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https://api.github.com/repos/huggingface/datasets/issues/875 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/875/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/875/comments | https://api.github.com/repos/huggingface/datasets/issues/875/events | https://github.com/huggingface/datasets/issues/875 | 748,194,311 | MDU6SXNzdWU3NDgxOTQzMTE= | 875 | bug in boolq dataset loading | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-22T08:18:34 | 2020-11-24T10:12:33 | 2020-11-24T10:12:33 | CONTRIBUTOR | null | null | null | Hi
I am trying to load boolq dataset:
```
import datasets
datasets.load_dataset("boolq")
```
I am getting the following errors, thanks for your help
```
>>> import datasets
2020-11-22 09:16:30.070332: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2020-11-22 09:16:30.070389: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
>>> datasets.load_dataset("boolq")
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets
Using custom data configuration default
Downloading and preparing dataset boolq/default (download: 8.36 MiB, generated: 7.47 MiB, post-processed: Unknown size, total: 15.83 MiB) to /idiap/temp/rkarimi/cache_home/datasets/boolq/default/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/874 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/874/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/874/comments | https://api.github.com/repos/huggingface/datasets/issues/874/events | https://github.com/huggingface/datasets/issues/874 | 748,193,140 | MDU6SXNzdWU3NDgxOTMxNDA= | 874 | trec dataset unavailable | {
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} | [] | closed | false | null | [] | null | 2 | 2020-11-22T08:09:36 | 2020-11-27T13:56:42 | 2020-11-27T13:56:42 | CONTRIBUTOR | null | null | null | Hi
when I try to load the trec dataset I am getting these errors, thanks for your help
`datasets.load_dataset("trec", split="train")
`
```
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/873/comments | https://api.github.com/repos/huggingface/datasets/issues/873/events | https://github.com/huggingface/datasets/issues/873 | 747,959,523 | MDU6SXNzdWU3NDc5NTk1MjM= | 873 | load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error | {
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} | [] | closed | false | null | [] | null | 5 | 2020-11-21T06:30:45 | 2020-12-03T11:04:15 | 2020-11-22T12:18:05 | NONE | null | null | null | ```
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
Stack trace:
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-6-2e06a8332652> in <module>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
608 download_config=download_config,
609 download_mode=download_mode,
--> 610 ignore_verifications=ignore_verifications,
611 )
612
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
513 if not downloaded_from_gcs:
514 self._download_and_prepare(
--> 515 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
516 )
517 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
568 split_dict = SplitDict(dataset_name=self.name)
569 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 570 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
571
572 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
I have ran the code on Google Colab | {
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https://api.github.com/repos/huggingface/datasets/issues/872 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/872/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/872/comments | https://api.github.com/repos/huggingface/datasets/issues/872/events | https://github.com/huggingface/datasets/pull/872 | 747,653,697 | MDExOlB1bGxSZXF1ZXN0NTI0ODM4NjEx | 872 | Add IndicGLUE dataset and Metrics | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-20T17:09:34 | 2020-11-25T17:01:11 | 2020-11-25T15:26:07 | CONTRIBUTOR | null | false | {
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} | Added IndicGLUE benchmark for evaluating models on 11 Indian Languages. The descriptions of the tasks and the corresponding paper can be found [here](https://indicnlp.ai4bharat.org/indic-glue/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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https://api.github.com/repos/huggingface/datasets/issues/871 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/871/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/871/comments | https://api.github.com/repos/huggingface/datasets/issues/871/events | https://github.com/huggingface/datasets/issues/871 | 747,470,136 | MDU6SXNzdWU3NDc0NzAxMzY= | 871 | terminate called after throwing an instance of 'google::protobuf::FatalException' | {
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} | [] | closed | false | null | [] | null | 2 | 2020-11-20T12:56:24 | 2020-12-12T21:16:32 | 2020-12-12T21:16:32 | CONTRIBUTOR | null | null | null | Hi
I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
run_t5_base_eval.sh: line 19: 5795 Aborted | {
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] | open | false | null | [] | null | 1 | 2020-11-19T23:51:31 | 2020-11-27T13:48:11 | null | NONE | null | null | null | I'm working on a project about rhyming verse using phonetic poetry and song lyrics, and line breaks are a vital part of the data.
I recently switched over to use the datasets library when my various corpora grew larger than my computer's memory. And so far, it is SO great.
But the first time I processed all of my data into a dataset, I hadn't realized the text loader script was processing the source files line-by-line and stripping off the newlines.
Once I caught the issue, I made my own data loader by modifying one line in the default text loader (changing `batch = batch.splitlines()` to `batch = batch.splitlines(True)` inside `_generate_tables`). And so I'm all set as far as my project is concerned.
But if my use case is more general, it seems like it'd be pretty trivial to add a kwarg to the default text loader called keeplinebreaks or something, which would default to False and get passed to `splitlines()`. | {
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https://api.github.com/repos/huggingface/datasets/issues/869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/869/comments | https://api.github.com/repos/huggingface/datasets/issues/869/events | https://github.com/huggingface/datasets/pull/869 | 746,495,711 | MDExOlB1bGxSZXF1ZXN0NTIzODc3OTkw | 869 | Update ner datasets infos | {
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} | Update the dataset_infos.json files for changes made in #850 regarding the ner datasets feature types (and the change to ClassLabel)
I also fixed the ner types of conll2003 | {
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https://api.github.com/repos/huggingface/datasets/issues/868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/868/comments | https://api.github.com/repos/huggingface/datasets/issues/868/events | https://github.com/huggingface/datasets/pull/868 | 745,889,882 | MDExOlB1bGxSZXF1ZXN0NTIzMzc2MzQ3 | 868 | Consistent metric outputs | {
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} | To automate the use of metrics, they should return consistent outputs.
In particular I'm working on adding a conversion of metrics to keras metrics.
To achieve this we need two things:
- have each metric return dictionaries of string -> floats since each keras metrics should return one float
- define in the metric info the different fields of the output dictionary
In this PR I'm adding these two features.
I also fixed a few bugs in some metrics
#867 needs to be merged first | {
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https://api.github.com/repos/huggingface/datasets/issues/867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/867/comments | https://api.github.com/repos/huggingface/datasets/issues/867/events | https://github.com/huggingface/datasets/pull/867 | 745,773,955 | MDExOlB1bGxSZXF1ZXN0NTIzMjc4MjI4 | 867 | Fix some metrics feature types | {
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} | Replace `int` feature type to `int32` since `int` is not a pyarrow dtype in those metrics:
- accuracy
- precision
- recall
- f1
I also added the sklearn citation and used keyword arguments to remove future warnings | {
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https://api.github.com/repos/huggingface/datasets/issues/866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/866/comments | https://api.github.com/repos/huggingface/datasets/issues/866/events | https://github.com/huggingface/datasets/issues/866 | 745,719,222 | MDU6SXNzdWU3NDU3MTkyMjI= | 866 | OSCAR from Inria group | {
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] | closed | false | null | [] | null | 2 | 2020-11-18T14:40:54 | 2020-11-18T15:01:30 | 2020-11-18T15:01:30 | NONE | null | null | null | ## Adding a Dataset
- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https://oscar-corpus.com/).
- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*
- **Paper:** *[here](https://hal.inria.fr/hal-02148693)*
- **Data:** *[here](https://oscar-corpus.com/)*
- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*
I am aware that you do offer the "colossal" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.
| {
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https://api.github.com/repos/huggingface/datasets/issues/865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/865/comments | https://api.github.com/repos/huggingface/datasets/issues/865/events | https://github.com/huggingface/datasets/issues/865 | 745,430,497 | MDU6SXNzdWU3NDU0MzA0OTc= | 865 | Have Trouble importing `datasets` | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-18T08:04:41 | 2020-11-18T08:16:35 | 2020-11-18T08:16:35 | CONTRIBUTOR | null | null | null | I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.
I cloned the newest version of datasets (master branch), and do `pip install -e .`.
Then, `import datasets` causes the error below.
```
~/workspace/Clone/datasets/src/datasets/utils/file_utils.py in <module>
116 sys.path.append(str(HF_MODULES_CACHE))
117
--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)
119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, "__init__.py")):
120 with open(os.path.join(HF_MODULES_CACHE, "__init__.py"), "w"):
~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/os.py in makedirs(name, mode, exist_ok)
221 return
222 try:
--> 223 mkdir(name, mode)
224 except OSError:
225 # Cannot rely on checking for EEXIST, since the operating system
FileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>/.cache/huggingface/modules'
```
The error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.
(I use Python 3.8, so `exist_ok` is expected to work.)
I've checked some environment variables, and they are set as below.
```
*** NameError: name 'HF_MODULES_CACHE' is not defined
*** NameError: name 'hf_cache_home' is not defined
*** NameError: name 'XDG_CACHE_HOME' is not defined
```
Should I set some environment variables before using this library?
And, do you have any idea why "No such file or directory" occurs even though the `exist_ok = True` option is set?
Thank you in advance. | {
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https://api.github.com/repos/huggingface/datasets/issues/864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/864/comments | https://api.github.com/repos/huggingface/datasets/issues/864/events | https://github.com/huggingface/datasets/issues/864 | 745,322,357 | MDU6SXNzdWU3NDUzMjIzNTc= | 864 | Unable to download cnn_dailymail dataset | {
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```
from datasets import load_dataset
train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
```
### Error
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-8-47c39c228935> in <module>()
1 from datasets import load_dataset
2
----> 3 train_dataset = load_dataset("cnn_dailymail", "3.0.0", split= 'train[:10%')
4 valid_dataset = load_dataset("cnn_dailymail","3.0.0", split="validation[:5%]")
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
609 download_config=download_config,
610 download_mode=download_mode,
--> 611 ignore_verifications=ignore_verifications,
612 )
613
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
469 if not downloaded_from_gcs:
470 self._download_and_prepare(
--> 471 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
472 )
473 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
524 split_dict = SplitDict(dataset_name=self.name)
525 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 526 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
527
528 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
Thanks for any suggestions. | {
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https://api.github.com/repos/huggingface/datasets/issues/863 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/863/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/863/comments | https://api.github.com/repos/huggingface/datasets/issues/863/events | https://github.com/huggingface/datasets/pull/863 | 744,954,534 | MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1 | 863 | Add clear_cache parameter in the test command | {
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} | For certain datasets like OSCAR #348 there are lots of different configurations and each one of them can take a lot of disk space.
I added a `--clear_cache` flag to the `datasets-cli test` command to be able to clear the cache after each configuration test to avoid filling up the disk. It should enable an easier generation for the `dataset_infos.json` file for OSCAR. | {
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] | null | 7 | 2020-11-17T13:48:59 | 2021-03-30T14:04:04 | 2021-03-22T12:04:55 | NONE | null | null | null | Hey guys,
I was trying to create a new bert model from scratch via _huggingface transformers + tokenizers + dataets_ (actually using this example script by your team: https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py). It was supposed to be a first test with a small 5 GB raw text file but I can't even end the preprocessing handled by datasets because this tiny 5 GB text file becomes more than 1 TB when processing. My system was running out of space and crashed prematurely.
I've done training from scratch via Google's bert repo in the past and I can remember that the resulting pretraining data can become quite big. But 5 GB becoming 1 TB was never the case. Is this considered normal or is it a bug?
I've used the following CMD:
`python xla_spawn.py --num_cores=8 run_mlm.py --model_type bert --config_name config.json --tokenizer_name tokenizer.json --train_file dataset_full.txt --do_train --output_dir out --max_steps 500000 --save_steps 2500 --save_total_limit 2 --prediction_loss_only --line_by_line --max_seq_length 128 --pad_to_max_length --preprocessing_num_workers 16 --per_device_train_batch_size 128 --overwrite_output_dir --debug`
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https://api.github.com/repos/huggingface/datasets/issues/860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/860/comments | https://api.github.com/repos/huggingface/datasets/issues/860/events | https://github.com/huggingface/datasets/issues/860 | 744,750,691 | MDU6SXNzdWU3NDQ3NTA2OTE= | 860 | wmt16 cs-en does not donwload | {
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] | open | false | null | [] | null | 0 | 2020-11-17T13:45:35 | 2020-11-27T13:47:04 | null | CONTRIBUTOR | null | null | null | Hi
I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "finetune_t5_trainer.py", line 109, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset
dataset = load_dataset("wmt16", self.pair, split=split)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz | {
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} | Previously the locking system of the lib was based on the file_lock package. However as noticed in #812 there were too many logs printed even when the datasets logging was set to warnings or errors.
For example
```python
import logging
logging.basicConfig(level=logging.INFO)
import datasets
datasets.set_verbosity_warning()
datasets.load_dataset("squad")
```
would still log the file lock events:
```
INFO:filelock:Lock 5737989232 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 5737989232 released on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 4393489968 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393489968 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393490808 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
INFO:filelock:Lock 4393490808 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
```
With the integration of file_lock in the library, the ouput is much cleaner:
```
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
```
Since the file_lock package is only a 450 lines file I think it's fine to have it inside the lib.
Fix #812 | {
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} | Hi,
I don't know how to add dummy data, since I create the validation set out of the last 1000 examples of the train set. If you have a suggestion, I am happy to implement it.
Cheers,
Joel | {
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https://api.github.com/repos/huggingface/datasets/issues/857 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/857/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/857/comments | https://api.github.com/repos/huggingface/datasets/issues/857/events | https://github.com/huggingface/datasets/pull/857 | 743,863,214 | MDExOlB1bGxSZXF1ZXN0NTIxNjg0ODIx | 857 | Use pandas reader in csv | {
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} | The pyarrow CSV reader has issues that the pandas one doesn't (see #836 ).
To fix that I switched to the pandas csv reader.
The new reader is compatible with all the pandas parameters to read csv files.
Moreover it reads csv by chunk in order to save RAM, while the pyarrow one loads everything in memory.
Fix #836
Fix #794
Breaking: now all the parameters to read to csv file can be used in the `load_dataset` kwargs when loading csv, and the previous pyarrow objects `pyarrow.csv.ReadOptions`, `pyarrow.csv.ParseOptions` and `pyarrow.csv.ConvertOptions` are not used anymore. | {
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} | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | {
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} | The kor_nli dataset had an issue with the csv reader that was not able to parse the lines correctly. Some lines were merged together for some reason.
I fixed that by iterating through the lines directly instead of using a csv reader.
I also changed the feature names to match the other NLI datasets (i.e. use "premise", "hypothesis", "label" features)
Fix #821 | {
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] | open | false | null | [] | null | 12 | 2020-11-16T09:31:51 | 2021-02-25T03:15:09 | null | CONTRIBUTOR | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
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File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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| {
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https://api.github.com/repos/huggingface/datasets/issues/852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/852/comments | https://api.github.com/repos/huggingface/datasets/issues/852/events | https://github.com/huggingface/datasets/issues/852 | 743,396,240 | MDU6SXNzdWU3NDMzOTYyNDA= | 852 | wmt cannot be downloaded | {
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] | closed | false | null | [] | null | 0 | 2020-11-16T01:04:41 | 2020-11-16T09:31:58 | 2020-11-16T09:31:58 | CONTRIBUTOR | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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https://api.github.com/repos/huggingface/datasets/issues/851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/851/comments | https://api.github.com/repos/huggingface/datasets/issues/851/events | https://github.com/huggingface/datasets/issues/851 | 743,343,278 | MDU6SXNzdWU3NDMzNDMyNzg= | 851 | Add support for other languages for rouge | {
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}
] | open | false | null | [] | null | 1 | 2020-11-15T20:57:45 | 2021-06-06T09:07:52 | null | NONE | null | null | null | I calculate rouge with
```
from datasets import load_metric
rouge = load_metric("rouge")
rouge_output = rouge.compute(predictions=['ΡΠ΅ΡΡ ΡΠ΅ΡΡ ΠΏΡΠΈΠ²Π΅Ρ'], references=['ΡΠ΅ΡΡ ΡΠ΅ΡΡ ΠΏΠΎΠΊΠ°'], rouge_types=[
"rouge2"])["rouge2"].mid
print(rouge_output)
```
the result is
`Score(precision=0.0, recall=0.0, fmeasure=0.0)`
It seems like the `rouge_score` library that this metric uses filters all non-alphanueric latin characters
in `rouge_scorer/tokenize.py` with `text = re.sub(r"[^a-z0-9]+", " ", six.ensure_str(text))`.
Please add support for other languages. | {
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https://api.github.com/repos/huggingface/datasets/issues/850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/850/comments | https://api.github.com/repos/huggingface/datasets/issues/850/events | https://github.com/huggingface/datasets/pull/850 | 742,369,419 | MDExOlB1bGxSZXF1ZXN0NTIwNTE0MDY3 | 850 | Create ClassLabel for labelling tasks datasets | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-13T11:07:22 | 2020-11-16T10:32:05 | 2020-11-16T10:31:58 | CONTRIBUTOR | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/850",
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} | This PR adds a specific `ClassLabel` for the datasets that are about a labelling task such as POS, NER or Chunking. | {
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https://api.github.com/repos/huggingface/datasets/issues/849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/849/comments | https://api.github.com/repos/huggingface/datasets/issues/849/events | https://github.com/huggingface/datasets/issues/849 | 742,263,333 | MDU6SXNzdWU3NDIyNjMzMzM= | 849 | Load amazon dataset | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-13T08:34:24 | 2020-11-17T07:22:59 | 2020-11-17T07:22:59 | CONTRIBUTOR | null | null | null | Hi,
I was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset.
Eg. what API usage is on the [website](https://huggingface.co/datasets/amazon_us_reviews)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews")
```
How it is when I tried (the error generated does point me to the right direction though)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews", 'Books_v1_00')
```
Also, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it? | {
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https://api.github.com/repos/huggingface/datasets/issues/848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/848/comments | https://api.github.com/repos/huggingface/datasets/issues/848/events | https://github.com/huggingface/datasets/issues/848 | 742,240,942 | MDU6SXNzdWU3NDIyNDA5NDI= | 848 | Error when concatenate_datasets | {
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} | [] | closed | false | null | [] | null | 4 | 2020-11-13T07:56:02 | 2020-11-13T17:40:59 | 2020-11-13T15:55:10 | NONE | null | null | null | Hello, when I concatenate two dataset loading from disk, I encountered a problem:
```
test_dataset = load_from_disk('data/test_dataset')
trn_dataset = load_from_disk('data/train_dataset')
train_dataset = concatenate_datasets([trn_dataset, test_dataset])
```
And it reported ValueError blow:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-74fa525512ca> in <module>
----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])
/opt/miniconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py in concatenate_datasets(dsets, info, split)
2547 "However datasets' indices {} come from memory and datasets' indices {} come from disk.".format(
2548 [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],
-> 2549 [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],
2550 )
2551 )
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
But it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:
```
trn_dataset._data_files
# output
[{'filename': 'data/train_dataset/csv-train.arrow', 'skip': 0, 'take': 593264}]
test_dataset._data_files
# output
[{'filename': 'data/test_dataset/csv-test.arrow', 'skip': 0, 'take': 424383}]
print([not dset._data_files for dset in [trn_dataset, test_dataset]])
# [False, False]
# And I tested the code the same as arrow_dataset, but nothing happened
dsets = [trn_dataset, test_dataset]
dsets_in_memory = [not dset._data_files for dset in dsets]
if any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):
raise ValueError(
"Datasets should ALL come from memory, or should ALL come from disk.\n"
"However datasets {} come from memory and datasets {} come from disk.".format(
[i for i in range(len(dsets)) if dsets_in_memory[i]],
[i for i in range(len(dsets)) if not dsets_in_memory[i]],
)
)
```
Any suggestions would be greatly appreciated!
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/847/comments | https://api.github.com/repos/huggingface/datasets/issues/847/events | https://github.com/huggingface/datasets/issues/847 | 742,179,495 | MDU6SXNzdWU3NDIxNzk0OTU= | 847 | multiprocessing in dataset map "can only test a child process" | {
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} | [] | open | false | null | [] | null | 8 | 2020-11-13T06:01:04 | 2021-02-01T16:53:28 | null | NONE | null | null | null | Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.
```
def tokenizer_fn(example):
return tokenizer.batch_encode_plus(example['text'])
ds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])
```
```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 156, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1510, in _map_single
for i in pbar:
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 228, in __iter__
for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1186, in __iter__
self.close()
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 251, in close
super(tqdm_notebook, self).close(*args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1291, in close
fp_write('')
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1288, in fp_write
self.fp.write(_unicode(s))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 91, in new_write
cb(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 598, in _console_callback
self._backend.interface.publish_output(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 146, in publish_output
self._publish_output(o)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 151, in _publish_output
self._publish(rec)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 431, in _publish
if self._process and not self._process.is_alive():
File "/usr/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
"""
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/846/comments | https://api.github.com/repos/huggingface/datasets/issues/846/events | https://github.com/huggingface/datasets/issues/846 | 741,885,174 | MDU6SXNzdWU3NDE4ODUxNzQ= | 846 | Add HoVer multi-hop fact verification dataset | {
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"name": "dataset request",
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] | closed | false | null | [] | null | 3 | 2020-11-12T19:55:46 | 2020-12-10T21:47:33 | 2020-12-10T21:47:33 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** HoVer
- **Description:** https://twitter.com/YichenJiang9/status/1326954363806429186 contains 20K claim verification examples
- **Paper:** https://arxiv.org/abs/2011.03088
- **Data:** https://hover-nlp.github.io/
- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/845/comments | https://api.github.com/repos/huggingface/datasets/issues/845/events | https://github.com/huggingface/datasets/pull/845 | 741,841,350 | MDExOlB1bGxSZXF1ZXN0NTIwMDg1NDMy | 845 | amazon description fields as bullets | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-12T18:50:41 | 2020-11-12T18:50:54 | 2020-11-12T18:50:54 | CONTRIBUTOR | null | false | {
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} | One more minor formatting change to amazon reviews's description (in addition to #844). Just reformatting the fields to display as a bulleted list in markdown. | {
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} | Just a quick formatting fix to hopefully make it render nicer on Viewer | {
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https://api.github.com/repos/huggingface/datasets/issues/843 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/843/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/843/comments | https://api.github.com/repos/huggingface/datasets/issues/843/events | https://github.com/huggingface/datasets/issues/843 | 741,531,121 | MDU6SXNzdWU3NDE1MzExMjE= | 843 | use_custom_baseline still produces errors for bertscore | {
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"name": "metric bug",
"color": "25b21e",
"default": false,
"description": "A bug in a metric script"
}
] | closed | false | null | [] | null | 5 | 2020-11-12T11:44:32 | 2021-08-31T10:06:44 | 2021-02-09T14:21:48 | NONE | null | null | null | `metric = load_metric('bertscore')`
`a1 = "random sentences"`
`b1 = "random sentences"`
`metric.compute(predictions = [a1], references = [b1], lang = 'en')`
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/stephen_chan/.cache/huggingface/modules/datasets_modules/metrics/bertscore/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363/bertscore.py", line 108, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`
Adding 'use_custom_baseline = False' as an argument produces this error
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
TypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`
This is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/842 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/842/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/842/comments | https://api.github.com/repos/huggingface/datasets/issues/842/events | https://github.com/huggingface/datasets/issues/842 | 741,208,428 | MDU6SXNzdWU3NDEyMDg0Mjg= | 842 | How to enable `.map()` pre-processing pipelines to support multi-node parallelism? | {
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} | [] | open | false | null | [] | null | 1 | 2020-11-12T02:04:38 | 2020-11-12T23:28:27 | null | NONE | null | null | null | Hi,
Currently, multiprocessing can be enabled for the `.map()` stages on a single node. However, in the case of multi-node training, (since more than one node would be available) I'm wondering if it's possible to extend the parallel processing among nodes, instead of only 1 node running the `.map()` while the other node is waiting for it to finish?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/841 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/841/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/841/comments | https://api.github.com/repos/huggingface/datasets/issues/841/events | https://github.com/huggingface/datasets/issues/841 | 740,737,448 | MDU6SXNzdWU3NDA3Mzc0NDg= | 841 | Can not reuse datasets already downloaded | {
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} | [] | closed | false | null | [] | null | 2 | 2020-11-11T12:42:15 | 2020-11-11T18:17:16 | 2020-11-11T18:17:16 | NONE | null | null | null | Hello,
I need to connect to a frontal node (with http proxy, no gpu) before connecting to a gpu node (but no http proxy, so can not use wget so on).
I successfully downloaded and reuse the wikipedia datasets in a frontal node.
When I connect to the gpu node, I supposed to use the downloaded datasets from cache, but failed and end with time out error.
On frontal node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Reusing dataset wikipedia (/linkhome/rech/genini01/uua34ms/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/f92599dfccab29832c442b82870fa8f6983e5b4ebbf5e6e2dcbe894e325339cd)
/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)
return torch._C._cuda_getDeviceCount() > 0
```
On gpu node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 160, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
TimeoutError: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 677, in urlopen
chunked=chunked,
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 172, in _new_conn
self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 727, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/retry.py", line 446, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 590, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 264, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return requests.head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 516, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
```
Any advice?Thanks!
| {
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https://api.github.com/repos/huggingface/datasets/issues/840 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/840/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/840/comments | https://api.github.com/repos/huggingface/datasets/issues/840/events | https://github.com/huggingface/datasets/pull/840 | 740,632,771 | MDExOlB1bGxSZXF1ZXN0NTE5MDg2NDUw | 840 | Update squad_v2.py | {
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} | Change lines 100 and 102 to prevent overwriting ```predictions``` variable. | {
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https://api.github.com/repos/huggingface/datasets/issues/839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/839/comments | https://api.github.com/repos/huggingface/datasets/issues/839/events | https://github.com/huggingface/datasets/issues/839 | 740,355,270 | MDU6SXNzdWU3NDAzNTUyNzA= | 839 | XSum dataset missing spaces between sentences | {
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} | [] | open | false | null | [] | null | 0 | 2020-11-11T00:34:43 | 2020-11-11T00:34:43 | null | NONE | null | null | null | I noticed that the XSum dataset has no space between sentences. This could lead to worse results for anyone training or testing on it. Here's an example (0th entry in the test set):
`The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!"` | {
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https://api.github.com/repos/huggingface/datasets/issues/838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/838/comments | https://api.github.com/repos/huggingface/datasets/issues/838/events | https://github.com/huggingface/datasets/pull/838 | 740,328,382 | MDExOlB1bGxSZXF1ZXN0NTE4ODM0NTE5 | 838 | CNN/Dailymail Dataset Card | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-10T23:56:43 | 2020-11-25T21:09:51 | 2020-11-25T21:09:50 | CONTRIBUTOR | null | false | {
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} | Link to the card page: https://github.com/mcmillanmajora/datasets/tree/cnn_dailymail_card/datasets/cnn_dailymail
One of the questions this dataset brings up is how we want to handle versioning of the cards to mirror versions of the dataset. The different versions of this dataset are used for different tasks (which may not be reflected in the versions that we currently have in the repo?), but it's only the structure that's changing rather than the content in this particular case, at least between versions 2.0.0 and 3.0.0. | {
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https://api.github.com/repos/huggingface/datasets/issues/837 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/837/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/837/comments | https://api.github.com/repos/huggingface/datasets/issues/837/events | https://github.com/huggingface/datasets/pull/837 | 740,250,215 | MDExOlB1bGxSZXF1ZXN0NTE4NzcwNDM5 | 837 | AlloCinΓ© dataset card | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-10T21:19:53 | 2020-11-25T21:56:27 | 2020-11-25T21:56:27 | CONTRIBUTOR | null | false | {
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} | Link to the card page: https://github.com/mcmillanmajora/datasets/blob/allocine_card/datasets/allocine/README.md
There wasn't as much information available for this dataset, so I'm wondering what's the best way to address open questions about the dataset. For example, where did the list of films that the dataset creator used come from?
I'm also wondering how best to go about talking about limitations when so little is known about the data. | {
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https://api.github.com/repos/huggingface/datasets/issues/836 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/836/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/836/comments | https://api.github.com/repos/huggingface/datasets/issues/836/events | https://github.com/huggingface/datasets/issues/836 | 740,187,613 | MDU6SXNzdWU3NDAxODc2MTM= | 836 | load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas | {
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] | closed | false | null | [] | null | 8 | 2020-11-10T19:35:40 | 2021-11-24T16:59:19 | 2020-11-19T17:35:38 | NONE | null | null | null | Hi All
I am trying to load a custom dataset and I am trying to load a single file to make sure the file is loading correctly:
dataset = load_dataset('csv', data_files=files)
When I run it I get:
Downloading and preparing dataset csv/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache/huggingface/datasets/csv/default-35575a1051604c88/0.0.0/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...
I am getting this error:
6a4ac4/csv.py in _generate_tables(self, files)
78 def _generate_tables(self, files):
79 for i, file in enumerate(files):
---> 80 pa_table = pac.read_csv(
81 file,
82 read_options=self.config.pa_read_options,
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**
The size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.
There is no issue reading the file with pandas. any idea what could be the issue?
When I am running a different CSV I do not get this line:
(download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)
Any ideas?
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https://api.github.com/repos/huggingface/datasets/issues/835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/835/comments | https://api.github.com/repos/huggingface/datasets/issues/835/events | https://github.com/huggingface/datasets/issues/835 | 740,102,210 | MDU6SXNzdWU3NDAxMDIyMTA= | 835 | Wikipedia postprocessing | {
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} | [] | closed | false | null | [] | null | 3 | 2020-11-10T17:26:38 | 2020-11-10T18:23:20 | 2020-11-10T17:49:21 | NONE | null | null | null | Hi, thanks for this library!
Running this code:
```py
import datasets
wikipedia = datasets.load_dataset("wikipedia", "20200501.de")
print(wikipedia['train']['text'][0])
```
I get:
```
mini|Ricardo Flores MagΓ³n
mini|Mexikanische RevolutionΓ€re, MagΓ³n in der Mitte anfΓΌhrend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des GemΓ€lde βTierra y Libertadβ von Idelfonso Carrara (?) von 1930.
Ricardo Flores MagΓ³n (* 16. September 1874 in San Antonio EloxochitlΓ‘n im mexikanischen Bundesstaat Oaxaca; β 22. November 1922 im BundesgefΓ€ngnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein fΓΌhrender anarchistischer Theoretiker und Aktivist, der die revolutionΓ€re mexikanische Bewegung radikal beeinflusste. MagΓ³n war GrΓΌnder der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World.
Politische Biografie
Journalistisch und politisch kΓ€mpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung βLand und Freiheitβ (Tierra y Libertad) populΓ€r. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte groΓen Einfluss auf die Landarbeiter. 1904 floh er in die USA und grΓΌndete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in GefΓ€ngnissen und im Exil und wurde 1918 in den USA wegen βBehinderung der Kriegsanstrengungenβ zu zwanzig Jahren GefΓ€ngnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass MagΓ³n von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM verΓΆffentlichte 1923 einen Beitrag, nachdem MagΓ³n von einem GefΓ€ngniswΓ€rter erschlagen wurde.
mini|Die BrΓΌder Ricardo (links) und Enrique Flores MagΓ³n (rechts) vor dem Los Angeles County Jail, 1917
[...]
```
so some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup?
Apologies if this has been asked before. | {
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https://api.github.com/repos/huggingface/datasets/issues/834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/834/comments | https://api.github.com/repos/huggingface/datasets/issues/834/events | https://github.com/huggingface/datasets/issues/834 | 740,082,890 | MDU6SXNzdWU3NDAwODI4OTA= | 834 | [GEM] add WikiLingua cross-lingual abstractive summarization dataset | {
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] | closed | false | null | [] | null | 2 | 2020-11-10T17:00:43 | 2021-04-15T12:04:09 | 2021-04-15T12:01:38 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WikiLingua
- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.
- **Paper:** https://arxiv.org/pdf/2010.03093.pdf
- **Data:** https://github.com/esdurmus/Wikilingua
- **Motivation:** Included in the GEM shared task. Multilingual.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/833/comments | https://api.github.com/repos/huggingface/datasets/issues/833/events | https://github.com/huggingface/datasets/issues/833 | 740,079,692 | MDU6SXNzdWU3NDAwNzk2OTI= | 833 | [GEM] add ASSET text simplification dataset | {
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] | closed | false | null | [] | null | 0 | 2020-11-10T16:56:30 | 2020-12-03T13:38:15 | 2020-12-03T13:38:15 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** ASSET
- **Description:** ASSET is a crowdsourced
multi-reference corpus for assessing sentence simplification in English where each simplification was produced by executing several rewriting transformations.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.424.pdf
- **Data:** https://github.com/facebookresearch/asset
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/832/comments | https://api.github.com/repos/huggingface/datasets/issues/832/events | https://github.com/huggingface/datasets/issues/832 | 740,077,228 | MDU6SXNzdWU3NDAwNzcyMjg= | 832 | [GEM] add WikiAuto text simplification dataset | {
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] | closed | false | null | [] | null | 0 | 2020-11-10T16:53:23 | 2020-12-03T13:38:08 | 2020-12-03T13:38:08 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WikiAuto
- **Description:** Sentences in English Wikipedia and their corresponding sentences in Simple English Wikipedia that are written with simpler grammar and word choices. A lot of lexical and syntactic paraphrasing.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.709.pdf
- **Data:** https://github.com/chaojiang06/wiki-auto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/831/comments | https://api.github.com/repos/huggingface/datasets/issues/831/events | https://github.com/huggingface/datasets/issues/831 | 740,071,697 | MDU6SXNzdWU3NDAwNzE2OTc= | 831 | [GEM] Add WebNLG dataset | {
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] | closed | false | null | [] | null | 0 | 2020-11-10T16:46:48 | 2020-12-03T13:38:01 | 2020-12-03T13:38:01 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WebNLG
- **Description:** WebNLG consists of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation of these triples (16,095 data inputs and 42,873 data-text pairs). The data is available in English and Russian
- **Paper:** https://www.aclweb.org/anthology/P17-1017.pdf
- **Data:** https://webnlg-challenge.loria.fr/download/
- **Motivation:** Included in the GEM shared task, multilingual
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/830/comments | https://api.github.com/repos/huggingface/datasets/issues/830/events | https://github.com/huggingface/datasets/issues/830 | 740,065,376 | MDU6SXNzdWU3NDAwNjUzNzY= | 830 | [GEM] add ToTTo Table-to-text dataset | {
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"name": "dataset request",
"color": "e99695",
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] | closed | false | null | [] | null | 1 | 2020-11-10T16:38:34 | 2020-12-10T13:06:02 | 2020-12-10T13:06:01 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** ToTTo
- **Description:** ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
- **Paper:** https://arxiv.org/abs/2004.14373
- **Data:** https://github.com/google-research-datasets/totto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/829/comments | https://api.github.com/repos/huggingface/datasets/issues/829/events | https://github.com/huggingface/datasets/issues/829 | 740,061,699 | MDU6SXNzdWU3NDAwNjE2OTk= | 829 | [GEM] add Schema-Guided Dialogue | {
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] | closed | false | null | [] | null | 0 | 2020-11-10T16:33:44 | 2020-12-03T13:37:50 | 2020-12-03T13:37:50 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** The Schema-Guided Dialogue Dataset
- **Description:** The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather.
- **Paper:** https://arxiv.org/pdf/2002.01359.pdf https://arxiv.org/pdf/2004.15006.pdf
- **Data:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/828/comments | https://api.github.com/repos/huggingface/datasets/issues/828/events | https://github.com/huggingface/datasets/pull/828 | 740,008,683 | MDExOlB1bGxSZXF1ZXN0NTE4NTcwMjY3 | 828 | Add writer_batch_size attribute to GeneratorBasedBuilder | {
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} | As specified in #741 one would need to specify a custom ArrowWriter batch size to avoid filling the RAM. Indeed the defaults buffer size is 10 000 examples but for multimodal datasets that contain images or videos we may want to reduce that. | {
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https://api.github.com/repos/huggingface/datasets/issues/827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/827/comments | https://api.github.com/repos/huggingface/datasets/issues/827/events | https://github.com/huggingface/datasets/issues/827 | 739,983,024 | MDU6SXNzdWU3Mzk5ODMwMjQ= | 827 | [GEM] MultiWOZ dialogue dataset | {
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] | open | false | null | [] | null | 1 | 2020-11-10T14:57:50 | 2020-12-12T13:42:30 | null | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)
- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts β there are no annotations from the user side.
- **Paper:** https://arxiv.org/pdf/2007.12720.pdf
- **Data:** https://github.com/budzianowski/multiwoz
- **Motivation:** Will likely be part of the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/826/comments | https://api.github.com/repos/huggingface/datasets/issues/826/events | https://github.com/huggingface/datasets/issues/826 | 739,976,716 | MDU6SXNzdWU3Mzk5NzY3MTY= | 826 | [GEM] Add E2E dataset | {
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] | closed | false | null | [] | null | 0 | 2020-11-10T14:50:40 | 2020-12-03T13:37:57 | 2020-12-03T13:37:57 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** E2E NLG dataset (for End-to-end natural language generation)
- **Description:**a dataset for training end-to-end, datadriven natural language generation systems in the restaurant domain, the datasets consists of 5,751 dialogue-act Meaning Representations (structured data) and 8.1 reference free-text utterances per dialogue-act on average
- **Paper:** https://arxiv.org/pdf/1706.09254.pdf https://arxiv.org/abs/1901.07931
- **Data:** http://www.macs.hw.ac.uk/InteractionLab/E2E/#data
- **Motivation:** This dataset will likely be included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/825 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/825/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/825/comments | https://api.github.com/repos/huggingface/datasets/issues/825/events | https://github.com/huggingface/datasets/pull/825 | 739,925,960 | MDExOlB1bGxSZXF1ZXN0NTE4NTAyNjgx | 825 | Add accuracy, precision, recall and F1 metrics | {
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} | This PR adds several single metrics, namely:
- Accuracy
- Precision
- Recall
- F1
They all uses under the hood the sklearn metrics of the same name. They allow different useful features when training a multilabel/multiclass model:
- have a macro/micro/per label/weighted/binary/per sample score
- score only the selected labels (usually what we call the positive labels) and ignore the negative ones. For example in case of a Named Entity Recognition task, positive labels are (`PERSON`, `LOCATION` or `ORGANIZATION`) and the negative one is `O`. | {
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https://api.github.com/repos/huggingface/datasets/issues/824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/824/comments | https://api.github.com/repos/huggingface/datasets/issues/824/events | https://github.com/huggingface/datasets/issues/824 | 739,896,526 | MDU6SXNzdWU3Mzk4OTY1MjY= | 824 | Discussion using datasets in offline mode | {
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] | open | false | null | [] | null | 7 | 2020-11-10T13:10:51 | 2021-01-20T14:05:04 | null | NONE | null | null | null | `datasets.load_dataset("csv", ...)` breaks if you have no connection (There is already this issue https://github.com/huggingface/datasets/issues/761 about it). It seems to be the same for metrics too.
I create this ticket to discuss a bit and gather what you have in mind or other propositions.
Here are some points to open discussion:
- if you want to prepare your code/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.
- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset("MY_PATH/csv.py", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.
- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least "freeze" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.
WDYT? (thks)
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https://api.github.com/repos/huggingface/datasets/issues/823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/823/comments | https://api.github.com/repos/huggingface/datasets/issues/823/events | https://github.com/huggingface/datasets/issues/823 | 739,815,763 | MDU6SXNzdWU3Mzk4MTU3NjM= | 823 | how processing in batch works in datasets | {
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] | closed | false | null | [] | null | 3 | 2020-11-10T11:11:17 | 2020-11-10T13:11:10 | 2020-11-10T13:11:09 | NONE | null | null | null | Hi,
I need to process my datasets before it is passed to dataloader in batch,
here is my codes
```
class AbstractTask(ABC):
task_name: str = NotImplemented
preprocessor: Callable = NotImplemented
split_to_data_split: Mapping[str, str] = NotImplemented
tokenizer: Callable = NotImplemented
max_source_length: str = NotImplemented
max_target_length: str = NotImplemented
# TODO: should not be a task item, but cannot see other ways.
tpu_num_cores: int = None
# The arguments set are for all tasks and needs to be kept common.
def __init__(self, config):
self.max_source_length = config['max_source_length']
self.max_target_length = config['max_target_length']
self.tokenizer = config['tokenizer']
self.tpu_num_cores = config['tpu_num_cores']
def _encode(self, batch) -> Dict[str, torch.Tensor]:
batch_encoding = self.tokenizer.prepare_seq2seq_batch(
[x["src_texts"] for x in batch],
tgt_texts=[x["tgt_texts"] for x in batch],
max_length=self.max_source_length,
max_target_length=self.max_target_length,
padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack
return_tensors="pt"
)
return batch_encoding.data
def data_split(self, split):
return self.split_to_data_split[split]
def get_dataset(self, split, n_obs=None):
split = self.data_split(split)
if n_obs is not None:
split = split+"[:{}]".format(n_obs)
dataset = load_dataset(self.task_name, split=split)
dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
dataset.set_format(type="torch", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])
return dataset
```
I call it like
`AutoTask.get(task, train_dataset_config).get_dataset(split="train", n_obs=data_args.n_train)
`
This gives the following error, to me because the data inside the dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks
File "finetune_multitask_trainer.py", line 192, in main
if training_args.do_train else None
File "finetune_multitask_trainer.py", line 191, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in get_dataset
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1236, in map
update_data = does_function_return_dict(test_inputs, test_indices)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1207, in does_function_return_dict
function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in <lambda>
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in _encode
[x["src_texts"] for x in batch],
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in <listcomp>
[x["src_texts"] for x in batch],
TypeError: string indices must be integers
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https://api.github.com/repos/huggingface/datasets/issues/822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/822/comments | https://api.github.com/repos/huggingface/datasets/issues/822/events | https://github.com/huggingface/datasets/issues/822 | 739,579,314 | MDU6SXNzdWU3Mzk1NzkzMTQ= | 822 | datasets freezes | {
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] | open | false | null | [] | null | 1 | 2020-11-10T05:10:19 | 2020-11-12T23:23:03 | null | NONE | null | null | null | Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks
dataset1 = load_dataset("squad", split="train[:10]")
dataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])
dataset2 = load_dataset("imdb", split="train[:10]")
dataset2 = dataset2.set_format(type="torch", columns=["text", "label"])
print(len(dataset1))
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https://api.github.com/repos/huggingface/datasets/issues/821 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/821/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/821/comments | https://api.github.com/repos/huggingface/datasets/issues/821/events | https://github.com/huggingface/datasets/issues/821 | 739,506,859 | MDU6SXNzdWU3Mzk1MDY4NTk= | 821 | `kor_nli` dataset doesn't being loaded properly | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-10T02:04:12 | 2020-11-16T13:59:12 | 2020-11-16T13:59:12 | NONE | null | null | null | There are two issues from `kor_nli` dataset
1. csv.DictReader failed to split features by tab
- Should not exist `None` value in label feature, but there it is.
```python
kor_nli_train['train'].unique('gold_label')
# ['neutral', 'entailment', 'contradiction', None]
```
- I found a reason why there is `None` values in label feature as following code
```python
from datasets import load_dataset
kor_nli_train = load_dataset('kor_nli', 'multi_nli')
for idx, example in enumerate(kor_nli_train['train']):
if example['gold_label'] is None:
print(idx, example)
break
# 16835 {'gold_label': None, 'sentence1': 'κ·Έλ μ μ μ μ κ°λ²Όμ΄ λ²
μ€ν¨ μλ§μ κ°μ§κ³ λ¬λ¦¬κΈ° μν΄ μ°μ μ²λΌ νμ μ€ν°λλ₯Ό λ£μλ€.\tμ μ μ μ λ€μΈμ’
μ¬μ±λ€κ³Ό ν¨κ» μλ λ°±μΈ λ¨μκ° μμλ€.\tentailment\nμ¬λ¦Όμ μ¬λΉ¨λ¦¬ μ·μ μ
μκ³ , μκ°μ μΌλ‘ λ―Έμ§κ·Όν λ¬Όμ λΏλ¦΄ μ μλ μμΉ¨ μΈνλ¬Όμ κΈ°κΊΌμ΄ κ°λμλ€.\tμ¬λ¦Όμ μ§μ₯μ λ¦μλ€.\tneutral\nλ΄μμμ κ·Έ μμ¬λ₯Ό ν΄λ΄€λλ°, κ±°κΈ°μ μκ³ κΈ°μ λ©μ§ μκ³ κΈ° λΆλΆμ μ리νκ³ λ°λ² νλ‘ λ§λ λλΉ€μ§ κ°μ κ±Έ κ°μ Έμλλ°, μ λ§ λλ¨ν΄.\tκ·Έλ€μ΄ κ±°κΈ°μ μ리νλ μ κ³ κΈ°λ μκ²Ήλ€. κ±°κΈ°μ μ λ λ¨Ήμ§ λ§λΌ.\tcontradiction\nν맀μμ μ£½μμμ λΈλΌμ΄μΈ λ°λ€ν... ν¬λ¦¬μ€ μΌλ¦¬\tν¬λ¦¬μ€ μΌλ¦¬λ μΈμΌμ¦λ§¨μ μ£½μμ μΈκΈνμ§ μλλ€.\tcontradiction\nκ·Έλ¬λ λμ μ리μ¬λ κ·Έλ₯ νκ° λ¬μ΄.\tμ€νκ° λλ λμ μ리μ¬λ νκ° λ¬λ€.\tneutral\nλ§μ§λ§ λ‘λ§μ 맹곡격 μ λ λ°€, 900λͺ
μ΄μμ μ λμΈ μλΉμλ€μ΄ λ‘λ§μΈλ€μκ² κ·Έλ€μ μ¬λ‘μ‘λ μΉλ¦¬λ₯Ό μ£ΌκΈ° 보λ€λ λλ μμ΄μ μ μ§λ λ€.\tλ‘λ§μΈλ€μ΄ κ·Έλ€μ ν¬νμ μΉλ¦¬νλλ‘ λ΄λ²λ €λκΈ° 보λ€λ 900λͺ
μ μ λμΈ μλΉμλ€μ΄ μμ΄νλ€.\tentailment\nμμΌλ‘ λ°μ¬νλΌ.\tλ°μ¬.\tneutral\nκ·Έλ¦¬κ³ λΉμ μ μ°λ¦¬ λ
μ΄ μμ΄μ»€μ μλ€λ κ²μ μκ³ μλ€. μ°λ¦¬ μ¬λλ€μ μ΄λ€ κ²μ΄ μΌλ§λ λ§μμ§ μ΄ν΄νμ§ λͺ»ν κ²μ΄λ€.\tλͺ¨λ μ¬λλ€μ μ°λ¦¬μ μΈ‘μ μμ€ν
μ΄ μ΄λ»κ² μλνλμ§ μκ³ μ΄ν΄ν©λλ€.\tcontradiction\nμ£Όλ―Έκ²μ€\tJumiygesλ λμμ μ΄λ¦μ΄λ€.\tneutral\nμ¬λμ μκΈ° λ―Όμ‘±μ λλ΄μΌ νλ€...\tμ¬λμ μ‘°κ΅μ 곡κ°ν΄μΌ νλ€.\tentailment\nλν PDD 63μ μ λΆμ μ
κ³κ° μ»΄ν¨ν° κΈ°λ° κ³΅κ²©μ λν΄ κ²½κ³ νκ³ λ°©μ΄ν μ€λΉλ₯Ό λ μν μ μλλ‘ μμ€ν
μ·¨μ½μ±, μν, μΉ¨μ
λ° μ΄μμ λν μ 보λ₯Ό 곡μ νλ λ©μ»€λμ¦μ μ립νλ κ²μ΄ μ€μνλ€λ κ²μ μΈμνμ΅λλ€.\tμ 보 μ μ‘ νλ‘ν μ½μ λ§λλ κ²μ μ€μνλ€.\tentailment\nμΉ΄ν λ§ νΌμμ λΈλΌ λ νλΈλ¦¬μΉ΄ λ°λ‘ λ¨μͺ½μλ νΌλ μ²΄κ° μλ €μ§ μ§ μ ν λλ¬Έμ νλ μ€νΈλ‘ λ§μΌμ΄λΌκ³ λΆλ Έλ 16μΈκΈ° λ‘μ§μμΈ λ©λ₯΄μΉ΄ν λμ€λ³΄(Mercato Nuovo)κ° μλ€.\tνΌμμ λΈλΌ λ νλΈλ¦¬μΉ΄μλ μΉ΄νκ° λ§μ΄ μλ€.\tentailment\nμ°λ¦¬κ° μ¬κΈ° μλ ν νΈλ¦°νμ΄ λ μ£Όμ λμ§ μ΄ν΄λ΄μΌκ² μ΄\tμ°λ¦¬λ νΈλ¦°νμ΄ λ¬΄μμ μ£Όμ λμ§ λ³΄λ λ° μκ°μ λλΉνμ§ μμ κ²μ΄λ€.\tcontradiction\nκ·Έλ¬λ μΌνΈμ‘±μ λ¬Ένμ κΈ°λ°μ κ°μ§ μμΌλλ κ΅νλ μ λ½μ μ ν₯ κΈ°λ
κ΅ μΈκ³μλ λ€λ₯΄κ² λ°μ νκ³ κ²°κ΅ λ‘λ§μ μ€μμ§κΆμ νμ μΌλ‘ λ체λμλ€.\tμμΌλλ κ΅νμλ μΌνΈμ‘±μ κΈ°μ§κ° μμλ€.\tentailment\nκΈμ, λ μ νμ μ¬μ§κ° μμ΄\tκΈμ, λμκ² λ§μ μ νκΆμ΄ μμ΄.\tcontradiction\nμ¬μ€, 곡μμ μΈ λ³΄μ₯μ μλ€.\tλ΄κ° μ° λ¬Όκ±΄μ λν 보μ¦μ΄ μμλ€.\tneutral\nλ νκΈ°μ°¨κΈ΄ νμ§λ§, μμμ λ₯΄ λΆλ₯΄μ ―μ μ¬λμ€λ¬μ΄ νΈμμμλ μΆμ λκ°μ΄ μμΎνλ€.\tμμμ λ₯΄ λΆλ₯΄κ²μμλ νΈμμμμ νλμ΄ μλλ₯΄κ³ λ°μ λΆμκΈ°λ₯Ό μ°μΆνλ€.\tcontradiction\nκ·Έμ μ¬ν μμμ΄ μ΄λ―Έ νΌμ‘λ€λ©΄ 곡격 μμλ νΌμ‘μ ν
μ§λ§ λ§μμμλ μ ν 곡ν©μ κΈ°λ―Έκ° λ³΄μ΄μ§ μμλ€.\tκ·Έλ μ λ§μμ΄ λΉν©νμ§ μμλμ§ μ μ μμλ€.\tneutral\nκ³Όκ±°μλ μ£½μμ μνμ΄ ν μ§μ ν맀λ₯Ό λ§λ λ° κ±°μ λμμ΄ λμ§ μμλ€.\tν μ§ ν맀λ μ΄λ ν μνλ κ΅ννμ§ μκ³ μ΄λ£¨μ΄μ§λ€.\tcontradiction\nμ΄λ μμ μ μ΄λ₯΄λ¬ λλ μ§κΈ λ€κ°μ€λ μλ‘μ΄ κ²λ€κ³Ό λμ€λ λ§μ μλ‘μ΄ κ²λ€μ΄ λ΄κ° λμ΄κ°κ³ μλ€κ³ λ§νλ μλλ‘ μ μ΄λ€κ³ μλ€.\tλλ μ¬μ ν λ΄κ° 보λ λͺ¨λ μλ‘μ΄ κ²μ μ¬λνλ€.\tcontradiction\nλ΄μ€μν¬λ 물리νμλ€μ΄ κ²½κΈ°μ₯ νμ¬μμ κ³ μλλ‘μ μλμ°¨ κ΅ν΅κ³Ό 보νμ κ΅ν΅μ κ°μ νκΈ° μν΄ μλΌμ μμ§μμ μ°κ΅¬νκ³ μλ€κ³ λ§νλ€.\tκ³ μλλ‘μ μλμ°¨ κ΅ν΅ νλ¦μ κ°μ νλ κ²μ 물리νμλ€μ΄ μλΌλ₯Ό μ°κ΅¬νλ μ΄μ μ€ νλμ΄λ€.\tentailment\nμΌλ§λ λ€λ₯Έκ°? κ·Έλ μ μ λ§μ λ©μΆμλ€κ° λ§μ μ΄μλ€.\tκ·Έλ κ·Έ μλ
κ° μ΄λμ μλμ§ μκ³ μΆμλ€.\tentailment\nκΈμ, κ·Έμκ² λ무 λ§μ κ²μ μ£Όμ§λ§.\tκ·Έλ ν¨μ¬ λ λ§μ κ²μ μꡬν κ²μ΄λ€.\tneutral\nμ무리 κ·Έμ μ°½μλ¬Όμ΄ μλ²½ν΄ λ³΄μΈλ€κ³ ν΄λ, κ·Έλ€μ λ―Ώλ κ²μ μλ§λ μ’μ μκ°μ΄ μλ κ²μ΄λ€.\'\tλμκΈ°λ₯Ό μ λ§λ λ€κ³ ν΄μ λκ΅°κ°λ₯Ό λ―Ώλ κ²μ μλ§ μ’μ§ μμ κ²μ΄λ€.\tneutral\nλ²μ€νλ§ κ·Έλ λΉμ(Bustling Gran Via)λ νΈν
, μμ , κ·Ήμ₯, λμ΄νΈν΄λ½, μΉ΄ν λ±μ΄ μ΄μ°λ¬μ Έ μ°μ±
κ³Ό μ°½κ°λ₯Ό λ³Ό μ μλ€.\tGran Viaλ νΈν
, μμ , κ·Ήμ₯, λμ΄νΈν΄λ½, μΉ΄νμ λ²νν μ‘°ν©μ΄λ€.\tentailment\nμ λΆ μΈμμ\tκ·Έ μ¬λ¬΄μ€μ μμ±ν΄μ μμΉν΄ μλ€.\tneutral\nμ€μ λ¬Έν μ μμ΄ μ΄λ μλμ§ μκ³ μΆλ€λ©΄ νμμ μμ΄λ²λ¦¬κ³ μ€λ¦¬μ½ 밸리μ λ λλͺ¬λλ₯Ό μκ°ν΄ 보λΌ.\tμ€μ λ¬Έν μ μμ λ λλͺ¬λμμ μΌμ΄λλ€.\tentailment\nκ·Έλ¦¬κ³ νλμ€λ¦°μ μ£Όμ§ μκΈ° μν΄ μΉ¨λ μμ μ¬λ €λ¨μ΄\tκ·Έλ
μ λ°©μλ νλμ€λ¦°μ΄ μλ€λ μ§νκ° μ ν μμλ€.\tcontradiction\nL.A.μ μΌμΈ μμ₯μ ν보νλ κ²μ λ§μκ³ μ λ ΄ν 그루λΈλ₯Ό μ‘κ³ , λμ΄ μλ νλΉμ μ¦κΈ°κ³ , μ μ ν λμ°λ¬Ό, κ½, ν₯, κ·Έλ¦¬κ³ κ°μ ― κ°λ‘μ΄λ₯Ό ꡬμ
νλ©΄μ νμ§μΈλ€κ³Ό μ΄μΈλ¦΄ μ μλ νλ₯ν λ°©λ²μ΄λ€.\tLAμ μΌμΈ μμ₯μ λμλ€λλ κ²μ μκ° λλΉλ€.\tcontradiction\nμλλ λ°μΌλ‘ λμ μλμ νμ¨μ λ΄μ¬μλ€. λ¨ ν λ², κ·Έλ¦¬κ³ λ§λ¦¬νμμ¬ λ§μ μ λ‘ λλ΄μλ κ²°μ¬μ΄ λ€μμ¬ μμλ€.\tμλλ μμ¬νκ³ λ§λ¦¬νμμ¬ λ§μ μ μ λ€ λ§μκΈ°λ‘ κ²°μ¬νλ€.\tentailment\n5 μμ Vajpayeeλ ν΅ μ€νμ μ±κ³΅μ μΈ μλ£λ₯Ό λ°ννλλ°, μΈλμΈλ€μ μ£ΌκΆμ νμλ‘ μ μ νμ§λ§ μ΄μ κ΅κ°μ μꡬμμ μΈλ κ΄κ³λ₯Ό 볡μ‘νκ² λ§λ€ μ μμ΅λλ€.\tμΈλλ μ±κ³΅μ μΈ ν΅μ€νμ ν μ μ΄ μλ€.\tcontradiction\nνλΌλ
Έ μμμ λ³΄ν΅ μΌλ§λ λ§μ κ²μ κ°μ§κ³ μλκ°?\tμ μ¬λλ€ μ€μ νλΌλ
Έ μμ κ°λ³Έ μ¬λ μμ΄?\tcontradiction\nκ·Έκ²μ μ 체μ μΈ ννμ μ°μν¨μ μ΄ν 건λνΈμμ κ°μ₯ μ λ³Ό μ μλ€. μλνλ©΄, λ‘λ§μ μλ μ± λ² λλ‘μ²λΌ, λμ κΈΈμν λ³ΈλΉ λ€λ‘ λ κ°κΉμ΄ κ³³μ μ¬λΌμ§κΈ° λλ¬Έμ΄λ€.\tμ± λ² λλ‘μ κΈΈμν λ³ΈλΉμ λμ κ°λ¦°λ€.\tentailment\nλΉμ μ μν΄μ΄ μ΄μ κ°λ°μ μΈ κΈ°μ¨μ κ°μ§κ³ λλλ₯Ό 그릴 κ²μ΄λΌκ³ μκ°νκ² μ§λ§, μλμ€; κ·Έλ κ·Έμ λͺ¨λ κ²½λ ₯μμ λ¨ ν μ λ§μ κ·Έλ Έκ³ , κ·Έκ²μ μ¬μν κ·Έλ¦Όμ΄λ€.\tκ·Έλ κ·Έκ²μ΄ κ·Έλ₯Ό λΆνΈνκ² λ§λ€μκΈ° λλ¬Έμ νλλ§ κ·Έλ Έλ€.\tneutral\nμ΄ μΈμμ μΈ νκ²½μ μλ λν¬ λ μ¨μ΄ 루λΈλ₯΄ λ°λ¬Όκ΄μ μΉ¨μ€μμ λ³Ό μ μλλ‘ κ³νλμλλ°, κ·Έ λΉμ κΆμ μ΄μμ΅λλ€.\tλν΄λ μΉμ κ·Έμ λͺ¨λ κΆμ μ μλ κ·Έμ μΉ¨μ€μμ 보λ κ²½μΉμ λ§μ κ΄μ¬μ κ°μ‘λ€.\tneutral\nκ·Έλ μ°λ¦¬μκ² λ¬Έ μ΄μ λ₯Ό 건λ€μ£Όκ³ λ κΈν λ λ¬λ€.\tκ·Έλ κΈ΄μ₯ν΄μ μ°λ¦¬μκ² μ΄μ λ₯Ό 빨리 μ£Όμλ€.\tneutral\nμμνλ λν μ΅μ’
κ·μΉμ OMBμ μ μΆνλ€.\tμμνλ λν μ΄ κ·μΉμ λ€λ₯Έ κ·Έλ£Ήμ μ μΆνμ§λ§ μ΅μ’
κ·μΉμ OMBκ° νκ°νκΈ° μν κ²μ΄ μμ΅λλ€.\tneutral\nμ μκ°κ²μ κ°λ³΄λ©΄ μ¬λ¦¬λΉμμ 볡μ νν©λ¬Ό κ°μ μ μΎν μ΄λ¦μ κ°μ§ μ νλ€μ μ°Ύμ μ μμ κ²λλ€.μ΄ μ νμ΄ λΏλ¦¬λ₯Ό λ΄λ¦¬λλ‘ λκΈ° μν΄ μ΄¬μμ μ λ¨λ λμ λ©ν¬μμ νλ νΈλ₯΄λͺ¬μ νΌν©λ¬Όμ΄μ£ .\tμ μ κ°κΎΈκΈ° κ°κ²μ μ νλ€μ μ’
μ’
κ·Έλ€μ λͺ©μ μ μ€λͺ
νκΈ° μν΄ κΈ°μ μ μΌλ‘λ κ³Όνμ μΌλ‘ νμλ μ΄λ¦(μ¬λ¦¬λΉμμ 볡μ νν©λ¬Όμ²λΌ)μ λΆμ¬λ°λλ€.\tneutral\nμ€νλ μ€νΈ μμ μ΄λ μ κ·Έλ
μ μ΄μΌκΈ°λ₯Ό λ°κΎΈμλμ§μ ν¨μ¬ λ κ΄μ¬μ΄ μμ κ²μ΄λ€.\tμ€νΈμ μ΄μΌκΈ°λ μ‘°κΈλ λ³νμ§ μμλ€.\tcontradiction\nλ¨νΈκ³Όμ λ§μ§λ§ λκ²°λ‘ λ§₯ν°μ΄λ λ
ΈλΌμ λ³μ μ λ무λ λ₯μνκ² μκ³ ν΄ μκΈ° λλ¬Έμ, κ·Έλ
μκ²λ λΉν©μ€λ¬μΈ μ λλ‘ κ°μμ€λ¬μ΄ κ²μ²λΌ 보μ΄μ§λ§, μ°λ¦¬μκ²λ κ°μ μ μΌλ‘ λΆκ°νΌν΄ 보μΈλ€.\tλ
ΈλΌμ λ³μ μ λΆλͺ
νκ³ νμ°μ μ΄μλ€.\tcontradiction\nμ΄μ§νΈ μ΅λ¨λ¨ λμμΈ μμ€μμ μ€λ μμ¬λ₯Ό ν΅ν΄ μ€μν μν μ ν΄μλ€.\tμμ€μμ μ΄μ§νΈ κ΅κ²½ λ°λ‘ μμ μμΉν΄ μμ΅λλ€.\tneutral\nκ·Έλ¬λ ν¨μ¬ λ μ°μν 건μΆμ ν°μΉλ μ μ±ν μΆ€μΈ Bharatanatyamμμ μνλ 108 κ°μ§ κΈ°λ³Έ ν¬μ¦λ₯Ό μλ° ν¨λμμ λ³Ό μ μμ΅λλ€.\tν¨λμ λν μλ°μ λ¬μ¬λ μΌλ°μ μΈ λͺ¨ν°λΈλ€.\tneutral\nνΈνλ‘κ² μ¬μ΄μ§ κ³λ¨μ μ μμ μ΄ν리μ νμμ κ°μ₯ νλ₯ν μμλΈ μ€ νλμ
λλ€.\tμλ¦λ€μ΄ μ μκ³Ό ν¬κ·ν κ½κ½μ΄ λͺ¨λ μ΄ν리μμ νμμ μΈ μ€νμΌμ 보μ¬μ€λ€.\tneutral\nμ, κ·Έλ¬μΌλ©΄ μ’μμ ν
λ°\tλλ κ·Έκ²μ λ€λ₯΄κ² ν κΈ°νλ₯Ό λͺΉμ κ°λ§νλ€.\tentailment\nννκ° λ μ±μ κΈ°μμ μ리μ‘κ³ μλ μμ μ€μΈ λμ μΌμ΄μμ€λ²κ·Έλ λ
Έλ²¨ ννμ μμμ μλ²νΈ μλ°μ΄μ²(1875λ
)μ μΆμμ§λ‘ λ리 μλ €μ Έ μλ€.\tμλ²νΈ μλ°μ΄μ²λ λ λ€ μΌμ΄μμ€λ²κ·Έ λ§μμ μμλ€.\tentailment\nκ³ κ°λλ λ¬Έμ κ° μλ λλΆλΆμ νμλ€μ΄ λ°κ²¬λ κ²μ 보μ₯νλ€.\tμ₯λΉ λ―Όκ°λλ λ¬Έμ νμ§μ κ΄λ ¨μ΄ μμ΅λλ€.\tcontradiction\nμ€λμ νμ€ν λ°λ°μ§ κ°μ λ μ΄μμ΄\tμ€λ μ¬λ¬΄μ€μ μλ λͺ¨λ μ¬λλ€μ λ°λ°μ§λ₯Ό μ
μλ€.\tneutral\nλͺ»μκΈ΄ ν±μλλ₯Ό μ
κ³ .\tκ·Έκ²μ λΆνμκ³Ό μ£Όν©μμ
λλ€.\tneutral\nμ΄μ£Ό λ
Έλ μμ©μ μ€ λ§μ΄ κ° κ·Έλ€μ νμ§ μμμ μ°λ€.\tλ
Έλ μμ©μμλ νμ§ μμμ μ¬λ μ΄μ£Ό λ
Έλμλ€μ μ¬μ§μ΄ μλ€.\tneutral\nκ·Έλ, κ·Έκ° μ μΈκ³λ₯Ό μ¬νν νμ κ·Έλ° κ±°μΌ\tκ·Έκ²μ μ¬λλ€μ μΈκ³ μ¬νμ λ°λ₯Έλ€.\tentailment\n건λνΈμ ν¬κ³ ν° μ°Έλ무 λͺ κ·Έλ£¨κ° μλ€.\tμ°λ¦¬λ μ¬κΈ° μ€ν¬λ μ΄λ€ μ’
λ₯μ λ―Έκ΅ λ무λ μλ€.\tcontradiction\nFort-de-Franceμμ μΆλ°νλ μλμ°¨λ μ¬κ°μ μΌλ‘, λΉμ μ μμΈ ? λ°λ€ ν¬λκ° κ·Έλμ μ 곡νλ μΎμ ν κ°μ λͺ¨λ ν΄λ³κ³Ό νΌν¬λ ν
μ΄λΈ, μ΄λ¦°μ΄ λ―ΈλλΌν, μλΉμ΄ μλ μλμ λμ°©ν μ μλ€.\tνλμ€ μμμμ μλμ°¨λ ν리λ₯Ό νκ³ μμΈλ‘ κ° μ μλ€.\tentailment\nκ·Έλ¦¬κ³ κ·Έκ²μ μ¨λΌλ°°λ§μ£Όκ° μμνλ λλ‘ μμ°μμ 50λ§ λ¬λ¬λ₯Ό μκ°νμ§ μμ κ²μ΄λΌλ κ²μ μλ―Ένλ€.\tμ¨λΌλ°°λ§ μ£Όλ μμ° μκ°μ νμ§ μμλ€. μλνλ©΄ κ·Έλ κ² νλ κ²μ λν μ΄κΈ° μ λΉμ±μ΄ μ λ° μ‘°μ¬μ λ§μμ§ μμκΈ° λλ¬Έμ΄λ€.\tneutral\nμμμ΄ λ¨Όμ μ΄ .. μ΄ .. λ
ΈμΈμ΄λ κ°μ‘±μ μμμμ 보λ΄λ κ²μ λν΄ μ΄λ»κ² μκ°νλ?\tκ°μ‘±μ μμμμ 보λ΄μ μ¬λ κ²μ λν΄ μ΄λ»κ² μκ°νλμ§ μ νμκ° μλ€.\tcontradiction\nλλ¨Έμ§λ λμκ² λ¬λ Έμ΄.\tλλ¨Έμ§λ λμκ² λ¬λ Έμ§λ§ μκ°μ΄ λ§μ§ μλ€.\tneutral\nμ-ν , 3μμ νλ³μ νλ κ²μ λν΄ κ±±μ νλ©΄ μ λλ€λ κ²μ μκ³ μλ 3μμ΄μΌ.\t3μμ κ·Έλ κ² λ₯μ§ μλ€.\tneutral\nκ·Έλ¦¬κ³ μ΄, κ·Έλ° μμ κ²λ€λ‘ λ€μ μμν΄λ΄. μμ§ ν¨μ¬ μΈ. μ΄, κ·Έ νΉλ³ν λͺ¨λΈ μ°¨λ 150λ¬λ¬μΌ.\tκ·Έ λͺ¨νμ°¨λ 4μ² λ¬λ¬κ° λ λ€.\tcontradiction\nλ΄μΌ λμκ°μΌ νλ€λ©΄, μΉΌμ΄ λ§νλ€.\tλμκ° μ μμ΄. μ€λμ μ λΌ. λ΄μΌμ μ λΌ. μ λ μ λΌ." μΉΌμ΄ λ§νλ€.', 'sentence2': 'contradiction'}
```
2. (Optional) Preferred to change the name of the features for the compatibility with `run_glue.py` in π€ Transformers
- `kor_nli` dataset has same data structure of multi_nli, xnli
- Changing the name of features and the feature type of 'gold_label' to ClassLabel might be helpful
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
```
If you don't mind, I would like to fix this.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/820/comments | https://api.github.com/repos/huggingface/datasets/issues/820/events | https://github.com/huggingface/datasets/pull/820 | 739,387,617 | MDExOlB1bGxSZXF1ZXN0NTE4MDYwMjQ0 | 820 | Update quail dataset to v1.3 | {
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} | Updated quail to most recent version, to address the problem originally discussed [here](https://github.com/huggingface/datasets/issues/806). | {
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https://api.github.com/repos/huggingface/datasets/issues/819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/819/comments | https://api.github.com/repos/huggingface/datasets/issues/819/events | https://github.com/huggingface/datasets/pull/819 | 739,250,624 | MDExOlB1bGxSZXF1ZXN0NTE3OTQ2MjYy | 819 | Make save function use deterministic global vars order | {
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} | The `dumps` function need to be deterministic for the caching mechanism.
However in #816 I noticed that one of dill's method to recursively check the globals of a function may return the globals in different orders each time it's used. To fix that I sort the globals by key in the `globs` dictionary.
I had to add a rectified `save_function` to the saving functions registry of the Pickler to make it work.
This should fix #816 | {
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https://api.github.com/repos/huggingface/datasets/issues/818 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/818/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/818/comments | https://api.github.com/repos/huggingface/datasets/issues/818/events | https://github.com/huggingface/datasets/pull/818 | 739,173,861 | MDExOlB1bGxSZXF1ZXN0NTE3ODgzMzk0 | 818 | Fix type hints pickling in python 3.6 | {
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} | Type hints can't be properly pickled in python 3.6. This was causing errors the `run_mlm.py` script from `transformers` with python 3.6
However Cloupickle proposed a [fix](https://github.com/cloudpipe/cloudpickle/pull/318/files) to make it work anyway.
The idea is just to implement the pickling/unpickling of parameterized type hints. There is one detail though: since in python 3.6 we can't use `isinstance` on type hints, then we can't use pickle saving functions registry directly. Therefore we just wrap the `save_global` method of the Pickler.
This should fix https://github.com/huggingface/transformers/issues/8212 for python 3.6 and make `run_mlm.py` support python 3.6
cc @sgugger | {
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https://api.github.com/repos/huggingface/datasets/issues/817 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/817/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/817/comments | https://api.github.com/repos/huggingface/datasets/issues/817/events | https://github.com/huggingface/datasets/issues/817 | 739,145,369 | MDU6SXNzdWU3MzkxNDUzNjk= | 817 | Add MRQA dataset | {
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- **Name:** MRQA
- **Description:** Collection of different (subsets of) QA datasets all converted to the same format to evaluate out-of-domain generalization (the datasets come from different domains, distributions, etc.). Some datasets are used for training and others are used for evaluation. This dataset was collected as part of MRQA 2019's shared task
- **Paper:** https://arxiv.org/abs/1910.09753
- **Data:** https://github.com/mrqa/MRQA-Shared-Task-2019
- **Motivation:** Out-of-domain generalization is becoming (has become) a de-factor evaluation for NLU systems
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html). | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-09T15:01:20 | 2020-11-11T15:20:50 | 2020-11-11T15:20:50 | MEMBER | null | null | null | Dill uses `dill.detect.globalvars` to get the globals used by a function in a recursive dump. `globalvars` returns a dictionary of all the globals that a dumped function needs. However the order of the keys in this dict is not deterministic and can cause caching issues.
To fix that one could register an implementation of dill's `save_function` in the `datasets` pickler that sorts the globals keys before dumping a function. | {
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https://api.github.com/repos/huggingface/datasets/issues/815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/815/comments | https://api.github.com/repos/huggingface/datasets/issues/815/events | https://github.com/huggingface/datasets/issues/815 | 738,842,092 | MDU6SXNzdWU3Mzg4NDIwOTI= | 815 | Is dataset iterative or not? | {
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I want to use your library for large-scale training, I am not sure if this is implemented as iterative datasets or not?
could you provide me with example how I can use datasets as iterative datasets?
thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/814/comments | https://api.github.com/repos/huggingface/datasets/issues/814/events | https://github.com/huggingface/datasets/issues/814 | 738,500,443 | MDU6SXNzdWU3Mzg1MDA0NDM= | 814 | Joining multiple datasets | {
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I have multiple iterative datasets from your library with different size and I want to join them in a way that each datasets is sampled equally, so smaller datasets more, larger one less, could you tell me how to implement this in pytorch? thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/813/comments | https://api.github.com/repos/huggingface/datasets/issues/813/events | https://github.com/huggingface/datasets/issues/813 | 738,489,852 | MDU6SXNzdWU3Mzg0ODk4NTI= | 813 | How to implement DistributedSampler with datasets | {
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I am using your datasets to define my dataloaders, and I am training finetune_trainer.py in huggingface repo on them.
I need a distributedSampler to be able to train the models on TPUs being able to distribute the load across the TPU cores. Could you tell me how I can implement the distribued sampler when using datasets in which datasets are iterative? To give you more context, I have multiple of datasets and I need to write sampler for this case. thanks. | {
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https://api.github.com/repos/huggingface/datasets/issues/812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/812/comments | https://api.github.com/repos/huggingface/datasets/issues/812/events | https://github.com/huggingface/datasets/issues/812 | 738,340,217 | MDU6SXNzdWU3MzgzNDAyMTc= | 812 | Too much logging | {
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"type": "User",
"site_admin": false
} | [] | closed | false | null | [] | null | 7 | 2020-11-07T23:56:30 | 2021-01-26T14:31:34 | 2020-11-16T17:06:42 | NONE | null | null | null | I'm doing this in the beginning of my script:
from datasets.utils import logging as datasets_logging
datasets_logging.set_verbosity_warning()
but I'm still getting these logs:
[2020-11-07 15:45:41,908][filelock][INFO] - Lock 139958278886176 acquired on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock
[2020-11-07 15:45:41,909][filelock][INFO] - Lock 139958278886176 released on /home/username/.cache/huggingface/datasets/cfe20ffaa80ef1c145a0a210d5b9cdce2b60002831e6ed0edc7ab9275d6f0d48.1bd4ccbce9de3dad0698d84674a19d6cc66a84db736a6398110bd196795dde7e.py.lock
using datasets version = 1.1.2 | {
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"url": "https://api.github.com/repos/huggingface/datasets/labels/nlp-viewer",
"name": "nlp-viewer",
"color": "94203D",
"default": false,
"description": ""
}
] | open | false | null | [] | null | 2 | 2020-11-07T17:08:58 | 2020-11-16T15:26:23 | null | NONE | null | null | null | Hello,
when I select amazon_us_reviews in nlp viewer, it shows error.
https://huggingface.co/nlp/viewer/?dataset=amazon_us_reviews

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https://api.github.com/repos/huggingface/datasets/issues/810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/810/comments | https://api.github.com/repos/huggingface/datasets/issues/810/events | https://github.com/huggingface/datasets/pull/810 | 737,878,370 | MDExOlB1bGxSZXF1ZXN0NTE2ODQzMzQ3 | 810 | Fix seqeval metric | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-06T16:11:43 | 2020-11-09T14:04:29 | 2020-11-09T14:04:28 | MEMBER | null | false | {
"url": "https://api.github.com/repos/huggingface/datasets/pulls/810",
"html_url": "https://github.com/huggingface/datasets/pull/810",
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} | The current seqeval metric returns the following error when computed:
```
~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/78a944d83252b5a16c9a2e49f057f4c6e02f18cc03349257025a8c9aea6524d8/seqeval.py in _compute(self, predictions, references, suffix)
102 scores = {}
103 for type_name, score in report.items():
--> 104 scores[type_name]["precision"] = score["precision"]
105 scores[type_name]["recall"] = score["recall"]
106 scores[type_name]["f1"] = score["f1-score"]
KeyError: 'LOC'
```
This is because the current code basically tries to do:
```
scores = {}
scores["LOC"]["precision"] = some_value
```
which does not work in python. This PR fixes that while keeping the previous nested structure of results, with the same keys. | {
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https://api.github.com/repos/huggingface/datasets/issues/809 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/809/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/809/comments | https://api.github.com/repos/huggingface/datasets/issues/809/events | https://github.com/huggingface/datasets/issues/809 | 737,832,701 | MDU6SXNzdWU3Mzc4MzI3MDE= | 809 | Add Google Taskmaster dataset | {
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] | closed | false | null | [] | null | 2 | 2020-11-06T15:10:41 | 2021-04-20T13:09:26 | 2021-04-20T13:09:26 | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Taskmaster
- **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations)
- **Paper:** https://arxiv.org/abs/1909.05358
- **Data:** https://github.com/google-research-datasets/Taskmaster
- **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/808 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/808/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/808/comments | https://api.github.com/repos/huggingface/datasets/issues/808/events | https://github.com/huggingface/datasets/pull/808 | 737,638,942 | MDExOlB1bGxSZXF1ZXN0NTE2NjQ0NDc0 | 808 | dataset(dgs): initial dataset loading script | {
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} | [] | closed | false | null | [] | null | 2 | 2020-11-06T10:14:43 | 2021-03-23T06:18:55 | 2021-03-23T06:18:55 | CONTRIBUTOR | null | false | {
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} | When trying to create dummy data I get:
> Dataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has t o be created with less guidance. Make sure you create the file dummy_data.
I am not sure how to manually create the dummy_data (what exactly it should contain)
Also note, this library says:
> ImportError: To be able to use this dataset, you need to install the following dependencies['pympi'] using 'pip install pympi' for instance'
When you actually need to `pip install pympi-ling`
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https://api.github.com/repos/huggingface/datasets/issues/807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/807/comments | https://api.github.com/repos/huggingface/datasets/issues/807/events | https://github.com/huggingface/datasets/issues/807 | 737,509,954 | MDU6SXNzdWU3Mzc1MDk5NTQ= | 807 | load_dataset for LOCAL CSV files report CONNECTION ERROR | {
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} | [] | closed | false | null | [] | null | 11 | 2020-11-06T06:33:04 | 2021-01-11T01:30:27 | 2020-11-14T05:30:34 | NONE | null | null | null | ## load_dataset for LOCAL CSV files report CONNECTION ERROR
- **Description:**
A local demo csv file:
```
import pandas as pd
import numpy as np
from datasets import load_dataset
import torch
import transformers
df = pd.DataFrame(np.arange(1200).reshape(300,4))
df.to_csv('test.csv', header=False, index=False)
print('datasets version: ', datasets.__version__)
print('pytorch version: ', torch.__version__)
print('transformers version: ', transformers.__version__)
# output:
datasets version: 1.1.2
pytorch version: 1.5.0
transformers version: 3.2.0
```
when I load data through `dataset`:
```
dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
```
Error infos:
```
ConnectionError Traceback (most recent call last)
<ipython-input-17-bbdadb9a0c78> in <module>
----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
588 # Download/copy dataset processing script
589 module_path, hash = prepare_module(
--> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
591 )
592
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
270 if script_version is not None:
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
306 user_agent=download_config.user_agent,
307 local_files_only=download_config.local_files_only,
--> 308 use_etag=download_config.use_etag,
309 )
310 elif os.path.exists(url_or_filename):
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)
473 elif response is not None and response.status_code == 404:
474 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 475 raise ConnectionError("Couldn't reach {}".format(url))
476
477 # Try a second time
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py
```
And I try to connect to the site with requests:
```
import requests
requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
```
Similarly Error occurs:
```
---------------------------------------------------------------------------
ConnectionRefusedError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
159 conn = connection.create_connection(
--> 160 (self._dns_host, self.port), self.timeout, **extra_kw
161 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
NewConnectionError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
676 headers=headers,
--> 677 chunked=chunked,
678 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
380 try:
--> 381 self._validate_conn(conn)
382 except (SocketTimeout, BaseSSLError) as e:
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 976 conn.connect()
977
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self)
307 # Add certificate verification
--> 308 conn = self._new_conn()
309 hostname = self.host
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
171 raise NewConnectionError(
--> 172 self, "Failed to establish a new connection: %s" % e
173 )
NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 retries=self.max_retries,
--> 449 timeout=timeout
450 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
724 retries = retries.increment(
--> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
726 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
438 if new_retry.is_exhausted():
--> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause))
440
MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
During handling of the above exception, another exception occurred:
ConnectionError Traceback (most recent call last)
<ipython-input-20-18cc3eb4a049> in <module>
1 import requests
2
----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs)
102
103 kwargs.setdefault('allow_redirects', False)
--> 104 return request('head', url, **kwargs)
105
106
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
528 }
529 send_kwargs.update(settings)
--> 530 resp = self.send(prep, **send_kwargs)
531
532 return resp
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs)
641
642 # Send the request
--> 643 r = adapter.send(request, **kwargs)
644
645 # Total elapsed time of the request (approximately)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
514 raise SSLError(e, request=request)
515
--> 516 raise ConnectionError(e, request=request)
517
518 except ClosedPoolError as e:
ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/806/comments | https://api.github.com/repos/huggingface/datasets/issues/806/events | https://github.com/huggingface/datasets/issues/806 | 737,215,430 | MDU6SXNzdWU3MzcyMTU0MzA= | 806 | Quail dataset urls are out of date | {
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} | [] | closed | false | null | [] | null | 3 | 2020-11-05T19:40:19 | 2020-11-10T14:02:51 | 2020-11-10T14:02:51 | CONTRIBUTOR | null | null | null | <h3>Code</h3>
```
from datasets import load_dataset
quail = load_dataset('quail')
```
<h3>Error</h3>
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml
```
As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. | {
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https://api.github.com/repos/huggingface/datasets/issues/805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/805/comments | https://api.github.com/repos/huggingface/datasets/issues/805/events | https://github.com/huggingface/datasets/issues/805 | 737,019,360 | MDU6SXNzdWU3MzcwMTkzNjA= | 805 | On loading a metric from datasets, I get the following error | {
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} | [] | open | false | null | [] | null | 1 | 2020-11-05T15:14:38 | 2020-11-09T09:12:35 | null | NONE | null | null | null | `from datasets import load_metric`
`metric = load_metric('bleurt')`
Traceback:
210 class _ArrayXDExtensionType(pa.PyExtensionType):
211
212 ndims: int = None
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType'
Any help will be appreciated. Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/804/comments | https://api.github.com/repos/huggingface/datasets/issues/804/events | https://github.com/huggingface/datasets/issues/804 | 736,858,507 | MDU6SXNzdWU3MzY4NTg1MDc= | 804 | Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') | {
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} | [] | closed | false | null | [] | null | 3 | 2020-11-05T11:38:01 | 2020-11-09T14:14:59 | 2020-11-09T14:14:58 | CONTRIBUTOR | null | null | null | # The issue
It's all in the title, it appears to be fine on the train and validation sets.
Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ?
# How to reproduce
```py
from datasets import load_dataset
kilt_tasks = load_dataset("kilt_tasks")
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
# both in "kilt_tasks"
In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])
Out[18]: False
# and "trivia_qa"
In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])
Out[13]: True
# appears to be fine on the train and validation sets.
In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])
Out[14]: False
In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])
Out[15]: False
In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])
Out[16]: True
In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])
Out[17]: True
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/803 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/803/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/803/comments | https://api.github.com/repos/huggingface/datasets/issues/803/events | https://github.com/huggingface/datasets/pull/803 | 736,818,917 | MDExOlB1bGxSZXF1ZXN0NTE1OTY1ODE2 | 803 | fix: typos in tutorial to map KILT and TriviaQA | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-05T10:42:00 | 2020-11-10T09:08:07 | 2020-11-10T09:08:07 | CONTRIBUTOR | null | false | {
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|
https://api.github.com/repos/huggingface/datasets/issues/802 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/802/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/802/comments | https://api.github.com/repos/huggingface/datasets/issues/802/events | https://github.com/huggingface/datasets/pull/802 | 736,296,343 | MDExOlB1bGxSZXF1ZXN0NTE1NTM1MDI0 | 802 | Add XGlue | {
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} | [] | closed | false | null | [] | null | 1 | 2020-11-04T17:29:54 | 2020-12-01T15:58:28 | 2020-12-01T15:58:27 | MEMBER | null | false | {
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} | Dataset is ready to merge. An important feature of this dataset is that for each config the train data is in English, while dev and test data are in multiple languages. Therefore, @lhoestq and I decided offline that we will give the dataset the following API, *e.g.* for
```python
load_dataset("xglue", "ner") # would give the splits 'train', 'validation.en', 'test.en', 'validation.es', 'test.es', ...
```
=> therefore one can load a single language test via
```python
load_dataset("xglue", "ner", split="test.es")
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/801 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/801/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/801/comments | https://api.github.com/repos/huggingface/datasets/issues/801/events | https://github.com/huggingface/datasets/issues/801 | 735,790,876 | MDU6SXNzdWU3MzU3OTA4NzY= | 801 | How to join two datasets? | {
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} | [] | closed | false | null | [] | null | 3 | 2020-11-04T03:53:11 | 2020-12-23T14:02:58 | 2020-12-23T14:02:58 | NONE | null | null | null | Hi,
I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels?
I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.
Thanks! | {
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"merged_at": "2020-11-03T18:44:10"
} | Switches the label order to be more intuitive for amazon reviews, #791. | {
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https://api.github.com/repos/huggingface/datasets/issues/798 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/798/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/798/comments | https://api.github.com/repos/huggingface/datasets/issues/798/events | https://github.com/huggingface/datasets/issues/798 | 735,518,805 | MDU6SXNzdWU3MzU1MTg4MDU= | 798 | Cannot load TREC dataset: ConnectionError | {
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] | open | false | null | [] | null | 9 | 2020-11-03T17:45:22 | 2021-11-19T11:47:52 | null | NONE | null | null | null | ## Problem
I cannot load "trec" dataset, it results with ConnectionError as shown below. I've tried on both Google Colab and locally.
* `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label')` returns <Response [302]>.
* `requests.head('http://cogcomp.org/Data/QA/QC/train_5500.label', allow_redirects=True)` raises `requests.exceptions.TooManyRedirects: Exceeded 30 redirects.`
* Opening `http://cogcomp.org/Data/QA/QC/train_5500.label' in a browser works, but opens a different address
* Increasing max_redirects to 100 doesn't help
Also, while debugging I've seen that requesting 'https://storage.googleapis.com/huggingface-nlp/cache/datasets/trec/default/1.1.0/dataset_info.json' returns <Response [404]> before, but it doesn't raise any errors. Not sure if that's relevant.
* datasets.__version__ == '1.1.2'
* requests.__version__ == '2.24.0'
## Error trace
```
>>> import datasets
>>> datasets.__version__
'1.1.2'
>>> dataset = load_dataset("trec", split="train")
Using custom data configuration default
Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /home/przemyslaw/.cache/huggingface/datasets/trec/default/1.1.0/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/przemyslaw/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/home/przemyslaw/.local/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
```
I would appreciate some suggestions here. | {
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https://api.github.com/repos/huggingface/datasets/issues/797 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/797/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/797/comments | https://api.github.com/repos/huggingface/datasets/issues/797/events | https://github.com/huggingface/datasets/issues/797 | 735,420,332 | MDU6SXNzdWU3MzU0MjAzMzI= | 797 | Token classification labels are strings and we don't have the list of labels | {
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] | open | false | null | [] | null | 3 | 2020-11-03T15:33:30 | 2020-11-10T14:07:11 | null | MEMBER | null | null | null | Not sure if this is an issue we want to fix or not, putting it here so it's not forgotten. Right now, in token classification datasets, the labels for NER, POS and the likes are typed as `Sequence` of `strings`, which is wrong in my opinion. These should be `Sequence` of `ClassLabel` or some types that gives easy access to the underlying labels.
The main problem for preprocessing those datasets is that the list of possible labels is not stored inside the `Dataset` object which makes converting the labels to IDs quite difficult (you either have to know the list of labels in advance or run a full pass through the dataset to get the list of labels, the `unique` method being useless with the type `Sequence[str]`). | {
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https://api.github.com/repos/huggingface/datasets/issues/796 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/796/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/796/comments | https://api.github.com/repos/huggingface/datasets/issues/796/events | https://github.com/huggingface/datasets/issues/796 | 735,414,881 | MDU6SXNzdWU3MzU0MTQ4ODE= | 796 | Seq2Seq Metrics QOL: Bleu, Rouge | {
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] | open | false | null | [] | null | 4 | 2020-11-03T15:26:29 | 2021-01-28T14:13:48 | null | CONTRIBUTOR | null | null | null | Putting all my QOL issues here, idt I will have time to propose fixes, but I didn't want these to be lost, in case they are useful. I tried using `rouge` and `bleu` for the first time and wrote down everything I didn't immediately understand:
+ Bleu expects tokenization, can I just kwarg it like sacrebleu?
+ different signatures, means that I would have had to add a lot of conditionals + pre and post processing: if I were going to replace the `calculate_rouge` and `calculate_bleu` functions here: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/utils.py#L61
#### What I tried
Rouge experience:
```python
rouge = load_metric('rouge')
rouge.add_batch(['hi im sam'], ['im daniel']) # fails
rouge.add_batch(predictions=['hi im sam'], references=['im daniel']) # works
rouge.compute() # huge messy output, but reasonable. Not worth integrating b/c don't want to rewrite all the postprocessing.
```
BLEU experience:
```python
bleu = load_metric('bleu')
bleu.add_batch(predictions=['hi im sam'], references=['im daniel'])
bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])
bleu.add_batch(predictions=[['hi im sam']], references=[['im daniel']])
```
All of these raise `ValueError: Got a string but expected a list instead: 'im daniel'`
#### Doc Typo
This says `dataset=load_metric(...)` which seems wrong, will cause `NameError`

cc @lhoestq, feel free to ignore. | {
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https://api.github.com/repos/huggingface/datasets/issues/795 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/795/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/795/comments | https://api.github.com/repos/huggingface/datasets/issues/795/events | https://github.com/huggingface/datasets/issues/795 | 735,198,265 | MDU6SXNzdWU3MzUxOTgyNjU= | 795 | Descriptions of raw and processed versions of wikitext are inverted | {
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] | open | false | null | [] | null | 1 | 2020-11-03T10:24:51 | 2020-11-10T14:05:45 | null | NONE | null | null | null | Nothing of importance, but it looks like the descriptions of wikitext-n-v1 and wikitext-n-raw-v1 are inverted for both n=2 and n=103. I just verified by loading them and the `<unk>` tokens are present in the non-raw versions, which confirms that it's a mere inversion of the descriptions and not of the datasets themselves.
Also it would be nice if those descriptions appeared in the dataset explorer.
https://github.com/huggingface/datasets/blob/87bd0864845ea0a1dd7167918dc5f341bf807bd3/datasets/wikitext/wikitext.py#L52 | {
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https://api.github.com/repos/huggingface/datasets/issues/794 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/794/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/794/comments | https://api.github.com/repos/huggingface/datasets/issues/794/events | https://github.com/huggingface/datasets/issues/794 | 735,158,725 | MDU6SXNzdWU3MzUxNTg3MjU= | 794 | self.options cannot be converted to a Python object for pickling | {
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] | closed | false | null | [] | null | 1 | 2020-11-03T09:27:34 | 2020-11-19T17:35:38 | 2020-11-19T17:35:38 | NONE | null | null | null | Hi,
Currently I am trying to load csv file with customized read_options. And the latest master seems broken if we pass the ReadOptions object.
Here is a code snippet
```python
from datasets import load_dataset
from pyarrow.csv import ReadOptions
load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024))
```
error is `self.options cannot be converted to a Python object for pickling`
Would you mind to take a look? Thanks!
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-28-ab83fec2ded4> in <module>
----> 1 load_dataset("csv", data_files=["out.csv"], read_options=ReadOptions(block_size=16*1024*1024))
/tmp/datasets/src/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
602 hash=hash,
603 features=features,
--> 604 **config_kwargs,
605 )
606
/tmp/datasets/src/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
162 name,
163 custom_features=features,
--> 164 **config_kwargs,
165 )
166
/tmp/datasets/src/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
281 )
282 else:
--> 283 suffix = Hasher.hash(config_kwargs_to_add_to_suffix)
284
285 if builder_config.data_files is not None:
/tmp/datasets/src/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
/tmp/datasets/src/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
/tmp/datasets/src/datasets/utils/py_utils.py in dumps(obj)
365 file = StringIO()
366 with _no_cache_fields(obj):
--> 367 dump(obj, file)
368 return file.getvalue()
369
/tmp/datasets/src/datasets/utils/py_utils.py in dump(obj, file)
337 def dump(obj, file):
338 """pickle an object to a file"""
--> 339 Pickler(file, recurse=True).dump(obj)
340 return
341
~/.local/lib/python3.6/site-packages/dill/_dill.py in dump(self, obj)
444 raise PicklingError(msg)
445 else:
--> 446 StockPickler.dump(self, obj)
447 stack.clear() # clear record of 'recursion-sensitive' pickled objects
448 return
/usr/lib/python3.6/pickle.py in dump(self, obj)
407 if self.proto >= 4:
408 self.framer.start_framing()
--> 409 self.save(obj)
410 self.write(STOP)
411 self.framer.end_framing()
/usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)
474 f = self.dispatch.get(t)
475 if f is not None:
--> 476 f(self, obj) # Call unbound method with explicit self
477 return
478
~/.local/lib/python3.6/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
931 # we only care about session the first pass thru
932 pickler._session = False
--> 933 StockPickler.save_dict(pickler, obj)
934 log.info("# D2")
935 return
/usr/lib/python3.6/pickle.py in save_dict(self, obj)
819
820 self.memoize(obj)
--> 821 self._batch_setitems(obj.items())
822
823 dispatch[dict] = save_dict
/usr/lib/python3.6/pickle.py in _batch_setitems(self, items)
850 k, v = tmp[0]
851 save(k)
--> 852 save(v)
853 write(SETITEM)
854 # else tmp is empty, and we're done
/usr/lib/python3.6/pickle.py in save(self, obj, save_persistent_id)
494 reduce = getattr(obj, "__reduce_ex__", None)
495 if reduce is not None:
--> 496 rv = reduce(self.proto)
497 else:
498 reduce = getattr(obj, "__reduce__", None)
~/.local/lib/python3.6/site-packages/pyarrow/_csv.cpython-36m-x86_64-linux-gnu.so in pyarrow._csv.ReadOptions.__reduce_cython__()
TypeError: self.options cannot be converted to a Python object for pickling
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/793 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/793/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/793/comments | https://api.github.com/repos/huggingface/datasets/issues/793/events | https://github.com/huggingface/datasets/pull/793 | 735,105,907 | MDExOlB1bGxSZXF1ZXN0NTE0NTU2NzY5 | 793 | [Datasets] fix discofuse links | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-03T08:03:45 | 2020-11-03T08:16:41 | 2020-11-03T08:16:40 | MEMBER | null | false | {
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} | The discofuse links were changed: https://github.com/google-research-datasets/discofuse/commit/d27641016eb5b3eb2af03c7415cfbb2cbebe8558.
The old links are broken
I changed the links and created the new dataset_infos.json.
Pinging @thomwolf @lhoestq for notification. | {
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https://api.github.com/repos/huggingface/datasets/issues/792 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/792/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/792/comments | https://api.github.com/repos/huggingface/datasets/issues/792/events | https://github.com/huggingface/datasets/issues/792 | 734,693,652 | MDU6SXNzdWU3MzQ2OTM2NTI= | 792 | KILT dataset: empty string in triviaqa input field | {
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Both train and test splits of the triviaqa dataset (part of the KILT benchmark) seem to have empty string in their input field (unlike the natural questions dataset, part of the same benchmark)
# Versions
KILT version is `1.0.0`
`datasets` version is `1.1.2`
[more here](https://gist.github.com/PaulLerner/3768c8d25f723edbac20d99b6a4056c1)
# How to reproduce
```py
In [1]: from datasets import load_dataset
In [4]: dataset = load_dataset("kilt_tasks")
# everything works fine, removed output for a better readibility
Dataset kilt_tasks downloaded and prepared to /people/lerner/.cache/huggingface/datasets/kilt_tasks/all_tasks/1.0.0/821c4295a2c35db2847585918d9c47d7f028f1a26b78825d8e77cd3aeb2621a1. Subsequent calls will reuse this data.
# empty string in triviaqa input field
In [36]: dataset['train_triviaqa'][0]
Out[36]:
{'id': 'dpql_5197',
'input': '',
'meta': {'left_context': '',
'mention': '',
'obj_surface': {'text': []},
'partial_evidence': {'end_paragraph_id': [],
'meta': [],
'section': [],
'start_paragraph_id': [],
'title': [],
'wikipedia_id': []},
'right_context': '',
'sub_surface': {'text': []},
'subj_aliases': {'text': []},
'template_questions': {'text': []}},
'output': {'answer': ['five Β£', '5 Β£', 'Β£5', 'five Β£'],
'meta': [],
'provenance': [{'bleu_score': [1.0],
'end_character': [248],
'end_paragraph_id': [30],
'meta': [],
'section': ['Section::::Question of legal tender.\n'],
'start_character': [246],
'start_paragraph_id': [30],
'title': ['Banknotes of the pound sterling'],
'wikipedia_id': ['270680']}]}}
In [35]: dataset['train_triviaqa']['input'][:10]
Out[35]: ['', '', '', '', '', '', '', '', '', '']
# same with test set
In [37]: dataset['test_triviaqa']['input'][:10]
Out[37]: ['', '', '', '', '', '', '', '', '', '']
# works fine with natural questions
In [34]: dataset['train_nq']['input'][:10]
Out[34]:
['how i.met your mother who is the mother',
'who had the most wins in the nfl',
'who played mantis guardians of the galaxy 2',
'what channel is the premier league on in france',
"god's not dead a light in the darkness release date",
'who is the current president of un general assembly',
'when do the eclipse supposed to take place',
'what is the name of the sea surrounding dubai',
'who holds the nba record for most points in a career',
'when did the new maze runner movie come out']
```
Stay safe :) | {
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} | [] | closed | false | null | [] | null | 3 | 2020-11-02T16:42:57 | 2020-11-03T20:15:06 | 2020-11-03T16:43:57 | CONTRIBUTOR | null | false | {
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} | Adds the Amazon US Reviews dataset as requested in #353. Converted from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/amazon_us_reviews). cc @clmnt @sshleifer | {
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https://api.github.com/repos/huggingface/datasets/issues/790 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/790/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/790/comments | https://api.github.com/repos/huggingface/datasets/issues/790/events | https://github.com/huggingface/datasets/issues/790 | 734,470,197 | MDU6SXNzdWU3MzQ0NzAxOTc= | 790 | Error running pip install -e ".[dev]" on MacOS 10.13.6: faiss/python does not exist | {
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} | [] | closed | false | null | [] | null | 2 | 2020-11-02T12:36:35 | 2020-11-10T14:05:02 | 2020-11-10T14:05:02 | NONE | null | null | null | I was following along with https://huggingface.co/docs/datasets/share_dataset.html#adding-tests-and-metadata-to-the-dataset when I ran into this error.
```sh
git clone https://github.com/huggingface/datasets
cd datasets
virtualenv venv -p python3 --system-site-packages
source venv/bin/activate
pip install -e ".[dev]"
```


Python 3.7.7
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https://api.github.com/repos/huggingface/datasets/issues/789 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/789/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/789/comments | https://api.github.com/repos/huggingface/datasets/issues/789/events | https://github.com/huggingface/datasets/pull/789 | 734,237,839 | MDExOlB1bGxSZXF1ZXN0NTEzODM1MzE0 | 789 | dataset(ncslgr): add initial loading script | {
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} | Its a small dataset, but its heavily annotated
https://www.bu.edu/asllrp/ncslgr.html

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https://api.github.com/repos/huggingface/datasets/issues/788 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/788/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/788/comments | https://api.github.com/repos/huggingface/datasets/issues/788/events | https://github.com/huggingface/datasets/issues/788 | 734,136,124 | MDU6SXNzdWU3MzQxMzYxMjQ= | 788 | failed to reuse cache | {
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} | [] | closed | false | null | [] | null | 0 | 2020-11-02T02:42:36 | 2020-11-02T12:26:15 | 2020-11-02T12:26:15 | NONE | null | null | null | I packed the `load_dataset ` in a function of class, and cached data in a directory. But when I import the class and use the function, the data still have to be downloaded again. The information (Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to ******) which logged to terminal shows the path is right to the cache directory, but the files still have to be downloaded again. | {
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https://api.github.com/repos/huggingface/datasets/issues/787 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/787/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/787/comments | https://api.github.com/repos/huggingface/datasets/issues/787/events | https://github.com/huggingface/datasets/pull/787 | 734,070,162 | MDExOlB1bGxSZXF1ZXN0NTEzNjk5MTQz | 787 | Adding nli_tr dataset | {
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} | Hello,
In this pull request, we have implemented the necessary interface to add our recent dataset [NLI-TR](https://github.com/boun-tabi/NLI-TR). The datasets will be presented on a full paper at EMNLP 2020 this month. [[arXiv link] ](https://arxiv.org/pdf/2004.14963.pdf)
The dataset is the neural machine translation of SNLI and MultiNLI datasets into Turkish. So, we followed a similar format with the original datasets hosted in the HuggingFace datasets hub.
Our dataset is designed to be accessed as follows by following the interface of the GLUE dataset that provides multiple datasets in a single interface over the HuggingFace datasets hub.
```
from datasets import load_dataset
multinli_tr = load_dataset("nli_tr", "multinli_tr")
snli_tr = load_dataset("nli_tr", "snli_tr")
```
Thanks for your help in reviewing our pull request. | {
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} | [] | open | false | null | [] | null | 1 | 2020-10-31T16:52:16 | 2020-11-09T08:38:38 | null | CONTRIBUTOR | null | null | null | forking this out of #741, this issue is only regarding multiprocessing
I'd love if there was a dataset configuration parameter `workers`, where when it is `1` it behaves as it does right now, and when its `>1` maybe `_generate_examples` can also get the `pool` and return an iterable using the pool.
In my use case, I would instead of:
```python
for datum in data:
yield self.load_datum(datum)
```
do:
```python
return pool.map(self.load_datum, data)
```
As the dataset in question, as an example, has **only** 7000 rows, and takes 10 seconds to load each row on average, it takes almost 20 hours to load the entire dataset.
If this was a larger dataset (and many such datasets exist), it would take multiple days to complete.
Using multiprocessing, for example, 40 cores, could speed it up dramatically. For this dataset, hopefully to fully load in under an hour. | {
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} | For reproducibility sake, it's best if there are defined dev and test splits.
The original paper author did not define splits for the entire dataset, not for the sample loaded via this library, so I decided to define:
- 5/7th for train
- 1/7th for dev
- 1/7th for test
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} | [] | closed | false | null | [] | null | 5 | 2020-10-31T11:40:00 | 2022-02-09T17:50:16 | 2020-11-25T15:42:13 | NONE | null | null | null | Hi, I tried to download Sundanese and Javanese wikipedia data with the following snippet
```
jv_wiki = datasets.load_dataset('wikipedia', '20200501.jv', beam_runner='DirectRunner')
su_wiki = datasets.load_dataset('wikipedia', '20200501.su', beam_runner='DirectRunner')
```
And I get the following error for these two languages:
Javanese
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json
```
Sundanese
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json
```
I found from https://github.com/huggingface/datasets/issues/577#issuecomment-688435085 that for small languages, they are directly downloaded and parsed from the Wikipedia dump site, but both of `https://dumps.wikimedia.org/jvwiki/20200501/dumpstatus.json` and `https://dumps.wikimedia.org/suwiki/20200501/dumpstatus.json` are no longer valid.
Any suggestions on how to handle this issue? Thanks! | {
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} | When you call `del` on a metric we want to make sure that the arrow attributes are not already deleted.
I just added `if hasattr(...)` to make sure it doesn't crash | {
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} | I added the train set that was built using the translated MNLI.
Now you can load the dataset specifying one language:
```python
from datasets import load_dataset
xnli_en = load_dataset("xnli", "en")
print(xnli_en["train"][0])
# {'hypothesis': 'Product and geography are what make cream skimming work .', 'label': 1, 'premise': 'Conceptually cream skimming has two basic dimensions - product and geography .'}
print(xnli_en["test"][0])
# {'hypothesis': 'I havent spoken to him again.', 'label': 2, 'premise': "Well, I wasn't even thinking about that, but I was so frustrated, and, I ended up talking to him again."}
```
Cc @sgugger | {
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} | This pull request adds the ASNQ dataset. It is a dataset for answer sentence selection derived from Google Natural Questions (NQ) dataset (Kwiatkowski et al. 2019). The dataset details can be found in the paper at https://arxiv.org/abs/1911.04118
The dataset is authored by Siddhant Garg, Thuy Vu and Alessandro Moschitti.
_Please note that I have no affiliation with the authors._
Repo: https://github.com/alexa/wqa_tanda
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} | [] | closed | false | null | [] | null | 2 | 2020-10-29T16:06:10 | 2020-10-29T21:21:27 | 2020-10-29T21:21:27 | CONTRIBUTOR | null | null | null | I read a csv file from disk and forgot so specify the right delimiter. When i read the csv file again specifying the right delimiter it had no effect since it was using the cached dataset. I am not sure if this is unwanted behavior since i can always specify `download_mode="force_redownload"`. But i think it would be nice if the information what `delimiter` or what `column_names` were used would influence the identifier of the cached dataset.
Small snippet to reproduce the behavior:
```python
import datasets
with open("dummy_data.csv", "w") as file:
file.write("test,this;text\n")
print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train").column_names)
# ["test", "this;text"]
print(datasets.load_dataset("csv", data_files="dummy_data.csv", split="train", delimiter=";").column_names)
# still ["test", "this;text"]
```
By the way, thanks a lot for this amazing library! :) | {
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https://api.github.com/repos/huggingface/datasets/issues/777 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/777/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/777/comments | https://api.github.com/repos/huggingface/datasets/issues/777/events | https://github.com/huggingface/datasets/pull/777 | 732,376,648 | MDExOlB1bGxSZXF1ZXN0NTEyMzI2ODM2 | 777 | Better error message for uninitialized metric | {
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} | When calling `metric.compute()` without having called `metric.add` or `metric.add_batch` at least once, the error was quite cryptic. I added a better error message
Fix #729 | {
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} | [] | closed | false | null | [] | null | 1 | 2020-10-29T14:04:06 | 2020-10-30T13:46:45 | 2020-10-30T13:23:52 | MEMBER | null | false | {
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} | The `text` dataset used to return only splits like train, test and validation. Other splits were ignored.
Now any split name is allowed.
I did the same for `json`, `pandas` and `csv`
Fix #735 | {
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