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639,439,052 | 275 | NonMatchingChecksumError when loading pubmed dataset | closed | I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.
The error is:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-2-7742dea167d0> in <module>()
----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')
2 df = pd.DataFrame(df)
3 gc.collect()
3 frames
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
431 verify_infos = not save_infos and not ignore_verifications
432 self._download_and_prepare(
--> 433 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
434 )
435 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
468 # Checksums verification
469 if verify_infos:
--> 470 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
471 for split_generator in split_generators:
472 if str(split_generator.split_info.name).lower() == "all":
/usr/local/lib/python3.6/dist-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I'm currently working on google colab.
That is quite strange because yesterday it was fine. | true | 2020-06-16T07:31:51Z | 2020-06-19T07:37:07Z | 2020-06-19T07:37:07Z | DavideStenner | NONE | null | null | 1 | 0 | 0 | 0 | null | false | [
"dataset bug"
] | https://github.com/huggingface/datasets/issues/275 | false | [
"For some reason the files are not available for unauthenticated users right now (like the download service of this package). Instead of downloading the right files, it downloads the html of the error.\r\nAccording to the error it should be back again in 24h.\r\n\r\n![image](https://user-images.githubusercontent.co... |
639,156,625 | 274 | PG-19 | closed | Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. | true | 2020-06-15T21:02:26Z | 2020-07-06T15:35:02Z | 2020-07-06T15:35:02Z | lucidrains | CONTRIBUTOR | null | null | 4 | 0 | 0 | 0 | null | false | [
"dataset request"
] | https://github.com/huggingface/datasets/issues/274 | false | [
"Sounds good! Do you want to give it a try?",
"Ok, I'll see if I can figure it out tomorrow!",
"Got around to this today, and so far so good, I'm able to download and load pg19 locally. However, I think there may be an issue with the dummy data, and testing in general.\r\n\r\nThe problem lies in the fact that e... |
638,968,054 | 273 | update cos_e to add cos_e v1.0 | closed | This PR updates the cos_e dataset to add v1.0 as requested here #163
@nazneenrajani | true | 2020-06-15T16:03:22Z | 2020-06-16T08:25:54Z | 2020-06-16T08:25:52Z | mariamabarham | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/273 | 2020-06-16T08:25:52Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/273 | true | [] |
638,307,313 | 272 | asd | closed | true | 2020-06-14T08:20:38Z | 2020-06-14T09:16:41Z | 2020-06-14T09:16:41Z | sn696 | NONE | https://github.com/huggingface/datasets/pull/272 | null | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/272 | true | [] | |
638,135,754 | 271 | Fix allociné dataset configuration | closed | This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | true | 2020-06-13T10:12:10Z | 2020-06-18T07:41:21Z | 2020-06-18T07:41:20Z | TheophileBlard | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/271 | null | 6 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/271 | true | [
"Actually when there is only one configuration, then you don't need to specify the configuration in `load_dataset`. You can run:\r\n```python\r\ndataset = load_dataset('allocine')\r\n```\r\nand it works.\r\n\r\nMaybe we should take that into account in the nlp viewer @srush ?",
"@lhoestq Just to understand the ex... |
638,121,617 | 270 | c4 dataset is not viewable in nlpviewer demo | closed | I get the following error when I try to view the c4 dataset in [nlpviewer](https://huggingface.co/nlp/viewer/)
```python
ModuleNotFoundError: No module named 'langdetect'
Traceback:
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp_viewer/run.py", line 54, in <module>
configs = get_confs(option.id)
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp_viewer/run.py", line 48, in get_confs
builder_cls = nlp.load.import_main_class(module_path, dataset=True)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/load.py", line 57, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4.py", line 29, in <module>
from .c4_utils import (
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4_utils.py", line 29, in <module>
import langdetect
``` | true | 2020-06-13T08:26:16Z | 2020-10-27T15:35:29Z | 2020-10-27T15:35:13Z | rajarsheem | NONE | null | null | 1 | 0 | 0 | 0 | null | false | [
"nlp-viewer"
] | https://github.com/huggingface/datasets/issues/270 | false | [
"C4 is too large to be shown in the viewer"
] |
638,106,774 | 269 | Error in metric.compute: missing `original_instructions` argument | closed | I'm running into an error using metrics for computation in the latest master as well as version 0.2.1. Here is a minimal example:
```python
import nlp
rte_metric = nlp.load_metric('glue', name="rte")
rte_metric.compute(
[0, 0, 1, 1],
[0, 1, 0, 1],
)
```
```
181 # Read the predictions and references
182 reader = ArrowReader(path=self.data_dir, info=None)
--> 183 self.data = reader.read_files(node_files)
184
185 # Release all of our locks
TypeError: read_files() missing 1 required positional argument: 'original_instructions'
```
I believe this might have been introduced with cc8d2508b75f7ba0e5438d0686ee02dcec43c7f4, which added the `original_instructions` argument. Elsewhere, an empty-string default is provided--perhaps that could be done here too? | true | 2020-06-13T06:26:54Z | 2020-06-18T07:41:44Z | 2020-06-18T07:41:44Z | zphang | NONE | null | null | 0 | 1 | 1 | 0 | null | false | [
"metric bug"
] | https://github.com/huggingface/datasets/issues/269 | false | [] |
637,848,056 | 268 | add Rotten Tomatoes Movie Review sentences sentiment dataset | closed | Sentence-level movie reviews v1.0 from here: http://www.cs.cornell.edu/people/pabo/movie-review-data/ | true | 2020-06-12T15:53:59Z | 2020-06-18T07:46:24Z | 2020-06-18T07:46:23Z | jxmorris12 | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/268 | 2020-06-18T07:46:23Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/268 | true | [
"@jplu @thomwolf @patrickvonplaten @lhoestq -- How do I request reviewers? Thanks."
] |
637,415,545 | 267 | How can I load/find WMT en-romanian? | closed | I believe it is from `wmt16`
When I run
```python
wmt = nlp.load_dataset('wmt16')
```
I get:
```python
AssertionError: The dataset wmt16 with config cs-en requires manual data.
Please follow the manual download instructions: Some of the wmt configs here, require a manual download.
Please look into wmt.py to see the exact path (and file name) that has to
be downloaded.
.
Manual data can be loaded with `nlp.load(wmt16, data_dir='<path/to/manual/data>')
```
There is no wmt.py,as the error message suggests, and wmt16.py doesn't have manual download instructions.
Any idea how to do this?
Thanks in advance! | true | 2020-06-12T01:09:37Z | 2020-06-19T08:24:19Z | 2020-06-19T08:24:19Z | sshleifer | CONTRIBUTOR | null | null | 1 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/267 | false | [
"I will take a look :-) "
] |
637,156,392 | 266 | Add sort, shuffle, test_train_split and select methods | closed | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | true | 2020-06-11T16:22:20Z | 2020-06-18T16:23:25Z | 2020-06-18T16:23:24Z | thomwolf | MEMBER | https://github.com/huggingface/datasets/pull/266 | 2020-06-18T16:23:23Z | 4 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/266 | true | [
"Nice !\r\n\r\nAlso it looks like we can have a train_test_split method for free:\r\n```python\r\ntrain_indices, test_indices = train_test_split(range(len(dataset)))\r\ntrain = dataset.sort(indices=train_indices)\r\ntest = dataset.sort(indices=test_indices)\r\n```\r\n\r\nand a shuffling method for free:\r\n```pytho... |
637,139,220 | 265 | Add pyarrow warning colab | closed | When a user installs `nlp` on google colab, then google colab doesn't update pyarrow, and the runtime needs to be restarted to use the updated version of pyarrow.
This is an issue because `nlp` requires the updated version to work correctly.
In this PR I added en error that is shown to the user in google colab if the user tries to `import nlp` without having restarted the runtime. The error tells the user to restart the runtime. | true | 2020-06-11T15:57:51Z | 2020-08-02T18:14:36Z | 2020-06-12T08:14:16Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/265 | 2020-06-12T08:14:16Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/265 | true | [] |
637,106,170 | 264 | Fix small issues creating dataset | closed | Fix many small issues mentioned in #249:
- don't force to install apache beam for commands
- fix None cache dir when using `dl_manager.download_custom`
- added new extras in `setup.py` named `dev` that contains tests and quality dependencies
- mock dataset sizes when running tests with dummy data
- add a note about the naming convention of datasets (camel case - snake case) in CONTRIBUTING.md
This should help users create their datasets.
Next step is the `add_dataset.md` docs :) | true | 2020-06-11T15:20:16Z | 2020-06-12T08:15:57Z | 2020-06-12T08:15:56Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/264 | 2020-06-12T08:15:56Z | 0 | 2 | 1 | 1 | false | false | [] | https://github.com/huggingface/datasets/pull/264 | true | [] |
637,028,015 | 263 | [Feature request] Support for external modality for language datasets | closed | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https://github.com/huggingface/nlp/pull/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data.
# Language + Vision
## Use case
Typically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html#seconddown) dataset.
Currently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features.
For all these types of features, people use one of the following formats:
1. [HD5F](https://pypi.org/project/h5py/)
2. [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)
3. [LMDB](https://lmdb.readthedocs.io/en/release/)
## Implementation considerations
I was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:
1. Download dataset
2. Download images associated with the dataset
3. Write a script that generates the visual features for every image and store them in a specific file
4. Create a DataLoader that maps the visual features to the corresponding language example
In my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https://github.com/pytorch/pytorch/issues/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.
For ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array.
Looking forward to hearing your thoughts about it! | true | 2020-06-11T13:42:18Z | 2022-02-10T13:26:35Z | 2022-02-10T13:26:35Z | aleSuglia | CONTRIBUTOR | null | null | 5 | 23 | 18 | 1 | null | false | [
"enhancement",
"generic discussion"
] | https://github.com/huggingface/datasets/issues/263 | false | [
"Thanks a lot, @aleSuglia for the very detailed and introductive feature request.\r\nIt seems like we could build something pretty useful here indeed.\r\n\r\nOne of the questions here is that Arrow doesn't have built-in support for generic \"tensors\" in records but there might be ways to do that in a clean way. We... |
636,702,849 | 262 | Add new dataset ANLI Round 1 | closed | Adding new dataset [ANLI](https://github.com/facebookresearch/anli/).
I'm not familiar with how to add new dataset. Let me know if there is any issue. I only include round 1 data here. There will be round 2, round 3 and more in the future with potentially different format. I think it will be better to separate them. | true | 2020-06-11T04:14:57Z | 2020-06-12T22:03:03Z | 2020-06-12T22:03:03Z | easonnie | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/262 | null | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/262 | true | [
"Hello ! Thanks for adding this one :)\r\n\r\nThis looks great, you just have to do the last steps to make the CI pass.\r\nI can see that two things are missing:\r\n1. the dummy data that is used to test that the script is working as expected\r\n2. the json file with all the infos about the dataset\r\n\r\nYou can s... |
636,372,380 | 261 | Downloading dataset error with pyarrow.lib.RecordBatch | closed | I am trying to download `sentiment140` and I have the following error
```
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
472 try:
473 # Prepare split will record examples associated to the split
--> 474 self._prepare_split(split_generator, **prepare_split_kwargs)
475 except OSError:
476 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
652 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
653 example = self.info.features.encode_example(record)
--> 654 writer.write(example)
655 num_examples, num_bytes = writer.finalize()
656
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write(self, example, writer_batch_size)
143 self._build_writer(pa_table=pa.Table.from_pydict(example))
144 if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size:
--> 145 self.write_on_file()
146
147 def write_batch(
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self)
127 else:
128 # All good
--> 129 self._write_array_on_file(pa_array)
130 self.current_rows = []
131
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array)
96 def _write_array_on_file(self, pa_array):
97 """Write a PyArrow Array"""
---> 98 pa_batch = pa.RecordBatch.from_struct_array(pa_array)
99 self._num_bytes += pa_array.nbytes
100 self.pa_writer.write_batch(pa_batch)
AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
```
I installed the last version and ran the following command:
```python
import nlp
sentiment140 = nlp.load_dataset('sentiment140', cache_dir='/content')
``` | true | 2020-06-10T16:04:19Z | 2020-06-11T14:35:12Z | 2020-06-11T14:35:12Z | cuent | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/261 | false | [
"When you install `nlp` for the first time on a Colab runtime, it updates the `pyarrow` library that was already on colab. This update shows this message on colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly... |
636,261,118 | 260 | Consistency fixes | closed | A few bugs I've found while hacking | true | 2020-06-10T13:44:42Z | 2020-06-11T10:34:37Z | 2020-06-11T10:34:36Z | julien-c | MEMBER | https://github.com/huggingface/datasets/pull/260 | 2020-06-11T10:34:36Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/260 | true | [] |
636,239,529 | 259 | documentation missing how to split a dataset | closed | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description:
> See the [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md) for more information.
But the guide seems to be missing.
To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this:
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`
because the imdb test data is sorted by class (probably not a good idea anyway) | true | 2020-06-10T13:18:13Z | 2023-03-14T13:56:07Z | 2020-06-18T22:20:24Z | fotisj | NONE | null | null | 7 | 2 | 2 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/259 | false | [
"this seems to work for my specific problem:\r\n\r\n`self.train_ds, self.test_ds, self.val_ds = map(_prepare_ds, ('train', 'test[:25%]+test[50%:75%]', 'test[75%:]'))`",
"Currently you can indeed split a dataset using `ds_test = nlp.load_dataset('imdb, split='test[:5000]')` (works also with percentages).\r\n\r\nHo... |
635,859,525 | 258 | Why is dataset after tokenization far more larger than the orginal one ? | closed | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M wikipedia-train.arrow
47511M tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? | true | 2020-06-10T01:27:07Z | 2020-06-10T12:46:34Z | 2020-06-10T12:46:34Z | richarddwang | CONTRIBUTOR | null | null | 4 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/258 | false | [
"Hi ! This is because `.map` added the new column `input_ids` to the dataset, and so all the other columns were kept. Therefore the dataset size increased a lot.\r\n If you want to only keep the `input_ids` column, you can stash the other ones by specifying `remove_columns=[\"title\", \"text\"]` in the arguments of... |
635,620,979 | 257 | Tokenizer pickling issue fix not landed in `nlp` yet? | closed | Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)
for split in dataset.keys():
dataset[split].map(lambda x: some_function(x, tokenizer))
```
```
06/09/2020 10:09:19 - INFO - nlp.builder - Constructing Dataset for split train[:10], from /home/sarahw/.cache/huggingface/datasets/cos_e/default/0.0.1
Traceback (most recent call last):
File "generation/input_to_label_and_rationale.py", line 390, in <module>
main()
File "generation/input_to_label_and_rationale.py", line 263, in main
dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 522, in map
cache_file_name = self._get_cache_file_path(function, cache_kwargs)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 381, in _get_cache_file_path
function_bytes = dumps(function)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 257, in dumps
dump(obj, file)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 250, in dump
Pickler(file).dump(obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 445, in dump
StockPickler.dump(self, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 485, in dump
self.save(obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1410, in save_function
pickler.save_reduce(_create_function, (obj.__code__,
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1147, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 884, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save
self.save_reduce(obj=obj, *rv)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce
save(state)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict
self._batch_setitems(obj.items())
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems
save(v)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save
self.save_reduce(obj=obj, *rv)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce
save(state)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict
self._batch_setitems(obj.items())
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems
save(v)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 576, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'Tokenizer' object
```
Fix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https://github.com/huggingface/tokenizers/issues/87), which I can't install with any package managers. | true | 2020-06-09T17:12:34Z | 2020-06-10T21:45:32Z | 2020-06-09T17:26:53Z | sarahwie | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/257 | false | [
"Yes, the new release of tokenizers solves this and should be out soon.\r\nIn the meantime, you can install it with `pip install tokenizers==0.8.0-dev2`",
"If others run into this issue, a quick fix is to use python 3.6 instead of 3.7+. Serialization differences between the 3rd party `dataclasses` package for 3.6... |
635,596,295 | 256 | [Feature request] Add a feature to dataset | closed | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | true | 2020-06-09T16:38:12Z | 2020-06-09T16:51:42Z | 2020-06-09T16:51:42Z | sarahwie | NONE | null | null | 5 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/256 | false | [
"Do you have an example of what you would like to do? (you can just add a field in the output of the unction you give to map and this will add this field in the output table)",
"Given another source of data loaded in, I want to pre-add it to the dataset so that it aligns with the indices of the arrow dataset prio... |
635,300,822 | 255 | Add dataset/piaf | closed | Small SQuAD-like French QA dataset [PIAF](https://www.aclweb.org/anthology/2020.lrec-1.673.pdf) | true | 2020-06-09T10:16:01Z | 2020-06-12T08:31:27Z | 2020-06-12T08:31:27Z | RachelKer | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/255 | 2020-06-12T08:31:27Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/255 | true | [
"Very nice !"
] |
635,057,568 | 254 | [Feature request] Be able to remove a specific sample of the dataset | closed | As mentioned in #117, it's currently not possible to remove a sample of the dataset.
But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.
I think it should be a feature. What do you think ?
---
Any work-around in the meantime ? | true | 2020-06-09T02:22:13Z | 2020-06-09T08:41:38Z | 2020-06-09T08:41:38Z | astariul | NONE | null | null | 1 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/254 | false | [
"Oh yes you can now do that with the `dataset.filter()` method that was added in #214 "
] |
634,791,939 | 253 | add flue dataset | closed | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue. | true | 2020-06-08T17:11:09Z | 2023-09-24T09:46:03Z | 2020-07-16T07:50:59Z | mariamabarham | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/253 | null | 10 | 1 | 1 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/253 | true | [
"The dummy data file was wrong. I only fixed it for the book config. Even though the tests are all green here, this should also be fixed for all other configs. Could you take a look there @mariamabarham ? ",
"Hi @mariamabarham \r\n\r\nFLUE can indeed become a very interesting benchmark for french NLP !\r\nUnfortu... |
634,563,239 | 252 | NonMatchingSplitsSizesError error when reading the IMDB dataset | closed | Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/load.py", line 517, in load_dataset
save_infos=save_infos,
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 363, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 421, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```
Am I overlooking something? Thanks! | true | 2020-06-08T12:26:24Z | 2021-08-27T15:20:58Z | 2020-06-08T14:01:26Z | antmarakis | NONE | null | null | 4 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/252 | false | [
"I just tried on my side and I didn't encounter your problem.\r\nApparently the script doesn't generate all the examples on your side.\r\n\r\nCan you provide the version of `nlp` you're using ?\r\nCan you try to clear your cache and re-run the code ?",
"I updated it, that was it, thanks!",
"Hello, I am facing t... |
634,544,977 | 251 | Better access to all dataset information | closed | Moves all the dataset info down one level from `dataset.info.XXX` to `dataset.XXX`
This way it's easier to access `dataset.feature['label']` for instance
Also, add the original split instructions used to create the dataset in `dataset.split`
Ex:
```
from nlp import load_dataset
stsb = load_dataset('glue', name='stsb', split='train')
stsb.split
>>> NamedSplit('train')
``` | true | 2020-06-08T11:56:50Z | 2020-06-12T08:13:00Z | 2020-06-12T08:12:58Z | thomwolf | MEMBER | https://github.com/huggingface/datasets/pull/251 | 2020-06-12T08:12:58Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/251 | true | [] |
634,416,751 | 250 | Remove checksum download in c4 | closed | There was a line from the original tfds script that was still there and causing issues when loading the c4 script. This one should fix #233 and allow anyone to load the c4 script to generate the dataset | true | 2020-06-08T09:13:00Z | 2020-08-25T07:04:56Z | 2020-06-08T09:16:59Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/250 | 2020-06-08T09:16:59Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/250 | true | [
"Commenting again in case [previous thread](https://github.com/huggingface/nlp/pull/233) was inactive.\r\n\r\n@lhoestq I am facing `IsADirectoryError` while downloading with this command.\r\nCan you pls look into it & help me.\r\nI'm using version 0.4.0 of `nlp`.\r\n\r\n```\r\ndataset = load_dataset(\"c4\", 'en', d... |
633,393,443 | 249 | [Dataset created] some critical small issues when I was creating a dataset | closed | Hi, I successfully created a dataset and has made a pr #248.
But I have encountered several problems when I was creating it, and those should be easy to fix.
1. Not found dataset_info.json
should be fixed by #241 , eager to wait it be merged.
2. Forced to install `apach_beam`
If we should install it, then it might be better to include it in the pakcage dependency or specified in `CONTRIBUTING.md`
```
Traceback (most recent call last):
File "nlp-cli", line 10, in <module>
from nlp.commands.run_beam import RunBeamCommand
File "/home/yisiang/nlp/src/nlp/commands/run_beam.py", line 6, in <module>
import apache_beam as beam
ModuleNotFoundError: No module named 'apache_beam'
```
3. `cached_dir` is `None`
```
File "/home/yisiang/nlp/src/nlp/datasets/bookscorpus/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c/bookscorpus.py", line 88, in _split_generators
downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)
File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 128, in download_custom
downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)
File "/home/yisiang/nlp/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 126, in url_to_downloaded_path
return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))
File "/home/yisiang/miniconda3/envs/nlppr/lib/python3.7/posixpath.py", line 80, in join
a = os.fspath(a)
```
This is because this line
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/src/nlp/commands/test.py#L30-L32
And I add `--cache_dir="...."` to `python nlp-cli test datasets/<your-dataset-folder> --save_infos --all_configs` in the doc, finally I could pass this error.
But it seems to ignore my arg and use `/home/yisiang/.cache/huggingface/datasets/bookscorpus/plain_text/1.0.0` as cahe_dir
4. There is no `pytest`
So maybe in the doc we should specify a step to install pytest
5. Not enough capacity in my `/tmp`
When run test for dummy data, I don't know why it ask me for 5.6g to download something,
```
def download_and_prepare
...
if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):
raise IOError(
"Not enough disk space. Needed: {} (download: {}, generated: {})".format(
utils.size_str(self.info.size_in_bytes or 0),
utils.size_str(self.info.download_size or 0),
> utils.size_str(self.info.dataset_size or 0),
)
)
E OSError: Not enough disk space. Needed: 5.62 GiB (download: 1.10 GiB, generated: 4.52 GiB)
```
I add a `processed_temp_dir="some/dir"; raw_temp_dir="another/dir"` to 71, and the test passed
https://github.com/huggingface/nlp/blob/a67a6c422dece904b65d18af65f0e024e839dbe8/tests/test_dataset_common.py#L70-L72
I suggest we can create tmp dir under the `/home/user/tmp` but not `/tmp`, because take our lab server for example, everyone use `/tmp` thus it has not much capacity. Or at least we can improve error message, so the user know is what directory has no space and how many has it lefted. Or we could do both.
6. name of datasets
I was surprised by the dataset name `books_corpus`, and didn't know it is from `class BooksCorpus(nlp.GeneratorBasedBuilder)` . I change it to `Bookscorpus` afterwards. I think this point shold be also on the doc.
7. More thorough doc to how to create `dataset.py`
I believe there will be.
**Feel free to close this issue** if you think these are solved. | true | 2020-06-07T12:58:54Z | 2020-06-12T08:28:51Z | 2020-06-12T08:28:51Z | richarddwang | CONTRIBUTOR | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/249 | false | [
"Thanks for noticing all these :) They should be easy to fix indeed",
"Alright I think I fixed all the problems you mentioned. Thanks again, that will be useful for many people.\r\nThere is still more work needed for point 7. but we plan to have some nice docs soon."
] |
633,390,427 | 248 | add Toronto BooksCorpus | closed | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | true | 2020-06-07T12:54:56Z | 2020-06-12T08:45:03Z | 2020-06-12T08:45:02Z | richarddwang | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/248 | 2020-06-12T08:45:02Z | 11 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/248 | true | [
"Thanks for adding this one !\r\n\r\nAbout the three points you mentioned:\r\n1. I think the `toronto_books_corpus` branch can be removed @mariamabarham ? \r\n2. You can use the download manager to download from google drive. For you case you can just do something like \r\n```python\r\nURL = \"https://drive.google.... |
632,380,078 | 247 | Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir | closed | This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different | true | 2020-06-06T11:02:10Z | 2020-06-08T09:18:16Z | 2020-06-08T09:18:14Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/247 | 2020-06-08T09:18:14Z | 3 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/247 | true | [
"That's great!\r\n\r\nI think it would be nice to test \"deterministic-ness\" of datasets in CI if we can do it (should be left for future PR of course)\r\n\r\nHere is a possibility (maybe there are other ways to do it): given that we may soon have efficient and large-scale hashing (cf our discussion on versioning/... |
632,380,054 | 246 | What is the best way to cache a dataset? | closed | For example if I want to use streamlit with a nlp dataset:
```
@st.cache
def load_data():
return nlp.load_dataset('squad')
```
This code raises the error "uncachable object"
Right now I just fixed with a constant for my specific case:
```
@st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})
```
But I was curious to know what is the best way in general | true | 2020-06-06T11:02:07Z | 2020-07-09T09:15:07Z | 2020-07-09T09:15:07Z | Mistobaan | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/246 | false | [
"Everything is already cached by default in 🤗nlp (in particular dataset\nloading and all the “map()” operations) so I don’t think you need to do any\nspecific caching in streamlit.\n\nTell us if you feel like it’s not the case.\n\nOn Sat, 6 Jun 2020 at 13:02, Fabrizio Milo <notifications@github.com> wrote:\n\n> Fo... |
631,985,108 | 245 | SST-2 test labels are all -1 | closed | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}
>>> list(l['label'] for l in glue['test'])
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 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-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
``` | true | 2020-06-05T21:41:42Z | 2021-12-08T00:47:32Z | 2020-06-06T16:56:41Z | jxmorris12 | CONTRIBUTOR | null | null | 10 | 1 | 1 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/245 | false | [
"this also happened to me with `nlp.load_dataset('glue', 'mnli')`",
"Yes, this is because the test sets for glue are hidden so the labels are\nnot publicly available. You can read the glue paper for more details.\n\nOn Sat, 6 Jun 2020 at 18:16, Jack Morris <notifications@github.com> wrote:\n\n> this also happened... |
631,869,155 | 244 | Add Allociné Dataset | closed | This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine.
Basically, it's a french "IMDB" dataset, with more reviews.
More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert). | true | 2020-06-05T19:19:26Z | 2020-06-11T07:47:26Z | 2020-06-11T07:47:26Z | TheophileBlard | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/244 | 2020-06-11T07:47:26Z | 3 | 1 | 1 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/244 | true | [
"great work @TheophileBlard ",
"LGTM, thanks a lot for adding dummy data tests :-) Was it difficult to create the correct dummy data folder? ",
"It was pretty easy actually. Documentation is on point !"
] |
631,735,848 | 243 | Specify utf-8 encoding for GLUE | closed | #242
This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug. | true | 2020-06-05T16:33:00Z | 2020-06-17T21:16:06Z | 2020-06-08T08:42:01Z | patpizio | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/243 | 2020-06-08T08:42:01Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/243 | true | [
"Thanks for fixing the encoding :)"
] |
631,733,683 | 242 | UnicodeDecodeError when downloading GLUE-MNLI | closed | When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
529
--> 530 for n, row in enumerate(reader):
531 if is_cola_non_test:
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. | true | 2020-06-05T16:30:01Z | 2020-06-09T16:06:47Z | 2020-06-08T08:45:03Z | patpizio | CONTRIBUTOR | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/242 | false | [
"It should be good now, thanks for noticing and fixing it ! I would say that it was because you are on windows but not 100% sure",
"On Windows Python supports Unicode almost everywhere, but one of the notable exceptions is open() where it uses the locale encoding schema. So platform independent python scripts wou... |
631,703,079 | 241 | Fix empty cache dir | closed | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239 | true | 2020-06-05T15:45:22Z | 2020-06-08T08:35:33Z | 2020-06-08T08:35:31Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/241 | 2020-06-08T08:35:31Z | 2 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/241 | true | [
"Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think",
"> Looks great! Will this change force all cached datasets to be redownloaded? But even if it does, it shoud not be a big problem, I think\r\n\r\nNo it shouldn't force to redo... |
631,434,677 | 240 | Deterministic dataset loading | closed | When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180
which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered
I think we should go through all the dataset scripts and make sure to have deterministic behavior.
A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name.
What do you think @lhoestq? | true | 2020-06-05T09:03:26Z | 2020-06-08T09:18:14Z | 2020-06-08T09:18:14Z | patrickvonplaten | CONTRIBUTOR | null | null | 4 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/240 | false | [
"Yes good point !",
"I think using `sorted(glob.glob())` would actually solve this problem. Can you think of other reasons why dataset loading might not be deterministic? @mariamabarham @yjernite @lhoestq @thomwolf . \r\n\r\nI can do a sweep through the dataset scripts and fix the glob.glob() if you guys are ok w... |
631,340,440 | 239 | [Creating new dataset] Not found dataset_info.json | closed | Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 139795325778640 acquired on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.load:Found main folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus
INFO:nlp.load:Found specific version folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9
INFO:nlp.load:Found script file from datasets/bookcorpus/bookcorpus.py to /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.py
INFO:nlp.load:Couldn't find dataset infos file at datasets/bookcorpus/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.json
INFO:filelock:Lock 139795325778640 released on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.builder:Overwrite dataset info from restored data version.
INFO:nlp.info:Loading Dataset info from /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0
Traceback (most recent call last):
File "nlp-cli", line 37, in <module>
service.run()
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/commands/test.py", line 78, in run
builders.append(builder_cls(name=config.name, data_dir=self._data_dir))
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/dataset_info.json'
```
btw, `ls /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/` show me nothing is in the directory.
I have also pushed the script to my fork [bookcorpus.py](https://github.com/richardyy1188/nlp/blob/bookcorpusdev/datasets/bookcorpus/bookcorpus.py). | true | 2020-06-05T06:15:04Z | 2020-06-07T13:01:04Z | 2020-06-07T13:01:04Z | richarddwang | CONTRIBUTOR | null | null | 5 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/239 | false | [
"I think you can just `rm` this directory and it should be good :)",
"@lhoestq - this seems to happen quite often (already the 2nd issue). Can we maybe delete this automatically?",
"Yes I have an idea of what's going on. I'm sure I can fix that",
"Hi, I rebase my local copy to `fix-empty-cache-dir`, and try t... |
631,260,143 | 238 | [Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. | closed | When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```
---
**What am I doing wrong / How can I hide this warning ?** | true | 2020-06-05T02:14:47Z | 2020-06-29T17:10:19Z | 2020-06-29T17:10:19Z | astariul | NONE | null | null | 1 | 1 | 1 | 0 | null | false | [
"metric bug"
] | https://github.com/huggingface/datasets/issues/238 | false | [
"This print statement comes from the official implementation of bert_score (see [here](https://github.com/Tiiiger/bert_score/blob/master/bert_score/utils.py#L343)). The warning shows up only if the attention mask outputs no candidate.\r\nRight now we want to only use official code for metrics to have fair evaluatio... |
631,199,940 | 237 | Can't download MultiNLI | closed | When I try to download MultiNLI with
```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-13-3b11f6be4cb9> in <module>
1 # Load a dataset and print the first examples in the training set
2 # nli_dataset = nlp.load_dataset('multi_nli')
----> 3 dataset = load_dataset('multi_nli')
4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')
5 # print(nli_dataset['train'][0])
~\Miniconda3\envs\nlp\lib\site-packages\nlp\load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
514
515 # Download and prepare data
--> 516 builder_instance.download_and_prepare(
517 download_config=download_config,
518 download_mode=download_mode,
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
417 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
418 verify_infos = not save_infos and not ignore_verifications
--> 419 self._download_and_prepare(
420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
455 split_dict = SplitDict(dataset_name=self.name)
456 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 457 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
458 # Checksums verification
459 if verify_infos:
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\multi_nli\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\multi_nli.py in _split_generators(self, dl_manager)
99 def _split_generators(self, dl_manager):
100
--> 101 downloaded_dir = dl_manager.download_and_extract(
102 "http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip"
103 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in download_and_extract(self, url_or_urls)
214 extracted_path(s): `str`, extracted paths of given URL(s).
215 """
--> 216 return self.extract(self.download(url_or_urls))
217
218 def get_recorded_sizes_checksums(self):
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in extract(self, path_or_paths)
194 path_or_paths.
195 """
--> 196 return map_nested(
197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
168 return tuple(mapped)
169 # Singleton
--> 170 return function(data_struct)
171
172
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in <lambda>(path)
195 """
196 return map_nested(
--> 197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
199
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
231 if is_zipfile(output_path):
232 with ZipFile(output_path, "r") as zip_file:
--> 233 zip_file.extractall(output_path_extracted)
234 zip_file.close()
235 elif tarfile.is_tarfile(output_path):
~\Miniconda3\envs\nlp\lib\zipfile.py in extractall(self, path, members, pwd)
1644
1645 for zipinfo in members:
-> 1646 self._extract_member(zipinfo, path, pwd)
1647
1648 @classmethod
~\Miniconda3\envs\nlp\lib\zipfile.py in _extract_member(self, member, targetpath, pwd)
1698
1699 with self.open(member, pwd=pwd) as source, \
-> 1700 open(targetpath, "wb") as target:
1701 shutil.copyfileobj(source, target)
1702
OSError: [Errno 22] Invalid argument: 'C:\\Users\\Python\\.cache\\huggingface\\datasets\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\multinli_1.0\\Icon\r'
``` | true | 2020-06-04T23:05:21Z | 2020-06-06T10:51:34Z | 2020-06-06T10:51:34Z | patpizio | CONTRIBUTOR | null | null | 3 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/237 | false | [
"You should use `load_dataset('glue', 'mnli')`",
"Thanks! I thought I had to use the same code displayed in the live viewer:\r\n```python\r\n!pip install nlp\r\nfrom nlp import load_dataset\r\ndataset = load_dataset('multi_nli', 'plain_text')\r\n```\r\nYour suggestion works, even if then I got a different issue (... |
631,099,875 | 236 | CompGuessWhat?! dataset | closed | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | true | 2020-06-04T19:45:50Z | 2020-06-11T09:43:42Z | 2020-06-11T07:45:21Z | aleSuglia | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/236 | 2020-06-11T07:45:21Z | 9 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/236 | true | [
"Hi @aleSuglia, thanks for this great PR. Indeed you can have both datasets in one file. You need to add a config class which will allows you to specify the different subdataset names and then you will be able to load them as follow.\r\nnlp.load_dataset(\"compguesswhat\", \"compguesswhat-gameplay\") \r\nnlp.load_d... |
630,952,297 | 235 | Add experimental datasets | closed | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset) | true | 2020-06-04T15:54:56Z | 2020-06-12T15:38:55Z | 2020-06-12T15:38:55Z | yjernite | MEMBER | https://github.com/huggingface/datasets/pull/235 | 2020-06-12T15:38:55Z | 6 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/235 | true | [
"I think it would be nicer to not create a new folder `datasets_experimental` , but just put your datasets also into the folder `datasets` for the following reasons:\r\n\r\n- From my point of view, the datasets are not very different from the other datasets (assuming that we soon have C4, and the beam datasets) so ... |
630,534,427 | 234 | Huggingface NLP, Uploading custom dataset | closed | Hello,
Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | true | 2020-06-04T05:59:06Z | 2020-07-06T09:33:26Z | 2020-07-06T09:33:26Z | Nouman97 | NONE | null | null | 4 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/234 | false | [
"What do you mean 'custom' ? You may want to elaborate on it when ask a question.\r\n\r\nAnyway, there are two things you may interested\r\n`nlp.Dataset.from_file` and `load_dataset(..., cache_dir=)`",
"To load a dataset you need to have a script that defines the format of the examples, the splits and the way to ... |
630,432,132 | 233 | Fail to download c4 english corpus | closed | i run following code to download c4 English corpus.
```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.cache/huggingface/datasets/c4/en/2.3.0...
Traceback (most recent call last):
File "download_corpus.py", line 38, in <module>
, data_dir='/home/adam/data/corpus/en/c4')
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 420, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 816, in _download_and_prepare
dl_manager, verify_infos=False, pipeline=pipeline,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 457, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/datasets/c4/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc/c4.py", line 175, in _split_generators
dl_manager.download_checksums(_CHECKSUMS_URL)
AttributeError: 'DownloadManager' object has no attribute 'download_checksums
```
can i get any advice? | true | 2020-06-04T01:06:38Z | 2021-01-08T07:17:32Z | 2020-06-08T09:16:59Z | donggyukimc | NONE | null | null | 5 | 3 | 3 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/233 | false | [
"Hello ! Thanks for noticing this bug, let me fix that.\r\n\r\nAlso for information, as specified in the changelog of the latest release, C4 currently needs to have a runtime for apache beam to work on. Apache beam is used to process this very big dataset and it can work on dataflow, spark, flink, apex, etc. You ca... |
630,029,568 | 232 | Nlp cli fix endpoints | closed | With this PR users will be able to upload their own datasets and metrics.
As mentioned in #181, I had to use the new endpoints and revert the use of dataclasses (just in case we have changes in the API in the future).
We now distinguish commands for datasets and commands for metrics:
```bash
nlp-cli upload_dataset <path/to/dataset>
nlp-cli upload_metric <path/to/metric>
nlp-cli s3_datasets {rm, ls}
nlp-cli s3_metrics {rm, ls}
```
Does it sound good to you @julien-c @thomwolf ? | true | 2020-06-03T14:10:39Z | 2020-06-08T09:02:58Z | 2020-06-08T09:02:57Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/232 | 2020-06-08T09:02:57Z | 1 | 1 | 1 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/232 | true | [
"LGTM 👍 "
] |
629,988,694 | 231 | Add .download to MockDownloadManager | closed | One method from the DownloadManager was missing and some users couldn't run the tests because of that.
@yjernite | true | 2020-06-03T13:20:00Z | 2020-06-03T14:25:56Z | 2020-06-03T14:25:55Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/231 | 2020-06-03T14:25:54Z | 0 | 1 | 1 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/231 | true | [] |
629,983,684 | 230 | Don't force to install apache beam for wikipedia dataset | closed | As pointed out in #227, we shouldn't force users to install apache beam if the processed dataset can be downloaded. I moved the imports of some datasets to avoid this problem | true | 2020-06-03T13:13:07Z | 2020-06-03T14:34:09Z | 2020-06-03T14:34:07Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/230 | 2020-06-03T14:34:07Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/230 | true | [] |
629,956,490 | 229 | Rename dataset_infos.json to dataset_info.json | closed | As the file required for the viewing in the live nlp viewer is named as dataset_info.json | true | 2020-06-03T12:31:44Z | 2020-06-03T12:52:54Z | 2020-06-03T12:48:33Z | aswin-giridhar | NONE | https://github.com/huggingface/datasets/pull/229 | null | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/229 | true | [
"\r\nThis was actually the right name. `dataset_infos.json` is used to have the infos of all the dataset configurations.\r\n\r\nOn the other hand `dataset_info.json` (without 's') is a cache file with the info of one specific configuration.\r\n\r\nTo fix #228, we probably just have to clear and reload the nlp-viewe... |
629,952,402 | 228 | Not able to access the XNLI dataset | closed | When I try to access the XNLI dataset, I get the following error. The option of plain_text get selected automatically and then I get the following error.
```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json'
Traceback:
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp_viewer/run.py", line 86, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp_viewer/run.py", line 72, in get
builder_instance = builder_cls(name=conf)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
```
Is it possible to see if the dataset_info.json is correctly placed? | true | 2020-06-03T12:25:14Z | 2020-07-17T17:44:22Z | 2020-07-17T17:44:22Z | aswin-giridhar | NONE | null | null | 4 | 1 | 1 | 0 | null | false | [
"nlp-viewer"
] | https://github.com/huggingface/datasets/issues/228 | false | [
"Added pull request to change the name of the file from dataset_infos.json to dataset_info.json",
"Thanks for reporting this bug !\r\nAs it seems to be just a cache problem, I closed your PR.\r\nI think we might just need to clear and reload the `xnli` cache @srush ? ",
"Update: The dataset_info.json error is g... |
629,845,704 | 227 | Should we still have to force to install apache_beam to download wikipedia ? | closed | Hi, first thanks to @lhoestq 's revolutionary work, I successfully downloaded processed wikipedia according to the doc. 😍😍😍
But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time.
Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ? | true | 2020-06-03T09:33:20Z | 2020-06-03T15:25:41Z | 2020-06-03T15:25:41Z | richarddwang | CONTRIBUTOR | null | null | 3 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/227 | false | [
"Thanks for your message 😊 \r\nIndeed users shouldn't have to install those dependencies",
"Got it, feel free to close this issue when you think it’s resolved.",
"It should be good now :)"
] |
628,344,520 | 226 | add BlendedSkillTalk dataset | closed | This PR add the BlendedSkillTalk dataset, which is used to fine tune the blenderbot. | true | 2020-06-01T10:54:45Z | 2020-06-03T14:37:23Z | 2020-06-03T14:37:22Z | mariamabarham | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/226 | 2020-06-03T14:37:22Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/226 | true | [
"Awesome :D"
] |
628,083,366 | 225 | [ROUGE] Different scores with `files2rouge` | closed | It seems that the ROUGE score of `nlp` is lower than the one of `files2rouge`.
Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.14574333776191592
rougeL 0.2321187823256159
`files2rouge` :
>Running ROUGE...
===========================
1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)
1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)
1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)
===========================
1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)
1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)
1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)
===========================
1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)
1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)
1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)
---
When using longer predictions/gold, the difference is bigger.
**How can I reproduce same score as `files2rouge` ?**
@lhoestq | true | 2020-06-01T00:50:36Z | 2020-06-03T15:27:18Z | 2020-06-03T15:27:18Z | astariul | NONE | null | null | 3 | 0 | 0 | 0 | null | false | [
"Metric discussion"
] | https://github.com/huggingface/datasets/issues/225 | false | [
"@Colanim unfortunately there are different implementations of the ROUGE metric floating around online which yield different results, and we had to chose one for the package :) We ended up including the one from the google-research repository, which does minimal post-processing before computing the P/R/F scores. If... |
627,791,693 | 224 | [Feature Request/Help] BLEURT model -> PyTorch | closed | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).
I had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried https://colab.research.google.com/drive/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!
Thank you muchly! | true | 2020-05-30T18:30:40Z | 2023-08-26T17:38:48Z | 2021-01-04T09:53:32Z | adamwlev | NONE | null | null | 6 | 1 | 1 | 0 | null | false | [
"enhancement"
] | https://github.com/huggingface/datasets/issues/224 | false | [
"Is there any update on this? \r\n\r\nThanks!",
"Hitting this error when using bleurt with PyTorch ...\r\n\r\n```\r\nUnrecognizedFlagError: Unknown command line flag 'f'\r\n```\r\n... and I'm assuming because it was built for TF specifically. Is there a way to use this metric in PyTorch?",
"We currently provid... |
627,683,386 | 223 | [Feature request] Add FLUE dataset | closed | Hi,
I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.). | true | 2020-05-30T08:52:15Z | 2020-12-03T13:39:33Z | 2020-12-03T13:39:33Z | lbourdois | NONE | null | null | 3 | 0 | 0 | 0 | null | false | [
"dataset request"
] | https://github.com/huggingface/datasets/issues/223 | false | [
"Hi @lbourdois, yes please share it with us",
"@mariamabarham \r\nI put all the datasets on this drive: https://1drv.ms/u/s!Ao2Rcpiny7RFinDypq7w-LbXcsx9?e=iVsEDh\r\n\r\n\r\nSome information : \r\n• For FLUE, the quote used is\r\n\r\n> @misc{le2019flaubert,\r\n> title={FlauBERT: Unsupervised Language Model Pre... |
627,586,690 | 222 | Colab Notebook breaks when downloading the squad dataset | closed | When I run the notebook in Colab
https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:
 | true | 2020-05-29T22:55:59Z | 2020-06-04T00:21:05Z | 2020-06-04T00:21:05Z | carlos-aguayo | NONE | null | null | 6 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/222 | false | [
"The notebook forces version 0.1.0. If I use the latest, things work, I'll run the whole notebook and create a PR.\r\n\r\nBut in the meantime, this issue gets fixed by changing:\r\n`!pip install nlp==0.1.0`\r\nto\r\n`!pip install nlp`",
"It still breaks very near the end\r\n\r\n![image](https://user-images.github... |
627,300,648 | 221 | Fix tests/test_dataset_common.py | closed | When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly. | true | 2020-05-29T14:12:15Z | 2020-06-01T12:20:42Z | 2020-05-29T15:02:23Z | tayciryahmed | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/221 | 2020-05-29T15:02:23Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/221 | true | [
"Thanks ! Good catch :)\r\n\r\nTo fix the CI you can do:\r\n1 - rebase from master\r\n2 - then run `make style` as specified in [CONTRIBUTING.md](https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md) ?"
] |
627,280,683 | 220 | dataset_arcd | closed | Added Arabic Reading Comprehension Dataset (ARCD): https://arxiv.org/abs/1906.05394 | true | 2020-05-29T13:46:50Z | 2020-05-29T14:58:40Z | 2020-05-29T14:57:21Z | tayciryahmed | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/220 | 2020-05-29T14:57:21Z | 2 | 1 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/220 | true | [
"you can rebase from master to fix the CI error :)",
"Awesome !"
] |
627,235,893 | 219 | force mwparserfromhell as third party | closed | This should fix your env because you had `mwparserfromhell ` as a first party for `isort` @patrickvonplaten | true | 2020-05-29T12:33:17Z | 2020-05-29T13:30:13Z | 2020-05-29T13:30:12Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/219 | 2020-05-29T13:30:12Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/219 | true | [] |
627,173,407 | 218 | Add Natual Questions and C4 scripts | closed | Scripts are ready !
However they are not processed nor directly available from gcp yet. | true | 2020-05-29T10:40:30Z | 2020-05-29T12:31:01Z | 2020-05-29T12:31:00Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/218 | 2020-05-29T12:31:00Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/218 | true | [] |
627,128,403 | 217 | Multi-task dataset mixing | open | It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks).
The [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning:
- **Examples-proportional mixing** - sample from tasks proportionally to their dataset size
- **Equal mixing** - sample uniformly from each task
- **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T.
Following this discussion https://github.com/huggingface/transformers/issues/4340 in [transformers](https://github.com/huggingface/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality.
Some method for combining datasets could be implemented ,e.g.
```
dataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...)
```
We would need a few additions:
- Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '?
- Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score.
It would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn.
I'm willing to write bits/most of this I just need some guidance on the interface and other library details so I can integrate it properly. | true | 2020-05-29T09:22:26Z | 2022-10-22T00:45:50Z | null | ghomasHudson | CONTRIBUTOR | null | null | 26 | 12 | 12 | 0 | null | false | [
"enhancement",
"generic discussion"
] | https://github.com/huggingface/datasets/issues/217 | false | [
"I like this feature! I think the first question we should decide on is how to convert all datasets into the same format. In T5, the authors decided to format every dataset into a text-to-text format. If the dataset had \"multiple\" inputs like MNLI, the inputs were concatenated. So in MNLI the input:\r\n\r\n> - **... |
626,896,890 | 216 | ❓ How to get ROUGE-2 with the ROUGE metric ? | closed | I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric.
---
I compute scores with :
```python
import nlp
rouge = nlp.load_metric('rouge')
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
rouge.add([lp], [lg])
score = rouge.compute()
```
then : _(print only the F-score for readability)_
```python
for k, s in score.items():
print(k, s.mid.fmeasure)
```
It gives :
>rouge1 0.7915168355671788
rougeL 0.7915168355671788
---
**How can I get the ROUGE-2 score ?**
Also, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ?
@lhoestq | true | 2020-05-28T23:47:32Z | 2020-06-01T00:04:35Z | 2020-06-01T00:04:35Z | astariul | NONE | null | null | 3 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/216 | false | [
"ROUGE-1 and ROUGE-L shouldn't return the same thing. This is weird",
"For the rouge2 metric you can do\r\n\r\n```python\r\nrouge = nlp.load_metric('rouge')\r\nwith open(\"pred.txt\") as p, open(\"ref.txt\") as g:\r\n for lp, lg in zip(p, g):\r\n rouge.add(lp, lg)\r\nscore = rouge.compute(rouge_types=[\... |
626,867,879 | 215 | NonMatchingSplitsSizesError when loading blog_authorship_corpus | closed | Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`.
```
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train',
num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323,
dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation',
num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'),
'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766,
dataset_name='blog_authorship_corpus')}]
```
Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on. | true | 2020-05-28T22:55:19Z | 2025-01-04T00:03:12Z | 2022-02-10T13:05:45Z | cedricconol | NONE | null | null | 12 | 0 | 0 | 0 | null | false | [
"dataset bug"
] | https://github.com/huggingface/datasets/issues/215 | false | [
"I just ran it on colab and got this\r\n```\r\n[{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812,\r\ndataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train',\r\nnum_bytes=611607465, num_examples=533285, dataset_name='blog_authorship_corpus')},\r\n{'expected': SplitInf... |
626,641,549 | 214 | [arrow_dataset.py] add new filter function | closed | The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples.
I think, filtering out examples is also a very common operation people would like to perform on datasets.
This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function.
Here is a sample code you can play around with:
```python
ds = nlp.load_dataset("squad", split="validation[:10%]")
def remove_under_idx_5(example, idx):
return idx < 5
def only_keep_examples_with_is_in_context(example):
return "is" in example["context"]
result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False)
result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False)
print("Original number of examples: {}".format(len(ds)))
print("First five examples number of examples: {}".format(len(result_keep_only_first_5)))
print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context)))
``` | true | 2020-05-28T16:21:40Z | 2020-05-29T11:43:29Z | 2020-05-29T11:32:20Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/214 | 2020-05-29T11:32:20Z | 13 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/214 | true | [
"I agree that a `.filter` method would be VERY useful and appreciated. I'm not a big fan of using `flatten_nested` as it completely breaks down the structure of the example and it may create bugs. Right now I think it may not work for nested structures. Maybe there's a simpler way that we've not figured out yet.",
... |
626,587,995 | 213 | better message if missing beam options | closed | WDYT @yjernite ?
For example:
```python
dataset = nlp.load_dataset('wikipedia', '20200501.aa')
```
Raises:
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.aa', beam_runner='DirectRunner')`
``` | true | 2020-05-28T15:06:57Z | 2020-05-29T09:51:17Z | 2020-05-29T09:51:16Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/213 | 2020-05-29T09:51:16Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/213 | true | [] |
626,580,198 | 212 | have 'add' and 'add_batch' for metrics | closed | This should fix #116
Previously the `.add` method of metrics expected a batch of examples.
Now `.add` expects one prediction/reference and `.add_batch` expects a batch.
I think it is more coherent with the way the ArrowWriter works. | true | 2020-05-28T14:56:47Z | 2020-05-29T10:41:05Z | 2020-05-29T10:41:04Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/212 | 2020-05-29T10:41:04Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/212 | true | [] |
626,565,994 | 211 | [Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type | closed | Running the following code
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, load_from_cache_file=False)
```
triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error.
On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works:
```
import nlp
ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards...
ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False)
```
. Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be?
**Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly. | true | 2020-05-28T14:38:14Z | 2020-07-23T10:15:16Z | 2020-07-23T10:15:16Z | patrickvonplaten | CONTRIBUTOR | null | null | 7 | 0 | 0 | 0 | null | false | [
"enhancement"
] | https://github.com/huggingface/datasets/issues/211 | false | [
"Here the full error trace:\r\n\r\n```\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-1-7aaf3f011358> in <module>\r\n 1 import nlp\r\n 2 ds = nlp.load_dataset(\"trivia_qa\", \"rc\", split=\"validation[:1%]\") # this might take 2.3 min to download but it's... |
626,504,243 | 210 | fix xnli metric kwargs description | closed | The text was wrong as noticed in #202 | true | 2020-05-28T13:21:44Z | 2020-05-28T13:22:11Z | 2020-05-28T13:22:10Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/210 | 2020-05-28T13:22:10Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/210 | true | [] |
626,405,849 | 209 | Add a Google Drive exception for small files | closed | I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive.
One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly.
Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` :
```
elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url)
```
I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing)
I avoid the error by adding an exception but there is maybe a proper way to do it.
Many thanks :hugs:
Best, | true | 2020-05-28T10:40:17Z | 2020-05-28T15:15:04Z | 2020-05-28T15:15:04Z | airKlizz | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/209 | 2020-05-28T15:15:04Z | 3 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/209 | true | [
"Can you run the style formatting tools to pass the code quality test?\r\n\r\nYou can find all the details in CONTRIBUTING.md: https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-contribute-to-nlp",
"Nice ! ",
"``make style`` done! Thanks for the approvals."
] |
626,398,519 | 208 | [Dummy data] insert config name instead of config | closed | Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself.
Also, @lhoestq fixed small import bug introduced by beam command I think. | true | 2020-05-28T10:28:19Z | 2020-05-28T12:48:01Z | 2020-05-28T12:48:00Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/208 | 2020-05-28T12:48:00Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/208 | true | [] |
625,932,200 | 207 | Remove test set from NLP viewer | closed | While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness. | true | 2020-05-27T18:32:07Z | 2022-02-10T13:17:45Z | 2022-02-10T13:17:45Z | chrisdonahue | NONE | null | null | 3 | 3 | 3 | 0 | null | false | [
"nlp-viewer"
] | https://github.com/huggingface/datasets/issues/207 | false | [
"~is the viewer also open source?~\r\n[is a streamlit app!](https://docs.streamlit.io/en/latest/getting_started.html)",
"Appears that [two thirds of those polled on Twitter](https://twitter.com/srush_nlp/status/1265734497632477185) are in favor of _some_ mechanism for averting eyeballs from the test data.",
"We... |
625,842,989 | 206 | [Question] Combine 2 datasets which have the same columns | closed | Hi,
I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german)
My issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution.
Hoping this is clear enough,
Thanks a lot 😊
Best | true | 2020-05-27T16:25:52Z | 2020-06-10T09:11:14Z | 2020-06-10T09:11:14Z | airKlizz | CONTRIBUTOR | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/206 | false | [
"We are thinking about ways to combine datasets for T5 in #217, feel free to share your thoughts about this.",
"Ok great! I will look at it. Thanks"
] |
625,839,335 | 205 | Better arrow dataset iter | closed | I tried to play around with `tf.data.Dataset.from_generator` and I found out that the `__iter__` that we have for `nlp.arrow_dataset.Dataset` ignores the format that has been set (torch or tensorflow).
With these changes I should be able to come up with a `tf.data.Dataset` that uses lazy loading, as asked in #193. | true | 2020-05-27T16:20:21Z | 2020-05-27T16:39:58Z | 2020-05-27T16:39:56Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/205 | 2020-05-27T16:39:56Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/205 | true | [] |
625,655,849 | 204 | Add Dataflow support + Wikipedia + Wiki40b | closed | # Add Dataflow support + Wikipedia + Wiki40b
## Support datasets processing with Apache Beam
Some datasets are too big to be processed on a single machine, for example: wikipedia, wiki40b, etc. Apache Beam allows to process datasets on many execution engines like Dataflow, Spark, Flink, etc.
To process such datasets with Beam, I added a command to run beam pipelines `nlp-cli run_beam path/to/dataset/script`. Then I used it to process the english + french wikipedia, and the english of wiki40b.
The processed arrow files are on GCS and are the result of a Dataflow job.
I added a markdown documentation file in `docs` that explains how to use it properly.
## Load already processed datasets
Now that we have those datasets already processed, I made it possible to load datasets that are already processed. You can do `load_dataset('wikipedia', '20200501.en')` and it will download the processed files from the Hugging Face GCS directly into the user's cache and be ready to use !
The Wikipedia dataset was already asked in #187 and this PR should soon allow to add Natural Questions as asked in #129
## Other changes in the code
To make things work, I had to do a few adjustments:
- add a `ship_files_with_pipeline` method to the `DownloadManager`. This is because beam pipelines can be run in the cloud and therefore need to have access to your downloaded data. I used it in the wikipedia script:
```python
if not pipeline.is_local():
downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)
```
- add parquet to arrow conversion. This is because the output of beam pipelines are parquet files so we need to convert them to arrow and have the arrow files on GCS
- add a test script with a dummy beam dataset
- minor adjustments to allow read/write operations on remote files using `apache_beam.io.filesystems.FileSystems` if we want (it can be connected to gcp, s3, hdfs, etc...) | true | 2020-05-27T12:32:49Z | 2020-05-28T08:10:35Z | 2020-05-28T08:10:34Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/204 | 2020-05-28T08:10:34Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/204 | true | [] |
625,515,488 | 203 | Raise an error if no config name for datasets like glue | closed | Some datasets like glue (see #130) and scientific_papers (see #197) have many configs.
For example for glue there are cola, sst2, mrpc etc.
Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to pick one of the available configs (as proposed in #152). For example for glue, the message looks like:
```
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')`
```
The error is raised if the config name is missing and if there are >=2 possible configs. | true | 2020-05-27T09:03:58Z | 2020-05-27T16:40:39Z | 2020-05-27T16:40:38Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/203 | 2020-05-27T16:40:38Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/203 | true | [] |
625,493,983 | 202 | Mistaken `_KWARGS_DESCRIPTION` for XNLI metric | closed | Hi!
The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
"""
```
But it should be something like:
```
_KWARGS_DESCRIPTION = """
Computes XNLI score which is just simple accuracy.
Args:
predictions: Predicted labels.
references: Ground truth labels.
Returns:
'accuracy': accuracy
``` | true | 2020-05-27T08:34:42Z | 2020-05-28T13:22:36Z | 2020-05-28T13:22:36Z | phiyodr | NONE | null | null | 1 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/202 | false | [
"Indeed, good catch ! thanks\r\nFixing it right now"
] |
625,235,430 | 201 | Fix typo in README | closed | true | 2020-05-26T22:18:21Z | 2020-05-26T23:40:31Z | 2020-05-26T23:00:56Z | LysandreJik | MEMBER | https://github.com/huggingface/datasets/pull/201 | 2020-05-26T23:00:56Z | 2 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/201 | true | [
"Amazing, @LysandreJik!",
"Really did my best!"
] | |
625,226,638 | 200 | [ArrowWriter] Set schema at first write example | closed | Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so).
I noticed that it was not done if the first example is added via `.write`, so I added it for coherence. | true | 2020-05-26T21:59:48Z | 2020-05-27T09:07:54Z | 2020-05-27T09:07:53Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/200 | 2020-05-27T09:07:53Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/200 | true | [
"Good point!\r\n\r\nI guess we could add this to `write_batch` as well (before using `self._schema` in the first line of this method)?"
] |
625,217,440 | 199 | Fix GermEval 2014 dataset infos | closed | Hi,
this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file. | true | 2020-05-26T21:41:44Z | 2020-05-26T21:50:24Z | 2020-05-26T21:50:24Z | stefan-it | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/199 | 2020-05-26T21:50:24Z | 2 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/199 | true | [
"Hopefully. this also fixes the dataset view on https://huggingface.co/nlp/viewer/ :)",
"Oh good catch ! This should fix it indeed"
] |
625,200,627 | 198 | Index outside of table length | closed | The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955).
> ValueError: Index (2000) outside of table length (2000).
> Traceback:
> File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
> exec(code, module.__dict__)
> File "/home/sasha/nlp_viewer/run.py", line 116, in <module>
> v = d[item][k]
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__
> output_all_columns=self._output_all_columns,
> File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem
> raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).") | true | 2020-05-26T21:09:40Z | 2020-05-26T22:43:49Z | 2020-05-26T22:43:49Z | casajarm | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/198 | false | [
"Sounds like something related to the nlp viewer @srush ",
"Fixed. "
] |
624,966,904 | 197 | Scientific Papers only downloading Pubmed | closed | Hi!
I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following:
```
dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.')
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.05k/5.05k [00:00<00:00, 2.66MB/s]
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.90k/4.90k [00:00<00:00, 2.42MB/s]
Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to ./scientific_papers/pubmed/1.1.1...
Downloading: 3.62GB [00:40, 90.5MB/s]
Downloading: 880MB [00:08, 101MB/s]
Dataset scientific_papers downloaded and prepared to ./scientific_papers/pubmed/1.1.1. Subsequent calls will reuse this data.
```
only a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something?
Thanks! | true | 2020-05-26T15:18:47Z | 2020-05-28T08:19:28Z | 2020-05-28T08:19:28Z | antmarakis | NONE | null | null | 3 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/197 | false | [
"Hi so there are indeed two configurations in the datasets as you can see [here](https://github.com/huggingface/nlp/blob/master/datasets/scientific_papers/scientific_papers.py#L81-L82).\r\n\r\nYou can load either one with:\r\n```python\r\ndataset = nlp.load_dataset('scientific_papers', 'pubmed')\r\ndataset = nlp.lo... |
624,901,266 | 196 | Check invalid config name | closed | As said in #194, we should raise an error if the config name has bad characters.
Bad characters are those that are not allowed for directory names on windows. | true | 2020-05-26T13:52:51Z | 2020-05-26T21:04:56Z | 2020-05-26T21:04:55Z | lhoestq | MEMBER | https://github.com/huggingface/datasets/pull/196 | 2020-05-26T21:04:55Z | 13 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/196 | true | [
"I think that's not related to the config name but the filenames in the dummy data. Mostly it occurs with files downloaded from drive. In that case the dummy file name is extracted from the google drive link and it corresponds to what comes after `https://drive.google.com/`\r\n\r\n",
"> I think that's not related... |
624,858,686 | 195 | [Dummy data command] add new case to command | closed | Qanta: #194 introduces a case that was not noticed before. This change in code helps community users to have an easier time creating the dummy data. | true | 2020-05-26T12:50:47Z | 2020-05-26T14:38:28Z | 2020-05-26T14:38:27Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/195 | 2020-05-26T14:38:27Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/195 | true | [
"@lhoestq - tiny change in the dummy data command, should be good to merge."
] |
624,854,897 | 194 | Add Dataset: Qanta | closed | Fixes dummy data for #169 @EntilZha | true | 2020-05-26T12:44:35Z | 2020-05-26T16:58:17Z | 2020-05-26T13:16:20Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/194 | 2020-05-26T13:16:20Z | 3 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/194 | true | [
"@lhoestq - the config name is rather special here: *E.g.* `mode=first,char_skip=25`. It includes `=` and `,` - will that be a problem for windows folders, you think? \r\n\r\nApart from that good to merge for me.",
"It's ok to have `=` and `,`.\r\nWindows doesn't like things like `?`, `:`, `/` etc.\r\n\r\nI'll ad... |
624,655,558 | 193 | [Tensorflow] Use something else than `from_tensor_slices()` | closed | In the example notebook, the TF Dataset is built using `from_tensor_slices()` :
```python
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x] for x in columns[:3]}
labels = {"output_1": train_tf_dataset["start_positions"]}
labels["output_2"] = train_tf_dataset["end_positions"]
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```
But according to [official tensorflow documentation](https://www.tensorflow.org/guide/data#consuming_numpy_arrays), this will load the entire dataset to memory.
**This defeats one purpose of this library, which is lazy loading.**
Is there any other way to load the `nlp` dataset into TF dataset lazily ?
---
For example, is it possible to use [Arrow dataset](https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset) ? If yes, is there any code example ? | true | 2020-05-26T07:19:14Z | 2020-10-27T15:28:11Z | 2020-10-27T15:28:11Z | astariul | NONE | null | null | 7 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/193 | false | [
"I guess we can use `tf.data.Dataset.from_generator` instead. I'll give it a try.",
"Is `tf.data.Dataset.from_generator` working on TPU ?",
"`from_generator` is not working on TPU, I met the following error :\r\n\r\n```\r\nFile \"/usr/local/lib/python3.6/contextlib.py\", line 88, in __exit__\r\n next(self.ge... |
624,397,592 | 192 | [Question] Create Apache Arrow dataset from raw text file | closed | Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide?
Is the worth of send it to you and add it to the NLP library?
Thanks, Manu | true | 2020-05-25T16:42:47Z | 2021-12-18T01:45:34Z | 2020-10-27T15:20:22Z | mrm8488 | CONTRIBUTOR | null | null | 4 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/192 | false | [
"We store every dataset in the Arrow format. This is convenient as it supports nested types and memory mapping. If you are curious feel free to check the [pyarrow documentation](https://arrow.apache.org/docs/python/)\r\n\r\nYou can use this library to load your covid papers by creating a dataset script. You can fin... |
624,394,936 | 191 | [Squad es] add dataset_infos | closed | @mariamabarham - was still about to upload this. Should have waited with my comment a bit more :D | true | 2020-05-25T16:35:52Z | 2020-05-25T16:39:59Z | 2020-05-25T16:39:58Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/191 | 2020-05-25T16:39:58Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/191 | true | [] |
624,124,600 | 190 | add squad Spanish v1 and v2 | closed | This PR add the Spanish Squad versions 1 and 2 datasets.
Fixes #164 | true | 2020-05-25T08:08:40Z | 2020-05-25T16:28:46Z | 2020-05-25T16:28:45Z | mariamabarham | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/190 | 2020-05-25T16:28:45Z | 5 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/190 | true | [
"Nice ! :) \r\nCan we group them into one dataset with two versions, instead of having two datasets ?",
"Yes sure, I can use the version as config name",
"@lhoestq can you check? I grouped them",
"Awesome :) feel free to merge after fixing the test in the CI",
"@mariamabarham - feel free to merge when you'r... |
624,048,881 | 189 | [Question] BERT-style multiple choice formatting | closed | Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior.
Thanks! | true | 2020-05-25T05:11:05Z | 2020-05-25T18:38:28Z | 2020-05-25T18:38:28Z | sarahwie | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/189 | false | [
"Hi @sarahwie, can you details this a little more?\r\n\r\nI'm not sure I understand what you refer to and what you mean when you say \"Previously, this was done by passing a list of InputFeatures to the dataloader instead of a list of InputFeature\"",
"I think I've resolved it. For others' reference: to convert f... |
623,890,430 | 188 | When will the remaining math_dataset modules be added as dataset objects | closed | Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help? | true | 2020-05-24T15:46:52Z | 2020-05-24T18:53:48Z | 2020-05-24T18:53:48Z | tylerroost | NONE | null | null | 3 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/188 | false | [
"On a similar note it would be nice to differentiate between train-easy, train-medium, and train-hard",
"Hi @tylerroost, we don't have a timeline for this at the moment.\r\nIf you want to give it a look we would be happy to review a PR on it.\r\nAlso, the library is one week old so everything is quite barebones, ... |
623,627,800 | 187 | [Question] How to load wikipedia ? Beam runner ? | closed | When `nlp.load_dataset('wikipedia')`, I got
* `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.`
* `AttributeError: 'NoneType' object has no attribute 'size'`
Could somebody tell me what should I do ?
# Env
On Colab,
```
git clone https://github.com/huggingface/nlp
cd nlp
pip install -q .
```
```
%pip install -q apache_beam mwparserfromhell
-> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible.
ERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible.
ERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible.
```
```
pip install -q apache-beam[interactive]
ERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible.
```
# The whole message
```
WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.
Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0...
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
44 frames
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-9-340aabccefff> in <module>()
----> 1 dset = nlp.load_dataset('wikipedia')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs)
370 verify_infos = not save_infos and not ignore_verifications
371 self._download_and_prepare(
--> 372 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
373 )
374 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
770 with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline:
771 super(BeamBasedBuilder, self)._download_and_prepare(
--> 772 dl_manager, pipeline=pipeline, verify_infos=False
773 ) # TODO{beam} verify infos
774
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)
501 def __exit__(self, exc_type, exc_val, exc_tb):
502 if not exc_type:
--> 503 self.run().wait_until_finish()
504
505 def visit(self, visitor):
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
481 return Pipeline.from_runner_api(
482 self.to_runner_api(use_fake_coders=True), self.runner,
--> 483 self._options).run(False)
484
485 if self._options.view_as(TypeOptions).runtime_type_check:
/usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api)
494 finally:
495 shutil.rmtree(tmpdir)
--> 496 return self.runner.run_pipeline(self, self._options)
497
498 def __enter__(self):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options)
128 runner = BundleBasedDirectRunner()
129
--> 130 return runner.run_pipeline(pipeline, options)
131
132
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_pipeline(self, pipeline, options)
553
554 self._latest_run_result = self.run_via_runner_api(
--> 555 pipeline.to_runner_api(default_environment=self._default_environment))
556 return self._latest_run_result
557
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_via_runner_api(self, pipeline_proto)
563 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to
564 # the teststream (if any), and all the stages).
--> 565 return self.run_stages(stage_context, stages)
566
567 @contextlib.contextmanager
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_stages(self, stage_context, stages)
704 stage,
705 pcoll_buffers,
--> 706 stage_context.safe_coders)
707 metrics_by_stage[stage.name] = stage_results.process_bundle.metrics
708 monitoring_infos_by_stage[stage.name] = (
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders)
1071 cache_token_generator=cache_token_generator)
1072
-> 1073 result, splits = bundle_manager.process_bundle(data_input, data_output)
1074
1075 def input_for(transform_id, input_id):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
-> 2334 for result, split_result in executor.map(execute, part_inputs):
2335
2336 split_result_list += split_result
/usr/lib/python3.6/concurrent/futures/_base.py in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
--> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.monotonic())
/usr/lib/python3.6/concurrent/futures/_base.py in result(self, timeout)
430 raise CancelledError()
431 elif self._state == FINISHED:
--> 432 return self.__get_result()
433 else:
434 raise TimeoutError()
/usr/lib/python3.6/concurrent/futures/_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
/usr/local/lib/python3.6/dist-packages/apache_beam/utils/thread_pool_executor.py in run(self)
42 # If the future wasn't cancelled, then attempt to execute it.
43 try:
---> 44 self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs))
45 except BaseException as exc:
46 # Even though Python 2 futures library has #set_exection(),
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in execute(part_map)
2329 self._registered,
2330 cache_token_generator=self._cache_token_generator)
-> 2331 return bundle_manager.process_bundle(part_map, expected_outputs)
2332
2333 with UnboundedThreadPoolExecutor() as executor:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs)
2243 process_bundle_descriptor_id=self._bundle_descriptor.id,
2244 cache_tokens=[next(self._cache_token_generator)]))
-> 2245 result_future = self._worker_handler.control_conn.push(process_bundle_req)
2246
2247 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in push(self, request)
1557 self._uid_counter += 1
1558 request.instruction_id = 'control_%s' % self._uid_counter
-> 1559 response = self.worker.do_instruction(request)
1560 return ControlFuture(request.instruction_id, response)
1561
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request)
413 # E.g. if register is set, this will call self.register(request.register))
414 return getattr(self, request_type)(
--> 415 getattr(request, request_type), request.instruction_id)
416 else:
417 raise NotImplementedError
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id)
448 with self.maybe_profile(instruction_id):
449 delayed_applications, requests_finalization = (
--> 450 bundle_processor.process_bundle(instruction_id))
451 monitoring_infos = bundle_processor.monitoring_infos()
452 monitoring_infos.extend(self.state_cache_metrics_fn())
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id)
837 for data in data_channel.input_elements(instruction_id,
838 expected_transforms):
--> 839 input_op_by_transform_id[data.transform_id].process_encoded(data.data)
840
841 # Finish all operations.
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values)
214 decoded_value = self.windowed_coder_impl.decode_from_stream(
215 input_stream, True)
--> 216 self.output(decoded_value)
217
218 def try_split(self, fraction_of_remainder, total_buffer_size):
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/usr/local/lib/python3.6/dist-packages/future/utils/__init__.py in raise_with_traceback(exc, traceback)
417 if traceback == Ellipsis:
418 _, _, traceback = sys.exc_info()
--> 419 raise exc.with_traceback(traceback)
420
421 else:
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result)
1080 for e in bundle[1]: # values
1081 writer.write(e)
-> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)]
1083
1084
/usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self)
421
422 def close(self):
--> 423 self.sink.close(self.temp_handle)
424 return self.temp_shard_path
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer)
536 def close(self, writer):
537 if len(self._buffer[0]) > 0:
--> 538 self._flush_buffer()
539 if self._record_batches_byte_size > 0:
540 self._write_batches(writer)
/usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self)
568 for x in arrays:
569 for b in x.buffers():
--> 570 size = size + b.size
571 self._record_batches_byte_size = self._record_batches_byte_size + size
AttributeError: 'NoneType' object has no attribute 'size' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles']
``` | true | 2020-05-23T10:18:52Z | 2020-05-25T00:12:02Z | 2020-05-25T00:12:02Z | richarddwang | CONTRIBUTOR | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/187 | false | [
"I have seen that somebody is hard working on easierly loadable wikipedia. #129 \r\nMaybe I should wait a few days for that version ?",
"Yes we (well @lhoestq) are very actively working on this."
] |
623,595,180 | 186 | Weird-ish: Not creating unique caches for different phases | closed | Sample code:
```python
import nlp
dataset = nlp.load_dataset('boolq')
def func1(x):
return x
def func2(x):
return None
train_output = dataset["train"].map(func1)
valid_output = dataset["validation"].map(func1)
print()
print(len(train_output), len(valid_output))
# Output: 9427 9427
```
The map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache.
What's weird is that the following doesn't seem to be an issue:
```python
train_output = dataset["train"].map(func2)
valid_output = dataset["validation"].map(func2)
print()
print(len(train_output), len(valid_output))
# 9427 3270
``` | true | 2020-05-23T06:40:58Z | 2020-05-23T20:22:18Z | 2020-05-23T20:22:17Z | zphang | NONE | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/186 | false | [
"Looks like a duplicate of #120.\r\nThis is already fixed on master. We'll do a new release on pypi soon",
"Good catch, it looks fixed.\r\n"
] |
623,172,484 | 185 | [Commands] In-detail instructions to create dummy data folder | closed | ### Dummy data command
This PR adds a new command `python nlp-cli dummy_data <path_to_dataset_folder>` that gives in-detail instructions on how to add the dummy data files.
It would be great if you can try it out by moving the current dummy_data folder of any dataset in `./datasets` with `mv datasets/<dataset_script>/dummy_data datasets/<dataset_name>/dummy_data_copy` and running the command `python nlp-cli dummy_data ./datasets/<dataset_name>` to see if you like the instructions.
### CONTRIBUTING.md
Also the CONTRIBUTING.md is made cleaner including a new section on "How to add a dataset".
### Current PRs
It would be nice if we can try out if this command helps current PRs, *e.g.* #169 to add a dataset. I comment on those PRs. | true | 2020-05-22T12:26:25Z | 2020-05-22T14:06:35Z | 2020-05-22T14:06:34Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/185 | 2020-05-22T14:06:34Z | 1 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/185 | true | [
"awesome !"
] |
623,120,929 | 184 | Use IndexError instead of ValueError when index out of range | closed | **`default __iter__ needs IndexError`**.
When I want to create a wrapper of arrow dataset to adapt to fastai,
I don't know how to initialize it, so I didn't use inheritance but use object composition.
I wrote sth like this.
```
clas HF_dataset():
def __init__(self, arrow_dataset):
self.dset = arrow_dataset
def __getitem__(self, i):
return self.my_get_item(self.dset)
```
But `for sample in my_dataset:` gave me `ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")` . This is because default `__iter__` will stop when it catched `IndexError`.
You can also see my [work](https://github.com/richardyy1188/Pretrain-MLM-and-finetune-on-GLUE-with-fastai/blob/master/GLUE_with_fastai.ipynb) that uses fastai2 to show/load batches from huggingface/nlp GLUE datasets
So I hope we can use `IndexError` instead to let other people who want to wrap it for any purpose won't be caught by this caveat.
BTW, I super appreciate your work, both transformers and nlp save my life. 💖💖💖💖💖💖💖 | true | 2020-05-22T10:43:42Z | 2020-05-28T08:31:18Z | 2020-05-28T08:31:18Z | richarddwang | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/184 | 2020-05-28T08:31:18Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/184 | true | [] |
623,054,270 | 183 | [Bug] labels of glue/ax are all -1 | closed | ```
ax = nlp.load_dataset('glue', 'ax')
for i in range(30): print(ax['test'][i]['label'], end=', ')
```
```
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
``` | true | 2020-05-22T08:43:36Z | 2020-05-22T22:14:05Z | 2020-05-22T22:14:05Z | richarddwang | CONTRIBUTOR | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/183 | false | [
"This is the test set given by the Glue benchmark. The labels are not provided, and therefore set to -1.",
"Ah, yeah. Why it didn’t occur to me. 😂\nThank you for your comment."
] |
622,646,770 | 182 | Update newsroom.py | closed | Updated the URL for Newsroom download so it's more robust to future changes. | true | 2020-05-21T17:07:43Z | 2020-05-22T16:38:23Z | 2020-05-22T16:38:23Z | yoavartzi | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/182 | 2020-05-22T16:38:23Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/182 | true | [] |
622,634,420 | 181 | Cannot upload my own dataset | closed | I look into `nlp-cli` and `user.py` to learn how to upload my own data.
It is supposed to work like this
- Register to get username, password at huggingface.co
- `nlp-cli login` and type username, passworld
- I have a single file to upload at `./ttc/ttc_freq_extra.csv`
- `nlp-cli upload ttc/ttc_freq_extra.csv`
But I got this error.
```
2020-05-21 16:33:52.722464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
About to upload file /content/ttc/ttc_freq_extra.csv to S3 under filename ttc/ttc_freq_extra.csv and namespace korakot
Proceed? [Y/n] y
Uploading... This might take a while if files are large
Traceback (most recent call last):
File "/usr/local/bin/nlp-cli", line 33, in <module>
service.run()
File "/usr/local/lib/python3.6/dist-packages/nlp/commands/user.py", line 234, in run
token=token, filename=filename, filepath=filepath, organization=self.args.organization
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 141, in presign_and_upload
urls = self.presign(token, filename=filename, organization=organization)
File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 132, in presign
return PresignedUrl(**d)
TypeError: __init__() got an unexpected keyword argument 'cdn'
``` | true | 2020-05-21T16:45:52Z | 2020-06-18T22:14:42Z | 2020-06-18T22:14:42Z | korakot | NONE | null | null | 6 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/181 | false | [
"It's my misunderstanding. I cannot just upload a csv. I need to write a dataset loading script too.",
"I now try with the sample `datasets/csv` folder. \r\n\r\n nlp-cli upload csv\r\n\r\nThe error is still the same\r\n\r\n```\r\n2020-05-21 17:20:56.394659: I tensorflow/stream_executor/platform/default/dso_loa... |
622,556,861 | 180 | Add hall of fame | closed | powered by https://github.com/sourcerer-io/hall-of-fame | true | 2020-05-21T14:53:48Z | 2020-05-22T16:35:16Z | 2020-05-22T16:35:14Z | clmnt | MEMBER | https://github.com/huggingface/datasets/pull/180 | 2020-05-22T16:35:14Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/180 | true | [] |
622,525,410 | 179 | [Feature request] separate split name and split instructions | closed | Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction.
This makes it impossible to have several training sets, which can occur when:
- A dataset corresponds to a collection of sub-datasets
- A dataset was built in stages, adding new examples at each stage
Would it be possible to have two separate fields in the Split class, a name /instruction and a unique ID that is used as the key in the builder's split_dict ? | true | 2020-05-21T14:10:51Z | 2020-05-22T13:31:08Z | 2020-05-22T13:31:07Z | yjernite | MEMBER | null | null | 2 | 0 | 0 | 0 | null | false | [] | https://github.com/huggingface/datasets/issues/179 | false | [
"If your dataset is a collection of sub-datasets, you should probably consider having one config per sub-dataset. For example for Glue, we have sst2, mnli etc.\r\nIf you want to have multiple train sets (for example one per stage). The easiest solution would be to name them `nlp.Split(\"train_stage1\")`, `nlp.Split... |
621,979,849 | 178 | [Manual data] improve error message for manual data in general | closed | `nlp.load("xsum")` now leads to the following error message:
I guess the manual download instructions for `xsum` can also be improved. | true | 2020-05-20T18:10:45Z | 2020-05-20T18:18:52Z | 2020-05-20T18:18:50Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/178 | 2020-05-20T18:18:50Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/178 | true | [] |
621,975,368 | 177 | Xsum manual download instruction | closed | true | 2020-05-20T18:02:41Z | 2020-05-20T18:16:50Z | 2020-05-20T18:16:49Z | mariamabarham | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/177 | 2020-05-20T18:16:49Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/177 | true | [] | |
621,934,638 | 176 | [Tests] Refactor MockDownloadManager | closed | Clean mock download manager class.
The print function was not of much help I think.
We should think about adding a command that creates the dummy folder structure for the user. | true | 2020-05-20T17:07:36Z | 2020-05-20T18:17:19Z | 2020-05-20T18:17:18Z | patrickvonplaten | CONTRIBUTOR | https://github.com/huggingface/datasets/pull/176 | 2020-05-20T18:17:18Z | 0 | 0 | 0 | 0 | false | false | [] | https://github.com/huggingface/datasets/pull/176 | true | [] |
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