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MDU6SXNzdWU3NDgxOTQzMTE=

bug in boolq dataset loading

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDgxOTMxNDA=

trec dataset unavailable

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDc5NTk1MjM=

load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error

closed

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``` 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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MDExOlB1bGxSZXF1ZXN0NTI0ODM4NjEx

Add IndicGLUE dataset and Metrics

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDc0NzAxMzY=

terminate called after throwing an instance of 'google::protobuf::FatalException'

closed

CONTRIBUTOR

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%|████████████████████████████████████████████████████████████████████████████████████████████████████| 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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MDU6SXNzdWU3NDcwMjE5OTY=

[Feature Request] Add optional parameter in text loading script to preserve linebreaks

open

NONE

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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MDExOlB1bGxSZXF1ZXN0NTIzODc3OTkw

Update ner datasets infos

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTIzMzc2MzQ3

Consistent metric outputs

open

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTIzMjc4MjI4

Fix some metrics feature types

closed

MEMBER

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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MDU6SXNzdWU3NDU3MTkyMjI=

OSCAR from Inria group

closed

NONE

## 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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MDU6SXNzdWU3NDU0MzA0OTc=

Have Trouble importing `datasets`

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDUzMjIzNTc=

Unable to download cnn_dailymail dataset

closed

NONE

### Script to reproduce the error ``` 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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MDExOlB1bGxSZXF1ZXN0NTIyNTk0Mjg1

Add clear_cache parameter in the test command

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTIyNTUzMzY1

Update head requests

closed

MEMBER

Get requests and Head requests didn't have the same parameters.

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MDU6SXNzdWU3NDQ3NTM0NTg=

Possible Bug: Small training/dataset file creates gigantic output

closed

NONE

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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MDU6SXNzdWU3NDQ3NTA2OTE=

wmt16 cs-en does not donwload

open

CONTRIBUTOR

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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https://github.com/huggingface/datasets/pull/859

MDExOlB1bGxSZXF1ZXN0NTIxNzI4MDM4

Integrate file_lock inside the lib for better logging control

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTIxNzE3ODQ4

Add SemEval-2010 task 8

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NTIxNjg0ODIx

Use pandas reader in csv

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTIxNjMzNTYz

Add open book corpus

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NTIxNTQ2Njkx

Fix kor nli csv reader

closed

MEMBER

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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MDU6SXNzdWU3NDM2NzUzNzY=

wmt16 does not download

open

CONTRIBUTOR

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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MDU6SXNzdWU3NDM0MjY1ODM=

concatenate_datasets support axis=0 or 1 ？

closed

NONE

I want to achieve the following result 

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MDU6SXNzdWU3NDMzOTYyNDA=

wmt cannot be downloaded

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDMzNDMyNzg=

Add support for other languages for rouge

open

NONE

I calculate rouge with ``` from datasets import load_metric rouge = load_metric("rouge") rouge_output = rouge.compute(predictions=['тест тест привет'], references=['тест тест пока'], 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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MDExOlB1bGxSZXF1ZXN0NTIwNTE0MDY3

Create ClassLabel for labelling tasks datasets

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDIyNjMzMzM=

Load amazon dataset

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDIyNDA5NDI=

Error when concatenate_datasets

closed

NONE

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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MDU6SXNzdWU3NDIxNzk0OTU=

multiprocessing in dataset map "can only test a child process"

open

NONE

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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MDU6SXNzdWU3NDE4ODUxNzQ=

Add HoVer multi-hop fact verification dataset

closed

MEMBER

## 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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MDExOlB1bGxSZXF1ZXN0NTIwMDg1NDMy

amazon description fields as bullets

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NTIwMDgwNzM5

add newlines to amazon desc

closed

CONTRIBUTOR

Just a quick formatting fix to hopefully make it render nicer on Viewer

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MDU6SXNzdWU3NDE1MzExMjE=

use_custom_baseline still produces errors for bertscore

closed

NONE

`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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MDU6SXNzdWU3NDEyMDg0Mjg=

How to enable `.map()` pre-processing pipelines to support multi-node parallelism?

open

NONE

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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MDU6SXNzdWU3NDA3Mzc0NDg=

Can not reuse datasets already downloaded

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NTE5MDg2NDUw

Update squad_v2.py

closed

CONTRIBUTOR

Change lines 100 and 102 to prevent overwriting ```predictions``` variable.

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MDU6SXNzdWU3NDAzNTUyNzA=

XSum dataset missing spaces between sentences

open

NONE

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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MDExOlB1bGxSZXF1ZXN0NTE4ODM0NTE5

CNN/Dailymail Dataset Card

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NTE4NzcwNDM5

AlloCiné dataset card

closed

CONTRIBUTOR

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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MDU6SXNzdWU3NDAxODc2MTM=

load_dataset with 'csv' is not working. while the same file is loading with 'text' mode or with pandas

closed

NONE

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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MDU6SXNzdWU3NDAxMDIyMTA=

Wikipedia postprocessing

closed

NONE

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 Magón mini|Mexikanische Revolutionäre, Magón in der Mitte anführend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des Gemälde „Tierra y Libertad“ von Idelfonso Carrara (?) von 1930. Ricardo Flores Magón (* 16. September 1874 in San Antonio Eloxochitlán im mexikanischen Bundesstaat Oaxaca; † 22. November 1922 im Bundesgefängnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein führender anarchistischer Theoretiker und Aktivist, der die revolutionäre mexikanische Bewegung radikal beeinflusste. Magón war Gründer der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World. Politische Biografie Journalistisch und politisch kämpfte 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 „Land und Freiheit“ (Tierra y Libertad) populär. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte großen Einfluss auf die Landarbeiter. 1904 floh er in die USA und gründete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in Gefängnissen und im Exil und wurde 1918 in den USA wegen „Behinderung der Kriegsanstrengungen“ zu zwanzig Jahren Gefängnis 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 Magón von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM veröffentlichte 1923 einen Beitrag, nachdem Magón von einem Gefängniswärter erschlagen wurde. mini|Die Brüder Ricardo (links) und Enrique Flores Magón (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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MDU6SXNzdWU3NDAwODI4OTA=

[GEM] add WikiLingua cross-lingual abstractive summarization dataset

closed

MEMBER

## 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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[GEM] add ASSET text simplification dataset

closed

MEMBER

## 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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MDU6SXNzdWU3NDAwNzcyMjg=

[GEM] add WikiAuto text simplification dataset

closed

MEMBER

## 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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[GEM] Add WebNLG dataset

closed

MEMBER

## 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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[GEM] add ToTTo Table-to-text dataset

closed

MEMBER

## 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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[GEM] add Schema-Guided Dialogue

closed

MEMBER

## 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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MDExOlB1bGxSZXF1ZXN0NTE4NTcwMjY3

Add writer_batch_size attribute to GeneratorBasedBuilder

closed

MEMBER

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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[GEM] MultiWOZ dialogue dataset

open

MEMBER

## 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 – 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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[GEM] Add E2E dataset

closed

MEMBER

## 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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MDExOlB1bGxSZXF1ZXN0NTE4NTAyNjgx

Add accuracy, precision, recall and F1 metrics

closed

CONTRIBUTOR

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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MDU6SXNzdWU3Mzk4OTY1MjY=

Discussion using datasets in offline mode

open

NONE

`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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MDU6SXNzdWU3Mzk4MTU3NjM=

how processing in batch works in datasets

closed

NONE

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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MDU6SXNzdWU3Mzk1NzkzMTQ=

datasets freezes

open

NONE

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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MDU6SXNzdWU3Mzk1MDY4NTk=

`kor_nli` dataset doesn't being loaded properly

closed

NONE

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': '그는 전쟁 전에 가벼운 벅스킨 암말을 가지고 달리기 위해 우유처럼 하얀 스터드를 넣었다.\t전쟁 전에 다인종 여성들과 함께 있는 백인 남자가 있었다.\tentailment\n슬림은 재빨리 옷을 입었고, 순간적으로 미지근한 물을 뿌릴 수 있는 아침 세탁물을 기꺼이 가두었다.\t슬림은 직장에 늦었다.\tneutral\n뉴욕에서 그 식사를 해봤는데, 거기서 소고기의 멋진 소고기 부분을 요리하고 바베큐로 만든 널빤지 같은 걸 가져왔는데, 정말 대단해.\t그들이 거기서 요리하는 쇠고기는 역겹다. 거기서 절대 먹지 마라.\tcontradiction\n판매원의 죽음에서 브라이언 데네히... 크리스 켈리\t크리스 켈리는 세일즈맨의 죽음을 언급하지 않는다.\tcontradiction\n그러는 동안 요리사는 그냥 화가 났어.\t스튜가 끓는 동안 요리사는 화가 났다.\tneutral\n마지막 로마의 맹공격 전날 밤, 900명 이상의 유대인 수비수들이 로마인들에게 그들을 사로잡는 승리를 주기 보다는 대량 자살을 저질렀다.\t로마인들이 그들의 포획에 승리하도록 내버려두기 보다는 900명의 유대인 수비수들이 자살했다.\tentailment\n앞으로 발사하라.\t발사.\tneutral\n그리고 당신은 우리 땅이 에이커에 있다는 것을 알고 있다. 우리 사람들은 어떤 것이 얼마나 많은지 이해하지 못할 것이다.\t모든 사람들은 우리의 측정 시스템이 어떻게 작동하는지 알고 이해합니다.\tcontradiction\n주미게스\tJumiyges는 도시의 이름이다.\tneutral\n사람은 자기 민족을 돌봐야 한다...\t사람은 조국에 공감해야 한다.\tentailment\n또한 PDD 63은 정부와 업계가 컴퓨터 기반 공격에 대해 경고하고 방어할 준비를 더 잘할 수 있도록 시스템 취약성, 위협, 침입 및 이상에 대한 정보를 공유하는 메커니즘을 수립하는 것이 중요하다는 것을 인식했습니다.\t정보 전송 프로토콜을 만드는 것은 중요하다.\tentailment\n카페 링 피아자 델라 레퓌블리카 바로 남쪽에는 피렌체가 알려진 짚 제품 때문에 한때 스트로 마켓이라고 불렸던 16세기 로지아인 메르카토 누오보(Mercato Nuovo)가 있다.\t피아자 델라 레퓌블리카에는 카페가 많이 있다.\tentailment\n우리가 여기 있는 한 트린판이 뭘 주웠는지 살펴봐야겠어\t우리는 트린판이 무엇을 주웠는지 보는 데 시간을 낭비하지 않을 것이다.\tcontradiction\n그러나 켈트족의 문화적 기반을 가진 아일랜드 교회는 유럽의 신흥 기독교 세계와는 다르게 발전했고 결국 로마와 중앙집권적 행정으로 대체되었다.\t아일랜드 교회에는 켈트족의 기지가 있었다.\tentailment\n글쎄, 넌 선택의 여지가 없어\t글쎄, 너에겐 많은 선택권이 있어.\tcontradiction\n사실, 공식적인 보장은 없다.\t내가 산 물건에 대한 보증이 없었다.\tneutral\n덜 활기차긴 하지만, 안시와 르 부르젯의 사랑스러운 호수에서도 삶은 똑같이 상쾌하다.\t안시와 르 부르겟에서는 호수에서의 활동이 서두르고 바쁜 분위기를 연출한다.\tcontradiction\n그의 여행 소식이 이미 퍼졌다면 공격 소식도 퍼졌을 테지만 마을에서는 전혀 공황의 기미가 보이지 않았다.\t그는 왜 마을이 당황하지 않았는지 알 수 없었다.\tneutral\n과거에는 죽음의 위협이 토지의 판매를 막는 데 거의 도움이 되지 않았다.\t토지 판매는 어떠한 위협도 교환하지 않고 이루어진다.\tcontradiction\n어느 시점에 이르러 나는 지금 다가오는 새로운 것들과 나오는 많은 새로운 것들이 내가 늙어가고 있다고 말하는 시대로 접어들고 있다.\t나는 여전히 내가 보는 모든 새로운 것을 사랑한다.\tcontradiction\n뉴스위크는 물리학자들이 경기장 행사에서 고속도로의 자동차 교통과 보행자 교통을 개선하기 위해 새떼의 움직임을 연구하고 있다고 말한다.\t고속도로의 자동차 교통 흐름을 개선하는 것은 물리학자들이 새떼를 연구하는 이유 중 하나이다.\tentailment\n얼마나 다른가? 그는 잠시 말을 멈추었다가 말을 이었다.\t그는 그 소녀가 어디에 있는지 알고 싶었다.\tentailment\n글쎄, 그에게 너무 많은 것을 주지마.\t그는 훨씬 더 많은 것을 요구할 것이다.\tneutral\n아무리 그의 창작물이 완벽해 보인다고 해도, 그들을 믿는 것은 아마도 좋은 생각이 아닐 것이다.\'\t도자기를 잘 만든다고 해서 누군가를 믿는 것은 아마 좋지 않을 것이다.\tneutral\n버스틀링 그란 비아(Bustling Gran Via)는 호텔, 상점, 극장, 나이트클럽, 카페 등이 어우러져 산책과 창가를 볼 수 있다.\tGran Via는 호텔, 상점, 극장, 나이트클럽, 카페의 번화한 조합이다.\tentailment\n정부 인쇄소\t그 사무실은 워싱턴에 위치해 있다.\tneutral\n실제 문화 전쟁이 어디 있는지 알고 싶다면 학원을 잊어버리고 실리콘 밸리와 레드몬드를 생각해 보라.\t실제 문화 전쟁은 레드몬드에서 일어난다.\tentailment\n그리고 페니실린을 주지 않기 위해 침대 위에 올려놨어\t그녀의 방에는 페니실린이 없다는 징후가 전혀 없었다.\tcontradiction\nL.A.의 야외 시장을 활보하는 것은 맛있고 저렴한 그루브를 잡고, 끝이 없는 햇빛을 즐기고, 신선한 농산물, 꽃, 향, 그리고 가젯 갈로어를 구입하면서 현지인들과 어울릴 수 있는 훌륭한 방법이다.\tLA의 야외 시장을 돌아다니는 것은 시간 낭비다.\tcontradiction\n안나는 밖으로 나와 안도의 한숨을 내쉬었다. 단 한 번, 그리고 마리후아쉬 맛의 술로 끝내자는 결심이 뒤섞여 있었다.\t안나는 안심하고 마리후아쉬 맛의 술을 다 마시기로 결심했다.\tentailment\n5 월에 Vajpayee는 핵 실험의 성공적인 완료를 발표했는데, 인도인들은 주권의 표시로 선전했지만 이웃 국가와 서구와의 인도 관계를 복잡하게 만들 수 있습니다.\t인도는 성공적인 핵실험을 한 적이 없다.\tcontradiction\n플라노 원에서 보통 얼마나 많은 것을 가지고 있는가?\t저 사람들 중에 플라노 원에 가본 사람 있어?\tcontradiction\n그것의 전체적인 형태의 우아함은 운하 건너편에서 가장 잘 볼 수 있다. 왜냐하면, 로마에 있는 성 베드로처럼, 돔은 길쭉한 본당 뒤로 더 가까운 곳에 사라지기 때문이다.\t성 베드로의 길쭉한 본당은 돔을 가린다.\tentailment\n당신은 수틴이 살에 강박적인 기쁨을 가지고 누드를 그릴 것이라고 생각하겠지만, 아니오; 그는 그의 모든 경력에서 단 한 점만을 그렸고, 그것은 사소한 그림이다.\t그는 그것이 그를 불편하게 만들었기 때문에 하나만 그렸다.\tneutral\n이 인상적인 풍경은 원래 나포 레온이 루브르 박물관의 침실에서 볼 수 있도록 계획되었는데, 그 당시 궁전이었습니다.\t나폴레옹은 그의 모든 궁전에 있는 그의 침실에서 보는 경치에 많은 관심을 가졌다.\tneutral\n그는 우리에게 문 열쇠를 건네주고는 급히 떠났다.\t그는 긴장해서 우리에게 열쇠를 빨리 주었다.\tneutral\n위원회는 또한 최종 규칙을 OMB에 제출했다.\t위원회는 또한 이 규칙을 다른 그룹에 제출했지만 최종 규칙은 OMB가 평가하기 위한 것이 었습니다.\tneutral\n정원가게에 가보면 올리비아의 복제 화합물 같은 유쾌한 이름을 가진 제품들을 찾을 수 있을 겁니다.이 제품이 뿌리를 내리도록 돕기 위해 촬영의 절단된 끝에 덩크슛을 하는 호르몬의 혼합물이죠.\t정원 가꾸기 가게의 제품들은 종종 그들의 목적을 설명하기 위해 기술적으로나 과학적으로 파생된 이름(올리비아의 복제 화합물처럼)을 부여받는다.\tneutral\n스타는 스틸 자신이나 왜 그녀의 이야기를 바꾸었는지에 훨씬 더 관심이 있을 것이다.\t스틸의 이야기는 조금도 변하지 않았다.\tcontradiction\n남편과의 마지막 대결로 맥티어는 노라의 변신을 너무나 능숙하게 예고해 왔기 때문에, 그녀에게는 당황스러울 정도로 갑작스러운 것처럼 보이지만, 우리에게는 감정적으로 불가피해 보인다.\t노라의 변신은 분명하고 필연적이었다.\tcontradiction\n이집트 최남단 도시인 아스완은 오랜 역사를 통해 중요한 역할을 해왔다.\t아스완은 이집트 국경 바로 위에 위치해 있습니다.\tneutral\n그러나 훨씬 더 우아한 건축적 터치는 신성한 춤인 Bharatanatyam에서 수행된 108 가지 기본 포즈를 시바 패널에서 볼 수 있습니다.\t패널에 대한 시바의 묘사는 일반적인 모티브다.\tneutral\n호화롭게 심어진 계단식 정원은 이탈리아 형식의 가장 훌륭한 앙상블 중 하나입니다.\t아름다운 정원과 희귀한 꽃꽂이 모두 이탈리아의 형식적인 스타일을 보여준다.\tneutral\n음, 그랬으면 좋았을 텐데\t나는 그것을 다르게 할 기회를 몹시 갈망한다.\tentailment\n폐허가 된 성의 기슭에 자리잡고 있는 예쁜 중세 도시 케이서스버그는 노벨 평화상 수상자 알버트 슈바이처(1875년)의 출생지로 널리 알려져 있다.\t알버트 슈바이처는 둘 다 케이서스버그 마을에 있었다.\tentailment\n고감도는 문제가 있는 대부분의 환자들이 발견될 것을 보장한다.\t장비 민감도는 문제 탐지와 관련이 없습니다.\tcontradiction\n오늘은 확실히 반바지 같은 날이었어\t오늘 사무실에 있는 모든 사람들은 반바지를 입었다.\tneutral\n못생긴 턱시도를 입고.\t그것은 분홍색과 주황색입니다.\tneutral\n이주 노동 수용소 오 마이 갓 그들은 판지 상자에 산다.\t노동 수용소에는 판지 상자에 사는 이주 노동자들의 사진이 있다.\tneutral\n그래, 그가 전 세계를 여행한 후에 그런 거야\t그것은 사람들의 세계 여행을 따른다.\tentailment\n건너편에 크고 큰 참나무 몇 그루가 있다.\t우리는 여기 오크나 어떤 종류의 미국 나무도 없다.\tcontradiction\nFort-de-France에서 출발하는 자동차나 여객선으로, 당신은 안세 ? 바다 포도가 그늘을 제공하는 쾌적한 갈색 모래 해변과 피크닉 테이블, 어린이 미끄럼틀, 식당이 있는 안느에 도착할 수 있다.\t프랑스 요새에서 자동차나 페리를 타고 안세로 갈 수 있다.\tentailment\n그리고 그것은 앨라배마주가 예상했던 대로 예산에서 50만 달러를 삭감하지 않을 것이라는 것을 의미한다.\t앨라배마 주는 예산 삭감을 하지 않았다. 왜냐하면 그렇게 하는 것에 대한 초기 정당성이 정밀 조사에 맞서지 않았기 때문이다.\tneutral\n알았어 먼저 어 .. 어 .. 노인이나 가족을 요양원에 보내는 것에 대해 어떻게 생각하니?\t가족을 요양원에 보내서 사는 것에 대해 어떻게 생각하는지 알 필요가 없다.\tcontradiction\n나머지는 너에게 달렸어.\t나머지는 너에게 달렸지만 시간이 많지 않다.\tneutral\n음-흠, 3월에 햇볕에 타는 것에 대해 걱정하면 안 된다는 것을 알고 있는 3월이야.\t3월은 그렇게 덥지 않다.\tneutral\n그리고 어, 그런 작은 것들로 다시 시작해봐. 아직 훨씬 싸. 어, 그 특별한 모델 차는 150달러야.\t그 모형차는 4천 달러가 든다.\tcontradiction\n내일 돌아가야 한다면, 칼이 말했다.\t돌아갈 수 없어. 오늘은 안 돼. 내일은 안 돼. 절대 안 돼." 칼이 말했다.', 'sentence2': 'contradiction'} ``` 2. (Optional) Preferred to change the name of the features for the compatibility with `run_glue.py` in 🤗 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://github.com/huggingface/datasets/pull/820

MDExOlB1bGxSZXF1ZXN0NTE4MDYwMjQ0

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://github.com/huggingface/datasets/pull/819

MDExOlB1bGxSZXF1ZXN0NTE3OTQ2MjYy

Make save function use deterministic global vars order

closed

MEMBER

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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MDExOlB1bGxSZXF1ZXN0NTE3ODgzMzk0

Fix type hints pickling in python 3.6

closed

MEMBER

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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MDU6SXNzdWU3MzkxNDUzNjk=

Add MRQA dataset

closed

MEMBER

## Adding a Dataset - **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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MDU6SXNzdWU3MzkxMDI2ODY=

[Caching] Dill globalvars() output order is not deterministic and can cause cache issues.

closed

MEMBER

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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MDU6SXNzdWU3Mzg4NDIwOTI=

Is dataset iterative or not?

closed

NONE

Hi 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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MDU6SXNzdWU3Mzg1MDA0NDM=

Joining multiple datasets

closed

NONE

Hi 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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MDU6SXNzdWU3Mzg0ODk4NTI=

How to implement DistributedSampler with datasets

open

NONE

Hi, 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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MDU6SXNzdWU3MzgzNDAyMTc=

Too much logging

closed

NONE

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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MDU6SXNzdWU3MzgyODAxMzI=

nlp viewer error

open

NONE

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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MDExOlB1bGxSZXF1ZXN0NTE2ODQzMzQ3

Fix seqeval metric

closed

MEMBER

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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MDU6SXNzdWU3Mzc4MzI3MDE=

Add Google Taskmaster dataset

closed

MEMBER

## 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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MDExOlB1bGxSZXF1ZXN0NTE2NjQ0NDc0

dataset(dgs): initial dataset loading script

closed

CONTRIBUTOR

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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MDU6SXNzdWU3Mzc1MDk5NTQ=

load_dataset for LOCAL CSV files report CONNECTION ERROR

closed

NONE

## 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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MDU6SXNzdWU3MzcyMTU0MzA=

Quail dataset urls are out of date

closed

CONTRIBUTOR

<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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MDU6SXNzdWU3MzcwMTkzNjA=

On loading a metric from datasets, I get the following error

open

NONE

`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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MDU6SXNzdWU3MzY4NTg1MDc=

Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa')

closed

CONTRIBUTOR

# 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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MDExOlB1bGxSZXF1ZXN0NTE1OTY1ODE2

fix: typos in tutorial to map KILT and TriviaQA

closed

CONTRIBUTOR

https://api.github.com/repos/huggingface/datasets/issues/803/timeline

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https://github.com/huggingface/datasets/pull/802

MDExOlB1bGxSZXF1ZXN0NTE1NTM1MDI0

Add XGlue

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MEMBER

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://github.com/huggingface/datasets/issues/801

MDU6SXNzdWU3MzU3OTA4NzY=

How to join two datasets?

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NONE

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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https://github.com/huggingface/datasets/pull/800

MDExOlB1bGxSZXF1ZXN0NTE1MTAyMjc3

Update loading_metrics.rst

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CONTRIBUTOR

Minor bug

https://api.github.com/repos/huggingface/datasets/issues/800/timeline

https://api.github.com/repos/huggingface/datasets/issues/799