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} | This PR disables testing for community datasets on aws.
It should fix the CI that is currently failing. | {
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} | Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability
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I would appreciate if you could help me with this issue. | {
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} | This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum. | {
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_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Traceback (most recent call last):
File "/Users/parasol_tree/Resource/019 - Github/AcademicEnglishToolkit /test.py", line 7, in <module>
import nlp
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/__init__.py", line 27, in <module>
from .arrow_dataset import Dataset
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/arrow_dataset.py", line 31, in <module>
from nlp.utils.py_utils import dumps
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/__init__.py", line 20, in <module>
from .download_manager import DownloadManager, GenerateMode
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/download_manager.py", line 25, in <module>
from .py_utils import flatten_nested, map_nested, size_str
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 244, in <module>
class Pickler(dill.Pickler):
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 247, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill' | {
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The imports were wrong | {
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} | I completed all the steps in https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset and push the code for ANLI. Please let me know if there are any errors. | {
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} | This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility. | {
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] | null | [] | 2020-06-18T14:48:45Z | 2020-06-22T17:30:46Z | 2020-06-22T17:30:46Z | CONTRIBUTOR | null | null | null | The citations are all of a different format, some have "```" and have text inside, others are proper bibtex.
Can we make it so that they all are proper citations, i.e. parse by the bibtex spec:
https://bibtexparser.readthedocs.io/en/master/ | {
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} | Some datasets are hosted on gcs (wikipedia for example). In this PR I make sure that, when a user loads such datasets, the file_instructions are built using the dataset_info.json from gcs and not from the info extracted from the local `dataset_infos.json` (the one that contain the info for each config). Indeed local files may end up outdated.
Furthermore, to avoid outdated dataset_infos.json, I now make sure that each time you run `load_dataset` it also tries to update the file locally.
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} | [] | closed | false | null | [] | null | [] | 2020-06-18T09:47:27Z | 2020-06-20T13:15:12Z | 2020-06-20T13:15:12Z | NONE | null | null | null | Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?
Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines.
Many thanks for your responses ahead of time and kind regards,
MFreidank | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-17T19:10:54Z | 2020-06-19T08:33:41Z | 2020-06-19T08:33:41Z | NONE | null | null | null | I can't seem to import squad v2 metrics.
**squad_metric = nlp.load_metric('squad_v2')**
**This throws me an error.:**
```
ImportError Traceback (most recent call last)
<ipython-input-8-170b6a170555> in <module>
----> 1 squad_metric = nlp.load_metric('squad_v2')
~/env/lib64/python3.6/site-packages/nlp/load.py in load_metric(path, name, process_id, num_process, data_dir, experiment_id, in_memory, download_config, **metric_init_kwargs)
426 """
427 module_path = prepare_module(path, download_config=download_config, dataset=False)
--> 428 metric_cls = import_main_class(module_path, dataset=False)
429 metric = metric_cls(
430 name=name,
~/env/lib64/python3.6/site-packages/nlp/load.py in import_main_class(module_path, dataset)
55 """
56 importlib.invalidate_caches()
---> 57 module = importlib.import_module(module_path)
58
59 if dataset:
/usr/lib64/python3.6/importlib/__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib64/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib64/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib64/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~/env/lib64/python3.6/site-packages/nlp/metrics/squad_v2/a15e787c76889174874386d3def75321f0284c11730d2a57e28fe1352c9b5c7a/squad_v2.py in <module>
16
17 import nlp
---> 18 from .evaluate import evaluate
19
20 _CITATION = """\
ImportError: cannot import name 'evaluate'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/279/comments | https://api.github.com/repos/huggingface/datasets/issues/279/events | https://github.com/huggingface/datasets/issues/279 | 640,611,692 | MDU6SXNzdWU2NDA2MTE2OTI= | 279 | Dataset Preprocessing Cache with .map() function not working as expected | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-17T17:17:21Z | 2021-07-06T21:43:28Z | 2021-04-18T23:43:49Z | NONE | null | null | null | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file.
Could you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess.
I was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/278/comments | https://api.github.com/repos/huggingface/datasets/issues/278/events | https://github.com/huggingface/datasets/issues/278 | 640,518,917 | MDU6SXNzdWU2NDA1MTg5MTc= | 278 | MemoryError when loading German Wikipedia | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-17T15:06:21Z | 2020-06-19T12:53:02Z | 2020-06-19T12:53:02Z | NONE | null | null | null | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train")
```
However, when I do so, I get the following error:
```
Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare
"\n\t`{}`".format(usage_example)
nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, 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.de', beam_runner='DirectRunner')`
```
So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.
This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?
My nlp version is 0.2.1.
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/277/comments | https://api.github.com/repos/huggingface/datasets/issues/277/events | https://github.com/huggingface/datasets/issues/277 | 640,163,053 | MDU6SXNzdWU2NDAxNjMwNTM= | 277 | Empty samples in glue/qqp | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-17T05:54:52Z | 2020-06-21T00:21:45Z | 2020-06-21T00:21:45Z | CONTRIBUTOR | null | null | null | ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that question 2 is empty string.
BTW, I have checked and these two are the only naughty ones in all splits of qqp. | {
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https://api.github.com/repos/huggingface/datasets/issues/276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/276/comments | https://api.github.com/repos/huggingface/datasets/issues/276/events | https://github.com/huggingface/datasets/pull/276 | 639,490,858 | MDExOlB1bGxSZXF1ZXN0NDM1MDY5Nzg5 | 276 | Fix metric compute (original_instructions missing) | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-16T08:52:01Z | 2020-06-18T07:41:45Z | 2020-06-18T07:41:44Z | MEMBER | null | false | {
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} | When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269 | {
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] | closed | false | null | [] | null | [] | 2020-06-16T07:31:51Z | 2020-06-19T07:37:07Z | 2020-06-19T07:37:07Z | NONE | null | null | null | 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.
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] | closed | false | null | [] | null | [] | 2020-06-15T21:02:26Z | 2020-07-06T15:35:02Z | 2020-07-06T15:35:02Z | CONTRIBUTOR | null | null | null | 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. | {
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} | This PR updates the cos_e dataset to add v1.0 as requested here #163
@nazneenrajani | {
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} | This is a patch for #244. According to the [live nlp viewer](url), the AllocinΓ© 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 AllocinΓ© dataset more convenient to use. | {
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```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
``` | {
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] | null | [] | 2020-06-13T06:26:54Z | 2020-06-18T07:41:44Z | 2020-06-18T07:41:44Z | NONE | null | null | null | 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? | {
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] | null | [] | 2020-06-12T01:09:37Z | 2020-06-19T08:24:19Z | 2020-06-19T08:24:19Z | CONTRIBUTOR | null | null | null | 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!
| {
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https://api.github.com/repos/huggingface/datasets/issues/266 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/266/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/266/comments | https://api.github.com/repos/huggingface/datasets/issues/266/events | https://github.com/huggingface/datasets/pull/266 | 637,156,392 | MDExOlB1bGxSZXF1ZXN0NDMzMTk1NDgw | 266 | Add sort, shuffle, test_train_split and select methods | {
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} | 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/265 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/265/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/265/comments | https://api.github.com/repos/huggingface/datasets/issues/265/events | https://github.com/huggingface/datasets/pull/265 | 637,139,220 | MDExOlB1bGxSZXF1ZXN0NDMzMTgxNDMz | 265 | Add pyarrow warning colab | {
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} | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/264 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/264/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/264/comments | https://api.github.com/repos/huggingface/datasets/issues/264/events | https://github.com/huggingface/datasets/pull/264 | 637,106,170 | MDExOlB1bGxSZXF1ZXN0NDMzMTU0ODQ4 | 264 | Fix small issues creating dataset | {
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} | 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 :) | {
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https://api.github.com/repos/huggingface/datasets/issues/263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/263/comments | https://api.github.com/repos/huggingface/datasets/issues/263/events | https://github.com/huggingface/datasets/issues/263 | 637,028,015 | MDU6SXNzdWU2MzcwMjgwMTU= | 263 | [Feature request] Support for external modality for language datasets | {
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] | closed | false | null | [] | null | [] | 2020-06-11T13:42:18Z | 2022-02-10T13:26:35Z | 2022-02-10T13:26:35Z | CONTRIBUTOR | null | null | null | # 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! | {
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https://api.github.com/repos/huggingface/datasets/issues/262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/262/comments | https://api.github.com/repos/huggingface/datasets/issues/262/events | https://github.com/huggingface/datasets/pull/262 | 636,702,849 | MDExOlB1bGxSZXF1ZXN0NDMyODI3Mzcz | 262 | Add new dataset ANLI Round 1 | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-11T04:14:57Z | 2020-06-12T22:03:03Z | 2020-06-12T22:03:03Z | CONTRIBUTOR | null | false | {
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} | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/261/comments | https://api.github.com/repos/huggingface/datasets/issues/261/events | https://github.com/huggingface/datasets/issues/261 | 636,372,380 | MDU6SXNzdWU2MzYzNzIzODA= | 261 | Downloading dataset error with pyarrow.lib.RecordBatch | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-10T16:04:19Z | 2020-06-11T14:35:12Z | 2020-06-11T14:35:12Z | NONE | null | null | null | 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')
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/260/comments | https://api.github.com/repos/huggingface/datasets/issues/260/events | https://github.com/huggingface/datasets/pull/260 | 636,261,118 | MDExOlB1bGxSZXF1ZXN0NDMyNDY3NDM5 | 260 | Consistency fixes | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-10T13:44:42Z | 2020-06-11T10:34:37Z | 2020-06-11T10:34:36Z | MEMBER | null | false | {
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} | A few bugs I've found while hacking | {
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https://api.github.com/repos/huggingface/datasets/issues/259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/259/comments | https://api.github.com/repos/huggingface/datasets/issues/259/events | https://github.com/huggingface/datasets/issues/259 | 636,239,529 | MDU6SXNzdWU2MzYyMzk1Mjk= | 259 | documentation missing how to split a dataset | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-10T13:18:13Z | 2020-06-18T22:20:24Z | 2020-06-18T22:20:24Z | NONE | null | null | null | 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)
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https://api.github.com/repos/huggingface/datasets/issues/258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/258/comments | https://api.github.com/repos/huggingface/datasets/issues/258/events | https://github.com/huggingface/datasets/issues/258 | 635,859,525 | MDU6SXNzdWU2MzU4NTk1MjU= | 258 | Why is dataset after tokenization far more larger than the orginal one ? | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-10T01:27:07Z | 2020-06-10T12:46:34Z | 2020-06-10T12:46:34Z | CONTRIBUTOR | null | null | null | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M wikipedia-train.arrow
47511M tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? | {
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https://api.github.com/repos/huggingface/datasets/issues/257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/257/comments | https://api.github.com/repos/huggingface/datasets/issues/257/events | https://github.com/huggingface/datasets/issues/257 | 635,620,979 | MDU6SXNzdWU2MzU2MjA5Nzk= | 257 | Tokenizer pickling issue fix not landed in `nlp` yet? | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-09T17:12:34Z | 2020-06-10T21:45:32Z | 2020-06-09T17:26:53Z | NONE | null | null | null | 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. | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-09T16:38:12Z | 2020-06-09T16:51:42Z | 2020-06-09T16:51:42Z | NONE | null | null | null | Is there a straightforward way to add a field to the arrow_dataset, prior to performing map? | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-09T10:16:01Z | 2020-06-12T08:31:27Z | 2020-06-12T08:31:27Z | CONTRIBUTOR | null | false | {
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} | Small SQuAD-like French QA dataset [PIAF](https://www.aclweb.org/anthology/2020.lrec-1.673.pdf) | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-09T02:22:13Z | 2020-06-09T08:41:38Z | 2020-06-09T08:41:38Z | NONE | null | null | null | 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 ? | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-08T17:11:09Z | 2020-07-16T07:50:59Z | 2020-07-16T07:50:59Z | CONTRIBUTOR | null | false | {
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} | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
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https://api.github.com/repos/huggingface/datasets/issues/252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/252/comments | https://api.github.com/repos/huggingface/datasets/issues/252/events | https://github.com/huggingface/datasets/issues/252 | 634,563,239 | MDU6SXNzdWU2MzQ1NjMyMzk= | 252 | NonMatchingSplitsSizesError error when reading the IMDB dataset | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-08T12:26:24Z | 2021-08-27T15:20:58Z | 2020-06-08T14:01:26Z | NONE | null | null | null | 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! | {
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} | 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')
``` | {
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} | 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 | {
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] | null | [] | 2020-06-07T12:58:54Z | 2020-06-12T08:28:51Z | 2020-06-12T08:28:51Z | CONTRIBUTOR | null | null | null | 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. | {
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} | 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/247/comments | https://api.github.com/repos/huggingface/datasets/issues/247/events | https://github.com/huggingface/datasets/pull/247 | 632,380,078 | MDExOlB1bGxSZXF1ZXN0NDI5MTMwMzQ2 | 247 | Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir | {
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} | 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 | {
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https://api.github.com/repos/huggingface/datasets/issues/246 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/246/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/246/comments | https://api.github.com/repos/huggingface/datasets/issues/246/events | https://github.com/huggingface/datasets/issues/246 | 632,380,054 | MDU6SXNzdWU2MzIzODAwNTQ= | 246 | What is the best way to cache a dataset? | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-06T11:02:07Z | 2020-07-09T09:15:07Z | 2020-07-09T09:15:07Z | NONE | null | null | null | 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
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https://api.github.com/repos/huggingface/datasets/issues/245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/245/comments | https://api.github.com/repos/huggingface/datasets/issues/245/events | https://github.com/huggingface/datasets/issues/245 | 631,985,108 | MDU6SXNzdWU2MzE5ODUxMDg= | 245 | SST-2 test labels are all -1 | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-05T21:41:42Z | 2021-12-08T00:47:32Z | 2020-06-06T16:56:41Z | CONTRIBUTOR | null | null | null | 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'])
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} | 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). | {
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This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug. | {
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} | [] | closed | false | null | [] | null | [] | 2020-06-05T16:30:01Z | 2020-06-09T16:06:47Z | 2020-06-08T08:45:03Z | CONTRIBUTOR | null | null | null | 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. | {
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} | 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
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} | [] | closed | false | null | [] | null | [] | 2020-06-05T09:03:26Z | 2020-06-08T09:18:14Z | 2020-06-08T09:18:14Z | MEMBER | null | null | null | 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? | {
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https://api.github.com/repos/huggingface/datasets/issues/239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/239/comments | https://api.github.com/repos/huggingface/datasets/issues/239/events | https://github.com/huggingface/datasets/issues/239 | 631,340,440 | MDU6SXNzdWU2MzEzNDA0NDA= | 239 | [Creating new dataset] Not found dataset_info.json | {
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] | null | [] | 2020-06-05T06:15:04Z | 2020-06-07T13:01:04Z | 2020-06-07T13:01:04Z | CONTRIBUTOR | null | null | null | 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).
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https://api.github.com/repos/huggingface/datasets/issues/238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/238/comments | https://api.github.com/repos/huggingface/datasets/issues/238/events | https://github.com/huggingface/datasets/issues/238 | 631,260,143 | MDU6SXNzdWU2MzEyNjAxNDM= | 238 | [Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. | {
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] | closed | false | null | [] | null | [] | 2020-06-05T02:14:47Z | 2020-06-29T17:10:19Z | 2020-06-29T17:10:19Z | NONE | null | null | null | 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 ?** | {
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https://api.github.com/repos/huggingface/datasets/issues/237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/237/comments | https://api.github.com/repos/huggingface/datasets/issues/237/events | https://github.com/huggingface/datasets/issues/237 | 631,199,940 | MDU6SXNzdWU2MzExOTk5NDA= | 237 | Can't download MultiNLI | {
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```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'
```
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} | 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? | {
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} | ## Adding an *experimental datasets* folder
After using the π€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)
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https://api.github.com/repos/huggingface/datasets/issues/234 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/234/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/234/comments | https://api.github.com/repos/huggingface/datasets/issues/234/events | https://github.com/huggingface/datasets/issues/234 | 630,534,427 | MDU6SXNzdWU2MzA1MzQ0Mjc= | 234 | Huggingface NLP, Uploading custom dataset | {
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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! | {
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```
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? | {
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} | 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 ? | {
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@yjernite | {
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```
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? | {
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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 ? | {
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] | null | [] | 2020-06-01T00:50:36Z | 2020-06-03T15:27:18Z | 2020-06-03T15:27:18Z | NONE | null | null | null | 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
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] | null | [] | 2020-05-30T18:30:40Z | 2021-09-02T15:02:17Z | 2021-01-04T09:53:32Z | NONE | null | null | null | 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! | {
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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.). | {
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https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:

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} | This should fix your env because you had `mwparserfromhell ` as a first party for `isort` @patrickvonplaten | {
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However they are not processed nor directly available from gcp yet. | {
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] | open | false | null | [] | null | [] | 2020-05-29T09:22:26Z | 2022-10-22T00:45:50Z | null | CONTRIBUTOR | null | null | null | 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.
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} | [] | closed | false | null | [] | null | [] | 2020-05-28T23:47:32Z | 2020-06-01T00:04:35Z | 2020-06-01T00:04:35Z | NONE | null | null | null | 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 | {
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"id": 2067388877,
"name": "dataset bug",
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] | closed | false | null | [] | null | [] | 2020-05-28T22:55:19Z | 2022-02-10T13:05:45Z | 2022-02-10T13:05:45Z | NONE | null | null | null | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/214/comments | https://api.github.com/repos/huggingface/datasets/issues/214/events | https://github.com/huggingface/datasets/pull/214 | 626,641,549 | MDExOlB1bGxSZXF1ZXN0NDI0NTk1NjIx | 214 | [arrow_dataset.py] add new filter function | {
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} | 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)))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/213 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/213/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/213/comments | https://api.github.com/repos/huggingface/datasets/issues/213/events | https://github.com/huggingface/datasets/pull/213 | 626,587,995 | MDExOlB1bGxSZXF1ZXN0NDI0NTUxODE3 | 213 | better message if missing beam options | {
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} | 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')`
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/212/comments | https://api.github.com/repos/huggingface/datasets/issues/212/events | https://github.com/huggingface/datasets/pull/212 | 626,580,198 | MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy | 212 | have 'add' and 'add_batch' for metrics | {
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} | 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. | {
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] | null | [] | 2020-05-28T14:38:14Z | 2020-07-23T10:15:16Z | 2020-07-23T10:15:16Z | MEMBER | null | null | null | 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. | {
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} | 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, | {
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} | Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself.
Also, @lhoestq fixed small import bug introduced by beam command I think. | {
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https://api.github.com/repos/huggingface/datasets/issues/206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/206/comments | https://api.github.com/repos/huggingface/datasets/issues/206/events | https://github.com/huggingface/datasets/issues/206 | 625,842,989 | MDU6SXNzdWU2MjU4NDI5ODk= | 206 | [Question] Combine 2 datasets which have the same columns | {
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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 π
Best | {
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} | 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. | {
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} | # 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...) | {
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} | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/202 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/202/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/202/comments | https://api.github.com/repos/huggingface/datasets/issues/202/events | https://github.com/huggingface/datasets/issues/202 | 625,493,983 | MDU6SXNzdWU2MjU0OTM5ODM= | 202 | Mistaken `_KWARGS_DESCRIPTION` for XNLI metric | {
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} | [] | closed | false | null | [] | null | [] | 2020-05-27T08:34:42Z | 2020-05-28T13:22:36Z | 2020-05-28T13:22:36Z | NONE | null | null | null | 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
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/201 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/201/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/201/comments | https://api.github.com/repos/huggingface/datasets/issues/201/events | https://github.com/huggingface/datasets/pull/201 | 625,235,430 | MDExOlB1bGxSZXF1ZXN0NDIzNDkzNTMw | 201 | Fix typo in README | {
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|
https://api.github.com/repos/huggingface/datasets/issues/200 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/200/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/200/comments | https://api.github.com/repos/huggingface/datasets/issues/200/events | https://github.com/huggingface/datasets/pull/200 | 625,226,638 | MDExOlB1bGxSZXF1ZXN0NDIzNDg2NTM0 | 200 | [ArrowWriter] Set schema at first write example | {
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} | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/199 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/199/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/199/comments | https://api.github.com/repos/huggingface/datasets/issues/199/events | https://github.com/huggingface/datasets/pull/199 | 625,217,440 | MDExOlB1bGxSZXF1ZXN0NDIzNDc4ODIx | 199 | Fix GermEval 2014 dataset infos | {
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} | [] | closed | false | null | [] | null | [] | 2020-05-26T21:41:44Z | 2020-05-26T21:50:24Z | 2020-05-26T21:50:24Z | CONTRIBUTOR | null | false | {
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} | Hi,
this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file. | {
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https://api.github.com/repos/huggingface/datasets/issues/198 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/198/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/198/comments | https://api.github.com/repos/huggingface/datasets/issues/198/events | https://github.com/huggingface/datasets/issues/198 | 625,200,627 | MDU6SXNzdWU2MjUyMDA2Mjc= | 198 | Index outside of table length | {
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} | [] | closed | false | null | [] | null | [] | 2020-05-26T21:09:40Z | 2020-05-26T22:43:49Z | 2020-05-26T22:43:49Z | NONE | null | null | null | 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}).") | {
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https://api.github.com/repos/huggingface/datasets/issues/197 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/197/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/197/comments | https://api.github.com/repos/huggingface/datasets/issues/197/events | https://github.com/huggingface/datasets/issues/197 | 624,966,904 | MDU6SXNzdWU2MjQ5NjY5MDQ= | 197 | Scientific Papers only downloading Pubmed | {
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} | [] | closed | false | null | [] | null | [] | 2020-05-26T15:18:47Z | 2020-05-28T08:19:28Z | 2020-05-28T08:19:28Z | NONE | null | null | null | 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%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 5.05k/5.05k [00:00<00:00, 2.66MB/s]
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 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! | {
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https://api.github.com/repos/huggingface/datasets/issues/196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/196/comments | https://api.github.com/repos/huggingface/datasets/issues/196/events | https://github.com/huggingface/datasets/pull/196 | 624,901,266 | MDExOlB1bGxSZXF1ZXN0NDIzMjIwMjIw | 196 | Check invalid config name | {
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} | 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. | {
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https://api.github.com/repos/huggingface/datasets/issues/195 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/195/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/195/comments | https://api.github.com/repos/huggingface/datasets/issues/195/events | https://github.com/huggingface/datasets/pull/195 | 624,858,686 | MDExOlB1bGxSZXF1ZXN0NDIzMTg1NTAy | 195 | [Dummy data command] add new case to command | {
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