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I'm having issue with loading a dataset with a custom `cache_dir`. Despite specifying the output dir, it is still downloaded to
`~/.cache`.
## Environment info
- `datasets` version: 1.1.3
- Platform: Linux-4.19.129-aufs-1-x86_64-with-debian-10.1
- Python version: 3.7.3
## The code I'm running:
```python
import datasets
from pathlib import Path
validation_dataset = datasets.load_dataset("natural_questions", split="validation[:5%]", cache_dir=Path("./data"))
```
## The output:
* The dataset is downloaded to my home directory's `.cache`
* A new empty directory named "`natural_questions` is created in the specified directory `.data`
* `tree data` in the shell outputs:
```
data
βββ natural_questions
βββ default
βββ 0.0.2
3 directories, 0 files
```
The output:
```
Downloading: 8.61kB [00:00, 5.11MB/s]
Downloading: 13.6kB [00:00, 7.89MB/s]
Using custom data configuration default
Downloading and preparing dataset natural_questions/default (download: 41.97 GiB, generated: 92.95 GiB, post-processed: Unknown size, total: 134.92 GiB) to ./data/natural_questions/default/0.0.2/867dbbaf9137c1b8
3ecb19f5eb80559e1002ea26e702c6b919cfa81a17a8c531...
Downloading: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββ| 13.6k/13.6k [00:00<00:00, 1.51MB/s]
Downloading: 7%|ββββ | 6.70G/97.4G [03:46<1:37:05, 15.6MB/s]
```
## Expected behaviour:
The dataset "Natural Questions" should be downloaded to the directory "./data"
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Seems like automatic dummy-data generation doesn't work if the builder is a `ArrowBasedBuilder`, do you think you could take a look @lhoestq ? | {
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] | closed | false | null | [] | null | [] | 2020-11-27T09:14:34Z | 2021-10-31T09:12:01Z | 2021-10-31T09:12:01Z | CONTRIBUTOR | null | null | null | I was looking through the dataset viewer and I like it a lot. Version numbers, citation information, everything's there! I've spotted a few issues/bugs though:
- the URL is still under `nlp`, perhaps an alias for `datasets` can be made
- when I remove a **feature** (and the feature list is empty), I get an error. This is probably expected, but perhaps a better error message can be shown to the user
```bash
IndexError: list index out of range
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 316, in <module>
st.table(style)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 122, in wrapped_method
return dg._enqueue_new_element_delta(marshall_element, delta_type, last_index)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 367, in _enqueue_new_element_delta
rv = marshall_element(msg.delta.new_element)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 120, in marshall_element
return method(dg, element, *args, **kwargs)
File "/home/sasha/streamlit/lib/streamlit/DeltaGenerator.py", line 2944, in table
data_frame_proto.marshall_data_frame(data, element.table)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 54, in marshall_data_frame
_marshall_styles(proto_df.style, df, styler)
File "/home/sasha/streamlit/lib/streamlit/elements/data_frame_proto.py", line 73, in _marshall_styles
translated_style = styler._translate()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/pandas/io/formats/style.py", line 351, in _translate
* (len(clabels[0]) - len(hidden_columns))
```
- there seems to be **an encoding issue** in the default view, the dataset examples are shown as raw monospace text, without a decent encoding. That makes it hard to read for languages that use a lot of special characters. Take for instance the [cs-en WMT19 set](https://huggingface.co/nlp/viewer/?dataset=wmt19&config=cs-en). This problem goes away when you enable "List view", because then some syntax highlighteris used, and the special characters are coded correctly.
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} | This PR adds a template for dataset cards, as well as a guide to filling out the template and a completed example for the ELI5 dataset, building on the work of @mcmillanmajora
New pull requests adding datasets should now have a README.md file which serves both to hold the tags we will have to index the datasets and as a data statement.
The template is designed to be pretty extensive. The idea is that the person who uploads the dataset should put in all the basic information (at least the Dataset Description section) and whatever else they feel comfortable adding and leave the `[More Information Needed]` annotation everywhere else as a placeholder.
We will then work with @mcmillanmajora to involve the data authors more directly in filling out the remaining information.
Direct links to:
- [Documentation](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README_guide.md)
- [Empty template](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/templates/README.md)
- [ELI5 example](https://github.com/yjernite/datasets/blob/add_dataset_card_doc/datasets/eli5/README.md) | {
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} | I made explicit the error message when a bad split name is used.
Also I wanted to allow the `-` symbol for split names but actually this symbol is used to name the arrow files `{dataset_name}-{dataset_split}.arrow` so we should probably keep it this way, i.e. not allowing the `-` symbol in split names. Moreover in the future we might want to use `{dataset_name}-{dataset_split}-{shard_id}_of_{n_shards}.arrow` and reuse the `-` symbol. | {
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} | Hi !
Currently we have three dataset cards : snli, cnn_dailymail and allocine.
For each one of those datasets a set of tag is defined. The set of tags contains fields like `multilinguality`, `task_ids`, `licenses` etc.
For certain datasets like `glue` for example, there exist several configurations: `sst2`, `mnli` etc. Therefore we should define one set of tags per configuration. However the current format used for tags only supports one set of tags per dataset.
In this PR I propose a simple change in the yaml format used for tags to allow for several sets of tags.
Let me know what you think, especially @julien-c let me know if it's good for you since it's going to be parsed by moon-landing | {
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} | I started making a small list of various datasets of reference in the documentation.
Since many datasets share a lot in common I think it's good to have a list of datasets scripts to get some inspiration from.
Let me know what you think, and if you have ideas of other datasets that we may add to this list, please let me know. | {
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} | This PR adds a `DEFAULT_CONFIG_NAME` class attribute to `DatasetBuilder`. This allows a dataset to have a specified default config name when a dataset has more than one config but the user does not specify it. For example, after defining `DEFAULT_CONFIG_NAME = "combined"` in PolyglotNER, a user can now do the following:
```python
ds = load_dataset("polyglot_ner")
```
which is equivalent to,
```python
ds = load_dataset("polyglot_ner", "combined")
```
In effect (for this particular dataset configuration), this means that if the user doesn't specify a language, they are given the combined dataset including all languages.
Since it doesn't always make sense to have a default config, this feature is opt-in. If `DEFAULT_CONFIG_NAME` is not defined and a user does not pass a config for a dataset with multiple configs available, a ValueError is raised like usual.
Let me know what you think about this approach @lhoestq @thomwolf and I'll add some documentation and define a default for some of our existing datasets. | {
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```python
"top": datasets.features.Sequence({
"middle": datasets.features.Sequence({
"bottom": datasets.Value("int32")
})
})
```
And I then create an example:
```python
yield 1, {
"top": [{
"middle": [
{"bottom": 1},
{"bottom": 2}
]
}]
}
```
I then load my dataset:
```python
train = load_dataset("my dataset")["train"]
```
and expect to be able to access `data[0]["top"][0]["middle"][0]`.
That is not the case. Here is `data[0]` as JSON:
```json
{"top": {"middle": [{"bottom": [1, 2]}]}}
```
Clearly different than the thing I inputted.
```json
{"top": [{"middle": [{"bottom": 1},{"bottom": 2}]}]}
``` | {
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] | open | false | null | [] | null | [] | 2020-11-25T14:32:21Z | 2021-09-09T17:03:40Z | null | CONTRIBUTOR | null | null | null | I set up a new dataset, with a sequence of arrays (really, I want to have an array of (None, 137, 2), and the first dimension is dynamic)
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(
{
"pose": datasets.features.Sequence(datasets.features.Array2D(shape=(137, 2), dtype="float32"))
}
),
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _generate_examples(self):
""" Yields examples. """
yield 1, {
"pose": [np.zeros(shape=(137, 2), dtype=np.float32)]
}
```
But this doesn't work -
> pyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type> | {
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} | It should be possible to use the wikipedia dataset with any `language` and `date`.
However it was not working as noticed in #784 . Indeed the custom wikipedia configurations were not enabled for some reason.
I fixed that and was able to run
```python
from datasets import load_dataset
load_dataset("./datasets/wikipedia", language="zh", date="20201120", beam_runner='DirectRunner')
```
cc @stvhuang @SamuelCahyawijaya
Fix #784 | {
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] | closed | false | null | [] | null | [] | 2020-11-25T12:47:58Z | 2021-01-13T11:31:25Z | 2021-01-13T11:31:25Z | CONTRIBUTOR | null | null | null | Hi,
I expect when importing `datasets` that nothing major happens in the background, and so the import should be insignificant.
When I load a metric, or a dataset, its fine that it takes time.
The following ranges from 3 to 9 seconds:
```
python -m timeit -n 1 -r 1 'from datasets import load_dataset'
```
edit:
sorry for the mis-tag, not sure how I added it. | {
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} | When adding a new dataset to the library, dummy data creation can take some time.
To make things easier I added a command line tool that automatically generates dummy data when possible.
The tool only supports certain data files types: txt, csv, tsv, jsonl, json and xml.
Here are some examples:
```
python datasets-cli dummy_data ./datasets/snli --auto_generate
python datasets-cli dummy_data ./datasets/squad --auto_generate --json_field data
python datasets-cli dummy_data ./datasets/iwslt2017 --auto_generate --xml_tag seg --match_text_files "train*" --n_lines 15
# --xml_tag seg => each sample corresponds to a "seg" tag in the xml tree
# --match_text_files "train*" => also match text files that don't have a proper text file extension (no suffix like ".txt" for example)
# --n_lines 15 => some text files have headers so we have to use at least 15 lines
```
and here is the command usage:
```
usage: datasets-cli <command> [<args>] dummy_data [-h] [--auto_generate]
[--n_lines N_LINES]
[--json_field JSON_FIELD]
[--xml_tag XML_TAG]
[--match_text_files MATCH_TEXT_FILES]
[--keep_uncompressed]
[--cache_dir CACHE_DIR]
path_to_dataset
positional arguments:
path_to_dataset Path to the dataset (example: ./datasets/squad)
optional arguments:
-h, --help show this help message and exit
--auto_generate Try to automatically generate dummy data
--n_lines N_LINES Number of lines or samples to keep when auto-
generating dummy data
--json_field JSON_FIELD
Optional, json field to read the data from when auto-
generating dummy data. In the json data files, this
field must point to a list of samples as json objects
(ex: the 'data' field for squad-like files)
--xml_tag XML_TAG Optional, xml tag name of the samples inside the xml
files when auto-generating dummy data.
--match_text_files MATCH_TEXT_FILES
Optional, a comma separated list of file patterns that
looks for line-by-line text files other than *.txt or
*.csv. Example: --match_text_files *.label
--keep_uncompressed Don't compress the dummy data folders when auto-
generating dummy data. Useful for debugging for to do
manual adjustements before compressing.
--cache_dir CACHE_DIR
Cache directory to download and cache files when auto-
generating dummy data
```
The command generates all the necessary `dummy_data.zip` files (one per config).
How it works:
- it runs the split_generators() method of the dataset script to download the original data files
- when downloading it records a mapping between the downloaded files and the corresponding expected dummy data files paths
- then for each data file it creates the dummy data file keeping only the first samples (the strategy depends on the type of file)
- finally it compresses the dummy data folders into dummy_zip files ready for dataset tests
Let me know if that makes sense or if you have ideas to improve this tool !
I also added a unit test. | {
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] | open | false | null | [] | null | [] | 2020-11-24T14:25:18Z | 2020-11-27T13:51:55Z | null | NONE | null | null | null | Hi,
I want to use the validation data *only* (of natural question).
I don't want to have the whole dataset cached in my machine, just the dev set.
Is this possible? I can't find a way to do it in the docs.
Thank you,
Sapir | {
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} | BoolQ is a dataset that used tf.io.gfile.copy to download the file from a GCP bucket.
It prevented the dataset to be downloaded twice because of a FileAlreadyExistsError.
Even though the error could be fixed by providing `overwrite=True` to the tf.io.gfile.copy call, I changed the script to use GCP download urls and use regular downloads instead and remove the tensorflow dependency.
Fix #875 | {
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] | closed | false | null | [] | null | [] | 2020-11-23T16:31:55Z | 2020-12-23T13:58:24Z | 2020-12-23T13:58:23Z | CONTRIBUTOR | null | null | null | ## Adding a Dataset
- **Name:** SQA (Sequential Question Answering) by Microsoft.
- **Description:** The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.
- **Paper:** https://www.microsoft.com/en-us/research/publication/search-based-neural-structured-learning-sequential-question-answering/
- **Data:** https://www.microsoft.com/en-us/download/details.aspx?id=54253
- **Motivation:** currently, the [Tapas](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html) algorithm by Google AI is being added to the Transformers library (see https://github.com/huggingface/transformers/pull/8113). It would be great to use that model in combination with this dataset, on which it achieves SOTA results (average question accuracy of 0.71).
Note 1: this dataset actually consists of 2 types of files:
1) TSV files, containing the questions, answer coordinates and answer texts (for training, dev and test)
2) a folder of csv files, which contain the actual tabular data
Note 2: if you download the dataset straight from the download link above, then you will see that the `answer_coordinates` and `answer_text` columns are string lists of string tuples and strings respectively, which is not ideal. It would be better to make them true Python lists of tuples and strings respectively (using `ast.literal_eval`), before uploading them to the HuggingFace hub.
Adding this would be great! Then we could possibly also add [WTQ (WikiTable Questions)](https://github.com/ppasupat/WikiTableQuestions) and [TabFact (Tabular Fact Checking)](https://github.com/wenhuchen/Table-Fact-Checking) on which TAPAS also achieves state-of-the-art results. Note that the TAPAS algorithm requires these datasets to first be converted into the SQA format.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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] | closed | false | null | [] | null | [] | 2020-11-23T14:28:28Z | 2022-10-05T12:23:32Z | 2022-10-05T12:23:32Z | CONTRIBUTOR | null | null | null | Hi
I am getting these errors trying to load boolq thanks
Traceback (most recent call last):
File "test.py", line 5, in <module>
data = AutoTask().get("boolq").get_dataset("train", n_obs=10)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 42, in get_dataset
dataset = self.load_dataset(split=split)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks/tasks.py", line 38, in load_dataset
return datasets.load_dataset(self.task.name, split=split)
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 150, in download_custom
get_from_cache(url, cache_dir=cache_dir, local_files_only=True, use_etag=False)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 472, in get_from_cache
f"Cannot find the requested files in the cached path at {cache_path} and outgoing traffic has been"
FileNotFoundError: Cannot find the requested files in the cached path at /idiap/home/rkarimi/.cache/huggingface/datasets/eaee069e38f6ceaa84de02ad088c34e63ec97671f2cd1910ddb16b10dc60808c and outgoing traffic has been disabled. To enable file online look-ups, set 'local_files_only' to False.
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] | open | false | null | [] | null | [] | 2020-11-23T09:17:22Z | 2020-12-23T09:53:08Z | null | NONE | null | null | null | In Sagemaker Im tring to load the data set from S3 path as follows
`train_path = 's3://xxxxxxxxxx/xxxxxxxxxx/train.csv'
valid_path = 's3://xxxxxxxxxx/xxxxxxxxxx/validation.csv'
test_path = 's3://xxxxxxxxxx/xxxxxxxxxx/test.csv'
data_files = {}
data_files["train"] = train_path
data_files["validation"] = valid_path
data_files["test"] = test_path
extension = train_path.split(".")[-1]
datasets = load_dataset(extension, data_files=data_files, s3_enabled=True)
print(datasets)`
I getting an error of
`algo-1-7plil_1 | File "main.py", line 21, in <module>
algo-1-7plil_1 | datasets = load_dataset(extension, data_files=data_files)
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/load.py", line 603, in load_dataset
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 155, in __init__
algo-1-7plil_1 | **config_kwargs,
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/site-packages/datasets/builder.py", line 305, in _create_builder_config
algo-1-7plil_1 | m.update(str(os.path.getmtime(data_file)))
algo-1-7plil_1 | File "/opt/conda/lib/python3.6/genericpath.py", line 55, in getmtime
algo-1-7plil_1 | return os.stat(filename).st_mtime
algo-1-7plil_1 | FileNotFoundError: [Errno 2] No such file or directory: 's3://lsmv-sagemaker/pubmedbert/test.csv`
But when im trying with pandas , it is able to load from S3
Does the datasets library support S3 path to load | {
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https://api.github.com/repos/huggingface/datasets/issues/877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/877/comments | https://api.github.com/repos/huggingface/datasets/issues/877/events | https://github.com/huggingface/datasets/issues/877 | 748,234,438 | MDU6SXNzdWU3NDgyMzQ0Mzg= | 877 | DataLoader(datasets) become more and more slowly within iterations | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-22T12:41:10Z | 2020-11-29T15:45:12Z | 2020-11-29T15:45:12Z | NONE | null | null | null | Hello, when I for loop my dataloader, the loading speed is becoming more and more slowly!
```
dataset = load_from_disk(dataset_path) # around 21,000,000 lines
lineloader = tqdm(DataLoader(dataset, batch_size=1))
for idx, line in enumerate(lineloader):
# do some thing for each line
```
In the begining, the loading speed is around 2000it/s, but after 1 minutes later, the speed is much slower, just around 800it/s.
And when I set `num_workers=4` in DataLoader, the loading speed is much lower, just 130it/s.
Could you please help me with this problem?
Thanks a lot! | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-22T08:24:43Z | 2021-11-26T11:07:16Z | 2020-12-24T17:38:47Z | CONTRIBUTOR | null | null | null | Hi
I am trying to load the imdb train dataset
`dataset = datasets.load_dataset("imdb", split="train")`
getting following errors, thanks for your help
```
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 558, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/info_utils.py", line 73, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=32660064, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='test', num_bytes=26476338, num_examples=20316, dataset_name='imdb')}, {'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=0, num_examples=0, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
>>> dataset = datasets.load_dataset("imdb", split="train")
```
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} | [] | closed | false | null | [] | null | [] | 2020-11-22T08:18:34Z | 2020-11-24T10:12:33Z | 2020-11-24T10:12:33Z | CONTRIBUTOR | null | null | null | Hi
I am trying to load boolq dataset:
```
import datasets
datasets.load_dataset("boolq")
```
I am getting the following errors, thanks for your help
```
>>> import datasets
2020-11-22 09:16:30.070332: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2020-11-22 09:16:30.070389: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
>>> datasets.load_dataset("boolq")
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets
Using custom data configuration default
Downloading and preparing dataset boolq/default (download: 8.36 MiB, generated: 7.47 MiB, post-processed: Unknown size, total: 15.83 MiB) to /idiap/temp/rkarimi/cache_home/datasets/boolq/default/0.1.0/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11...
cahce dir /idiap/temp/rkarimi/cache_home/datasets
cahce dir /idiap/temp/rkarimi/cache_home/datasets/downloads
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/boolq/2987db1f15deaa19500ae24de560eabeaf1f8ef51df88c0470beeec72943bf11/boolq.py", line 74, in _split_generators
downloaded_files = dl_manager.download_custom(urls_to_download, tf.io.gfile.copy)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 149, in download_custom
custom_download(url, path)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/tensorflow/python/lib/io/file_io.py", line 516, in copy_v2
compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite)
tensorflow.python.framework.errors_impl.AlreadyExistsError: file already exists
``` | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-22T08:09:36Z | 2020-11-27T13:56:42Z | 2020-11-27T13:56:42Z | CONTRIBUTOR | null | null | null | Hi
when I try to load the trec dataset I am getting these errors, thanks for your help
`datasets.load_dataset("trec", split="train")
`
```
File "<stdin>", line 1, in <module>
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File " /idiap/home/rkarimi/.cache/huggingface/modules/datasets_modules/datasets/trec/ca4248481ad244f235f4cf277186cad2ee8769f975119a2bbfc41b8932b88bd7/trec.py", line 140, in _split_generators
dl_files = dl_manager.download_and_extract(_URLs)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 477, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://cogcomp.org/Data/QA/QC/train_5500.label
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/873 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/873/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/873/comments | https://api.github.com/repos/huggingface/datasets/issues/873/events | https://github.com/huggingface/datasets/issues/873 | 747,959,523 | MDU6SXNzdWU3NDc5NTk1MjM= | 873 | load_dataset('cnn_dalymail', '3.0.0') gives a 'Not a directory' error | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-21T06:30:45Z | 2022-05-05T07:19:59Z | 2020-11-22T12:18:05Z | NONE | null | null | null | ```
from datasets import load_dataset
dataset = load_dataset('cnn_dailymail', '3.0.0')
```
Stack trace:
```
---------------------------------------------------------------------------
NotADirectoryError Traceback (most recent call last)
<ipython-input-6-2e06a8332652> in <module>()
1 from datasets import load_dataset
----> 2 dataset = load_dataset('cnn_dailymail', '3.0.0')
5 frames
/usr/local/lib/python3.6/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
608 download_config=download_config,
609 download_mode=download_mode,
--> 610 ignore_verifications=ignore_verifications,
611 )
612
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
513 if not downloaded_from_gcs:
514 self._download_and_prepare(
--> 515 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
516 )
517 # Sync info
/usr/local/lib/python3.6/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
568 split_dict = SplitDict(dataset_name=self.name)
569 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 570 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
571
572 # Checksums verification
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _split_generators(self, dl_manager)
252 def _split_generators(self, dl_manager):
253 dl_paths = dl_manager.download_and_extract(_DL_URLS)
--> 254 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN)
255 # Generate shared vocabulary
256
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _subset_filenames(dl_paths, split)
153 else:
154 logging.fatal("Unsupported split: %s", split)
--> 155 cnn = _find_files(dl_paths, "cnn", urls)
156 dm = _find_files(dl_paths, "dm", urls)
157 return cnn + dm
/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602/cnn_dailymail.py in _find_files(dl_paths, publisher, url_dict)
132 else:
133 logging.fatal("Unsupported publisher: %s", publisher)
--> 134 files = sorted(os.listdir(top_dir))
135
136 ret_files = []
NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories'
```
I have ran the code on Google Colab | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-20T17:09:34Z | 2020-11-25T17:01:11Z | 2020-11-25T15:26:07Z | CONTRIBUTOR | null | false | {
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} | Added IndicGLUE benchmark for evaluating models on 11 Indian Languages. The descriptions of the tasks and the corresponding paper can be found [here](https://indicnlp.ai4bharat.org/indic-glue/)
- [x] Followed the instructions in CONTRIBUTING.md
- [x] Ran the tests successfully
- [x] Created the dummy data | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-20T12:56:24Z | 2020-12-12T21:16:32Z | 2020-12-12T21:16:32Z | CONTRIBUTOR | null | null | null | Hi
I am using the dataset "iwslt2017-en-nl", and after downloading it I am getting this error when trying to evaluate it on T5-base with seq2seq_trainer.py in the huggingface repo could you assist me please? thanks
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 63/63 [02:47<00:00, 2.18s/it][libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1505] CHECK failed: (index) >= (0):
terminate called after throwing an instance of 'google::protobuf::FatalException'
what(): CHECK failed: (index) >= (0):
run_t5_base_eval.sh: line 19: 5795 Aborted | {
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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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} | 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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} | 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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} | Replace `int` feature type to `int32` since `int` is not a pyarrow dtype in those metrics:
- accuracy
- precision
- recall
- f1
I also added the sklearn citation and used keyword arguments to remove future warnings | {
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https://api.github.com/repos/huggingface/datasets/issues/866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/866/comments | https://api.github.com/repos/huggingface/datasets/issues/866/events | https://github.com/huggingface/datasets/issues/866 | 745,719,222 | MDU6SXNzdWU3NDU3MTkyMjI= | 866 | OSCAR from Inria group | {
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] | closed | false | null | [] | null | [] | 2020-11-18T14:40:54Z | 2020-11-18T15:01:30Z | 2020-11-18T15:01:30Z | NONE | null | null | null | ## Adding a Dataset
- **Name:** *OSCAR* (Open Super-large Crawled ALMAnaCH coRpus), multilingual parsing of Common Crawl (separate crawls for many different languages), [here](https://oscar-corpus.com/).
- **Description:** *OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.*
- **Paper:** *[here](https://hal.inria.fr/hal-02148693)*
- **Data:** *[here](https://oscar-corpus.com/)*
- **Motivation:** *useful for unsupervised tasks in separate languages. In an ideal world, your team would be able to obtain the unshuffled version, that could be used to train GPT-2-like models (the shuffled version, I suppose, could be used for translation).*
I am aware that you do offer the "colossal" Common Crawl dataset already, but this has the advantage to be available in many subcorpora for different languages.
| {
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https://api.github.com/repos/huggingface/datasets/issues/865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/865/comments | https://api.github.com/repos/huggingface/datasets/issues/865/events | https://github.com/huggingface/datasets/issues/865 | 745,430,497 | MDU6SXNzdWU3NDU0MzA0OTc= | 865 | Have Trouble importing `datasets` | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-18T08:04:41Z | 2020-11-18T08:16:35Z | 2020-11-18T08:16:35Z | CONTRIBUTOR | null | null | null | I'm failing to import transformers (v4.0.0-dev), and tracing the cause seems to be failing to import datasets.
I cloned the newest version of datasets (master branch), and do `pip install -e .`.
Then, `import datasets` causes the error below.
```
~/workspace/Clone/datasets/src/datasets/utils/file_utils.py in <module>
116 sys.path.append(str(HF_MODULES_CACHE))
117
--> 118 os.makedirs(HF_MODULES_CACHE, exist_ok=True)
119 if not os.path.exists(os.path.join(HF_MODULES_CACHE, "__init__.py")):
120 with open(os.path.join(HF_MODULES_CACHE, "__init__.py"), "w"):
~/.pyenv/versions/anaconda3-2020.07/lib/python3.8/os.py in makedirs(name, mode, exist_ok)
221 return
222 try:
--> 223 mkdir(name, mode)
224 except OSError:
225 # Cannot rely on checking for EEXIST, since the operating system
FileNotFoundError: [Errno 2] No such file or directory: '<MY_HOME_DIRECTORY>/.cache/huggingface/modules'
```
The error occurs in `os.makedirs` in `file_utils.py`, even though `exist_ok = True` option is set.
(I use Python 3.8, so `exist_ok` is expected to work.)
I've checked some environment variables, and they are set as below.
```
*** NameError: name 'HF_MODULES_CACHE' is not defined
*** NameError: name 'hf_cache_home' is not defined
*** NameError: name 'XDG_CACHE_HOME' is not defined
```
Should I set some environment variables before using this library?
And, do you have any idea why "No such file or directory" occurs even though the `exist_ok = True` option is set?
Thank you in advance. | {
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```
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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} | 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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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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I am trying with wmt16, cs-en pair, thanks for the help, perhaps similar to the ro-en issue. thanks
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "finetune_t5_trainer.py", line 109, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/home/rabeeh/internship/seq2seq/tasks/tasks.py", line 82, in get_dataset
dataset = load_dataset("wmt16", self.pair, split=split)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/rabeeh/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/opt/conda/envs/internship/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz | {
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} | Previously the locking system of the lib was based on the file_lock package. However as noticed in #812 there were too many logs printed even when the datasets logging was set to warnings or errors.
For example
```python
import logging
logging.basicConfig(level=logging.INFO)
import datasets
datasets.set_verbosity_warning()
datasets.load_dataset("squad")
```
would still log the file lock events:
```
INFO:filelock:Lock 5737989232 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 5737989232 released on /Users/quentinlhoest/.cache/huggingface/datasets/44801f118d500eff6114bfc56ab4e6def941f1eb14b70ac1ecc052e15cdac49d.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py.lock
INFO:filelock:Lock 4393489968 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393489968 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
INFO:filelock:Lock 4393490808 acquired on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
INFO:filelock:Lock 4393490808 released on /Users/quentinlhoest/.cache/huggingface/datasets/_Users_quentinlhoest_.cache_huggingface_datasets_squad_plain_text_1.0.0_1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41.lock
```
With the integration of file_lock in the library, the ouput is much cleaner:
```
Reusing dataset squad (/Users/quentinlhoest/.cache/huggingface/datasets/squad/plain_text/1.0.0/1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41)
```
Since the file_lock package is only a 450 lines file I think it's fine to have it inside the lib.
Fix #812 | {
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} | Hi,
I don't know how to add dummy data, since I create the validation set out of the last 1000 examples of the train set. If you have a suggestion, I am happy to implement it.
Cheers,
Joel | {
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} | The pyarrow CSV reader has issues that the pandas one doesn't (see #836 ).
To fix that I switched to the pandas csv reader.
The new reader is compatible with all the pandas parameters to read csv files.
Moreover it reads csv by chunk in order to save RAM, while the pyarrow one loads everything in memory.
Fix #836
Fix #794
Breaking: now all the parameters to read to csv file can be used in the `load_dataset` kwargs when loading csv, and the previous pyarrow objects `pyarrow.csv.ReadOptions`, `pyarrow.csv.ParseOptions` and `pyarrow.csv.ConvertOptions` are not used anymore. | {
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} | Adds book corpus based on Shawn Presser's [work](https://github.com/soskek/bookcorpus/issues/27) @richarddwang, the author of the original BookCorpus dataset, suggested it should be named [OpenBookCorpus](https://github.com/huggingface/datasets/issues/486). I named it BookCorpusOpen to be easily located alphabetically. But, of course, we can rename it if needed.
It contains 17868 dataset items; each item contains two fields: title and text. The title is the name of the book (just the file name) while the text contains unprocessed book text. Note that bookcorpus is pre-segmented into a sentence while this bookcorpus is not. This is intentional (see https://github.com/huggingface/datasets/issues/486) as some users might want to further process the text themselves.
@lhoestq and others please review this PR thoroughly. cc @shawwn | {
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} | The kor_nli dataset had an issue with the csv reader that was not able to parse the lines correctly. Some lines were merged together for some reason.
I fixed that by iterating through the lines directly instead of using a csv reader.
I also changed the feature names to match the other NLI datasets (i.e. use "premise", "hypothesis", "label" features)
Fix #821 | {
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] | closed | false | null | [] | null | [] | 2020-11-16T09:31:51Z | 2022-10-05T12:27:42Z | 2022-10-05T12:27:42Z | CONTRIBUTOR | null | null | null | Hi, I appreciate your help with the following error, thanks
>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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>>> from datasets import load_dataset
>>> dataset = load_dataset("wmt16", "ro-en", split="train")
Downloading and preparing dataset wmt16/ro-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wmt16/ro-en/1.0.0/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py", line 611, in load_dataset
ignore_verifications=ignore_verifications,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 476, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/builder.py", line 531, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/root/.cache/huggingface/modules/datasets_modules/datasets/wmt16/7b2c4443a7d34c2e13df267eaa8cab4c62dd82f6b62b0d9ecc2e3a673ce17308/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 254, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/download_manager.py", line 179, in download
num_proc=download_config.num_proc,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 225, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 181, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/py_utils.py", line 163, in _single_map_nested
return function(data_struct)
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/root/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 475, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach http://opus.nlpl.eu/download.php?f=SETIMES/v2/tmx/en-ro.tmx.gz | {
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https://api.github.com/repos/huggingface/datasets/issues/850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/850/comments | https://api.github.com/repos/huggingface/datasets/issues/850/events | https://github.com/huggingface/datasets/pull/850 | 742,369,419 | MDExOlB1bGxSZXF1ZXN0NTIwNTE0MDY3 | 850 | Create ClassLabel for labelling tasks datasets | {
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"url": "https://api.github.com/users/jplu"
} | [] | closed | false | null | [] | null | [] | 2020-11-13T11:07:22Z | 2020-11-16T10:32:05Z | 2020-11-16T10:31:58Z | CONTRIBUTOR | null | false | {
"diff_url": "https://github.com/huggingface/datasets/pull/850.diff",
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"merged_at": "2020-11-16T10:31:58Z",
"patch_url": "https://github.com/huggingface/datasets/pull/850.patch",
"url": "https://api.github.com/repos/huggingface/datasets/pulls/850"
} | This PR adds a specific `ClassLabel` for the datasets that are about a labelling task such as POS, NER or Chunking. | {
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https://api.github.com/repos/huggingface/datasets/issues/849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/849/comments | https://api.github.com/repos/huggingface/datasets/issues/849/events | https://github.com/huggingface/datasets/issues/849 | 742,263,333 | MDU6SXNzdWU3NDIyNjMzMzM= | 849 | Load amazon dataset | {
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"url": "https://api.github.com/users/bhavitvyamalik"
} | [] | closed | false | null | [] | null | [] | 2020-11-13T08:34:24Z | 2020-11-17T07:22:59Z | 2020-11-17T07:22:59Z | CONTRIBUTOR | null | null | null | Hi,
I was going through amazon_us_reviews dataset and found that example API usage given on website is different from the API usage while loading dataset.
Eg. what API usage is on the [website](https://huggingface.co/datasets/amazon_us_reviews)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews")
```
How it is when I tried (the error generated does point me to the right direction though)
```
from datasets import load_dataset
dataset = load_dataset("amazon_us_reviews", 'Books_v1_00')
```
Also, there is some issue with formatting as it's not showing bullet list in description with new line. Can I work on it? | {
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https://api.github.com/repos/huggingface/datasets/issues/848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/848/comments | https://api.github.com/repos/huggingface/datasets/issues/848/events | https://github.com/huggingface/datasets/issues/848 | 742,240,942 | MDU6SXNzdWU3NDIyNDA5NDI= | 848 | Error when concatenate_datasets | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-13T07:56:02Z | 2020-11-13T17:40:59Z | 2020-11-13T15:55:10Z | NONE | null | null | null | Hello, when I concatenate two dataset loading from disk, I encountered a problem:
```
test_dataset = load_from_disk('data/test_dataset')
trn_dataset = load_from_disk('data/train_dataset')
train_dataset = concatenate_datasets([trn_dataset, test_dataset])
```
And it reported ValueError blow:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-38-74fa525512ca> in <module>
----> 1 train_dataset = concatenate_datasets([trn_dataset, test_dataset])
/opt/miniconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py in concatenate_datasets(dsets, info, split)
2547 "However datasets' indices {} come from memory and datasets' indices {} come from disk.".format(
2548 [i for i in range(len(dsets)) if indices_mappings_in_memory[i]],
-> 2549 [i for i in range(len(dsets)) if not indices_mappings_in_memory[i]],
2550 )
2551 )
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
But it's curious both of my datasets loading from disk, so I check the source code in `arrow_dataset.py` about the Error:
```
trn_dataset._data_files
# output
[{'filename': 'data/train_dataset/csv-train.arrow', 'skip': 0, 'take': 593264}]
test_dataset._data_files
# output
[{'filename': 'data/test_dataset/csv-test.arrow', 'skip': 0, 'take': 424383}]
print([not dset._data_files for dset in [trn_dataset, test_dataset]])
# [False, False]
# And I tested the code the same as arrow_dataset, but nothing happened
dsets = [trn_dataset, test_dataset]
dsets_in_memory = [not dset._data_files for dset in dsets]
if any(dset_in_memory != dsets_in_memory[0] for dset_in_memory in dsets_in_memory):
raise ValueError(
"Datasets should ALL come from memory, or should ALL come from disk.\n"
"However datasets {} come from memory and datasets {} come from disk.".format(
[i for i in range(len(dsets)) if dsets_in_memory[i]],
[i for i in range(len(dsets)) if not dsets_in_memory[i]],
)
)
```
Any suggestions would be greatly appreciated!
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/847/comments | https://api.github.com/repos/huggingface/datasets/issues/847/events | https://github.com/huggingface/datasets/issues/847 | 742,179,495 | MDU6SXNzdWU3NDIxNzk0OTU= | 847 | multiprocessing in dataset map "can only test a child process" | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-13T06:01:04Z | 2022-10-05T12:22:51Z | 2022-10-05T12:22:51Z | NONE | null | null | null | Using a dataset with a single 'text' field and a fast tokenizer in a jupyter notebook.
```
def tokenizer_fn(example):
return tokenizer.batch_encode_plus(example['text'])
ds_tokenized = text_dataset.map(tokenizer_fn, batched=True, num_proc=6, remove_columns=['text'])
```
```
---------------------------------------------------------------------------
RemoteTraceback Traceback (most recent call last)
RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/multiprocess/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 156, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/fingerprint.py", line 163, in wrapper
out = func(self, *args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 1510, in _map_single
for i in pbar:
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 228, in __iter__
for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1186, in __iter__
self.close()
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/notebook.py", line 251, in close
super(tqdm_notebook, self).close(*args, **kwargs)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1291, in close
fp_write('')
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/tqdm/std.py", line 1288, in fp_write
self.fp.write(_unicode(s))
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/lib/redirect.py", line 91, in new_write
cb(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/wandb_run.py", line 598, in _console_callback
self._backend.interface.publish_output(name, data)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 146, in publish_output
self._publish_output(o)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 151, in _publish_output
self._publish(rec)
File "/home/jovyan/share/users/tlaurent/invitae-bert/ve/lib/python3.6/site-packages/wandb/sdk/interface/interface.py", line 431, in _publish
if self._process and not self._process.is_alive():
File "/usr/lib/python3.6/multiprocessing/process.py", line 134, in is_alive
assert self._parent_pid == os.getpid(), 'can only test a child process'
AssertionError: can only test a child process
"""
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/846 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/846/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/846/comments | https://api.github.com/repos/huggingface/datasets/issues/846/events | https://github.com/huggingface/datasets/issues/846 | 741,885,174 | MDU6SXNzdWU3NDE4ODUxNzQ= | 846 | Add HoVer multi-hop fact verification dataset | {
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{
"color": "e99695",
"default": false,
"description": "Requesting to add a new dataset",
"id": 2067376369,
"name": "dataset request",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request"
}
] | closed | false | null | [] | null | [] | 2020-11-12T19:55:46Z | 2020-12-10T21:47:33Z | 2020-12-10T21:47:33Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** HoVer
- **Description:** https://twitter.com/YichenJiang9/status/1326954363806429186 contains 20K claim verification examples
- **Paper:** https://arxiv.org/abs/2011.03088
- **Data:** https://hover-nlp.github.io/
- **Motivation:** There are still few multi-hop information extraction benchmarks (HotpotQA, which dataset wase based off, notwithstanding)
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
| {
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https://api.github.com/repos/huggingface/datasets/issues/845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/845/comments | https://api.github.com/repos/huggingface/datasets/issues/845/events | https://github.com/huggingface/datasets/pull/845 | 741,841,350 | MDExOlB1bGxSZXF1ZXN0NTIwMDg1NDMy | 845 | amazon description fields as bullets | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-12T18:50:41Z | 2020-11-12T18:50:54Z | 2020-11-12T18:50:54Z | CONTRIBUTOR | null | false | {
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} | One more minor formatting change to amazon reviews's description (in addition to #844). Just reformatting the fields to display as a bulleted list in markdown. | {
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} | Just a quick formatting fix to hopefully make it render nicer on Viewer | {
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https://api.github.com/repos/huggingface/datasets/issues/843 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/843/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/843/comments | https://api.github.com/repos/huggingface/datasets/issues/843/events | https://github.com/huggingface/datasets/issues/843 | 741,531,121 | MDU6SXNzdWU3NDE1MzExMjE= | 843 | use_custom_baseline still produces errors for bertscore | {
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{
"color": "25b21e",
"default": false,
"description": "A bug in a metric script",
"id": 2067393914,
"name": "metric bug",
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"url": "https://api.github.com/repos/huggingface/datasets/labels/metric%20bug"
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] | closed | false | null | [] | null | [] | 2020-11-12T11:44:32Z | 2021-08-31T10:06:44Z | 2021-02-09T14:21:48Z | NONE | null | null | null | `metric = load_metric('bertscore')`
`a1 = "random sentences"`
`b1 = "random sentences"`
`metric.compute(predictions = [a1], references = [b1], lang = 'en')`
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/stephen_chan/.cache/huggingface/modules/datasets_modules/metrics/bertscore/361e597a01a41d6cf95d94bbfb01dea16261687abc0c6c74cc9930f80488f363/bertscore.py", line 108, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() missing 1 required positional argument: 'use_custom_baseline'`
Adding 'use_custom_baseline = False' as an argument produces this error
`Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/stephen_chan/.local/lib/python3.6/site-packages/datasets/metric.py", line 393, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
TypeError: _compute() got an unexpected keyword argument 'use_custom_baseline'`
This is on Ubuntu 18.04, Python 3.6.9, datasets version 1.1.2 | {
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https://api.github.com/repos/huggingface/datasets/issues/842 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/842/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/842/comments | https://api.github.com/repos/huggingface/datasets/issues/842/events | https://github.com/huggingface/datasets/issues/842 | 741,208,428 | MDU6SXNzdWU3NDEyMDg0Mjg= | 842 | How to enable `.map()` pre-processing pipelines to support multi-node parallelism? | {
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} | [] | open | false | null | [] | null | [] | 2020-11-12T02:04:38Z | 2022-10-12T16:10:51Z | null | NONE | null | null | null | Hi,
Currently, multiprocessing can be enabled for the `.map()` stages on a single node. However, in the case of multi-node training, (since more than one node would be available) I'm wondering if it's possible to extend the parallel processing among nodes, instead of only 1 node running the `.map()` while the other node is waiting for it to finish?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/841 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/841/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/841/comments | https://api.github.com/repos/huggingface/datasets/issues/841/events | https://github.com/huggingface/datasets/issues/841 | 740,737,448 | MDU6SXNzdWU3NDA3Mzc0NDg= | 841 | Can not reuse datasets already downloaded | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-11T12:42:15Z | 2020-11-11T18:17:16Z | 2020-11-11T18:17:16Z | NONE | null | null | null | Hello,
I need to connect to a frontal node (with http proxy, no gpu) before connecting to a gpu node (but no http proxy, so can not use wget so on).
I successfully downloaded and reuse the wikipedia datasets in a frontal node.
When I connect to the gpu node, I supposed to use the downloaded datasets from cache, but failed and end with time out error.
On frontal node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Reusing dataset wikipedia (/linkhome/rech/genini01/uua34ms/.cache/huggingface/datasets/wikipedia/20200501.en/1.0.0/f92599dfccab29832c442b82870fa8f6983e5b4ebbf5e6e2dcbe894e325339cd)
/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.)
return torch._C._cuda_getDeviceCount() > 0
```
On gpu node:
```
>>> from datasets import load_dataset
>>> dataset = load_dataset('wikipedia', '20200501.en')
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 160, in _new_conn
(self._dns_host, self.port), self.timeout, **extra_kw
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
TimeoutError: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 677, in urlopen
chunked=chunked,
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connection.py", line 172, in _new_conn
self, "Failed to establish a new connection: %s" % e
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 449, in send
timeout=timeout
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/connectionpool.py", line 727, in urlopen
method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/urllib3/util/retry.py", line 446, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 590, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/load.py", line 264, in prepare_module
head_hf_s3(path, filename=name, dataset=dataset)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 200, in head_hf_s3
return requests.head(hf_bucket_url(identifier=identifier, filename=filename, use_cdn=use_cdn, dataset=dataset))
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 104, in head
return request('head', url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/api.py", line 61, in request
return session.request(method=method, url=url, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/linkhome/rech/genini01/uua34ms/work/anaconda3/envs/pytorch_pip170_cuda102/lib/python3.6/site-packages/requests/adapters.py", line 516, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/datasets/datasets/wikipedia/wikipedia.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x14b7b73e4908>: Failed to establish a new connection: [Errno 110] Connection timed out',))
```
Any advice?Thanks!
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} | Change lines 100 and 102 to prevent overwriting ```predictions``` variable. | {
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https://api.github.com/repos/huggingface/datasets/issues/839 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/839/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/839/comments | https://api.github.com/repos/huggingface/datasets/issues/839/events | https://github.com/huggingface/datasets/issues/839 | 740,355,270 | MDU6SXNzdWU3NDAzNTUyNzA= | 839 | XSum dataset missing spaces between sentences | {
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} | [] | open | false | null | [] | null | [] | 2020-11-11T00:34:43Z | 2020-11-11T00:34:43Z | null | NONE | null | null | null | I noticed that the XSum dataset has no space between sentences. This could lead to worse results for anyone training or testing on it. Here's an example (0th entry in the test set):
`The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!"` | {
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https://api.github.com/repos/huggingface/datasets/issues/838 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/838/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/838/comments | https://api.github.com/repos/huggingface/datasets/issues/838/events | https://github.com/huggingface/datasets/pull/838 | 740,328,382 | MDExOlB1bGxSZXF1ZXN0NTE4ODM0NTE5 | 838 | CNN/Dailymail Dataset Card | {
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} | 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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} | 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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] | closed | false | null | [] | null | [] | 2020-11-10T19:35:40Z | 2021-11-24T16:59:19Z | 2020-11-19T17:35:38Z | NONE | null | null | null | Hi All
I am trying to load a custom dataset and I am trying to load a single file to make sure the file is loading correctly:
dataset = load_dataset('csv', data_files=files)
When I run it I get:
Downloading and preparing dataset csv/default-35575a1051604c88 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) tocache/huggingface/datasets/csv/default-35575a1051604c88/0.0.0/49187751790fa4d820300fd4d0707896e5b941f1a9c644652645b866716a4ac4...
I am getting this error:
6a4ac4/csv.py in _generate_tables(self, files)
78 def _generate_tables(self, files):
79 for i, file in enumerate(files):
---> 80 pa_table = pac.read_csv(
81 file,
82 read_options=self.config.pa_read_options,
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/anaconda2/envs/nlp/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
**ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)**
The size of the file is 3.5 GB. When I try smaller files I do not have an issue. When I load it with 'text' parser I can see all data but it is not what I need.
There is no issue reading the file with pandas. any idea what could be the issue?
When I am running a different CSV I do not get this line:
(download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size)
Any ideas?
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https://api.github.com/repos/huggingface/datasets/issues/835 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/835/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/835/comments | https://api.github.com/repos/huggingface/datasets/issues/835/events | https://github.com/huggingface/datasets/issues/835 | 740,102,210 | MDU6SXNzdWU3NDAxMDIyMTA= | 835 | Wikipedia postprocessing | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-10T17:26:38Z | 2020-11-10T18:23:20Z | 2020-11-10T17:49:21Z | NONE | null | null | null | Hi, thanks for this library!
Running this code:
```py
import datasets
wikipedia = datasets.load_dataset("wikipedia", "20200501.de")
print(wikipedia['train']['text'][0])
```
I get:
```
mini|Ricardo Flores MagΓ³n
mini|Mexikanische RevolutionΓ€re, MagΓ³n in der Mitte anfΓΌhrend, gegen die Diktatur von Porfirio Diaz, Ausschnitt des GemΓ€lde βTierra y Libertadβ von Idelfonso Carrara (?) von 1930.
Ricardo Flores MagΓ³n (* 16. September 1874 in San Antonio EloxochitlΓ‘n im mexikanischen Bundesstaat Oaxaca; β 22. November 1922 im BundesgefΓ€ngnis Leavenworth im US-amerikanischen Bundesstaat Kansas) war als Journalist, Gewerkschafter und Literat ein fΓΌhrender anarchistischer Theoretiker und Aktivist, der die revolutionΓ€re mexikanische Bewegung radikal beeinflusste. MagΓ³n war GrΓΌnder der Partido Liberal Mexicano und Mitglied der Industrial Workers of the World.
Politische Biografie
Journalistisch und politisch kΓ€mpfte er und sein Bruder sehr kompromisslos gegen die Diktatur Porfirio Diaz. Philosophisch und politisch orientiert an radikal anarchistischen Idealen und den Erfahrungen seiner indigenen Vorfahren bei der gemeinschaftlichen Bewirtschaftung des Gemeindelandes, machte er die Forderung βLand und Freiheitβ (Tierra y Libertad) populΓ€r. Besonders Francisco Villa und Emiliano Zapata griffen die Forderung Land und Freiheit auf. Seine Philosophie hatte groΓen Einfluss auf die Landarbeiter. 1904 floh er in die USA und grΓΌndete 1906 die Partido Liberal Mexicano. Im Exil lernte er u. a. Emma Goldman kennen. Er verbrachte die meiste Zeit seines Lebens in GefΓ€ngnissen und im Exil und wurde 1918 in den USA wegen βBehinderung der Kriegsanstrengungenβ zu zwanzig Jahren GefΓ€ngnis verurteilt. Zu seinem Tod gibt es drei verschiedene Theorien. Offiziell starb er an Herzversagen. Librado Rivera, der die Leiche mit eigenen Augen gesehen hat, geht davon aus, dass MagΓ³n von einem Mitgefangenen erdrosselt wurde. Die staatstreue Gewerkschaftszeitung CROM verΓΆffentlichte 1923 einen Beitrag, nachdem MagΓ³n von einem GefΓ€ngniswΓ€rter erschlagen wurde.
mini|Die BrΓΌder Ricardo (links) und Enrique Flores MagΓ³n (rechts) vor dem Los Angeles County Jail, 1917
[...]
```
so some Markup like `mini|` is still left. Should I run another parser on this text before feeding it to an ML model or is this a known imperfection of parsing Wiki markup?
Apologies if this has been asked before. | {
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https://api.github.com/repos/huggingface/datasets/issues/834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/834/comments | https://api.github.com/repos/huggingface/datasets/issues/834/events | https://github.com/huggingface/datasets/issues/834 | 740,082,890 | MDU6SXNzdWU3NDAwODI4OTA= | 834 | [GEM] add WikiLingua cross-lingual abstractive summarization dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T17:00:43Z | 2021-04-15T12:04:09Z | 2021-04-15T12:01:38Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WikiLingua
- **Description:** The dataset includes ~770k article and summary pairs in 18 languages from WikiHow. The gold-standard article-summary alignments across languages were extracted by aligning the images that are used to describe each how-to step in an article.
- **Paper:** https://arxiv.org/pdf/2010.03093.pdf
- **Data:** https://github.com/esdurmus/Wikilingua
- **Motivation:** Included in the GEM shared task. Multilingual.
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/833 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/833/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/833/comments | https://api.github.com/repos/huggingface/datasets/issues/833/events | https://github.com/huggingface/datasets/issues/833 | 740,079,692 | MDU6SXNzdWU3NDAwNzk2OTI= | 833 | [GEM] add ASSET text simplification dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T16:56:30Z | 2020-12-03T13:38:15Z | 2020-12-03T13:38:15Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** ASSET
- **Description:** ASSET is a crowdsourced
multi-reference corpus for assessing sentence simplification in English where each simplification was produced by executing several rewriting transformations.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.424.pdf
- **Data:** https://github.com/facebookresearch/asset
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/832/comments | https://api.github.com/repos/huggingface/datasets/issues/832/events | https://github.com/huggingface/datasets/issues/832 | 740,077,228 | MDU6SXNzdWU3NDAwNzcyMjg= | 832 | [GEM] add WikiAuto text simplification dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T16:53:23Z | 2020-12-03T13:38:08Z | 2020-12-03T13:38:08Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WikiAuto
- **Description:** Sentences in English Wikipedia and their corresponding sentences in Simple English Wikipedia that are written with simpler grammar and word choices. A lot of lexical and syntactic paraphrasing.
- **Paper:** https://www.aclweb.org/anthology/2020.acl-main.709.pdf
- **Data:** https://github.com/chaojiang06/wiki-auto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/831/comments | https://api.github.com/repos/huggingface/datasets/issues/831/events | https://github.com/huggingface/datasets/issues/831 | 740,071,697 | MDU6SXNzdWU3NDAwNzE2OTc= | 831 | [GEM] Add WebNLG dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T16:46:48Z | 2020-12-03T13:38:01Z | 2020-12-03T13:38:01Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** WebNLG
- **Description:** WebNLG consists of Data/Text pairs where the data is a set of triples extracted from DBpedia and the text is a verbalisation of these triples (16,095 data inputs and 42,873 data-text pairs). The data is available in English and Russian
- **Paper:** https://www.aclweb.org/anthology/P17-1017.pdf
- **Data:** https://webnlg-challenge.loria.fr/download/
- **Motivation:** Included in the GEM shared task, multilingual
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/830 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/830/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/830/comments | https://api.github.com/repos/huggingface/datasets/issues/830/events | https://github.com/huggingface/datasets/issues/830 | 740,065,376 | MDU6SXNzdWU3NDAwNjUzNzY= | 830 | [GEM] add ToTTo Table-to-text dataset | {
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- **Name:** ToTTo
- **Description:** ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
- **Paper:** https://arxiv.org/abs/2004.14373
- **Data:** https://github.com/google-research-datasets/totto
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/829/comments | https://api.github.com/repos/huggingface/datasets/issues/829/events | https://github.com/huggingface/datasets/issues/829 | 740,061,699 | MDU6SXNzdWU3NDAwNjE2OTk= | 829 | [GEM] add Schema-Guided Dialogue | {
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] | closed | false | null | [] | null | [] | 2020-11-10T16:33:44Z | 2020-12-03T13:37:50Z | 2020-12-03T13:37:50Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** The Schema-Guided Dialogue Dataset
- **Description:** The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, ranging from banks and events to media, calendar, travel, and weather.
- **Paper:** https://arxiv.org/pdf/2002.01359.pdf https://arxiv.org/pdf/2004.15006.pdf
- **Data:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
- **Motivation:** Included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/828/comments | https://api.github.com/repos/huggingface/datasets/issues/828/events | https://github.com/huggingface/datasets/pull/828 | 740,008,683 | MDExOlB1bGxSZXF1ZXN0NTE4NTcwMjY3 | 828 | Add writer_batch_size attribute to GeneratorBasedBuilder | {
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} | As specified in #741 one would need to specify a custom ArrowWriter batch size to avoid filling the RAM. Indeed the defaults buffer size is 10 000 examples but for multimodal datasets that contain images or videos we may want to reduce that. | {
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https://api.github.com/repos/huggingface/datasets/issues/827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/827/comments | https://api.github.com/repos/huggingface/datasets/issues/827/events | https://github.com/huggingface/datasets/issues/827 | 739,983,024 | MDU6SXNzdWU3Mzk5ODMwMjQ= | 827 | [GEM] MultiWOZ dialogue dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T14:57:50Z | 2022-10-05T12:31:13Z | 2022-10-05T12:31:13Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** MultiWOZ (Multi-Domain Wizard-of-Oz)
- **Description:** 10k annotated human-human dialogues. Each dialogue consists of a goal, multiple user and system utterances as well as a belief state. Only system utterances are annotated with dialogue acts β there are no annotations from the user side.
- **Paper:** https://arxiv.org/pdf/2007.12720.pdf
- **Data:** https://github.com/budzianowski/multiwoz
- **Motivation:** Will likely be part of the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/826/comments | https://api.github.com/repos/huggingface/datasets/issues/826/events | https://github.com/huggingface/datasets/issues/826 | 739,976,716 | MDU6SXNzdWU3Mzk5NzY3MTY= | 826 | [GEM] Add E2E dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-10T14:50:40Z | 2020-12-03T13:37:57Z | 2020-12-03T13:37:57Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** E2E NLG dataset (for End-to-end natural language generation)
- **Description:**a dataset for training end-to-end, datadriven natural language generation systems in the restaurant domain, the datasets consists of 5,751 dialogue-act Meaning Representations (structured data) and 8.1 reference free-text utterances per dialogue-act on average
- **Paper:** https://arxiv.org/pdf/1706.09254.pdf https://arxiv.org/abs/1901.07931
- **Data:** http://www.macs.hw.ac.uk/InteractionLab/E2E/#data
- **Motivation:** This dataset will likely be included in the GEM shared task
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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} | [] | closed | false | null | [] | null | [] | 2020-11-10T13:50:35Z | 2020-11-11T19:23:48Z | 2020-11-11T19:23:43Z | CONTRIBUTOR | null | false | {
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} | This PR adds several single metrics, namely:
- Accuracy
- Precision
- Recall
- F1
They all uses under the hood the sklearn metrics of the same name. They allow different useful features when training a multilabel/multiclass model:
- have a macro/micro/per label/weighted/binary/per sample score
- score only the selected labels (usually what we call the positive labels) and ignore the negative ones. For example in case of a Named Entity Recognition task, positive labels are (`PERSON`, `LOCATION` or `ORGANIZATION`) and the negative one is `O`. | {
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] | closed | false | null | [] | null | [] | 2020-11-10T13:10:51Z | 2022-02-15T10:32:36Z | 2022-02-15T10:32:36Z | NONE | null | null | null | `datasets.load_dataset("csv", ...)` breaks if you have no connection (There is already this issue https://github.com/huggingface/datasets/issues/761 about it). It seems to be the same for metrics too.
I create this ticket to discuss a bit and gather what you have in mind or other propositions.
Here are some points to open discussion:
- if you want to prepare your code/datasets on your machine (having internet connexion) but run it on another offline machine (not having internet connexion), it won't work as is, even if you have all files locally on this machine.
- AFAIK, you can make it work if you manually put the python files (csv.py for example) on this offline machine and change your code to `datasets.load_dataset("MY_PATH/csv.py", ...)`. But it would be much better if you could run ths same code without modification if files are available locally.
- I've also been considering the requirement of downloading Python code and execute on your machine to use datasets. This can be an issue in a professional context. Downloading a CSV/H5 file is acceptable, downloading an executable script can open many security issues. We certainly need a mechanism to at least "freeze" the dataset code you retrieved once so that you can review it if you want and then be sure you use this one everywhere and not a version dowloaded from internet.
WDYT? (thks)
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https://api.github.com/repos/huggingface/datasets/issues/823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/823/comments | https://api.github.com/repos/huggingface/datasets/issues/823/events | https://github.com/huggingface/datasets/issues/823 | 739,815,763 | MDU6SXNzdWU3Mzk4MTU3NjM= | 823 | how processing in batch works in datasets | {
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] | closed | false | null | [] | null | [] | 2020-11-10T11:11:17Z | 2020-11-10T13:11:10Z | 2020-11-10T13:11:09Z | NONE | null | null | null | Hi,
I need to process my datasets before it is passed to dataloader in batch,
here is my codes
```
class AbstractTask(ABC):
task_name: str = NotImplemented
preprocessor: Callable = NotImplemented
split_to_data_split: Mapping[str, str] = NotImplemented
tokenizer: Callable = NotImplemented
max_source_length: str = NotImplemented
max_target_length: str = NotImplemented
# TODO: should not be a task item, but cannot see other ways.
tpu_num_cores: int = None
# The arguments set are for all tasks and needs to be kept common.
def __init__(self, config):
self.max_source_length = config['max_source_length']
self.max_target_length = config['max_target_length']
self.tokenizer = config['tokenizer']
self.tpu_num_cores = config['tpu_num_cores']
def _encode(self, batch) -> Dict[str, torch.Tensor]:
batch_encoding = self.tokenizer.prepare_seq2seq_batch(
[x["src_texts"] for x in batch],
tgt_texts=[x["tgt_texts"] for x in batch],
max_length=self.max_source_length,
max_target_length=self.max_target_length,
padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack
return_tensors="pt"
)
return batch_encoding.data
def data_split(self, split):
return self.split_to_data_split[split]
def get_dataset(self, split, n_obs=None):
split = self.data_split(split)
if n_obs is not None:
split = split+"[:{}]".format(n_obs)
dataset = load_dataset(self.task_name, split=split)
dataset = dataset.map(self.preprocessor, remove_columns=dataset.column_names)
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
dataset.set_format(type="torch", columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])
return dataset
```
I call it like
`AutoTask.get(task, train_dataset_config).get_dataset(split="train", n_obs=data_args.n_train)
`
This gives the following error, to me because the data inside the dataset = dataset.map(lambda batch: self._encode(batch), batched=True) is not processed in batch, could you tell me how I can process dataset in batch inside my function? thanks
File "finetune_multitask_trainer.py", line 192, in main
if training_args.do_train else None
File "finetune_multitask_trainer.py", line 191, in <dictcomp>
split="train", n_obs=data_args.n_train) for task in data_args.task}
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in get_dataset
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1236, in map
update_data = does_function_return_dict(test_inputs, test_indices)
File "/idiap/user/rkarimi/libs/anaconda3/envs/internship/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1207, in does_function_return_dict
function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 56, in <lambda>
dataset = dataset.map(lambda batch: self._encode(batch), batched=True)
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in _encode
[x["src_texts"] for x in batch],
File "/remote/idiap.svm/user.active/rkarimi/dev/internship/seq2seq/tasks.py", line 37, in <listcomp>
[x["src_texts"] for x in batch],
TypeError: string indices must be integers
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https://api.github.com/repos/huggingface/datasets/issues/822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/822/comments | https://api.github.com/repos/huggingface/datasets/issues/822/events | https://github.com/huggingface/datasets/issues/822 | 739,579,314 | MDU6SXNzdWU3Mzk1NzkzMTQ= | 822 | datasets freezes | {
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] | open | false | null | [] | null | [] | 2020-11-10T05:10:19Z | 2020-11-12T23:23:03Z | null | NONE | null | null | null | Hi, I want to load these two datasets and convert them to Dataset format in torch and the code freezes for me, could you have a look please? thanks
dataset1 = load_dataset("squad", split="train[:10]")
dataset1 = dataset1.set_format(type='torch', columns=['context', 'answers', 'question'])
dataset2 = load_dataset("imdb", split="train[:10]")
dataset2 = dataset2.set_format(type="torch", columns=["text", "label"])
print(len(dataset1))
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https://api.github.com/repos/huggingface/datasets/issues/821 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/821/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/821/comments | https://api.github.com/repos/huggingface/datasets/issues/821/events | https://github.com/huggingface/datasets/issues/821 | 739,506,859 | MDU6SXNzdWU3Mzk1MDY4NTk= | 821 | `kor_nli` dataset doesn't being loaded properly | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-10T02:04:12Z | 2020-11-16T13:59:12Z | 2020-11-16T13:59:12Z | NONE | null | null | null | There are two issues from `kor_nli` dataset
1. csv.DictReader failed to split features by tab
- Should not exist `None` value in label feature, but there it is.
```python
kor_nli_train['train'].unique('gold_label')
# ['neutral', 'entailment', 'contradiction', None]
```
- I found a reason why there is `None` values in label feature as following code
```python
from datasets import load_dataset
kor_nli_train = load_dataset('kor_nli', 'multi_nli')
for idx, example in enumerate(kor_nli_train['train']):
if example['gold_label'] is None:
print(idx, example)
break
# 16835 {'gold_label': None, 'sentence1': 'κ·Έλ μ μ μ μ κ°λ²Όμ΄ λ²
μ€ν¨ μλ§μ κ°μ§κ³ λ¬λ¦¬κΈ° μν΄ μ°μ μ²λΌ νμ μ€ν°λλ₯Ό λ£μλ€.\tμ μ μ μ λ€μΈμ’
μ¬μ±λ€κ³Ό ν¨κ» μλ λ°±μΈ λ¨μκ° μμλ€.\tentailment\nμ¬λ¦Όμ μ¬λΉ¨λ¦¬ μ·μ μ
μκ³ , μκ°μ μΌλ‘ λ―Έμ§κ·Όν λ¬Όμ λΏλ¦΄ μ μλ μμΉ¨ μΈνλ¬Όμ κΈ°κΊΌμ΄ κ°λμλ€.\tμ¬λ¦Όμ μ§μ₯μ λ¦μλ€.\tneutral\nλ΄μμμ κ·Έ μμ¬λ₯Ό ν΄λ΄€λλ°, κ±°κΈ°μ μκ³ κΈ°μ λ©μ§ μκ³ κΈ° λΆλΆμ μ리νκ³ λ°λ² νλ‘ λ§λ λλΉ€μ§ κ°μ κ±Έ κ°μ Έμλλ°, μ λ§ λλ¨ν΄.\tκ·Έλ€μ΄ κ±°κΈ°μ μ리νλ μ κ³ κΈ°λ μκ²Ήλ€. κ±°κΈ°μ μ λ λ¨Ήμ§ λ§λΌ.\tcontradiction\nν맀μμ μ£½μμμ λΈλΌμ΄μΈ λ°λ€ν... ν¬λ¦¬μ€ μΌλ¦¬\tν¬λ¦¬μ€ μΌλ¦¬λ μΈμΌμ¦λ§¨μ μ£½μμ μΈκΈνμ§ μλλ€.\tcontradiction\nκ·Έλ¬λ λμ μ리μ¬λ κ·Έλ₯ νκ° λ¬μ΄.\tμ€νκ° λλ λμ μ리μ¬λ νκ° λ¬λ€.\tneutral\nλ§μ§λ§ λ‘λ§μ 맹곡격 μ λ λ°€, 900λͺ
μ΄μμ μ λμΈ μλΉμλ€μ΄ λ‘λ§μΈλ€μκ² κ·Έλ€μ μ¬λ‘μ‘λ μΉλ¦¬λ₯Ό μ£ΌκΈ° 보λ€λ λλ μμ΄μ μ μ§λ λ€.\tλ‘λ§μΈλ€μ΄ κ·Έλ€μ ν¬νμ μΉλ¦¬νλλ‘ λ΄λ²λ €λκΈ° 보λ€λ 900λͺ
μ μ λμΈ μλΉμλ€μ΄ μμ΄νλ€.\tentailment\nμμΌλ‘ λ°μ¬νλΌ.\tλ°μ¬.\tneutral\nκ·Έλ¦¬κ³ λΉμ μ μ°λ¦¬ λ
μ΄ μμ΄μ»€μ μλ€λ κ²μ μκ³ μλ€. μ°λ¦¬ μ¬λλ€μ μ΄λ€ κ²μ΄ μΌλ§λ λ§μμ§ μ΄ν΄νμ§ λͺ»ν κ²μ΄λ€.\tλͺ¨λ μ¬λλ€μ μ°λ¦¬μ μΈ‘μ μμ€ν
μ΄ μ΄λ»κ² μλνλμ§ μκ³ μ΄ν΄ν©λλ€.\tcontradiction\nμ£Όλ―Έκ²μ€\tJumiygesλ λμμ μ΄λ¦μ΄λ€.\tneutral\nμ¬λμ μκΈ° λ―Όμ‘±μ λλ΄μΌ νλ€...\tμ¬λμ μ‘°κ΅μ 곡κ°ν΄μΌ νλ€.\tentailment\nλν PDD 63μ μ λΆμ μ
κ³κ° μ»΄ν¨ν° κΈ°λ° κ³΅κ²©μ λν΄ κ²½κ³ νκ³ λ°©μ΄ν μ€λΉλ₯Ό λ μν μ μλλ‘ μμ€ν
μ·¨μ½μ±, μν, μΉ¨μ
λ° μ΄μμ λν μ 보λ₯Ό 곡μ νλ λ©μ»€λμ¦μ μ립νλ κ²μ΄ μ€μνλ€λ κ²μ μΈμνμ΅λλ€.\tμ 보 μ μ‘ νλ‘ν μ½μ λ§λλ κ²μ μ€μνλ€.\tentailment\nμΉ΄ν λ§ νΌμμ λΈλΌ λ νλΈλ¦¬μΉ΄ λ°λ‘ λ¨μͺ½μλ νΌλ μ²΄κ° μλ €μ§ μ§ μ ν λλ¬Έμ νλ μ€νΈλ‘ λ§μΌμ΄λΌκ³ λΆλ Έλ 16μΈκΈ° λ‘μ§μμΈ λ©λ₯΄μΉ΄ν λμ€λ³΄(Mercato Nuovo)κ° μλ€.\tνΌμμ λΈλΌ λ νλΈλ¦¬μΉ΄μλ μΉ΄νκ° λ§μ΄ μλ€.\tentailment\nμ°λ¦¬κ° μ¬κΈ° μλ ν νΈλ¦°νμ΄ λ μ£Όμ λμ§ μ΄ν΄λ΄μΌκ² μ΄\tμ°λ¦¬λ νΈλ¦°νμ΄ λ¬΄μμ μ£Όμ λμ§ λ³΄λ λ° μκ°μ λλΉνμ§ μμ κ²μ΄λ€.\tcontradiction\nκ·Έλ¬λ μΌνΈμ‘±μ λ¬Ένμ κΈ°λ°μ κ°μ§ μμΌλλ κ΅νλ μ λ½μ μ ν₯ κΈ°λ
κ΅ μΈκ³μλ λ€λ₯΄κ² λ°μ νκ³ κ²°κ΅ λ‘λ§μ μ€μμ§κΆμ νμ μΌλ‘ λ체λμλ€.\tμμΌλλ κ΅νμλ μΌνΈμ‘±μ κΈ°μ§κ° μμλ€.\tentailment\nκΈμ, λ μ νμ μ¬μ§κ° μμ΄\tκΈμ, λμκ² λ§μ μ νκΆμ΄ μμ΄.\tcontradiction\nμ¬μ€, 곡μμ μΈ λ³΄μ₯μ μλ€.\tλ΄κ° μ° λ¬Όκ±΄μ λν 보μ¦μ΄ μμλ€.\tneutral\nλ νκΈ°μ°¨κΈ΄ νμ§λ§, μμμ λ₯΄ λΆλ₯΄μ ―μ μ¬λμ€λ¬μ΄ νΈμμμλ μΆμ λκ°μ΄ μμΎνλ€.\tμμμ λ₯΄ λΆλ₯΄κ²μμλ νΈμμμμ νλμ΄ μλλ₯΄κ³ λ°μ λΆμκΈ°λ₯Ό μ°μΆνλ€.\tcontradiction\nκ·Έμ μ¬ν μμμ΄ μ΄λ―Έ νΌμ‘λ€λ©΄ 곡격 μμλ νΌμ‘μ ν
μ§λ§ λ§μμμλ μ ν 곡ν©μ κΈ°λ―Έκ° λ³΄μ΄μ§ μμλ€.\tκ·Έλ μ λ§μμ΄ λΉν©νμ§ μμλμ§ μ μ μμλ€.\tneutral\nκ³Όκ±°μλ μ£½μμ μνμ΄ ν μ§μ ν맀λ₯Ό λ§λ λ° κ±°μ λμμ΄ λμ§ μμλ€.\tν μ§ ν맀λ μ΄λ ν μνλ κ΅ννμ§ μκ³ μ΄λ£¨μ΄μ§λ€.\tcontradiction\nμ΄λ μμ μ μ΄λ₯΄λ¬ λλ μ§κΈ λ€κ°μ€λ μλ‘μ΄ κ²λ€κ³Ό λμ€λ λ§μ μλ‘μ΄ κ²λ€μ΄ λ΄κ° λμ΄κ°κ³ μλ€κ³ λ§νλ μλλ‘ μ μ΄λ€κ³ μλ€.\tλλ μ¬μ ν λ΄κ° 보λ λͺ¨λ μλ‘μ΄ κ²μ μ¬λνλ€.\tcontradiction\nλ΄μ€μν¬λ 물리νμλ€μ΄ κ²½κΈ°μ₯ νμ¬μμ κ³ μλλ‘μ μλμ°¨ κ΅ν΅κ³Ό 보νμ κ΅ν΅μ κ°μ νκΈ° μν΄ μλΌμ μμ§μμ μ°κ΅¬νκ³ μλ€κ³ λ§νλ€.\tκ³ μλλ‘μ μλμ°¨ κ΅ν΅ νλ¦μ κ°μ νλ κ²μ 물리νμλ€μ΄ μλΌλ₯Ό μ°κ΅¬νλ μ΄μ μ€ νλμ΄λ€.\tentailment\nμΌλ§λ λ€λ₯Έκ°? κ·Έλ μ μ λ§μ λ©μΆμλ€κ° λ§μ μ΄μλ€.\tκ·Έλ κ·Έ μλ
κ° μ΄λμ μλμ§ μκ³ μΆμλ€.\tentailment\nκΈμ, κ·Έμκ² λ무 λ§μ κ²μ μ£Όμ§λ§.\tκ·Έλ ν¨μ¬ λ λ§μ κ²μ μꡬν κ²μ΄λ€.\tneutral\nμ무리 κ·Έμ μ°½μλ¬Όμ΄ μλ²½ν΄ λ³΄μΈλ€κ³ ν΄λ, κ·Έλ€μ λ―Ώλ κ²μ μλ§λ μ’μ μκ°μ΄ μλ κ²μ΄λ€.\'\tλμκΈ°λ₯Ό μ λ§λ λ€κ³ ν΄μ λκ΅°κ°λ₯Ό λ―Ώλ κ²μ μλ§ μ’μ§ μμ κ²μ΄λ€.\tneutral\nλ²μ€νλ§ κ·Έλ λΉμ(Bustling Gran Via)λ νΈν
, μμ , κ·Ήμ₯, λμ΄νΈν΄λ½, μΉ΄ν λ±μ΄ μ΄μ°λ¬μ Έ μ°μ±
κ³Ό μ°½κ°λ₯Ό λ³Ό μ μλ€.\tGran Viaλ νΈν
, μμ , κ·Ήμ₯, λμ΄νΈν΄λ½, μΉ΄νμ λ²νν μ‘°ν©μ΄λ€.\tentailment\nμ λΆ μΈμμ\tκ·Έ μ¬λ¬΄μ€μ μμ±ν΄μ μμΉν΄ μλ€.\tneutral\nμ€μ λ¬Έν μ μμ΄ μ΄λ μλμ§ μκ³ μΆλ€λ©΄ νμμ μμ΄λ²λ¦¬κ³ μ€λ¦¬μ½ 밸리μ λ λλͺ¬λλ₯Ό μκ°ν΄ 보λΌ.\tμ€μ λ¬Έν μ μμ λ λλͺ¬λμμ μΌμ΄λλ€.\tentailment\nκ·Έλ¦¬κ³ νλμ€λ¦°μ μ£Όμ§ μκΈ° μν΄ μΉ¨λ μμ μ¬λ €λ¨μ΄\tκ·Έλ
μ λ°©μλ νλμ€λ¦°μ΄ μλ€λ μ§νκ° μ ν μμλ€.\tcontradiction\nL.A.μ μΌμΈ μμ₯μ ν보νλ κ²μ λ§μκ³ μ λ ΄ν 그루λΈλ₯Ό μ‘κ³ , λμ΄ μλ νλΉμ μ¦κΈ°κ³ , μ μ ν λμ°λ¬Ό, κ½, ν₯, κ·Έλ¦¬κ³ κ°μ ― κ°λ‘μ΄λ₯Ό ꡬμ
νλ©΄μ νμ§μΈλ€κ³Ό μ΄μΈλ¦΄ μ μλ νλ₯ν λ°©λ²μ΄λ€.\tLAμ μΌμΈ μμ₯μ λμλ€λλ κ²μ μκ° λλΉλ€.\tcontradiction\nμλλ λ°μΌλ‘ λμ μλμ νμ¨μ λ΄μ¬μλ€. λ¨ ν λ², κ·Έλ¦¬κ³ λ§λ¦¬νμμ¬ λ§μ μ λ‘ λλ΄μλ κ²°μ¬μ΄ λ€μμ¬ μμλ€.\tμλλ μμ¬νκ³ λ§λ¦¬νμμ¬ λ§μ μ μ λ€ λ§μκΈ°λ‘ κ²°μ¬νλ€.\tentailment\n5 μμ Vajpayeeλ ν΅ μ€νμ μ±κ³΅μ μΈ μλ£λ₯Ό λ°ννλλ°, μΈλμΈλ€μ μ£ΌκΆμ νμλ‘ μ μ νμ§λ§ μ΄μ κ΅κ°μ μꡬμμ μΈλ κ΄κ³λ₯Ό 볡μ‘νκ² λ§λ€ μ μμ΅λλ€.\tμΈλλ μ±κ³΅μ μΈ ν΅μ€νμ ν μ μ΄ μλ€.\tcontradiction\nνλΌλ
Έ μμμ λ³΄ν΅ μΌλ§λ λ§μ κ²μ κ°μ§κ³ μλκ°?\tμ μ¬λλ€ μ€μ νλΌλ
Έ μμ κ°λ³Έ μ¬λ μμ΄?\tcontradiction\nκ·Έκ²μ μ 체μ μΈ ννμ μ°μν¨μ μ΄ν 건λνΈμμ κ°μ₯ μ λ³Ό μ μλ€. μλνλ©΄, λ‘λ§μ μλ μ± λ² λλ‘μ²λΌ, λμ κΈΈμν λ³ΈλΉ λ€λ‘ λ κ°κΉμ΄ κ³³μ μ¬λΌμ§κΈ° λλ¬Έμ΄λ€.\tμ± λ² λλ‘μ κΈΈμν λ³ΈλΉμ λμ κ°λ¦°λ€.\tentailment\nλΉμ μ μν΄μ΄ μ΄μ κ°λ°μ μΈ κΈ°μ¨μ κ°μ§κ³ λλλ₯Ό 그릴 κ²μ΄λΌκ³ μκ°νκ² μ§λ§, μλμ€; κ·Έλ κ·Έμ λͺ¨λ κ²½λ ₯μμ λ¨ ν μ λ§μ κ·Έλ Έκ³ , κ·Έκ²μ μ¬μν κ·Έλ¦Όμ΄λ€.\tκ·Έλ κ·Έκ²μ΄ κ·Έλ₯Ό λΆνΈνκ² λ§λ€μκΈ° λλ¬Έμ νλλ§ κ·Έλ Έλ€.\tneutral\nμ΄ μΈμμ μΈ νκ²½μ μλ λν¬ λ μ¨μ΄ 루λΈλ₯΄ λ°λ¬Όκ΄μ μΉ¨μ€μμ λ³Ό μ μλλ‘ κ³νλμλλ°, κ·Έ λΉμ κΆμ μ΄μμ΅λλ€.\tλν΄λ μΉμ κ·Έμ λͺ¨λ κΆμ μ μλ κ·Έμ μΉ¨μ€μμ 보λ κ²½μΉμ λ§μ κ΄μ¬μ κ°μ‘λ€.\tneutral\nκ·Έλ μ°λ¦¬μκ² λ¬Έ μ΄μ λ₯Ό 건λ€μ£Όκ³ λ κΈν λ λ¬λ€.\tκ·Έλ κΈ΄μ₯ν΄μ μ°λ¦¬μκ² μ΄μ λ₯Ό 빨리 μ£Όμλ€.\tneutral\nμμνλ λν μ΅μ’
κ·μΉμ OMBμ μ μΆνλ€.\tμμνλ λν μ΄ κ·μΉμ λ€λ₯Έ κ·Έλ£Ήμ μ μΆνμ§λ§ μ΅μ’
κ·μΉμ OMBκ° νκ°νκΈ° μν κ²μ΄ μμ΅λλ€.\tneutral\nμ μκ°κ²μ κ°λ³΄λ©΄ μ¬λ¦¬λΉμμ 볡μ νν©λ¬Ό κ°μ μ μΎν μ΄λ¦μ κ°μ§ μ νλ€μ μ°Ύμ μ μμ κ²λλ€.μ΄ μ νμ΄ λΏλ¦¬λ₯Ό λ΄λ¦¬λλ‘ λκΈ° μν΄ μ΄¬μμ μ λ¨λ λμ λ©ν¬μμ νλ νΈλ₯΄λͺ¬μ νΌν©λ¬Όμ΄μ£ .\tμ μ κ°κΎΈκΈ° κ°κ²μ μ νλ€μ μ’
μ’
κ·Έλ€μ λͺ©μ μ μ€λͺ
νκΈ° μν΄ κΈ°μ μ μΌλ‘λ κ³Όνμ μΌλ‘ νμλ μ΄λ¦(μ¬λ¦¬λΉμμ 볡μ νν©λ¬Όμ²λΌ)μ λΆμ¬λ°λλ€.\tneutral\nμ€νλ μ€νΈ μμ μ΄λ μ κ·Έλ
μ μ΄μΌκΈ°λ₯Ό λ°κΎΈμλμ§μ ν¨μ¬ λ κ΄μ¬μ΄ μμ κ²μ΄λ€.\tμ€νΈμ μ΄μΌκΈ°λ μ‘°κΈλ λ³νμ§ μμλ€.\tcontradiction\nλ¨νΈκ³Όμ λ§μ§λ§ λκ²°λ‘ λ§₯ν°μ΄λ λ
ΈλΌμ λ³μ μ λ무λ λ₯μνκ² μκ³ ν΄ μκΈ° λλ¬Έμ, κ·Έλ
μκ²λ λΉν©μ€λ¬μΈ μ λλ‘ κ°μμ€λ¬μ΄ κ²μ²λΌ 보μ΄μ§λ§, μ°λ¦¬μκ²λ κ°μ μ μΌλ‘ λΆκ°νΌν΄ 보μΈλ€.\tλ
ΈλΌμ λ³μ μ λΆλͺ
νκ³ νμ°μ μ΄μλ€.\tcontradiction\nμ΄μ§νΈ μ΅λ¨λ¨ λμμΈ μμ€μμ μ€λ μμ¬λ₯Ό ν΅ν΄ μ€μν μν μ ν΄μλ€.\tμμ€μμ μ΄μ§νΈ κ΅κ²½ λ°λ‘ μμ μμΉν΄ μμ΅λλ€.\tneutral\nκ·Έλ¬λ ν¨μ¬ λ μ°μν 건μΆμ ν°μΉλ μ μ±ν μΆ€μΈ Bharatanatyamμμ μνλ 108 κ°μ§ κΈ°λ³Έ ν¬μ¦λ₯Ό μλ° ν¨λμμ λ³Ό μ μμ΅λλ€.\tν¨λμ λν μλ°μ λ¬μ¬λ μΌλ°μ μΈ λͺ¨ν°λΈλ€.\tneutral\nνΈνλ‘κ² μ¬μ΄μ§ κ³λ¨μ μ μμ μ΄ν리μ νμμ κ°μ₯ νλ₯ν μμλΈ μ€ νλμ
λλ€.\tμλ¦λ€μ΄ μ μκ³Ό ν¬κ·ν κ½κ½μ΄ λͺ¨λ μ΄ν리μμ νμμ μΈ μ€νμΌμ 보μ¬μ€λ€.\tneutral\nμ, κ·Έλ¬μΌλ©΄ μ’μμ ν
λ°\tλλ κ·Έκ²μ λ€λ₯΄κ² ν κΈ°νλ₯Ό λͺΉμ κ°λ§νλ€.\tentailment\nννκ° λ μ±μ κΈ°μμ μ리μ‘κ³ μλ μμ μ€μΈ λμ μΌμ΄μμ€λ²κ·Έλ λ
Έλ²¨ ννμ μμμ μλ²νΈ μλ°μ΄μ²(1875λ
)μ μΆμμ§λ‘ λ리 μλ €μ Έ μλ€.\tμλ²νΈ μλ°μ΄μ²λ λ λ€ μΌμ΄μμ€λ²κ·Έ λ§μμ μμλ€.\tentailment\nκ³ κ°λλ λ¬Έμ κ° μλ λλΆλΆμ νμλ€μ΄ λ°κ²¬λ κ²μ 보μ₯νλ€.\tμ₯λΉ λ―Όκ°λλ λ¬Έμ νμ§μ κ΄λ ¨μ΄ μμ΅λλ€.\tcontradiction\nμ€λμ νμ€ν λ°λ°μ§ κ°μ λ μ΄μμ΄\tμ€λ μ¬λ¬΄μ€μ μλ λͺ¨λ μ¬λλ€μ λ°λ°μ§λ₯Ό μ
μλ€.\tneutral\nλͺ»μκΈ΄ ν±μλλ₯Ό μ
κ³ .\tκ·Έκ²μ λΆνμκ³Ό μ£Όν©μμ
λλ€.\tneutral\nμ΄μ£Ό λ
Έλ μμ©μ μ€ λ§μ΄ κ° κ·Έλ€μ νμ§ μμμ μ°λ€.\tλ
Έλ μμ©μμλ νμ§ μμμ μ¬λ μ΄μ£Ό λ
Έλμλ€μ μ¬μ§μ΄ μλ€.\tneutral\nκ·Έλ, κ·Έκ° μ μΈκ³λ₯Ό μ¬νν νμ κ·Έλ° κ±°μΌ\tκ·Έκ²μ μ¬λλ€μ μΈκ³ μ¬νμ λ°λ₯Έλ€.\tentailment\n건λνΈμ ν¬κ³ ν° μ°Έλ무 λͺ κ·Έλ£¨κ° μλ€.\tμ°λ¦¬λ μ¬κΈ° μ€ν¬λ μ΄λ€ μ’
λ₯μ λ―Έκ΅ λ무λ μλ€.\tcontradiction\nFort-de-Franceμμ μΆλ°νλ μλμ°¨λ μ¬κ°μ μΌλ‘, λΉμ μ μμΈ ? λ°λ€ ν¬λκ° κ·Έλμ μ 곡νλ μΎμ ν κ°μ λͺ¨λ ν΄λ³κ³Ό νΌν¬λ ν
μ΄λΈ, μ΄λ¦°μ΄ λ―ΈλλΌν, μλΉμ΄ μλ μλμ λμ°©ν μ μλ€.\tνλμ€ μμμμ μλμ°¨λ ν리λ₯Ό νκ³ μμΈλ‘ κ° μ μλ€.\tentailment\nκ·Έλ¦¬κ³ κ·Έκ²μ μ¨λΌλ°°λ§μ£Όκ° μμνλ λλ‘ μμ°μμ 50λ§ λ¬λ¬λ₯Ό μκ°νμ§ μμ κ²μ΄λΌλ κ²μ μλ―Ένλ€.\tμ¨λΌλ°°λ§ μ£Όλ μμ° μκ°μ νμ§ μμλ€. μλνλ©΄ κ·Έλ κ² νλ κ²μ λν μ΄κΈ° μ λΉμ±μ΄ μ λ° μ‘°μ¬μ λ§μμ§ μμκΈ° λλ¬Έμ΄λ€.\tneutral\nμμμ΄ λ¨Όμ μ΄ .. μ΄ .. λ
ΈμΈμ΄λ κ°μ‘±μ μμμμ 보λ΄λ κ²μ λν΄ μ΄λ»κ² μκ°νλ?\tκ°μ‘±μ μμμμ 보λ΄μ μ¬λ κ²μ λν΄ μ΄λ»κ² μκ°νλμ§ μ νμκ° μλ€.\tcontradiction\nλλ¨Έμ§λ λμκ² λ¬λ Έμ΄.\tλλ¨Έμ§λ λμκ² λ¬λ Έμ§λ§ μκ°μ΄ λ§μ§ μλ€.\tneutral\nμ-ν , 3μμ νλ³μ νλ κ²μ λν΄ κ±±μ νλ©΄ μ λλ€λ κ²μ μκ³ μλ 3μμ΄μΌ.\t3μμ κ·Έλ κ² λ₯μ§ μλ€.\tneutral\nκ·Έλ¦¬κ³ μ΄, κ·Έλ° μμ κ²λ€λ‘ λ€μ μμν΄λ΄. μμ§ ν¨μ¬ μΈ. μ΄, κ·Έ νΉλ³ν λͺ¨λΈ μ°¨λ 150λ¬λ¬μΌ.\tκ·Έ λͺ¨νμ°¨λ 4μ² λ¬λ¬κ° λ λ€.\tcontradiction\nλ΄μΌ λμκ°μΌ νλ€λ©΄, μΉΌμ΄ λ§νλ€.\tλμκ° μ μμ΄. μ€λμ μ λΌ. λ΄μΌμ μ λΌ. μ λ μ λΌ." μΉΌμ΄ λ§νλ€.', 'sentence2': 'contradiction'}
```
2. (Optional) Preferred to change the name of the features for the compatibility with `run_glue.py` in π€ Transformers
- `kor_nli` dataset has same data structure of multi_nli, xnli
- Changing the name of features and the feature type of 'gold_label' to ClassLabel might be helpful
```python
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"premise": datasets.Value("string"),
"hypothesis": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
}
),
```
If you don't mind, I would like to fix this.
Thanks! | {
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} | Updated quail to most recent version, to address the problem originally discussed [here](https://github.com/huggingface/datasets/issues/806). | {
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} | The `dumps` function need to be deterministic for the caching mechanism.
However in #816 I noticed that one of dill's method to recursively check the globals of a function may return the globals in different orders each time it's used. To fix that I sort the globals by key in the `globs` dictionary.
I had to add a rectified `save_function` to the saving functions registry of the Pickler to make it work.
This should fix #816 | {
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} | 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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- **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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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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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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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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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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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
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using datasets version = 1.1.2 | {
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] | closed | false | null | [] | null | [] | 2020-11-07T17:08:58Z | 2022-02-15T10:51:44Z | 2022-02-14T15:24:20Z | NONE | null | null | null | Hello,
when I select amazon_us_reviews in nlp viewer, it shows error.
https://huggingface.co/nlp/viewer/?dataset=amazon_us_reviews
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https://api.github.com/repos/huggingface/datasets/issues/810 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/810/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/810/comments | https://api.github.com/repos/huggingface/datasets/issues/810/events | https://github.com/huggingface/datasets/pull/810 | 737,878,370 | MDExOlB1bGxSZXF1ZXN0NTE2ODQzMzQ3 | 810 | Fix seqeval metric | {
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"url": "https://api.github.com/users/sgugger"
} | [] | closed | false | null | [] | null | [] | 2020-11-06T16:11:43Z | 2020-11-09T14:04:29Z | 2020-11-09T14:04:28Z | MEMBER | null | false | {
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} | The current seqeval metric returns the following error when computed:
```
~/.cache/huggingface/modules/datasets_modules/metrics/seqeval/78a944d83252b5a16c9a2e49f057f4c6e02f18cc03349257025a8c9aea6524d8/seqeval.py in _compute(self, predictions, references, suffix)
102 scores = {}
103 for type_name, score in report.items():
--> 104 scores[type_name]["precision"] = score["precision"]
105 scores[type_name]["recall"] = score["recall"]
106 scores[type_name]["f1"] = score["f1-score"]
KeyError: 'LOC'
```
This is because the current code basically tries to do:
```
scores = {}
scores["LOC"]["precision"] = some_value
```
which does not work in python. This PR fixes that while keeping the previous nested structure of results, with the same keys. | {
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https://api.github.com/repos/huggingface/datasets/issues/809 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/809/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/809/comments | https://api.github.com/repos/huggingface/datasets/issues/809/events | https://github.com/huggingface/datasets/issues/809 | 737,832,701 | MDU6SXNzdWU3Mzc4MzI3MDE= | 809 | Add Google Taskmaster dataset | {
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] | closed | false | null | [] | null | [] | 2020-11-06T15:10:41Z | 2021-04-20T13:09:26Z | 2021-04-20T13:09:26Z | MEMBER | null | null | null | ## Adding a Dataset
- **Name:** Taskmaster
- **Description:** A large dataset of task-oriented dialogue with annotated goals (55K dialogues covering entertainment and travel reservations)
- **Paper:** https://arxiv.org/abs/1909.05358
- **Data:** https://github.com/google-research-datasets/Taskmaster
- **Motivation:** One of few annotated datasets of this size for goal-oriented dialogue
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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https://api.github.com/repos/huggingface/datasets/issues/808 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/808/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/808/comments | https://api.github.com/repos/huggingface/datasets/issues/808/events | https://github.com/huggingface/datasets/pull/808 | 737,638,942 | MDExOlB1bGxSZXF1ZXN0NTE2NjQ0NDc0 | 808 | dataset(dgs): initial dataset loading script | {
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"url": "https://api.github.com/users/AmitMY"
} | [] | closed | false | null | [] | null | [] | 2020-11-06T10:14:43Z | 2021-03-23T06:18:55Z | 2021-03-23T06:18:55Z | CONTRIBUTOR | null | false | {
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} | When trying to create dummy data I get:
> Dataset datasets with config None seems to already open files in the method `_split_generators(...)`. You might consider to instead only open files in the method `_generate_examples(...)` instead. If this is not possible the dummy data has t o be created with less guidance. Make sure you create the file dummy_data.
I am not sure how to manually create the dummy_data (what exactly it should contain)
Also note, this library says:
> ImportError: To be able to use this dataset, you need to install the following dependencies['pympi'] using 'pip install pympi' for instance'
When you actually need to `pip install pympi-ling`
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https://api.github.com/repos/huggingface/datasets/issues/807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/807/comments | https://api.github.com/repos/huggingface/datasets/issues/807/events | https://github.com/huggingface/datasets/issues/807 | 737,509,954 | MDU6SXNzdWU3Mzc1MDk5NTQ= | 807 | load_dataset for LOCAL CSV files report CONNECTION ERROR | {
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"url": "https://api.github.com/users/shexuan"
} | [] | closed | false | null | [] | null | [] | 2020-11-06T06:33:04Z | 2021-01-11T01:30:27Z | 2020-11-14T05:30:34Z | NONE | null | null | null | ## load_dataset for LOCAL CSV files report CONNECTION ERROR
- **Description:**
A local demo csv file:
```
import pandas as pd
import numpy as np
from datasets import load_dataset
import torch
import transformers
df = pd.DataFrame(np.arange(1200).reshape(300,4))
df.to_csv('test.csv', header=False, index=False)
print('datasets version: ', datasets.__version__)
print('pytorch version: ', torch.__version__)
print('transformers version: ', transformers.__version__)
# output:
datasets version: 1.1.2
pytorch version: 1.5.0
transformers version: 3.2.0
```
when I load data through `dataset`:
```
dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
```
Error infos:
```
ConnectionError Traceback (most recent call last)
<ipython-input-17-bbdadb9a0c78> in <module>
----> 1 dataset = load_dataset('csv', data_files='./test.csv', delimiter=',', autogenerate_column_names=False)
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, script_version, **config_kwargs)
588 # Download/copy dataset processing script
589 module_path, hash = prepare_module(
--> 590 path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
591 )
592
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/load.py in prepare_module(path, script_version, download_config, download_mode, dataset, force_local_path, **download_kwargs)
266 file_path = hf_github_url(path=path, name=name, dataset=dataset, version=script_version)
267 try:
--> 268 local_path = cached_path(file_path, download_config=download_config)
269 except FileNotFoundError:
270 if script_version is not None:
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
306 user_agent=download_config.user_agent,
307 local_files_only=download_config.local_files_only,
--> 308 use_etag=download_config.use_etag,
309 )
310 elif os.path.exists(url_or_filename):
~/.conda/envs/py36/lib/python3.6/site-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag)
473 elif response is not None and response.status_code == 404:
474 raise FileNotFoundError("Couldn't find file at {}".format(url))
--> 475 raise ConnectionError("Couldn't reach {}".format(url))
476
477 # Try a second time
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py
```
And I try to connect to the site with requests:
```
import requests
requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
```
Similarly Error occurs:
```
---------------------------------------------------------------------------
ConnectionRefusedError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
159 conn = connection.create_connection(
--> 160 (self._dns_host, self.port), self.timeout, **extra_kw
161 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
83 if err is not None:
---> 84 raise err
85
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/connection.py in create_connection(address, timeout, source_address, socket_options)
73 sock.bind(source_address)
---> 74 sock.connect(sa)
75 return sock
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
NewConnectionError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
676 headers=headers,
--> 677 chunked=chunked,
678 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
380 try:
--> 381 self._validate_conn(conn)
382 except (SocketTimeout, BaseSSLError) as e:
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in _validate_conn(self, conn)
975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock`
--> 976 conn.connect()
977
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in connect(self)
307 # Add certificate verification
--> 308 conn = self._new_conn()
309 hostname = self.host
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connection.py in _new_conn(self)
171 raise NewConnectionError(
--> 172 self, "Failed to establish a new connection: %s" % e
173 )
NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
MaxRetryError Traceback (most recent call last)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
448 retries=self.max_retries,
--> 449 timeout=timeout
450 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
724 retries = retries.increment(
--> 725 method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
726 )
~/.conda/envs/py36/lib/python3.6/site-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
438 if new_retry.is_exhausted():
--> 439 raise MaxRetryError(_pool, url, error or ResponseError(cause))
440
MaxRetryError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
During handling of the above exception, another exception occurred:
ConnectionError Traceback (most recent call last)
<ipython-input-20-18cc3eb4a049> in <module>
1 import requests
2
----> 3 requests.head("https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/csv/csv.py")
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in head(url, **kwargs)
102
103 kwargs.setdefault('allow_redirects', False)
--> 104 return request('head', url, **kwargs)
105
106
~/.conda/envs/py36/lib/python3.6/site-packages/requests/api.py in request(method, url, **kwargs)
59 # cases, and look like a memory leak in others.
60 with sessions.Session() as session:
---> 61 return session.request(method=method, url=url, **kwargs)
62
63
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
528 }
529 send_kwargs.update(settings)
--> 530 resp = self.send(prep, **send_kwargs)
531
532 return resp
~/.conda/envs/py36/lib/python3.6/site-packages/requests/sessions.py in send(self, request, **kwargs)
641
642 # Send the request
--> 643 r = adapter.send(request, **kwargs)
644
645 # Total elapsed time of the request (approximately)
~/.conda/envs/py36/lib/python3.6/site-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)
514 raise SSLError(e, request=request)
515
--> 516 raise ConnectionError(e, request=request)
517
518 except ClosedPoolError as e:
ConnectionError: HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/1.1.2/datasets/csv/csv.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f3cceda5e48>: Failed to establish a new connection: [Errno 111] Connection refused',))
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/806/comments | https://api.github.com/repos/huggingface/datasets/issues/806/events | https://github.com/huggingface/datasets/issues/806 | 737,215,430 | MDU6SXNzdWU3MzcyMTU0MzA= | 806 | Quail dataset urls are out of date | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-05T19:40:19Z | 2020-11-10T14:02:51Z | 2020-11-10T14:02:51Z | CONTRIBUTOR | null | null | null | <h3>Code</h3>
```
from datasets import load_dataset
quail = load_dataset('quail')
```
<h3>Error</h3>
```
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/text-machine-lab/quail/master/quail_v1.2/xml/ordered/quail_1.2_train.xml
```
As per [quail v1.3 commit](https://github.com/text-machine-lab/quail/commit/506501cfa34d9ec6c042d31026ba6fea6bcec8ff) it looks like the location and suggested ordering has changed. In [https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58](https://github.com/huggingface/datasets/blob/master/datasets/quail/quail.py#L52-L58) the quail v1.2 datasets are being pointed to, which don't exist anymore. | {
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https://api.github.com/repos/huggingface/datasets/issues/805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/805/comments | https://api.github.com/repos/huggingface/datasets/issues/805/events | https://github.com/huggingface/datasets/issues/805 | 737,019,360 | MDU6SXNzdWU3MzcwMTkzNjA= | 805 | On loading a metric from datasets, I get the following error | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-05T15:14:38Z | 2022-02-14T15:32:59Z | 2022-02-14T15:32:59Z | NONE | null | null | null | `from datasets import load_metric`
`metric = load_metric('bleurt')`
Traceback:
210 class _ArrayXDExtensionType(pa.PyExtensionType):
211
212 ndims: int = None
AttributeError: module 'pyarrow' has no attribute 'PyExtensionType'
Any help will be appreciated. Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/804/comments | https://api.github.com/repos/huggingface/datasets/issues/804/events | https://github.com/huggingface/datasets/issues/804 | 736,858,507 | MDU6SXNzdWU3MzY4NTg1MDc= | 804 | Empty output/answer in TriviaQA test set (both in 'kilt_tasks' and 'trivia_qa') | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-05T11:38:01Z | 2020-11-09T14:14:59Z | 2020-11-09T14:14:58Z | CONTRIBUTOR | null | null | null | # The issue
It's all in the title, it appears to be fine on the train and validation sets.
Is there some kind of mapping to do like for the questions (see https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md) ?
# How to reproduce
```py
from datasets import load_dataset
kilt_tasks = load_dataset("kilt_tasks")
trivia_qa = load_dataset('trivia_qa', 'unfiltered.nocontext')
# both in "kilt_tasks"
In [18]: any([output['answer'] for output in kilt_tasks['test_triviaqa']['output']])
Out[18]: False
# and "trivia_qa"
In [13]: all([answer['value'] == '<unk>' for answer in trivia_qa['test']['answer']])
Out[13]: True
# appears to be fine on the train and validation sets.
In [14]: all([answer['value'] == '<unk>' for answer in trivia_qa['train']['answer']])
Out[14]: False
In [15]: all([answer['value'] == '<unk>' for answer in trivia_qa['validation']['answer']])
Out[15]: False
In [16]: any([output['answer'] for output in kilt_tasks['train_triviaqa']['output']])
Out[16]: True
In [17]: any([output['answer'] for output in kilt_tasks['validation_triviaqa']['output']])
Out[17]: True
``` | {
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|
https://api.github.com/repos/huggingface/datasets/issues/802 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/802/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/802/comments | https://api.github.com/repos/huggingface/datasets/issues/802/events | https://github.com/huggingface/datasets/pull/802 | 736,296,343 | MDExOlB1bGxSZXF1ZXN0NTE1NTM1MDI0 | 802 | Add XGlue | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-04T17:29:54Z | 2022-04-28T08:15:36Z | 2020-12-01T15:58:27Z | MEMBER | null | false | {
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} | Dataset is ready to merge. An important feature of this dataset is that for each config the train data is in English, while dev and test data are in multiple languages. Therefore, @lhoestq and I decided offline that we will give the dataset the following API, *e.g.* for
```python
load_dataset("xglue", "ner") # would give the splits 'train', 'validation.en', 'test.en', 'validation.es', 'test.es', ...
```
=> therefore one can load a single language test via
```python
load_dataset("xglue", "ner", split="test.es")
```
Close #749. | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-04T03:53:11Z | 2020-12-23T14:02:58Z | 2020-12-23T14:02:58Z | NONE | null | null | null | Hi,
I'm wondering if it's possible to join two (preprocessed) datasets with the same number of rows but different labels?
I'm currently trying to create paired sentences for BERT from `wikipedia/'20200501.en`, and I couldn't figure out a way to create a paired sentence using `.map()` where the second sentence is **not** the next sentence (i.e., from a different article) of the first sentence.
Thanks! | {
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} | [] | closed | false | null | [] | null | [] | 2020-11-04T02:57:11Z | 2020-11-11T15:28:32Z | 2020-11-11T15:28:32Z | CONTRIBUTOR | null | false | {
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