T14:37:24
| 2023-07-25T17:43:04
| 2023-07-25T17:43:04
|
lucadiliello
|
[
"bug"
] |
## Describe the bug
Wikipedia english dumps contain many wikipedia paragraphs like "References", "Category:" and "See Also" that should not be used for training.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
w = load_dataset('wikipedia', '20200501.en')
print(w['train'][0]['text'])
```
> 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the "famous historical and cultural market towns in China".\n\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year. wood block prints using vivid colourschemes to portray traditional scenes of children\'s games often interwoven with auspiciouse objects.\n\n, it had 27 residential communities () and 25 villages under its administration.\n\nShi Family Grand Courtyard\n\nShi Family Grand Courtyard (Tiānjīn Shí Jiā Dà Yuàn, 天津石家大院) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\n\nShi\'s ancestor came from Dong\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\n\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\n\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\n\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\' construction, local folk art and customs, and traditional period furnishings and crafts.\n\n**See also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'**
## Expected results
I expect no junk in the data.
## Actual results
Specify the actual results or traceback.
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 3.0.0
| false
|
961,652,046
|
https://api.github.com/repos/huggingface/datasets/issues/2762
|
https://github.com/huggingface/datasets/issues/2762
| 2,762
|
Add RVL-CDIP dataset
|
closed
| 3
| 2021-08-05T09:57:05
| 2022-04-21T17:15:41
| 2022-04-21T17:15:41
|
NielsRogge
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** RVL-CDIP
- **Description:** The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images. The images are sized so their largest dimension does not exceed 1000 pixels.
- **Paper:** https://www.cs.cmu.edu/~aharley/icdar15/
- **Data:** https://www.cs.cmu.edu/~aharley/rvl-cdip/
- **Motivation:** I'm currently adding LayoutLMv2 and LayoutXLM to HuggingFace Transformers. LayoutLM (v1) already exists in the library. This dataset has a large value for document image classification (i.e. classifying scanned documents). LayoutLM models obtain SOTA on this dataset, so would be great to directly use it in notebooks.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
961,568,287
|
https://api.github.com/repos/huggingface/datasets/issues/2761
|
https://github.com/huggingface/datasets/issues/2761
| 2,761
|
Error loading C4 realnewslike dataset
|
closed
| 4
| 2021-08-05T08:16:58
| 2021-08-08T19:44:34
| 2021-08-08T19:44:34
|
danshirron
|
[
"bug"
] |
## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
| false
|
961,372,667
|
https://api.github.com/repos/huggingface/datasets/issues/2760
|
https://github.com/huggingface/datasets/issues/2760
| 2,760
|
Add Nuswide dataset
|
open
| 0
| 2021-08-05T03:00:41
| 2021-12-08T12:06:23
| null |
shivangibithel
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** *NUSWIDE*
- **Description:** *[A Real-World Web Image Dataset from National University of Singapore](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html)*
- **Paper:** *[here](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/nuswide-civr2009.pdf)*
- **Data:** *[here](https://github.com/wenting-zhao/nuswide)*
- **Motivation:** *This dataset is a benchmark in the Text Retrieval task.*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
960,206,575
|
https://api.github.com/repos/huggingface/datasets/issues/2758
|
https://github.com/huggingface/datasets/pull/2758
| 2,758
|
Raise ManualDownloadError when loading a dataset that requires previous manual download
|
closed
| 0
| 2021-08-04T10:19:55
| 2021-08-04T11:36:30
| 2021-08-04T11:36:30
|
albertvillanova
|
[] |
This PR implements the raising of a `ManualDownloadError` when loading a dataset that requires previous manual download, and this is missing.
The `ManualDownloadError` is raised whether the dataset is loaded in normal or streaming mode.
Close #2749.
cc: @severo
| true
|
959,984,081
|
https://api.github.com/repos/huggingface/datasets/issues/2757
|
https://github.com/huggingface/datasets/issues/2757
| 2,757
|
Unexpected type after `concatenate_datasets`
|
closed
| 2
| 2021-08-04T07:10:39
| 2021-08-04T16:01:24
| 2021-08-04T16:01:23
|
JulesBelveze
|
[
"bug"
] |
## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`.
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.
## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
num_rows: 502
})
>>> for f in featurized_teacher.features:
print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])
>>> featurized_student
Dataset({
features: ['s_features', 's_labels'],
num_rows: 502
})
>>> for f in featurized_student.features:
print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.
Am I doing something wrong here? Any idea on how to fix this unexpected behavior?
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0
| false
|
959,255,646
|
https://api.github.com/repos/huggingface/datasets/issues/2756
|
https://github.com/huggingface/datasets/pull/2756
| 2,756
|
Fix metadata JSON for ubuntu_dialogs_corpus dataset
|
closed
| 0
| 2021-08-03T15:48:59
| 2021-08-04T09:43:25
| 2021-08-04T09:43:25
|
albertvillanova
|
[] |
Related to #2743.
| true
|
959,115,888
|
https://api.github.com/repos/huggingface/datasets/issues/2755
|
https://github.com/huggingface/datasets/pull/2755
| 2,755
|
Fix metadata JSON for turkish_movie_sentiment dataset
|
closed
| 0
| 2021-08-03T13:25:44
| 2021-08-04T09:06:54
| 2021-08-04T09:06:53
|
albertvillanova
|
[] |
Related to #2743.
| true
|
959,105,577
|
https://api.github.com/repos/huggingface/datasets/issues/2754
|
https://github.com/huggingface/datasets/pull/2754
| 2,754
|
Generate metadata JSON for telugu_books dataset
|
closed
| 0
| 2021-08-03T13:14:52
| 2021-08-04T08:49:02
| 2021-08-04T08:49:02
|
albertvillanova
|
[] |
Related to #2743.
| true
|
959,036,995
|
https://api.github.com/repos/huggingface/datasets/issues/2753
|
https://github.com/huggingface/datasets/pull/2753
| 2,753
|
Generate metadata JSON for reclor dataset
|
closed
| 0
| 2021-08-03T11:52:29
| 2021-08-04T08:07:15
| 2021-08-04T08:07:15
|
albertvillanova
|
[] |
Related to #2743.
| true
|
959,023,608
|
https://api.github.com/repos/huggingface/datasets/issues/2752
|
https://github.com/huggingface/datasets/pull/2752
| 2,752
|
Generate metadata JSON for lm1b dataset
|
closed
| 0
| 2021-08-03T11:34:56
| 2021-08-04T06:40:40
| 2021-08-04T06:40:39
|
albertvillanova
|
[] |
Related to #2743.
| true
|
959,021,262
|
https://api.github.com/repos/huggingface/datasets/issues/2751
|
https://github.com/huggingface/datasets/pull/2751
| 2,751
|
Update metadata for wikihow dataset
|
closed
| 0
| 2021-08-03T11:31:57
| 2021-08-03T15:52:09
| 2021-08-03T15:52:09
|
albertvillanova
|
[] |
Update metadata for wikihow dataset:
- Remove leading new line character in description and citation
- Update metadata JSON
- Remove no longer necessary `urls_checksums/checksums.txt` file
Related to #2748.
| true
|
958,984,730
|
https://api.github.com/repos/huggingface/datasets/issues/2750
|
https://github.com/huggingface/datasets/issues/2750
| 2,750
|
Second concatenation of datasets produces errors
|
closed
| 5
| 2021-08-03T10:47:04
| 2022-01-19T14:23:43
| 2022-01-19T14:19:05
|
Aktsvigun
|
[
"bug"
] |
Hi,
I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.
```
from datasets import load_dataset, concatenate_datasets
data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs
```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}
Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```
I am using `datasets` v.1.11.0
| false
|
958,968,748
|
https://api.github.com/repos/huggingface/datasets/issues/2749
|
https://github.com/huggingface/datasets/issues/2749
| 2,749
|
Raise a proper exception when trying to stream a dataset that requires to manually download files
|
closed
| 2
| 2021-08-03T10:26:27
| 2021-08-09T08:53:35
| 2021-08-04T11:36:30
|
severo
|
[
"bug"
] |
## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| false
|
958,889,041
|
https://api.github.com/repos/huggingface/datasets/issues/2748
|
https://github.com/huggingface/datasets/pull/2748
| 2,748
|
Generate metadata JSON for wikihow dataset
|
closed
| 0
| 2021-08-03T08:55:40
| 2021-08-03T10:17:51
| 2021-08-03T10:17:51
|
albertvillanova
|
[] |
Related to #2743.
| true
|
958,867,627
|
https://api.github.com/repos/huggingface/datasets/issues/2747
|
https://github.com/huggingface/datasets/pull/2747
| 2,747
|
add multi-proc in `to_json`
|
closed
| 17
| 2021-08-03T08:30:13
| 2021-10-19T18:24:21
| 2021-09-13T13:56:37
|
bhavitvyamalik
|
[] |
Closes #2663. I've tried adding multiprocessing in `to_json`. Here's some benchmarking I did to compare the timings of current version (say v1) and multi-proc version (say v2). I did this with `cpu_count` 4 (2015 Macbook Air)
1. Dataset name: `ascent_kb` - 8.9M samples (all samples were used, reporting this for a single run)
v1- ~225 seconds for converting whole dataset to json
v2- ~200 seconds for converting whole dataset to json
2. Dataset name: `lama` - 1.3M samples (all samples were used, reporting this for 2 runs)
v1- ~26 seconds for converting whole dataset to json
v2- ~23.6 seconds for converting whole dataset to json
I think it's safe to say that v2 is 10% faster as compared to v1. Timings may improve further with better configuration.
The only bottleneck I feel is writing to file from the output list. If we can improve that aspect then timings may improve further.
Let me know if any changes/improvements can be done in this @stas00, @lhoestq, @albertvillanova. @lhoestq even suggested to extend this work with other export methods as well like `csv` or `parquet`.
| true
|
958,551,619
|
https://api.github.com/repos/huggingface/datasets/issues/2746
|
https://github.com/huggingface/datasets/issues/2746
| 2,746
|
Cannot load `few-nerd` dataset
|
closed
| 6
| 2021-08-02T22:18:57
| 2021-11-16T08:51:34
| 2021-08-03T19:45:43
|
Mehrad0711
|
[
"bug"
] |
## Describe the bug
Cannot load `few-nerd` dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```
## Actual results
Executing above code will give the following error:
```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
| false
|
958,269,579
|
https://api.github.com/repos/huggingface/datasets/issues/2745
|
https://github.com/huggingface/datasets/pull/2745
| 2,745
|
added semeval18_emotion_classification dataset
|
closed
| 7
| 2021-08-02T15:39:55
| 2021-10-29T09:22:05
| 2021-09-21T09:48:35
|
maxpel
|
[] |
I added the data set of SemEval 2018 Task 1 (Subtask 5) for emotion detection in three languages.
```
datasets-cli test datasets/semeval18_emotion_classification/ --save_infos --all_configs
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_semeval18_emotion_classification
```
Both commands ran successfully.
I couldn't create the dummy data (the files are tsvs but have .txt ending, maybe that's the problem?) and therefore the test on the dummy data fails, maybe someone can help here.
I also formatted the code:
```
black --line-length 119 --target-version py36 datasets/semeval18_emotion_classification/
isort datasets/semeval18_emotion_classification/
flake8 datasets/semeval18_emotion_classification/
```
That's the publication for reference:
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001
| true
|
958,146,637
|
https://api.github.com/repos/huggingface/datasets/issues/2744
|
https://github.com/huggingface/datasets/pull/2744
| 2,744
|
Fix key by recreating metadata JSON for journalists_questions dataset
|
closed
| 0
| 2021-08-02T13:27:53
| 2021-08-03T09:25:34
| 2021-08-03T09:25:33
|
albertvillanova
|
[] |
Close #2743.
| true
|
958,119,251
|
https://api.github.com/repos/huggingface/datasets/issues/2743
|
https://github.com/huggingface/datasets/issues/2743
| 2,743
|
Dataset JSON is incorrect
|
closed
| 2
| 2021-08-02T13:01:26
| 2021-08-03T10:06:57
| 2021-08-03T09:25:33
|
severo
|
[
"bug"
] |
## Describe the bug
The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.
The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Steps to reproduce the bug
Look at the files.
## Expected results
The first key should be `plain_text`:
```json
{
"plain_text": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Actual results
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
| false
|
958,114,064
|
https://api.github.com/repos/huggingface/datasets/issues/2742
|
https://github.com/huggingface/datasets/issues/2742
| 2,742
|
Improve detection of streamable file types
|
closed
| 1
| 2021-08-02T12:55:09
| 2021-11-12T17:18:10
| 2021-11-12T17:18:10
|
severo
|
[
"enhancement",
"dataset-viewer"
] |
**Is your feature request related to a problem? Please describe.**
```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```
raises
```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:
```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160 yes 1
302221719412830209 yes 1
289761704907268096 yes 1
513820885032378369 yes %
```
Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.
**Describe the solution you'd like**
In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.
**Describe alternatives you've considered**
Add a variable in the dataset script to set the data format by hand.
| false
|
957,979,559
|
https://api.github.com/repos/huggingface/datasets/issues/2741
|
https://github.com/huggingface/datasets/issues/2741
| 2,741
|
Add Hypersim dataset
|
open
| 0
| 2021-08-02T10:06:50
| 2021-12-08T12:06:51
| null |
osanseviero
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** Hypersim
- **Description:** photorealistic synthetic dataset for holistic indoor scene understanding
- **Paper:** *link to the dataset paper if available*
- **Data:** https://github.com/apple/ml-hypersim
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
957,911,035
|
https://api.github.com/repos/huggingface/datasets/issues/2740
|
https://github.com/huggingface/datasets/pull/2740
| 2,740
|
Update release instructions
|
closed
| 0
| 2021-08-02T08:46:00
| 2021-08-02T14:39:56
| 2021-08-02T14:39:56
|
albertvillanova
|
[] |
Update release instructions.
| true
|
957,751,260
|
https://api.github.com/repos/huggingface/datasets/issues/2739
|
https://github.com/huggingface/datasets/pull/2739
| 2,739
|
Pass tokenize to sacrebleu only if explicitly passed by user
|
closed
| 0
| 2021-08-02T05:09:05
| 2021-08-03T04:23:37
| 2021-08-03T04:23:37
|
albertvillanova
|
[] |
Next `sacrebleu` release (v2.0.0) will remove `sacrebleu.DEFAULT_TOKENIZER`: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15
This PR passes `tokenize` to `sacrebleu` only if explicitly passed by the user, otherwise it will not pass it (and `sacrebleu` will use its default, no matter where it is and how it is called).
Close: #2737.
| true
|
957,517,746
|
https://api.github.com/repos/huggingface/datasets/issues/2738
|
https://github.com/huggingface/datasets/pull/2738
| 2,738
|
Sunbird AI Ugandan low resource language dataset
|
closed
| 4
| 2021-08-01T15:18:00
| 2022-10-03T09:37:30
| 2022-10-03T09:37:30
|
ak3ra
|
[
"dataset contribution"
] |
Multi-way parallel text corpus of 5 key Ugandan languages for the task of machine translation.
| true
|
957,124,881
|
https://api.github.com/repos/huggingface/datasets/issues/2737
|
https://github.com/huggingface/datasets/issues/2737
| 2,737
|
SacreBLEU update
|
closed
| 5
| 2021-07-30T23:53:08
| 2021-09-22T10:47:41
| 2021-08-03T04:23:37
|
devrimcavusoglu
|
[
"bug"
] |
With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
| false
|
956,895,199
|
https://api.github.com/repos/huggingface/datasets/issues/2736
|
https://github.com/huggingface/datasets/issues/2736
| 2,736
|
Add Microsoft Building Footprints dataset
|
open
| 1
| 2021-07-30T16:17:08
| 2021-12-08T12:09:03
| null |
albertvillanova
|
[
"dataset request",
"vision"
] |
## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @sashavor
| false
|
956,889,365
|
https://api.github.com/repos/huggingface/datasets/issues/2735
|
https://github.com/huggingface/datasets/issues/2735
| 2,735
|
Add Open Buildings dataset
|
open
| 0
| 2021-07-30T16:08:39
| 2021-07-31T05:01:25
| null |
albertvillanova
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** Open Buildings
- **Description:** A dataset of building footprints to support social good applications.
Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa.
See: "Mapping Africa's Buildings with Satellite Imagery" https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html
- **Paper:** https://arxiv.org/abs/2107.12283
- **Data:** https://sites.research.google/open-buildings/
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @osanseviero
| false
|
956,844,874
|
https://api.github.com/repos/huggingface/datasets/issues/2734
|
https://github.com/huggingface/datasets/pull/2734
| 2,734
|
Update BibTeX entry
|
closed
| 0
| 2021-07-30T15:22:51
| 2021-07-30T15:47:58
| 2021-07-30T15:47:58
|
albertvillanova
|
[] |
Update BibTeX entry.
| true
|
956,725,476
|
https://api.github.com/repos/huggingface/datasets/issues/2733
|
https://github.com/huggingface/datasets/pull/2733
| 2,733
|
Add missing parquet known extension
|
closed
| 0
| 2021-07-30T13:01:20
| 2021-07-30T13:24:31
| 2021-07-30T13:24:30
|
lhoestq
|
[] |
This code was failing because the parquet extension wasn't recognized:
```python
from datasets import load_dataset
base_url = "https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/"
data_files = {"train": base_url + "wikipedia-train.parquet"}
wiki = load_dataset("parquet", data_files=data_files, split="train", streaming=True)
```
It raises
```python
NotImplementedError: Extraction protocol for file at https://storage.googleapis.com/huggingface-nlp/cache/datasets/wikipedia/20200501.en/1.0.0/wikipedia-train.parquet is not implemented yet
```
I added `parquet` to the list of known extensions
EDIT: added pickle, conllu, xml extensions as well
| true
|
956,676,360
|
https://api.github.com/repos/huggingface/datasets/issues/2732
|
https://github.com/huggingface/datasets/pull/2732
| 2,732
|
Updated TTC4900 Dataset
|
closed
| 2
| 2021-07-30T11:52:14
| 2021-07-30T16:00:51
| 2021-07-30T15:58:14
|
yavuzKomecoglu
|
[] |
- The source address of the TTC4900 dataset of [@savasy](https://github.com/savasy) has been updated for direct download.
- Updated readme.
| true
|
956,087,452
|
https://api.github.com/repos/huggingface/datasets/issues/2731
|
https://github.com/huggingface/datasets/pull/2731
| 2,731
|
Adding to_tf_dataset method
|
closed
| 7
| 2021-07-29T18:10:25
| 2021-09-16T13:50:54
| 2021-09-16T13:50:54
|
Rocketknight1
|
[] |
Oh my **god** do not merge this yet, it's just a draft.
I've added a method (via a mixin) to the `arrow_dataset.Dataset` class that automatically converts our Dataset classes to TF Dataset classes ready for training. It hopefully has most of the features we want, including streaming from disk (no need to load the whole dataset in memory!), correct shuffling, variable-length batches to reduce compute, and correct support for unusual padding. It achieves that by calling the tokenizer `pad` method in the middle of a TF compute graph via a very hacky call to `tf.py_function`, which is heretical but seems to work.
A number of issues need to be resolved before it's ready to merge, though:
1) Is a MixIn the right way to do this? Do other classes besides `arrow_dataset.Dataset` need this method too?
2) Needs an argument to support constant-length batches for TPU training - this is easy to add and I'll do it soon.
3) Needs the user to supply the list of columns to drop from the arrow `Dataset`. Is there some automatic way to get the columns we want, or see which columns were added by the tokenizer?
4) Assumes the label column is always present and always called "label" - this is probably not great, but I'm not sure what the 'correct' thing to do here is.
| true
|
955,987,834
|
https://api.github.com/repos/huggingface/datasets/issues/2730
|
https://github.com/huggingface/datasets/issues/2730
| 2,730
|
Update CommonVoice with new release
|
open
| 3
| 2021-07-29T15:59:59
| 2021-08-07T16:19:19
| null |
yjernite
|
[
"dataset request"
] |
## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| false
|
955,920,489
|
https://api.github.com/repos/huggingface/datasets/issues/2729
|
https://github.com/huggingface/datasets/pull/2729
| 2,729
|
Fix IndexError while loading Arabic Billion Words dataset
|
closed
| 0
| 2021-07-29T14:47:02
| 2021-07-30T13:03:55
| 2021-07-30T13:03:55
|
albertvillanova
|
[
"bug"
] |
Catch `IndexError` and ignore that record.
Close #2727.
| true
|
955,892,970
|
https://api.github.com/repos/huggingface/datasets/issues/2728
|
https://github.com/huggingface/datasets/issues/2728
| 2,728
|
Concurrent use of same dataset (already downloaded)
|
open
| 4
| 2021-07-29T14:18:38
| 2021-08-02T07:25:57
| null |
PierreColombo
|
[
"bug"
] |
## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
| false
|
955,812,149
|
https://api.github.com/repos/huggingface/datasets/issues/2727
|
https://github.com/huggingface/datasets/issues/2727
| 2,727
|
Error in loading the Arabic Billion Words Corpus
|
closed
| 2
| 2021-07-29T12:53:09
| 2021-07-30T13:03:55
| 2021-07-30T13:03:55
|
M-Salti
|
[
"bug"
] |
## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
| false
|
955,674,388
|
https://api.github.com/repos/huggingface/datasets/issues/2726
|
https://github.com/huggingface/datasets/pull/2726
| 2,726
|
Typo fix `tokenize_exemple`
|
closed
| 0
| 2021-07-29T10:03:37
| 2021-07-29T12:00:25
| 2021-07-29T12:00:25
|
shabie
|
[] |
There is a small typo in the main README.md
| true
|
955,020,776
|
https://api.github.com/repos/huggingface/datasets/issues/2725
|
https://github.com/huggingface/datasets/pull/2725
| 2,725
|
Pass use_auth_token to request_etags
|
closed
| 0
| 2021-07-28T16:13:29
| 2021-07-28T16:38:02
| 2021-07-28T16:38:02
|
albertvillanova
|
[] |
Fix #2724.
| true
|
954,919,607
|
https://api.github.com/repos/huggingface/datasets/issues/2724
|
https://github.com/huggingface/datasets/issues/2724
| 2,724
|
404 Error when loading remote data files from private repo
|
closed
| 3
| 2021-07-28T14:24:23
| 2021-07-29T04:58:49
| 2021-07-28T16:38:01
|
albertvillanova
|
[
"bug"
] |
## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
| false
|
954,864,104
|
https://api.github.com/repos/huggingface/datasets/issues/2723
|
https://github.com/huggingface/datasets/pull/2723
| 2,723
|
Fix en subset by modifying dataset_info with correct validation infos
|
closed
| 0
| 2021-07-28T13:36:19
| 2021-07-28T15:22:23
| 2021-07-28T15:22:23
|
thomasw21
|
[] |
- Related to: #2682
We correct the values of `en` subset concerning the expected validation values (both `num_bytes` and `num_examples`.
Instead of having:
`{"name": "validation", "num_bytes": 828589180707, "num_examples": 364868892, "dataset_name": "c4"}`
We replace with correct values:
`{"name": "validation", "num_bytes": 825767266, "num_examples": 364608, "dataset_name": "c4"}`
There are still issues with validation with other subsets, but I can't download all the files, unzip to check for the correct number of bytes. (If you have a fast way to obtain those values for other subsets, I can do this in this PR ... otherwise I can't spend those resources)
| true
|
954,446,053
|
https://api.github.com/repos/huggingface/datasets/issues/2722
|
https://github.com/huggingface/datasets/issues/2722
| 2,722
|
Missing cache file
|
closed
| 2
| 2021-07-28T03:52:07
| 2022-03-21T08:27:51
| 2022-03-21T08:27:51
|
PosoSAgapo
|
[
"bug"
] |
Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
| false
|
954,238,230
|
https://api.github.com/repos/huggingface/datasets/issues/2721
|
https://github.com/huggingface/datasets/pull/2721
| 2,721
|
Deal with the bad check in test_load.py
|
closed
| 1
| 2021-07-27T20:23:23
| 2021-07-28T09:58:34
| 2021-07-28T08:53:18
|
mariosasko
|
[] |
This PR removes a check that's been added in #2684. My intention with this check was to capture an URL in the error message, but instead, it captures a substring of the previous regex match in the test function. Another option would be to replace this check with:
```python
m_paths = re.findall(r"\S*_dummy/_dummy.py\b", str(exc_info.value)) # on Linux this will match an URL as well as a local_path due to different os.sep, so take the last element (an URL always comes last in the list)
assert len(m_paths) > 0 and is_remote_url(m_paths[-1]) # is_remote_url comes from datasets.utils.file_utils
```
@lhoestq Let me know which one of these two approaches (delete or replace) do you prefer?
| true
|
954,024,426
|
https://api.github.com/repos/huggingface/datasets/issues/2720
|
https://github.com/huggingface/datasets/pull/2720
| 2,720
|
fix: 🐛 fix two typos
|
closed
| 0
| 2021-07-27T15:50:17
| 2021-07-27T18:38:17
| 2021-07-27T18:38:16
|
severo
|
[] | true
|
|
953,932,416
|
https://api.github.com/repos/huggingface/datasets/issues/2719
|
https://github.com/huggingface/datasets/issues/2719
| 2,719
|
Use ETag in streaming mode to detect resource updates
|
open
| 0
| 2021-07-27T14:17:09
| 2021-10-22T09:36:08
| null |
severo
|
[
"enhancement",
"dataset-viewer"
] |
**Is your feature request related to a problem? Please describe.**
I want to cache data I generate from processing a dataset I've loaded in streaming mode, but I've currently no way to know if the remote data has been updated or not, thus I don't know when to invalidate my cache.
**Describe the solution you'd like**
Take the ETag of the data files into account and provide it (directly or through a hash) to give a signal that I can invalidate my cache.
**Describe alternatives you've considered**
None
| false
|
953,360,663
|
https://api.github.com/repos/huggingface/datasets/issues/2718
|
https://github.com/huggingface/datasets/pull/2718
| 2,718
|
New documentation structure
|
closed
| 5
| 2021-07-26T23:15:13
| 2021-09-13T17:20:53
| 2021-09-13T17:20:52
|
stevhliu
|
[] |
Organize Datasets documentation into four documentation types to improve clarity and discoverability of content.
**Content to add in the very short term (feel free to add anything I'm missing):**
- A discussion on why Datasets uses Arrow that includes some context and background about why we use Arrow. Would also be great to talk about Datasets speed and performance here, and if you can share any benchmarking/tests you did, that would be awesome! Finally, a discussion about how memory-mapping frees the user from RAM constraints would be very helpful.
- Explain why you would want to disable or override verifications when loading a dataset.
- If possible, include a code sample of when the number of elements in the field of an output dictionary aren’t the same as the other fields in the output dictionary (taken from the [note](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset) here).
| true
|
952,979,976
|
https://api.github.com/repos/huggingface/datasets/issues/2717
|
https://github.com/huggingface/datasets/pull/2717
| 2,717
|
Fix shuffle on IterableDataset that disables batching in case any functions were mapped
|
closed
| 0
| 2021-07-26T14:42:22
| 2021-07-26T18:04:14
| 2021-07-26T16:30:06
|
amankhandelia
|
[] |
Made a very minor change to fix the issue#2716. Added the missing argument in the constructor call.
As discussed in the bug report, the change is made to prevent the `shuffle` method call from resetting the value of `batched` attribute in `MappedExamplesIterable`
Fix #2716.
| true
|
952,902,778
|
https://api.github.com/repos/huggingface/datasets/issues/2716
|
https://github.com/huggingface/datasets/issues/2716
| 2,716
|
Calling shuffle on IterableDataset will disable batching in case any functions were mapped
|
closed
| 3
| 2021-07-26T13:24:59
| 2021-07-26T18:04:43
| 2021-07-26T18:04:43
|
amankhandelia
|
[
"bug"
] |
When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`
I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
| false
|
952,845,229
|
https://api.github.com/repos/huggingface/datasets/issues/2715
|
https://github.com/huggingface/datasets/pull/2715
| 2,715
|
Update PAN-X data URL in XTREME dataset
|
closed
| 1
| 2021-07-26T12:21:17
| 2021-07-26T13:27:59
| 2021-07-26T13:27:59
|
albertvillanova
|
[] |
Related to #2710, #2691.
| true
|
952,580,820
|
https://api.github.com/repos/huggingface/datasets/issues/2714
|
https://github.com/huggingface/datasets/issues/2714
| 2,714
|
add more precise information for size
|
open
| 1
| 2021-07-26T07:11:03
| 2021-07-26T09:16:25
| null |
pennyl67
|
[
"enhancement"
] |
For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets.
| false
|
952,515,256
|
https://api.github.com/repos/huggingface/datasets/issues/2713
|
https://github.com/huggingface/datasets/pull/2713
| 2,713
|
Enumerate all ner_tags values in WNUT 17 dataset
|
closed
| 0
| 2021-07-26T05:22:16
| 2021-07-26T09:30:55
| 2021-07-26T09:30:55
|
albertvillanova
|
[] |
This PR does:
- Enumerate all ner_tags in dataset card Data Fields section
- Add all metadata tags to dataset card
Close #2709.
| true
|
951,723,326
|
https://api.github.com/repos/huggingface/datasets/issues/2710
|
https://github.com/huggingface/datasets/pull/2710
| 2,710
|
Update WikiANN data URL
|
closed
| 1
| 2021-07-23T16:29:21
| 2021-07-26T09:34:23
| 2021-07-26T09:34:23
|
albertvillanova
|
[] |
WikiANN data source URL is no longer accessible: 404 error from Dropbox.
We have decided to host it at Hugging Face. This PR updates the data source URL, the metadata JSON file and the dataset card.
Close #2691.
| true
|
951,534,757
|
https://api.github.com/repos/huggingface/datasets/issues/2709
|
https://github.com/huggingface/datasets/issues/2709
| 2,709
|
Missing documentation for wnut_17 (ner_tags)
|
closed
| 1
| 2021-07-23T12:25:32
| 2021-07-26T09:30:55
| 2021-07-26T09:30:55
|
maxpel
|
[
"bug"
] |
On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner!
| false
|
951,092,660
|
https://api.github.com/repos/huggingface/datasets/issues/2708
|
https://github.com/huggingface/datasets/issues/2708
| 2,708
|
QASC: incomplete training set
|
closed
| 2
| 2021-07-22T21:59:44
| 2021-07-23T13:30:07
| 2021-07-23T13:30:07
|
danyaljj
|
[
"bug"
] |
## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
| false
|
950,812,945
|
https://api.github.com/repos/huggingface/datasets/issues/2707
|
https://github.com/huggingface/datasets/issues/2707
| 2,707
|
404 Not Found Error when loading LAMA dataset
|
closed
| 3
| 2021-07-22T15:52:33
| 2021-07-26T14:29:07
| 2021-07-26T14:29:07
|
dwil2444
|
[] |
The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py`
| false
|
950,606,561
|
https://api.github.com/repos/huggingface/datasets/issues/2706
|
https://github.com/huggingface/datasets/pull/2706
| 2,706
|
Update BibTeX entry
|
closed
| 0
| 2021-07-22T12:29:29
| 2021-07-22T12:43:00
| 2021-07-22T12:43:00
|
albertvillanova
|
[] |
Update BibTeX entry.
| true
|
950,488,583
|
https://api.github.com/repos/huggingface/datasets/issues/2705
|
https://github.com/huggingface/datasets/issues/2705
| 2,705
|
404 not found error on loading WIKIANN dataset
|
closed
| 1
| 2021-07-22T09:55:50
| 2021-07-23T08:07:32
| 2021-07-23T08:07:32
|
ronbutan
|
[
"bug"
] |
## Describe the bug
Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| false
|
950,483,980
|
https://api.github.com/repos/huggingface/datasets/issues/2704
|
https://github.com/huggingface/datasets/pull/2704
| 2,704
|
Fix pick default config name message
|
closed
| 0
| 2021-07-22T09:49:43
| 2021-07-22T10:02:41
| 2021-07-22T10:02:40
|
lhoestq
|
[] |
The error message to tell which config name to load is not displayed.
This is because in the code it was considering the config kwargs to be non-empty, which is a special case for custom configs created on the fly. It appears after this change: https://github.com/huggingface/datasets/pull/2659
I fixed that by making the config kwargs empty by default, even if default parameters are passed
Fix https://github.com/huggingface/datasets/issues/2703
| true
|
950,482,284
|
https://api.github.com/repos/huggingface/datasets/issues/2703
|
https://github.com/huggingface/datasets/issues/2703
| 2,703
|
Bad message when config name is missing
|
closed
| 0
| 2021-07-22T09:47:23
| 2021-07-22T10:02:40
| 2021-07-22T10:02:40
|
lhoestq
|
[] |
When loading a dataset that have several configurations, we expect to see an error message if the user doesn't specify a config name.
However in `datasets` 1.10.0 and 1.10.1 it doesn't show the right message:
```python
import datasets
datasets.load_dataset("glue")
```
raises
```python
AttributeError: 'BuilderConfig' object has no attribute 'text_features'
```
instead of
```python
ValueError: Config name is missing.
Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']
Example of usage:
`load_dataset('glue', 'cola')`
```
| false
|
950,448,159
|
https://api.github.com/repos/huggingface/datasets/issues/2702
|
https://github.com/huggingface/datasets/pull/2702
| 2,702
|
Update BibTeX entry
|
closed
| 0
| 2021-07-22T09:04:39
| 2021-07-22T09:17:39
| 2021-07-22T09:17:38
|
albertvillanova
|
[] |
Update BibTeX entry.
| true
|
950,422,403
|
https://api.github.com/repos/huggingface/datasets/issues/2701
|
https://github.com/huggingface/datasets/pull/2701
| 2,701
|
Fix download_mode docstrings
|
closed
| 0
| 2021-07-22T08:30:25
| 2021-07-22T09:33:31
| 2021-07-22T09:33:31
|
albertvillanova
|
[
"documentation"
] |
Fix `download_mode` docstrings.
| true
|
950,276,325
|
https://api.github.com/repos/huggingface/datasets/issues/2700
|
https://github.com/huggingface/datasets/issues/2700
| 2,700
|
from datasets import Dataset is failing
|
closed
| 1
| 2021-07-22T03:51:23
| 2021-07-22T07:23:45
| 2021-07-22T07:09:07
|
kswamy15
|
[
"bug"
] |
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
| false
|
950,221,226
|
https://api.github.com/repos/huggingface/datasets/issues/2699
|
https://github.com/huggingface/datasets/issues/2699
| 2,699
|
cannot combine splits merging and streaming?
|
open
| 5
| 2021-07-22T01:13:25
| 2024-04-08T13:26:46
| null |
eyaler
|
[
"bug"
] |
this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits
| false
|
950,159,867
|
https://api.github.com/repos/huggingface/datasets/issues/2698
|
https://github.com/huggingface/datasets/pull/2698
| 2,698
|
Ignore empty batch when writing
|
closed
| 0
| 2021-07-21T22:35:30
| 2021-07-26T14:56:03
| 2021-07-26T13:25:26
|
pcuenca
|
[] |
This prevents an schema update with unknown column types, as reported in #2644.
This is my first attempt at fixing the issue. I tested the following:
- First batch returned by a batched map operation is empty.
- An intermediate batch is empty.
- `python -m unittest tests.test_arrow_writer` passes.
However, `arrow_writer` looks like a pretty generic interface, I'm not sure if there are other uses I may have overlooked. Let me know if that's the case, or if a better approach would be preferable.
| true
|
950,021,623
|
https://api.github.com/repos/huggingface/datasets/issues/2697
|
https://github.com/huggingface/datasets/pull/2697
| 2,697
|
Fix import on Colab
|
closed
| 1
| 2021-07-21T19:03:38
| 2021-07-22T07:09:08
| 2021-07-22T07:09:07
|
nateraw
|
[] |
Fix #2695, fix #2700.
| true
|
949,901,726
|
https://api.github.com/repos/huggingface/datasets/issues/2696
|
https://github.com/huggingface/datasets/pull/2696
| 2,696
|
Add support for disable_progress_bar on Windows
|
closed
| 1
| 2021-07-21T16:34:53
| 2021-07-26T13:31:14
| 2021-07-26T09:38:37
|
mariosasko
|
[] |
This PR is a continuation of #2667 and adds support for `utils.disable_progress_bar()` on Windows when using multiprocessing. This [answer](https://stackoverflow.com/a/6596695/14095927) on SO explains it nicely why the current approach (with calling `utils.is_progress_bar_enabled()` inside `Dataset._map_single`) would not work on Windows.
| true
|
949,864,823
|
https://api.github.com/repos/huggingface/datasets/issues/2695
|
https://github.com/huggingface/datasets/issues/2695
| 2,695
|
Cannot import load_dataset on Colab
|
closed
| 5
| 2021-07-21T15:52:51
| 2021-07-22T07:26:25
| 2021-07-22T07:09:07
|
bayartsogt-ya
|
[
"bug"
] |
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| false
|
949,844,722
|
https://api.github.com/repos/huggingface/datasets/issues/2694
|
https://github.com/huggingface/datasets/pull/2694
| 2,694
|
fix: 🐛 change string format to allow copy/paste to work in bash
|
closed
| 0
| 2021-07-21T15:30:40
| 2021-07-22T10:41:47
| 2021-07-22T10:41:47
|
severo
|
[] |
Before: copy/paste resulted in an error because the square bracket
characters `[]` are special characters in bash
| true
|
949,797,014
|
https://api.github.com/repos/huggingface/datasets/issues/2693
|
https://github.com/huggingface/datasets/pull/2693
| 2,693
|
Fix OSCAR Esperanto
|
closed
| 0
| 2021-07-21T14:43:50
| 2021-07-21T14:53:52
| 2021-07-21T14:53:51
|
lhoestq
|
[] |
The Esperanto part (original) of OSCAR has the wrong number of examples:
```python
from datasets import load_dataset
raw_datasets = load_dataset("oscar", "unshuffled_original_eo")
```
raises
```python
NonMatchingSplitsSizesError:
[{'expected': SplitInfo(name='train', num_bytes=314188336, num_examples=121171, dataset_name='oscar'),
'recorded': SplitInfo(name='train', num_bytes=314064514, num_examples=121168, dataset_name='oscar')}]
```
I updated the number of expected examples in dataset_infos.json
cc @sgugger
| true
|
949,765,484
|
https://api.github.com/repos/huggingface/datasets/issues/2692
|
https://github.com/huggingface/datasets/pull/2692
| 2,692
|
Update BibTeX entry
|
closed
| 0
| 2021-07-21T14:23:35
| 2021-07-21T15:31:41
| 2021-07-21T15:31:40
|
albertvillanova
|
[] |
Update BibTeX entry
| true
|
949,758,379
|
https://api.github.com/repos/huggingface/datasets/issues/2691
|
https://github.com/huggingface/datasets/issues/2691
| 2,691
|
xtreme / pan-x cannot be downloaded
|
closed
| 5
| 2021-07-21T14:18:05
| 2021-07-26T09:34:22
| 2021-07-26T09:34:22
|
severo
|
[
"bug"
] |
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| false
|
949,574,500
|
https://api.github.com/repos/huggingface/datasets/issues/2690
|
https://github.com/huggingface/datasets/pull/2690
| 2,690
|
Docs details
|
closed
| 1
| 2021-07-21T10:43:14
| 2021-07-27T18:40:54
| 2021-07-27T18:40:54
|
severo
|
[] |
Some comments here:
- the code samples assume the expected libraries have already been installed. Maybe add a section at start, or add it to every code sample. Something like `pip install datasets transformers torch 'datasets[streaming]'` (maybe just link to https://huggingface.co/docs/datasets/installation.html + a one-liner that installs all the requirements / alternatively a requirements.txt file)
- "If you’d like to play with the examples, you must install it from source." in https://huggingface.co/docs/datasets/installation.html: it's not clear to me what this means (what are these "examples"?)
- in https://huggingface.co/docs/datasets/loading_datasets.html: "or AWS bucket if it’s not already stored in the library". It's the only place in the doc (aside from the docstring https://huggingface.co/docs/datasets/package_reference/loading_methods.html?highlight=aws bucket#datasets.list_datasets) where the "AWS bucket" is mentioned. It's not easy to understand what this means. Maybe explain more, and link to https://s3.amazonaws.com/datasets.huggingface.co and/or https://huggingface.co/docs/datasets/filesystems.html.
- example in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files is obsoleted by https://github.com/huggingface/datasets/pull/2326. Also: see https://github.com/huggingface/datasets/issues/2691 for a bug on this specific dataset.
- in https://huggingface.co/docs/datasets/loading_datasets.html#manually-downloading-files the doc says "After you’ve downloaded the files, you can point to the folder hosting them locally with the data_dir argument as follows:", but the following example does not show how to use `data_dir`
- in https://huggingface.co/docs/datasets/loading_datasets.html#csv-files, it would be nice to have an URL to the csv loader reference (but I'm not sure there is one in the API reference). This comment applies in many places in the doc: I would want the API reference to contain doc for all the code/functions/classes... and I would want a lot more links inside the doc pointing to the API entries.
- in the API reference (docstrings) I would prefer "SOURCE" to link to github instead of a copy of the code inside the docs site (eg. https://github.com/huggingface/datasets/blob/master/src/datasets/load.py#L711 instead of https://huggingface.co/docs/datasets/_modules/datasets/load.html#load_dataset)
- it seems like not all the API is exposed in the doc. For example, there is no doc for [`disable_progress_bar`](https://github.com/huggingface/datasets/search?q=disable_progress_bar), see https://huggingface.co/docs/datasets/search.html?q=disable_progress_bar, even if the code contains docstrings. Does it mean that the function is not officially supported? (otherwise, maybe it also deserves a mention in https://huggingface.co/docs/datasets/package_reference/logging_methods.html)
- in https://huggingface.co/docs/datasets/loading_datasets.html?highlight=most%20efficient%20format%20have%20json%20files%20consisting%20multiple%20json%20objects#json-files, "The most efficient format is to have JSON files consisting of multiple JSON objects, one per line, representing individual data rows:", maybe link to https://en.wikipedia.org/wiki/JSON_streaming#Line-delimited_JSON and give it a name ("line-delimited JSON"? "JSON Lines" as in https://huggingface.co/docs/datasets/processing.html#exporting-a-dataset-to-csv-json-parquet-or-to-python-objects ?)
- in https://huggingface.co/docs/datasets/loading_datasets.html, for the local files sections, it would be nice to provide sample csv / json / text files to download, so that it's easier for the reader to try to load them (instead: they won't try)
- the doc explains how to shard a dataset, but does not explain why and when a dataset should be sharded (I have no idea... for [parallelizing](https://huggingface.co/docs/datasets/processing.html#multiprocessing)?). It does neither give an idea of the number of shards a dataset typically should have and why.
- the code example in https://huggingface.co/docs/datasets/processing.html#mapping-in-a-distributed-setting does not work, because `training_args` has not been defined before in the doc.
| true
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.