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"In 0.4.0, the assertion in `concatenate_datasets ` is on the features, and not the schema.\r\nCould you try to update `nlp` ?\r\n\r\nAlso, since 0.4.0, you can use `dset_wikipedia.cast_(dset_books.features)` to avoid the schema cast hack.",
"Or maybe the assertion comes from elsewhere ?",
"I'm using the master... | 1,597,105,666,000 | 1,598,458,639,000 | 1,598,458,639,000 | CONTRIBUTOR | null | Here's the code I'm trying to run:
```python
dset_wikipedia = nlp.load_dataset("wikipedia", "20200501.en", split="train", cache_dir=args.cache_dir)
dset_wikipedia.drop(columns=["title"])
dset_wikipedia.features.pop("title")
dset_books = nlp.load_dataset("bookcorpus", split="train", cache_dir=args.cache_dir)
dse... | {
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"I did the release on github, and updated the doc :)\r\nSorry for the delay",
"Thanks!"
] | 1,597,103,997,000 | 1,597,164,607,000 | 1,597,164,607,000 | CONTRIBUTOR | null | 0.4.0 was released on PyPi, but not on GitHub. This means [the documentation](https://huggingface.co/nlp/) is still displaying from 0.3.0, and that there's no tag to easily clone the 0.4.0 version of the repo. | {
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`nlp.load_dataset("wikipedia", "20200501.en", split="train", dir="/tmp/wikipedia")`
gives an error if apache_beam is not installed, stemming from
https://github.com/huggingface/nlp/blob/38eb2413de54ee804b0be81781bd65ac4a748ced/src/nlp/builder.py#L981-L988
This succeeded without the dependency in ve... | {
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"I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.... | 1,597,080,771,000 | 1,597,122,454,000 | null | MEMBER | null | I have encountered multiple issues while trying to:
```
import nlp
dataset = nlp.load_dataset('wmt16', 'ru-en')
metric = nlp.load_metric('wmt16')
```
1. I had to do `pip install -e ".[dev]" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and no... | {
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"It looks like you need to rebase from master to fix the CI. Could you do that please ?"
] | 1,597,066,631,000 | 1,598,870,566,000 | 1,598,870,566,000 | CONTRIBUTOR | null | I am using the latest elasticsearch binary and master of nlp. For me elasticsearch searches failed because the resultant "id_" returned for searches are strings, but our library assumes them to be integers. | {
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"Yes indeed it looks like some `'` and spaces are missing (for example in `dont` or `didnt`).\r\nDo you know if there exist some copies without this issue ?\r\nHow would you fix this issue on the current data exactly ? I can see that the data is raw text (not tokenized) so I'm not sure I understand how you would do... | 1,596,956,004,000 | 1,601,467,264,000 | null | CONTRIBUTOR | null | It seem that the bookcoprus data downloaded through the library was pretokenized with NLTK's Treebank tokenizer, which changes the text in incompatible ways to how, for instance, BERT's wordpiece tokenizer works. For example, "didn't" becomes "did" + "n't", and double quotes are changed to `` and '' for start and end q... | {
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import nlp
dataset = nlp.load_dataset('xtreme', 'PAWS-X.en')
dataset['test'][0]
```
prints the following
```
{'label': 'label', 'sentence1': 'sentence1', 'sentence2': 'sentence2'}
```
dataset['test'][0] should probably be the first item in the dataset, not just a dictionary mapping the column names t... | {
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"Thanks for adding this mirror link :)\r\n\r\nCould you run the following command to update the json file `dataset_infos.json` used to verify the integrity of the downloaded file ?\r\n\r\n```\r\nnlp-cli test ./datasets/rotten_tomatoes --save_infos --ignore_verifications\r\n```",
"done! @lhoestq ",
"the build_do... | 1,596,813,945,000 | 1,598,276,017,000 | 1,598,276,017,000 | CONTRIBUTOR | null | {
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"found a mirror: https://storage.googleapis.com/seldon-datasets/sentence_polarity_v1/rt-polaritydata.tar.gz",
"fixed in #484 ",
"Closing this one. Thanks again @jxmorris12 for taking care of this :)"
] | 1,596,813,121,000 | 1,599,557,794,000 | 1,599,557,793,000 | CONTRIBUTOR | null | In an interesting twist of events, the individual who created the movie review seems to have left Cornell, and their webpage has been removed, along with the movie review dataset (http://www.cs.cornell.edu/people/pabo/movie-review-data/rt-polaritydata.tar.gz). It's not downloadable anymore. | {
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"This comes from an overflow in pyarrow's array.\r\nIt is stuck inside the loop that reduces the batch size to avoid the overflow.\r\nI'll take a look",
"I created a PR to fix the issue.\r\nIt was due to an overflow check that handled badly an empty list.\r\n\r\nYou can try the changes by using \r\n```\r\n!pip in... | 1,596,788,615,000 | 1,597,241,626,000 | 1,597,190,115,000 | NONE | null | Hi Huggingface Team!
Thank you guys once again for this amazing repo.
I have tried to prepare ELI5 to train with T5, based on [this wonderful notebook of Suraj Patil](https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb)
However, when I run `dataset.map()` on ELI5 to prepare `input_text, ta... | {
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"Not sure why the AWS test is failing - perhaps I made too many concurrent CI builds 😢. Can someone please rerun the CI to check the error is not on my end?",
"I pushed an improved docstring and the unit tests now pass, which suggests the previous failure on AWS was simply a timeout error. \r\n\r\nFor some reaso... | 1,596,744,129,000 | 1,597,911,368,000 | 1,597,911,368,000 | MEMBER | null | ## Description
This PR applies utf-8 encoding for all instances of `with open(...) as f` to all Python files in `datasets/`. As suggested by @thomwolf in #468 , we use regular expressions and the following function
```python
def apply_encoding_on_file_open(filepath: str):
"""Apply UTF-8 encoding for all insta... | {
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"Looks good to me as well but we'll want to add a test indeed.\r\nYou can add one if you have time @TevenLeScao.\r\nOtherwise, we'll do it when we are back with Quentin. ",
"I fixed it in #494 "
] | 1,596,713,825,000 | 1,597,221,370,000 | 1,597,221,370,000 | MEMBER | null | As observed for example in #469 , currently `__getitem__` does not convert the data to the dataset format when indexing by column. This is a hotfix that imitates functional 0.3.0. code. In the future it'd probably be nice to have a test there. | {
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"Really nice !\r\nThanks for adding this one.\r\n\r\nI noticed that there are some '-' that are left in the description in the middle of some workds. It migh come from copy-pasting the pdf paper. ex: `im-provement`. Could you fix that please ?",
"@lhoestq \r\nLinebreaks have been removed! Note that there are stil... | 1,596,669,180,000 | 1,597,844,349,000 | 1,597,844,349,000 | CONTRIBUTOR | null | Added the METEOR metric. Can be used like this:
```python
import nlp
meteor = nlp.load_metric('metrics/meteor')
meteor.compute(["some string", "some string"], ["some string", "some similar string"])
# {'meteor': 0.6411637931034483}
meteor.add("some string", "some string")
meteor.add('some string", "some simila... | {
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"Nevermind, I restarted my python session and it worked fine...\r\n\r\n---\r\n\r\nI had an authentification error, and I authenticated from another terminal. After that, no more error but it was not working. Restarting the sessions makes it work :)"
] | 1,596,589,712,000 | 1,596,590,497,000 | 1,596,590,496,000 | NONE | null | Previously, I was writing TFRecords manually to GCP bucket with : `with tf.io.TFRecordWriter('gs://my_bucket/x.tfrecord')`
Since `0.4.0` is out with the `export()` function, I tried it. But it seems TFRecords cannot be directly written to GCP bucket.
`dataset.export('local.tfrecord')` works fine,
but `dataset.... | {
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"Thanks for reporting this issue\r\n\r\nThere was a bug where numpy arrays would get returned instead of tensorflow tensors.\r\nThis is fixed on master.\r\n\r\nI tried to re-run the colab and encountered this error instead:\r\n\r\n```\r\nAttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no at... | 1,596,583,095,000 | 1,627,970,535,000 | 1,627,970,535,000 | NONE | null | with nlp 0.4.0, the TensorFlow example in Overview.ipynb throws the following exceptions:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-48907f2ad433> in <module>
----> 1 features = {x: trai... | {
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"> Also, a little out of my depth there, but would there be a way to have the default pip install checklist command not require mysql and mariadb to be installed? Feels like that might be a source of confusion for users.\r\n\r\nI removed the pattern dependency, mysql is not a requirement anymore. I'm not sure where... | 1,596,565,925,000 | 1,599,161,648,000 | null | NONE | null | Sorry for the large pull request.
- Added checklists as datasets. I can't run `test_load_real_dataset` (see #474), but I can load the datasets successfully as shown in the example notebook
- Added a checklist wrapper | {
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"Cool thanks, I made those changes. LMK if you think it's ready for merge.",
"Ok to merge for me"
] | 1,596,555,149,000 | 1,597,698,848,000 | 1,597,698,847,000 | CONTRIBUTOR | null | A few misc. bugs and QOL improvements that I've come across in using the library. Let me know if you don't like any of them and I can adjust/remove them.
1. Printing datasets without a description field throws an error when formatting the `single_line_description`. This fixes that, and also adds some formatting to t... | {
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"The `data_dir` parameter has been removed. Now the error is `ValueError: Config name is missing`\r\n\r\nAs mentioned in #470 I think we can have one test with the first config of BUILDER_CONFIGS, and another test that runs all of the configs in BUILDER_CONFIGS",
"This was fixed in #527 \r\n\r\nClosing this one, ... | 1,596,498,396,000 | 1,599,490,393,000 | 1,599,490,393,000 | NONE | null | It a dataset has custom `BUILDER_CONFIGS` with non-keyword arguments (or keyword arguments with non default values), the config is not loaded during the test and causes an error.
I think the problem is that `test_load_real_dataset` calls `load_dataset` with `data_dir=temp_data_dir` ([here](https://github.com/huggingfa... | {
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"This PR was already approved by @lhoestq in #456 . This one just make style to remove some typos"
] | 1,596,453,302,000 | 1,596,453,730,000 | 1,596,453,729,000 | CONTRIBUTOR | null | opening new PR for CRD3 dataset (ACL2020) to fix the circle CI problems | {
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https://api.github.com/repos/huggingface/datasets/issues/470 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/470/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/470/comments | https://api.github.com/repos/huggingface/datasets/issues/470/events | https://github.com/huggingface/datasets/pull/470 | 671,952,276 | MDExOlB1bGxSZXF1ZXN0NDYyMDc0MzQ0 | 470 | Adding IWSLT 2017 dataset. | {
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"Ok I tried to add the dummy dataset (I actually modified the dummy_data command to generate them for me because it was too painful to do that manually).\r\n\r\nThe dummy_data test seems to work:\r\n```bash\r\nRUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_iwslt2017\... | 1,596,448,359,000 | 1,599,482,010,000 | 1,599,482,010,000 | CONTRIBUTOR | null | Created a [IWSLT 2017](https://sites.google.com/site/iwsltevaluation2017/TED-tasks) dataset script for the *multilingual data*.
```
Bilingual data: {Arabic, German, French, Japanese, Korean, Chinese} <-> English
Multilingual data: German, English, Italian, Dutch, Romanian. (Any pair)
```
I'm unsure how to h... | {
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"Hi ! Did you try to set the output format to pytorch ? (or tensorflow if you're using tensorflow)\r\nIt can be done with `dataset.set_format(\"torch\", columns=columns)` (or \"tensorflow\").\r\n\r\nNote that for pytorch, string columns can't be converted to `torch.Tensor`, so you have to specify in `columns=` the... | 1,596,440,909,000 | 1,603,357,466,000 | null | NONE | null | I trying to build multi label text classifier model using Transformers lib.
I'm using Transformers NLP to load the data set, while calling trainer.train() method. It throws the following error
File "C:\***\arrow_dataset.py", line 343, in _convert_outputs
v = command(v)
TypeError: new(): invalid data type ... | {
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"Indeed. Solution 1 is the simplest.\r\n\r\nThis is actually a recurring problem.\r\nI think we should scan all the datasets with regexpr to fix the use of `open()` without encodings.\r\nAnd probably add a test in the CI to forbid using this in the future.",
"I'm happy to tackle the broader problem - will open a ... | 1,596,377,110,000 | 1,597,911,368,000 | 1,597,911,368,000 | MEMBER | null | Hi 🤗 team!
## Description of the problem
I'm running into a `UnicodeDecodeError` while trying to load the PAN-X subset the XTREME dataset:
```
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
<ipython-inp... | {
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"Thanks!"
] | 1,596,358,777,000 | 1,596,376,347,000 | 1,596,359,934,000 | CONTRIBUTOR | null | Fix typo from dictionnary -> dictionary | {
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"The cast function is now called inside `features.encode_example`.\r\nI also added `encode_batch` that was missing.\r\n\r\nMoreover I used the cast function in `Dataset.map` to support torch/tensorflow tensors or numpy arrays inputs.\r\n\r\nThere are tests for tensors inputs in metrics and in .map",
"I think we c... | 1,596,279,825,000 | 1,597,677,300,000 | 1,597,677,299,000 | MEMBER | null | - Disallow the use of positional arguments to avoid `predictions` vs `references` mistakes
- Allow to directly feed numpy/pytorch/tensorflow/pandas objects in metrics | {
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"One note on features inference:\r\n\r\nif an arrow type is `struct of items` where each item is a `list`, then we return a `dict` in which each item is a `Sequence`.\r\nIt means that we don't use the Sequence <-> dict swap when we infer features.\r\n\r\nIt's fine because the swap is generally used in dataset scrip... | 1,596,206,601,000 | 1,596,220,053,000 | 1,596,220,052,000 | MEMBER | null | When applying a transform like `map`, some features were lost (and inferred features were used).
It was the case for ClassLabel, Translation, etc.
To fix that, I did some modifications in the `ArrowWriter`:
- added the `update_features` parameter. When it's `True`, then the features specified by the user (if any... | {
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These methods are added to `Dataset` as well as `DatasetDict`.
Added tests for these new methods and add the methods to the doc.
Naming follows th... | {
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"I think the problem is related to `wiki_dpr` dataset which is making the circle CI failed as you can see:\r\n```\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_wiki_dpr\r\nFAILED tests/test_hf_gcp.py::TestDatasetOnHfGcp::test_script_synced_with_s3_wiki_dpr/dummy_psgs_w100_no_embeddings\r... | 1,596,196,252,000 | 1,598,280,882,000 | 1,598,280,882,000 | CONTRIBUTOR | null | New pull request that should correct the previous errors.
The load_real_data stills fails because it is looking for a default dataset URL that does not exists, this does not happen when loading the dataset with load_dataset | {
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Therefore the next time you called map, it was loading an incomplete arrow file.
We had the same issue with select if there was a bad indice at one point.
To fix that I used temporary files that are renamed once everything... | {
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- rename the `flatten`, `drop` and `dictionary_encode_column` methods in `flatten_`, `drop_` and `dictionary_encode_column_` to indicate that these methods have in-place effects as discussed in #166. From now on we s... | {
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Also changed the function call to use named rather than positional argum... | {
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Add tests to these for `Dataset` and `DatasetDict`.
Fix some bugs uncovered by the tests for `pandas` formating. | {
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"Sorry one nit: Could we use named arguments for the call to BLEURT?\r\n\r\ni.e. \r\n scores = self.scorer.score(references=references, candidates=predictions)\r\n\r\n(i.e. so it is less bug prone)\r\n",
"Following up on Ankur's comment---we are going to drop support for\npositional (not named) arguments i... | 1,596,046,112,000 | 1,596,203,774,000 | 1,596,203,774,000 | MEMBER | null | This PR adds the BLEURT metric to the library.
The BLEURT `Metric` downloads a TF checkpoint corresponding to its `config_name` at creation (in the `_info` function). Default is set to `bleurt-base-128`.
Note that the default in the original package is `bleurt-tiny-128`, but they throw a warning and recommend usi... | {
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"Hi ! Glad you managed to fix the version issue.\r\n\r\nThe command `\r\npython nlp-cli dummy_data datasets/guardian_authorship --save_infos --all_configs` is supposed to generate a json file `dataset_infos.json` next to your dataset script, but I can't see it in the PR.\r\nCan you make sure you have the json file ... | 1,595,989,437,000 | 1,597,936,197,000 | 1,597,936,076,000 | CONTRIBUTOR | null | A new dataset: Guardian news articles for authorship attribution
**tests passed:**
python nlp-cli dummy_data datasets/guardian_authorship --save_infos --all_configs
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_guardian_authorship
**Tests failed:**
Real data:... | {
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"I think this is the way to go but I’m afraid this might be a little slow. I was thinking that we could use a high quality very fast non crypto hash like xxhash for these stuff (hashing data files)",
"Yep good idea, I'll take a look",
"I tested the hashing speed [here](https://colab.research.google.com/drive/1h... | 1,595,953,851,000 | 1,596,031,043,000 | 1,596,031,042,000 | MEMBER | null | The cache dir for csv/json/txt datasets was always the same. This is an issue because it should be different depending on the data files provided by the user.
To fix that, I added a line that use the hash of the data files provided by the user to define the cache dir.
This should fix #444 | {
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#353 | {
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"> Awesome !\r\n> Good job on parsing these files :O\r\n> \r\n> Do you think it would be hard to get the two other split configurations ?\r\n\r\nIt shouldn't be that hard, I think I can consider different config names for each split ",
"> > Awesome !\r\n> > Good job on parsing these files :O\r\n> > Do you think i... | 1,595,926,692,000 | 1,596,453,031,000 | 1,596,453,031,000 | CONTRIBUTOR | null | This PR adds the `Reuters_21578` dataset https://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html
#353
The datasets is a lit of `.sgm` files which are a bit different from xml file indeed `xml.etree` couldn't be used to read files. I consider them as text file (to avoid using external library) and read ... | {
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"Could you run `make style` to fix the code_quality fail ?\r\nYou'll need `black` and `isort` that you can install by doing `pip install -e .[quality]`",
"Thanks @lhoestq\r\nI fixed the styling",
"Thank you :)"
] | 1,595,926,222,000 | 1,595,948,547,000 | 1,595,948,547,000 | CONTRIBUTOR | null | Following issue #445
Added a test to recognize import errors of all metrics | {
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#445 | {
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"This issue was resolved by #447 "
] | 1,595,921,490,000 | 1,595,941,136,000 | 1,595,941,136,000 | CONTRIBUTOR | null | Latest Version 0.3.0
When loading the metric "sacrebleu" there is an import error due to the wrong path
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"Same here !",
"This is the only fix I could come up with without touching the repo's code.\r\n```python\r\nfrom nlp.builder import FORCE_REDOWNLOAD\r\ndataset = load_dataset('csv', data_file='./a.csv', download_mode=FORCE_REDOWNLOAD, version='0.0.1')\r\n```\r\nYou'll have to change the version each time you want... | 1,595,855,286,000 | 1,596,031,042,000 | 1,596,031,042,000 | NONE | null | I used load a file called 'a.csv' by
```
dataset = load_dataset('csv', data_file='./a.csv')
```
And after a while, I tried to load another csv called 'b.csv'
```
dataset = load_dataset('csv', data_file='./b.csv')
```
However, the new dataset seems to remain the old 'a.csv' and not loading new csv file.
Even... | {
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"This seems to be fixed in a non-released version. \r\n\r\nInstalling nlp from source\r\n```\r\ngit clone https://github.com/huggingface/nlp\r\ncd nlp\r\npip install .\r\n```\r\nsolves the issue. "
] | 1,595,852,017,000 | 1,595,855,111,000 | 1,595,855,111,000 | CONTRIBUTOR | null | Saving a formatted torch dataset to file using `torch.save()`. Loading the same file fails during unpickling:
```python
>>> import torch
>>> import nlp
>>> squad = nlp.load_dataset("squad.py", split="train")
>>> squad
Dataset(features: {'source_text': Value(dtype='string', id=None), 'target_text': Value(dtype... | {
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I've just realised you provide the the [Glue Diagnostics Data](https://huggingface.co/nlp/viewer/?dataset=glue&config=ax) without labels, indicating in the `GlueConfig` that you've only a test set.
Yet, the data with labels is available, too (see als... | {
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"This one is ready for review now",
"I changed to using features only, instead of info.\r\nLet mw know if it sounds good to you now @thomwolf "
] | 1,595,843,401,000 | 1,596,113,477,000 | 1,596,113,476,000 | MEMBER | null | Added `features` argument in `nlp.load_dataset`.
If they don't match the data type, it raises a `ValueError`.
It's a draft PR because #440 needs to be merged first. | {
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The writer used to overwrite the user specified features with inferred features.
I also added tests to make sure it doesn't happen again. | {
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"`DPRContextEncoder` and `DPRContextEncoderTokenizer` will be available in the next release of `transformers`.\r\n\r\nRight now you can experiment with it by installing `transformers` from the master branch.\r\nYou can also check the docs of DPR [here](https://huggingface.co/transformers/master/model_doc/dpr.html).... | 1,595,823,917,000 | 1,603,849,584,000 | 1,603,849,584,000 | NONE | null | It seems the DPRContextEncoder, DPRContextEncoderTokenizer cited[ in this documentation](https://huggingface.co/nlp/faiss_and_ea.html) is not implemented ? It didnot work with the standard nlp installation . Also, I couldn't find or use it with the latest nlp install from github in Colab. Is there any dependency on t... | {
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"Thanks Sam, we now have a very detailed tutorial and template on how to add a new dataset to the library. It typically take 1-2 hours to add one. Do you want to give it a try ?\r\nThe tutorial on writing a new dataset loading script is here: https://huggingface.co/nlp/add_dataset.html\r\nAnd the part on how to sha... | 1,595,799,784,000 | 1,598,281,935,000 | null | CONTRIBUTOR | null | **Links:**
[iwslt](https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/iwslt.html)
Don't know if that link is up to date.
[ittb](http://www.cfilt.iitb.ac.in/iitb_parallel/)
**Motivation**: replicate mbart finetuning results (table below)
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"There is an interesting design question here (cc @lhoestq).\r\n\r\nI guess the labels form a closed set so we could also use a [nlp.ClassLabel](https://huggingface.co/nlp/package_reference/main_classes.html#nlp.ClassLabel) instead of a string. The differences will be mainly that:\r\n- the labels are stored as inte... | 1,595,688,297,000 | 1,596,097,695,000 | 1,596,097,695,000 | MEMBER | null | Hi 🤗
In response to the discussion in #425 @lewtun and I made some fixes to the repo. In the original XTREME implementation the PAN-X dataset for named entity recognition loaded each word/tag pair as a single row and the sentence relation was lost. With the fix each row contains the list of all words in a single sen... | {
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"Indeed, we’ll make a new PyPi release next week to solve this. Cc @lhoestq ",
"+1! this is the reason our tests are failing at [TextAttack](https://github.com/QData/TextAttack) \r\n\r\n(Though it's worth noting if we fixed the version number of pyarrow to 0.16.0 that would fix our problem too. But in this case w... | 1,595,682,320,000 | 1,597,910,898,000 | 1,597,910,898,000 | NONE | null | With latest PyArrow 1.0.0 installed, I get the following exception . Restarting colab has the same issue
ImportWarning: To use `nlp`, the module `pyarrow>=0.16.0` is required, and the current version of `pyarrow` doesn't match this condition. If you are running this in a Google Colab, you should probably just rest... | {
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"This was fixed in #434 \r\nWe'll do a release later this week to include this fix.\r\nThanks for reporting",
"I dont know if the fix was made but the problem is still present : \r\nInstaled with pip : NLP 0.3.0 // pyarrow 1.0.0 \r\nOS : archlinux with kernel zen 5.8.5",
"Yes it was fixed in `nlp>=0.4.0`\r\nYou... | 1,595,648,679,000 | 1,599,587,835,000 | 1,596,472,652,000 | NONE | null | The following PR raised ImportWarning at `pyarrow ==1.0.0` https://github.com/huggingface/nlp/pull/265/files | {
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"Great, thanks!"
] | 1,595,636,213,000 | 1,595,658,994,000 | 1,595,658,994,000 | CONTRIBUTOR | null | Fix check for pyarrow in __init__.py. Previously would raise an error for pyarrow >= 1.0.0 | {
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"Hi @ArneBinder, we have a few \"generic\" datasets which are intended to load data files with a predefined format:\r\n- csv: https://github.com/huggingface/nlp/tree/master/datasets/csv\r\n- json: https://github.com/huggingface/nlp/tree/master/datasets/json\r\n- text: https://github.com/huggingface/nlp/tree/master/... | 1,595,611,657,000 | 1,596,190,997,000 | null | NONE | null | I have written a generic dataset for corpora created with the Brat annotation tool ([specification](https://brat.nlplab.org/standoff.html), [dataset code](https://github.com/ArneBinder/nlp/blob/brat/datasets/brat/brat.py)). Now I wonder how to use that to create specific dataset instances. What's the recommended way to... | {
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"Ok for this but I think we may want to use the general `filelock` method we are using at other places in the library instead of filecmp (in particular `filelock` take care of being an atomic operation which is safer for concurrent processes)",
"Ok I see.\r\nWhy not use filelock in this case then ?",
"I think w... | 1,595,603,457,000 | 1,596,301,902,000 | 1,596,097,528,000 | CONTRIBUTOR | null | When loading shards on several processes, each process upon loading the dataset will overwrite dataset_infos.json in <package path>/datasets/<dataset name>/<hash>/dataset_infos.json. It does so every time, even when the target file already exists and is identical. Because multiple processes rewrite the same file in par... | {
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"I was using a hack in `wiki_dpr` to download the index from GCS even for the configurations without the embeddings.\r\nHowever as GCS is something internal, I changed the logic to add a download step for indexes directly in the dataset script, using the `DownloadManager`.\r\n\r\nThis change was directly linked to ... | 1,595,582,959,000 | 1,596,186,304,000 | 1,596,186,303,000 | MEMBER | null | Previously if you tried to do
```python
from nlp import load_dataset
wiki = load_dataset("wiki_dpr", "psgs_w100_with_nq_embeddings", split="train[:100]", with_index=True)
```
Then you'd get an error `Index size should match Dataset size...`
This was because it was trying to use the full index (21M elements).
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"I did the changes in the docstrings and I added a type check in each `DatasetDict` method to make sure all values are of type `Dataset`",
"Awesome, do you mind adding these in the doc as well?",
"I added it to the docs (processing + main classes)",
"I'm trying to follow along with the following about dataset... | 1,595,519,029,000 | 1,596,502,913,000 | 1,596,013,582,000 | MEMBER | null | ## Add DatasetDict
### Overview
When you call `load_dataset` it can return a dictionary of datasets if there are several splits (train/test for example).
If you wanted to apply dataset transforms you had to iterate over each split and apply the transform.
Instead of returning a dict, it now returns a `nlp.Dat... | {
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"Thanks @RachelKer for this PR.\r\n\r\nI think the dummy_data structure does not also match. In the `_split_generator` you have something like `os.path.join(downloaded_files[\"validation\"], lang+'_val.jsonl')` but in you dummy_data you have `os.path.join(downloaded_files[\"validation\"], lang+\"_val.zip\", lang+'... | 1,595,505,159,000 | 1,596,195,980,000 | 1,596,195,980,000 | CONTRIBUTOR | null | Hello,
The tests for the load_real_data fail, as there is no default language subset to download it looks for a file that does not exist. This bug does not happen when using the load_dataset function, as it asks you to specify a language if you do not, so I submit this PR anyway. The dataset is avalaible on : https... | {
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### The issue
The steps of beam datasets processing is the following:
- download the source files and send them in a remote storage (gcs)
- process the files using a beam runner (dataflow)
- save output in remote storage (gcs)
- convert outp... | {
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"Yes that's definitely something we plan to add ^^",
"Yes, that would be nice. We could take a look at what tensorflow `tf.data` does under the hood for instance.",
"So `tf.data.Dataset.map()` returns a `ParallelMapDataset` if `num_parallel_calls is not None` [link](https://github.com/tensorflow/tensorflow/blob... | 1,595,480,441,000 | 1,615,541,652,000 | 1,599,490,084,000 | NONE | null | It would be nice to be able to speed up `dataset.map` or `dataset.filter`. Perhaps this is as easy as sharding the dataset sending each shard to a process/thread/dask pool and using the new `nlp.concatenate_dataset()` function to join them all together? | {
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"Thanks for noticing ! This looks more reasonable indeed.\r\nFeel free to open a PR",
"Hi @lhoestq \r\nI made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file. More specifically the `\"features\"` part of the PAN-X.LANG dataset:\r\n\... | 1,595,449,760,000 | 1,596,375,034,000 | 1,596,375,034,000 | MEMBER | null | Hi 🤗 team!
## Description of the problem
Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:
```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
dataset_train = dataset['tr... | {
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#353 | {
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"I had to make `SplitDict` serializable to be able to copy `DatasetInfo` objects properly.\r\nSerialization was also asked in #389 ",
"One thing I forgot to say here, is that we also want to use the features arguments of `load_dataset` (which goes in the builder’s config) to override the default features of a dat... | 1,595,343,167,000 | 1,595,668,114,000 | 1,595,499,317,000 | MEMBER | null | ## New logic for `nlp.Features` in datasets
Previously, it was confusing to have `features` and pyarrow's `schema` in `nlp.Dataset`.
However `features` is supposed to be the front-facing object to define the different fields of a dataset, while `schema` is only used to write arrow files.
Changes:
- Remove `sche... | {
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"What about the other PR #419 ?",
"Oh this is the PR where I ran make quality and make style and some previous files from master were changed",
"Oh right ! Let me fix the style myself if you don't mind"
] | 1,595,275,709,000 | 1,595,434,120,000 | 1,595,434,119,000 | CONTRIBUTOR | null | make quality and make style ran on scripts | {
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- added arrow schema to features conversion (it's going to be useful to fix #342 )
- make flatten handle deep features (useful for tfrecords conversion in #339 )
- add tests for flatten and features conversions
- the reader now returns the kwargs to instantiate a Dataset (fix circular dependencies) | {
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Signed-off-by: lordtt13 <thakurtanmay72@yahoo.com> | {
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"I think the problem is the way you wrote your urls. Try the following structure to see `https://drive.google.com/uc?export=download&id=your_file_id` . \r\n\r\n@lhoestq ",
"Oh sorry, I think `_get_drive_url` is doing that. \r\n\r\nHave you tried to use `dl_manager.download_and_extract(_get_drive_url(_TRAIN_URL)`... | 1,595,256,722,000 | 1,595,259,572,000 | 1,595,259,572,000 | CONTRIBUTOR | null | Hello there, I followed the template to create a download script of my own, which works fine for me, although I had to shun the dl_manager because it was downloading nothing from the drive links and instead use gdown.
This is the script for me:
```python
class EmoConfig(nlp.BuilderConfig):
"""BuilderConfig ... | {
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This should fix #304 | {
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"great, I think I did not download the data the way you do, but yours is more reasonable."
] | 1,595,239,757,000 | 1,595,319,346,000 | 1,595,319,344,000 | MEMBER | null | Fix #412 | {
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```
>>>import nlp
>>>from nlp import load_dataset
>>>dataset = load_dataset('wmt19', 'kk-en')
>>>dataset["train"]["translation"][0]
{'kk': 'Trumpian Uncertainty', 'en': 'Трамптық белгісіздік'}
>>>dataset["validation"]["translation"][0]
{'kk': 'Ақша-несие... | {
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"`from_dict` was added in #350 that was unfortunately not included in the 0.3.0 release. It's going to be included in the next release that will be out pretty soon though.\r\nRight now if you want to use `from_dict` you have to install the package from the master branch\r\n```\r\npip install git+https://github.com/... | 1,595,142,516,000 | 1,595,298,077,000 | 1,595,298,077,000 | NONE | null | AttributeError: type object 'Dataset' has no attribute 'from_dict' | {
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"Unfortunately it's not possible to download only the dev set of NQ.\r\n\r\nI think we could add a way to download only the test set by adding a custom configuration to the processing script though.",
"Ok, got it. I think this could be a valuable feature - especially for large datasets like NQ, but potentially al... | 1,595,068,103,000 | 1,644,573,021,000 | 1,644,573,021,000 | NONE | null | Maybe I missed that in the docs, but is there a way to only download the dev set of natural questions (~1 GB)?
As we want to benchmark QA models on different datasets, I would like to avoid downloading the 41GB of training data.
I tried
```
dataset = nlp.load_dataset('natural_questions', split="validation", bea... | {
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"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] | 1,595,066,100,000 | 1,595,319,344,000 | 1,595,319,344,000 | MEMBER | null | Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPho... | {
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dataset homepage: https://homes.cs.washington.edu/~msap/social-bias-frames/ | {
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#353 | {
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"It was fixed in 2ddd18d139d3047c9c3abe96e1e7d05bb360132c.\r\nCould you pull the latest changes from master @morganmcg1 ?",
"Thanks @lhoestq, works fine now!"
] | 1,594,982,188,000 | 1,595,342,092,000 | 1,595,342,092,000 | NONE | null | `train_test_split` is giving me an error when I try and call it:
`'dict' object has no attribute 'deepcopy'`
## To reproduce
```
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.train_test_split(test_size=0.2)
```
## Full Stacktrace
```
-------------------------------------------... | {
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These tests make sure that they're always available. It also makes sure that their scripts are in sync between S3 and the repo.
This should avoid future issues like #407 | {
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"Fixed. Could you try again @mitchellgordon95 ?\r\nIt was due a file not being updated on S3.\r\n\r\nWe need to make sure all the datasets scripts get updated properly @julien-c ",
"Works for me! Thanks.",
"I found the same issue with almost any language other than English. (For English, it works). Will someone... | 1,594,943,283,000 | 1,610,451,676,000 | 1,594,995,868,000 | CONTRIBUTOR | null | There may or may not be a regression for the pre-processed Wikipedia dataset. This was working fine 10 commits ago (without having Apache Beam available):
```
nlp.load_dataset('wikipedia', "20200501.en", split='train')
```
And now, having pulled master, I get:
```
Downloading and preparing dataset wikipedia... | {
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"I think the slowness here probably come from the fact that we are copying from and to python.\r\n\r\n@lhoestq for all the `select`-based methods I think we should stay in Arrow format and update the writer so that it can accept Arrow tables or batches as well. What do you think?",
"> @lhoestq for all the `select... | 1,594,934,513,000 | 1,599,489,926,000 | 1,599,489,925,000 | CONTRIBUTOR | null | Consider shuffling bookcorpus:
```
dataset = nlp.load_dataset('bookcorpus', split='train')
dataset.shuffle()
```
According to tqdm, this will take around 2.5 hours on my machine to complete (even with the faster version of select from #405). I've also tried with `keep_in_memory=True` and `writer_batch_size=1000`... | {
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```
dataset = nlp.load_dataset('bookcorpus', split='train')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1, load_from_cache_file=False)
end = time.time()
print(f'{end - start}')
start = time.time()
dataset.select(np.arange(1000), reader_batch_size=1000, load_fr... | {
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In this PR I allow the user to specify numpy's seed when instantiating a metric with `load_metric`.
The seed is set only when `compute` is called, and reset afterwards.
Moreover when calling `compute` with the same metric instance (i.e. same experiment... | {
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I fixed it by using to_pydict/to_pylist instead.
Fix #387
It was mentioned in https://github.com/huggingface/transformers/issues/5729
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#336 | {
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"What does the `nlp-cli dummy_data` command returns ?",
"`test.json` -> `test` \r\nand \r\n`train.json` -> `train`\r\n\r\nas shown by the `nlp-cli dummy_data` command ;-)",
"LGTM for merge @lhoestq - I let you merge if you want to."
] | 1,594,889,699,000 | 1,596,694,580,000 | 1,596,694,579,000 | CONTRIBUTOR | null | add Web Question dataset
#336
Maybe @patrickvonplaten you can help with the dummy_data structure? it still broken | {
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#336 | {
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"Thanks!"
] | 1,594,874,278,000 | 1,594,882,188,000 | 1,594,882,177,000 | CONTRIBUTOR | null | In "Formatting the dataset" part, "The two toehr modifications..." should be "The two other modifications..." ,the word "other" wrong spelled as "toehr". | {
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"Do you mind adding a link to the much more extended pages on adding and sharing a dataset in the new documentation?",
"Sure, I will do that too"
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https://api.github.com/repos/huggingface/datasets/issues/397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/397/comments | https://api.github.com/repos/huggingface/datasets/issues/397/events | https://github.com/huggingface/datasets/pull/397 | 657,510,856 | MDExOlB1bGxSZXF1ZXN0NDQ5NjE1MDA4 | 397 | Add contiguous sharding | {
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"events_url":... | [] | null | [] | 1,594,832,578,000 | 1,595,005,171,000 | 1,595,005,171,000 | CONTRIBUTOR | null | This makes dset.shard() play nice with nlp.concatenate_datasets(). When I originally wrote the shard() method, I was thinking about a distributed training scenario, but https://github.com/huggingface/nlp/pull/389 also uses it for splitting the dataset for distributed preprocessing.
Usage:
```
nlp.concatenate_datas... | {
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Fix #395 | {
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```python
import nlp
w = nlp.load_dataset("wikipedia", "20200501.en", split="train")
w.select([0])
```
This is caused by [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/arrow_dataset.py#L626) for some reason, that ... | {
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#378 | {
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For downloaded files, we are using the `downloads` directory to make things easier to navigate, but extracted files were still placed at the root of the cache directory. To fix that I changed the directory for extracted files to ca... | {
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