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MDU6SXNzdWU2ODI0Nzg4MzM=

dictionnary typo in docs

closed

CONTRIBUTOR

Many places dictionary is spelled dictionnary, not sure if its on purpose or not. Fixed in this pr: https://github.com/huggingface/nlp/pull/521

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https://github.com/huggingface/datasets/pull/521

MDExOlB1bGxSZXF1ZXN0NDcwNzEyNzgz

Fix dictionnary (dictionary) typo

closed

CONTRIBUTOR

This error happens many times I'm thinking maybe its spelled like this on purpose?

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https://github.com/huggingface/datasets/pull/520

MDExOlB1bGxSZXF1ZXN0NDcwNTI4MDE0

Transform references for sacrebleu

closed

CONTRIBUTOR

Currently it is impossible to use sacrebleu when len(predictions) != the number of references per prediction (very uncommon), due to a strange format expected by sacrebleu. If one passes in the data to `nlp.metric.compute()` in sacrebleu format, `nlp` throws an error due to mismatching lengths between predictions and references. If one uses a more standard format where predictions and references are lists of the same length, sacrebleu throws an error. This PR transforms reference data in a more standard format into the [unusual format](https://github.com/mjpost/sacreBLEU#using-sacrebleu-from-python) expected by sacrebleu.

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MDU6SXNzdWU2ODIxOTM4ODI=

[BUG] Metrics throwing new error on master since 0.4.0

closed

CONTRIBUTOR

The following error occurs when passing in references of type `List[List[str]]` to metrics like bleu. Wasn't happening on 0.4.0 but happening now on master. ``` File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 226, in compute self.add_batch(predictions=predictions, references=references) File "/usr/local/lib/python3.7/site-packages/nlp/metric.py", line 242, in add_batch batch = self.info.features.encode_batch(batch) File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in encode_batch encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column] File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 527, in <listcomp> encoded_batch[key] = [encode_nested_example(self[key], cast_to_python_objects(obj)) for obj in column] File "/usr/local/lib/python3.7/site-packages/nlp/features.py", line 456, in encode_nested_example raise ValueError("Got a string but expected a list instead: '{}'".format(obj)) ```

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https://github.com/huggingface/datasets/pull/518

MDExOlB1bGxSZXF1ZXN0NDcwNDE0ODE1

[METRICS, breaking] Refactor caching behavior, pickle/cloudpickle metrics and dataset, add tests on metrics

closed

MEMBER

Move the acquisition of the filelock at a later stage during metrics processing so it can be pickled/cloudpickled after instantiation. Also add some tests on pickling, concurrent but separate metric instances and concurrent and distributed metric instances. Changes significantly the caching behavior for the metrics: - if the metric is used in a non-distributed setup (most common case) we try to find a free cache file using UUID instead of asking for an `experiment_id` if we can't lock the cache file this allows to use several instances of the same metrics in parallel. - if the metrics is used in a distributed setup we ask for an `experiment_id` if we can't lock the cache file (because all the nodes need to have related cache file names for the final sync. - after the computation, we free the locks and delete all the cache files. Breaking: Some arguments for Metrics initialization have been removed for simplicity (`version`...) and some have been renamed for consistency with the rest of the library (`in_memory` => `keep_in_memory`). Also remove the `_has_transformers` detection in utils to avoid importing transformers everytime during loading.

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https://github.com/huggingface/datasets/issues/517

MDU6SXNzdWU2ODE4OTY5NDQ=

add MLDoc dataset

open

CONTRIBUTOR

Hi, I am recommending that someone add MLDoc, a multilingual news topic classification dataset. - Here's a link to the Github: https://github.com/facebookresearch/MLDoc - and the paper: http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf Looks like the dataset contains news stories in multiple languages that can be classified into four hierarchical groups: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social) and MCAT (Markets). There are 13 languages: Dutch, French, German, Chinese, Japanese, Russian, Portuguese, Spanish, Latin American Spanish, Italian, Danish, Norwegian, and Swedish

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https://github.com/huggingface/datasets/pull/516

MDExOlB1bGxSZXF1ZXN0NDcwMTY5NTA0

[Breaking] Rename formated to formatted

closed

MEMBER

`formated` is not correct but `formatted` is

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https://github.com/huggingface/datasets/pull/515

MDExOlB1bGxSZXF1ZXN0NDcwMTY5MTQ0

Fix batched map for formatted dataset

closed

MEMBER

If you had a dataset formatted as numpy for example, and tried to do a batched map, then it would crash because one of the elements from the inputs was missing for unchanged columns (ex: batch of length 999 instead of 1000). The happened during the creation of the `pa.Table`, since columns had different lengths.

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MDU6SXNzdWU2ODEyNTYzNDg=

dataset.shuffle(keep_in_memory=True) is never allowed

closed

CONTRIBUTOR

As of commit ef4aac2, the usage of the parameter `keep_in_memory=True` is never possible: `dataset.select(keep_in_memory=True)` The commit added the lines ```python # lines 994-996 in src/nlp/arrow_dataset.py assert ( not keep_in_memory or cache_file_name is None ), "Please use either `keep_in_memory` or `cache_file_name` but not both." ``` This affects both `shuffle()` as `select()` is a sub-routine, and `map()` that has the same check. I'd love to fix this myself, but unsure what the intention of the assert is given the rest of the logic in the function concerning `ccache_file_name` and `keep_in_memory`.

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https://github.com/huggingface/datasets/pull/513

MDExOlB1bGxSZXF1ZXN0NDY5NjQxMjg1

[speedup] Use indices mappings instead of deepcopy for all the samples reordering methods

closed

MEMBER

Use an indices mapping instead of rewriting the dataset for all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`). Added a `flatten_indices` method which copy the dataset to a new table to remove the indices mapping with tests. All the samples re-ordering/selection methods should be a lot faster. The downside is that iterating on very large batch of the dataset might be a little slower when we have changed the order of the samples since with in these case we use `pyarrow.Table.take` instead of `pyarrow.Table.slice`. There is no free lunch but the speed of iterating over the dataset is rarely the bottleneck. *Backward breaking change*: the `cache_file_name` argument in all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`) is now called `indices_cache_file_name` on purpose to make it explicit to the user that this caching file is used for caching the indices mapping and not the dataset itself.

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MDExOlB1bGxSZXF1ZXN0NDY5NTc2NzE3

Delete CONTRIBUTING.md

closed

NONE

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MDU6SXNzdWU2ODEwNTU1NTM=

dataset.shuffle() and select() resets format. Intended?

closed

CONTRIBUTOR

Calling `dataset.shuffle()` or `dataset.select()` on a dataset resets its format set by `dataset.set_format()`. Is this intended or an oversight? When working on quite large datasets that require a lot of preprocessing I find it convenient to save the processed dataset to file using `torch.save("dataset.pt")`. Later loading the dataset object using `torch.load("dataset.pt")`, which conserves the defined format before saving. I do shuffling and selecting (for controlling dataset size) after loading the data from .pt-file, as it's convenient whenever you train multiple models with varying sizes of the same dataset. The obvious workaround for this is to set the format again after using `dataset.select()` or `dataset.shuffle()`. _I guess this is more of a discussion on the design philosophy of the functions. Please let me know if this is not the right channel for these kinds of discussions or if they are not wanted at all!_ #### How to reproduce: ```python import nlp from transformers import T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("t5-base") def create_features(batch): context_encoding = tokenizer.batch_encode_plus(batch["context"]) return {"input_ids": context_encoding["input_ids"]} dataset = nlp.load_dataset("cosmos_qa", split="train") dataset = dataset.map(create_features, batched=True) dataset.set_format(type="torch", columns=["input_ids"]) dataset[0] # {'input_ids': tensor([ 1804, 3525, 1602, ... 0, 0])} dataset = dataset.shuffle() dataset[0] # {'id': '3Q9(...)20', 'context': "Good Old War an (...) play ?', 'answer0': 'None of the above choices .', 'answer1': 'This person likes music and likes to see the show , they will see other bands play .', (...) 'input_ids': [1804, 3525, 1602, ... , 0, 0]} ```

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MDU6SXNzdWU2ODA4MjM2NDQ=

Version of numpy to use the library

closed

NONE

Thank you so much for your excellent work! I would like to use nlp library in my project. While importing nlp, I am receiving the following error `AttributeError: module 'numpy.random' has no attribute 'Generator'` Numpy version in my project is 1.16.0. May I learn which numpy version is used for the nlp library. Thanks in advance.

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MDU6SXNzdWU2Nzk3MTE1ODU=

Converting TensorFlow dataset example

closed

NONE

Hi, I want to use TensorFlow datasets with this repo, I noticed you made some conversion script, can you give a simple example of using it? Thanks

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MDU6SXNzdWU2Nzk3MDU3MzQ=

TypeError: Receiver() takes no arguments

closed

NONE

I am trying to load a wikipedia data set ``` import nlp from nlp import load_dataset dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=data_path, beam_runner='DirectRunner') #dataset = load_dataset('wikipedia', '20200501.sv', cache_dir=data_path, beam_runner='DirectRunner') ``` This fails in the apache beam runner. ``` Traceback (most recent call last): File "D:/ML/wikiembedding/gpt2_sv.py", line 36, in <module> dataset = load_dataset("wikipedia", "20200501.en", split="train", cache_dir=my_cache_dir, beam_runner='DirectRunner') File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\load.py", line 548, in load_dataset builder_instance.download_and_prepare( File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare self._download_and_prepare( File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\nlp\builder.py", line 969, in _download_and_prepare pipeline_results = pipeline.run() File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\pipeline.py", line 534, in run return self.runner.run_pipeline(self, self._options) .... File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\bundle_processor.py", line 218, in process_encoded self.output(decoded_value) File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\apache_beam\runners\worker\operations.py", line 332, in output cython.cast(Receiver, self.receivers[output_index]).receive(windowed_value) File "C:\Users\seto\AppData\Local\Programs\Python\Python38\lib\site-packages\Cython\Shadow.py", line 167, in cast return type(*args) TypeError: Receiver() takes no arguments ``` This is run on a Windows 10 machine with python 3.8. I get the same error loading the swedish wikipedia dump.

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MDU6SXNzdWU2Nzk0MDA2ODM=

Errors when I use

closed

NONE

I tried the following example code from https://huggingface.co/deepset/roberta-base-squad2 and got errors I am using **transformers 3.0.2** code . from transformers.pipelines import pipeline from transformers.modeling_auto import AutoModelForQuestionAnswering from transformers.tokenization_auto import AutoTokenizer model_name = "deepset/roberta-base-squad2" nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) The errors are : res = nlp(QA_input) File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in __call__ for s, e, score in zip(starts, ends, scores) File ".local/lib/python3.6/site-packages/transformers/pipelines.py", line 1316, in <listcomp> for s, e, score in zip(starts, ends, scores) KeyError: 0

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https://github.com/huggingface/datasets/pull/506

MDExOlB1bGxSZXF1ZXN0NDY3OTkwNjc2

fix dataset.map for function without outputs

closed

MEMBER

As noticed in #505 , giving a function that doesn't return anything in `.map` raises an error because of an unreferenced variable. I fixed that and added tests. Thanks @avloss for reporting

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MDExOlB1bGxSZXF1ZXN0NDY3NjgxMjY4

tmp_file referenced before assignment

closed

NONE

Just learning about this library - so might've not set up all the flags correctly, but was getting this error about "tmp_file".

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https://github.com/huggingface/datasets/pull/504

MDExOlB1bGxSZXF1ZXN0NDY3NjUxOTA5

Added downloading to Hyperpartisan news detection

closed

CONTRIBUTOR

Following the discussion on Slack and #349, I've updated the hyperpartisan dataset to pull directly from Zenodo rather than manual install, which should make this dataset much more accessible. Many thanks to @johanneskiesel ! Currently doesn't pass `test_load_real_dataset` - I'm using `self.config.name` which is `default` in this test. Might be related to #474

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MDExOlB1bGxSZXF1ZXN0NDY3NjI3MTEw

CompGuessWhat?! 0.2.0

closed

CONTRIBUTOR

We updated some metadata information associated with the dataset. In addition, we've updated the `create_dummy_data.py` script to generate data samples for the dataset.

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https://github.com/huggingface/datasets/pull/502

MDExOlB1bGxSZXF1ZXN0NDY3NDc1MDg0

Fix tokenizers caching

closed

MEMBER

I've found some cases where the caching didn't work properly for tokenizers: 1. if a tokenizer has a regex pattern, then the caching would be inconsistent across sessions 2. if a tokenizer has a cache attribute that changes after some calls, the the caching would not work after cache updates 3. if a tokenizer is used inside a function, the caching of this function would result in the same cache file for different tokenizers 4. if `unique_no_split_tokens`'s attribute is not the same across sessions (after loading a tokenizer) then the caching could be inconsistent To fix that, this is what I did: 1. register a specific `save_regex` function for pickle that makes regex dumps deterministic 2. ignore cache attribute of some tokenizers before dumping 3. enable recursive dump by default for all dumps 4. make `unique_no_split_tokens` deterministic in https://github.com/huggingface/transformers/pull/6461 I also added tests to make sure that tokenizers hashing works as expected. In the future we should find a way to test if hashing also works across session (maybe using two CI jobs ? or by hardcoding a tokenizer's hash ?)

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MDU6SXNzdWU2Nzc5NTI4OTM=

Caching doesn't work for map (non-deterministic)

closed

NONE

The caching functionality doesn't work reliably when tokenizing a dataset. Here's a small example to reproduce it. ```python import nlp import transformers def main(): ds = nlp.load_dataset("reddit", split="train[:500]") tokenizer = transformers.AutoTokenizer.from_pretrained("gpt2") def convert_to_features(example_batch): input_str = example_batch["body"] encodings = tokenizer(input_str, add_special_tokens=True, truncation=True) return encodings ds = ds.map(convert_to_features, batched=True) if __name__ == "__main__": main() ``` Roughly 3/10 times, this example recomputes the tokenization. Is this expected behaviour?

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MDExOlB1bGxSZXF1ZXN0NDY2ODk0NTk0

Use hnsw in wiki_dpr

closed

MEMBER

The HNSW faiss index is much faster that regular Flat index.

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MDExOlB1bGxSZXF1ZXN0NDY2Nzg1MjAy

Narrativeqa (with full text)

closed

CONTRIBUTOR

Following the uploading of the full text data in #309, I've added the full text to the narrativeqa dataset. Few notes: - Had some encoding issues using the default `open` so am using `open(encoding="latin-1"...` which seems to fix it. Looks fine. - Can't get the dummy data to work. Currently putting stuff at: ``` dummy |---- 0.0.0 |- dummy_data.zip |-master.zip | |- narrativeqa-master | |- documents.csv | |- qaps.csv | |- third_party ...... | | - narrativeqa_full_text.zip | | - 001.content | | - .... ``` Not sure what I'm messing up here (probably something obvious).

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MDExOlB1bGxSZXF1ZXN0NDY2Njg5NTcy

dont use beam fs to save info for local cache dir

closed

MEMBER

If the cache dir is local, then we shouldn't use beam's filesystem to save the dataset info Fix #490

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MDExOlB1bGxSZXF1ZXN0NDY2MjQ2NDQ3

skip header in PAWS-X

closed

MEMBER

This should fix #485 I also updated the `dataset_infos.json` file that is used to verify the integrity of the generated splits (the number of examples was reduced by one). Note that there are new fields in `dataset_infos.json` introduced in the latest release 0.4.0 corresponding to post processing info. I removed them in this case when I ran `nlp-cli ./datasets/xtreme --save_infos` to keep backward compatibility (versions 0.3.0 can't load these fields). I think I'll change the logic so that `nlp-cli test` doesn't create these fields for dataset with no post processing

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MDExOlB1bGxSZXF1ZXN0NDY2MjE1Mjg1

fix bad type in overflow check

closed

MEMBER

When writing an arrow file and inferring the features, the overflow check could fail if the first example had a `null` field. This is because we were not using the inferred features to do this check, and we could end up with arrays that don't match because of a type mismatch (`null` vs `string` for example). This should fix #482

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MDExOlB1bGxSZXF1ZXN0NDY2MTY5MTA3

stack vectors in pytorch and tensorflow

closed

MEMBER

When the format of a dataset is set to pytorch or tensorflow, and if the dataset has vectors in it, they were not stacked together as tensors when calling `dataset[i:i + batch_size][column]` or `dataset[column]`. I added support for stacked tensors for both pytorch and tensorflow. For ragged tensors, they are stacked only for tensorflow as pytorch doesn't support ragged tensors.

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MDExOlB1bGxSZXF1ZXN0NDY2MTExOTQz

Fix numpy stacking

closed

MEMBER

When getting items using a column name as a key, numpy arrays were not stacked. I fixed that and added some tests. There is another issue that still needs to be fixed though: when getting items using a column name as a key, pytorch tensors are not stacked (it outputs a list of tensors). This PR should help with the to fix this issue.

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MDExOlB1bGxSZXF1ZXN0NDY1ODIxOTA0

Fix wmt zh-en url

closed

CONTRIBUTOR

I verified that ``` wget https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-zh.tar.gz.00 ``` runs in 2 minutes.

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MDU6SXNzdWU2NzY0OTUwNjQ=

nlp.Features does not distinguish between nullable and non-nullable types in PyArrow schema

closed

CONTRIBUTOR

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) dset = nlp.concatenate_datasets([dset_wikipedia, dset_books]) ``` This fails because they have different schemas, despite having identical features. ```python assert dset_wikipedia.features == dset_books.features # True assert dset_wikipedia._data.schema == dset_books._data.schema # False ``` The Wikipedia dataset has 'text: string', while the BookCorpus dataset has 'text: string not null'. Currently I hack together a working schema match with the following line, but it would be better if this was handled in Features themselves. ```python dset_wikipedia._data = dset_wikipedia.data.cast(dset_books._data.schema) ```

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MDU6SXNzdWU2NzY0ODYyNzU=

No 0.4.0 release on GitHub

closed

CONTRIBUTOR

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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MDU6SXNzdWU2NzY0ODIyNDI=

Loading preprocessed Wikipedia dataset requires apache_beam

closed

CONTRIBUTOR

Running `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 version 0.3.0. This seems like an unnecessary dependency to process some dataset info if you're using the already-preprocessed version. Could it be removed?

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MDU6SXNzdWU2NzY0NTYyNTc=

ug

closed

NONE

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MDU6SXNzdWU2NzYyOTk5OTM=

issues with downloading datasets for wmt16 and wmt19

closed

CONTRIBUTOR

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 now it worked. So it must have been some outdated dependencies that `pip install -e ".[dev]" ` fixed. 2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for. I tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below) 3. my machine has crushed and when I retried I got: ``` Traceback (most recent call last): File "./download.py", line 9, in <module> dataset = nlp.load_dataset('wmt16', 'ru-en') File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 549, in load_dataset download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 449, in download_and_prepare with incomplete_dir(self._cache_dir) as tmp_data_dir: File "/home/stas/anaconda3/envs/main/lib/python3.7/contextlib.py", line 112, in __enter__ return next(self.gen) File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 422, in incomplete_dir os.makedirs(tmp_dir) File "/home/stas/anaconda3/envs/main/lib/python3.7/os.py", line 221, in makedirs mkdir(name, mode) FileExistsError: [Errno 17] File exists: '/home/stas/.cache/huggingface/datasets/wmt16/ru-en/1.0.0/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete' ``` it can't handle resumes. but neither allows a new start. Had to delete it manually. 4. and finally when it downloaded the dataset, it then failed to fetch the metrics: ``` Traceback (most recent call last): File "./download.py", line 15, in <module> metric = nlp.load_metric('wmt16') File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 442, in load_metric module_path, hash = prepare_module(path, download_config=download_config, dataset=False) File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 258, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 198, in cached_path local_files_only=download_config.local_files_only, File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 356, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/metrics/wmt16/wmt16.py ``` 5. If I run the same code with `wmt19`, it fails too: ``` ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz ```

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MDExOlB1bGxSZXF1ZXN0NDY1NTA1NjQy

Fix elasticsearch result ids returning as strings

closed

CONTRIBUTOR

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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MDU6SXNzdWU2NzU2NDkwMzQ=

Bookcorpus data contains pretokenized text

closed

CONTRIBUTOR

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 quotes, respectively. On my own projects, I just run the data through NLTK's TreebankWordDetokenizer to reverse the tokenization (as best as possible). I think it would be beneficial to apply this transformation directly on your remote cached copy of the dataset. If you choose to do so, I would also suggest to use my fork of NLTK that fixes several bugs in their detokenizer (I've opened a pull-request, but they've yet to respond): https://github.com/nltk/nltk/pull/2575

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MDU6SXNzdWU2NzU1OTUzOTM=

PAWS dataset first item is header

closed

CONTRIBUTOR

``` 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 to themselves. Probably just need to ignore the first row in the dataset by default or something like that.

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MDExOlB1bGxSZXF1ZXN0NDY0NjY1NTU4

update mirror for RT dataset

closed

CONTRIBUTOR

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MDU6SXNzdWU2NzUwODA2OTQ=

rotten tomatoes movie review dataset taken down

closed

CONTRIBUTOR

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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MDU6SXNzdWU2NzQ4NTExNDc=

Bugs : dataset.map() is frozen on ELI5

closed

NONE

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, target_text`, `dataset.map` is **frozen** in the first hundreds examples. On the contrary, this works totally fine on SQUAD (80,000 examples). Both `nlp` version 0.3.0 and 0.4.0 cause frozen process . Also try various `pyarrow` versions from 0.16.0 / 0.17.0 / 1.0.0 also have the same frozen process. Reproducible code can be found on [this colab notebook ](https://colab.research.google.com/drive/14wttOTv3ky74B_c0kv5WrbgQjCF2fYQk?usp=sharing), where I also show that the same mapping function works fine on SQUAD, so the problem is likely due to ELI5 somehow. ---------------------------------------- **More Info :** instead of `map`, if I run `for` loop and apply function by myself, there's no error and can finish within 10 seconds. However, `nlp dataset` is immutable (I couldn't manually assign a new key-value to `dataset `object) I also notice that SQUAD texts are quite clean while ELI5 texts contain many special characters, not sure if this is the cause ?

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MDExOlB1bGxSZXF1ZXN0NDY0MjM2MTA1

Apply utf-8 encoding to all datasets

closed

MEMBER

## 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 instances where a non-binary file is opened.""" with open(filepath, 'r', encoding='utf-8') as input_file: regexp = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)") input_text = input_file.read() match = regexp.search(input_text) if match: output = regexp.sub(lambda m: m.group()[:-1]+', encoding="utf-8")', input_text) with open(filepath, 'w', encoding='utf-8') as output_file: output_file.write(output) ``` to perform the replacement. Note: 1. I excluded all _**binary files**_ from the search since it's possible some objects are opened for which the encoding doesn't make sense. Please correct me if I'm wrong and I'll tweak the regexp accordingly 2. There were two edge cases where the regexp failed (e.g. two `open` instances on a single line), but I decided to just fix these manually in the interest of time. 3. I only applied the replacement to files in `datasets/`. Let me know if this should be extended to other places like `metrics/` 4. I have implemented a unit test that should catch missing encodings in future CI runs Closes #468 and possibly #347

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MDExOlB1bGxSZXF1ZXN0NDYzOTcwNjQ2

Column indexing hotfix

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NDYzNjkxMjA0

add METEOR metric

closed

CONTRIBUTOR

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 similar string") meteor.compute() # {'meteor': 0.6411637931034483} ``` Uses [NLTK's implementation](https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score), [(source)](https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py)

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MDU6SXNzdWU2NzMxNzgzMTc=

Export TFRecord to GCP bucket

closed

NONE

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.export('gs://my_bucket/x.tfrecord')` does not work. There is no error message, I just can't find the file on my bucket... --- Looking at the code, `nlp` is using `tf.data.experimental.TFRecordWriter`, while I was using `tf.io.TFRecordWriter`. **What's the difference between those 2 ? How can I write TFRecords files directly to GCP bucket ?** @jarednielsen @lhoestq

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MDU6SXNzdWU2NzMxNDIxNDM=

Overview.ipynb throws exceptions with nlp 0.4.0

closed

NONE

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: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} 2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])} 3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1]) 4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8) <ipython-input-5-48907f2ad433> in <dictcomp>(.0) ----> 1 features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]} 2 labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])} 3 labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1]) 4 tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8) AttributeError: 'numpy.ndarray' object has no attribute 'to_tensor'

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MDExOlB1bGxSZXF1ZXN0NDYyOTMyMTgx

CheckList

closed

NONE

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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MDExOlB1bGxSZXF1ZXN0NDYyODQzMzQz

misc. bugs and quality of life

closed

CONTRIBUTOR

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 the repr to make it slightly more readable. ``` >>> print(list_datasets()[0]) nlp.ObjectInfo( id='aeslc', description='A collection of email messages of employees in the Enron Corporation.There are two features: - email_body: email body text. - subject_line: email subject text.', files=[nlp.S3Object('aeslc.py'), nlp.S3Object('dataset_infos.json'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/dev/allen-p_inbox_29.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/test/allen-p_inbox_24.subject'), nlp.S3Object('dummy/1.0.0/dummy_data-zip-extracted/dummy_data/AESLC-master/enron_subject_line/train/allen-p_inbox_20.subject'), nlp.S3Object('dummy/1.0.0/dummy_data.zip'), nlp.S3Object('urls_checksums/checksums.txt')] ) ``` 2. Add id-only option to `list_datasets` and `list_metrics` to allow the user to easily print out just the names of the datasets & metrics. I often found myself annoyed that this took so many strokes to do. ```python [dataset.id for dataset in list_datasets()] # before list_datasets(id_only=True) # after ``` 3. Fix null-seed randomization caching. When using `train_test_split` and `shuffle`, the computation was being cached even without a seed or generator being passed. The result was that calling `.shuffle` more than once on the same dataset didn't do anything without passing a distinct seed or generator. Likewise with `train_test_split`. 4. Indexing by iterables of bool. I added support for passing an iterable of type bool to `_getitem` as a numpy/pandas-like indexing method. Let me know if you think it's redundant with `filter` (I know it's not optimal memory-wise), but I think it's nice to have as a lightweight alternative to do simple things without having to create a copy of the entire dataset, e.g. ```python dataset[dataset['label'] == 0] # numpy-like bool indexing to look at instances with labels of 0 ``` 5. Add an `input_column` argument to `map` and `filter`, which allows you to filter/map on a particular column rather than passing the whole dict to the function. Also adds `fn_kwargs` to be passed to the function. I think these together make mapping much cleaner in many cases such as mono-column tokenization: ```python # before dataset = dataset.map(lambda batch: tokenizer(batch["text"]) # after dataset = dataset.map(tokenizer, input_column="text") dataset = dataset.map(tokenizer, input_column="text", fn_kwargs={"truncation": True, "padding": True}) ```

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MDU6SXNzdWU2NzI0MDczMzA=

test_load_real_dataset when config has BUILDER_CONFIGS that matter

closed

NONE

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/huggingface/nlp/blob/master/tests/test_dataset_common.py#L200)). This causes [this line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L201) to always be false because `config_kwargs` is not `None`. [This line](https://github.com/huggingface/nlp/blob/master/src/nlp/builder.py#L222) will be run instead, which doesn't use `BUILDER_CONFIGS`. For an example, you can try running the test for lince: ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lince` which yields > E TypeError: __init__() missing 3 required positional arguments: 'colnames', 'classes', and 'label_column'

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MDExOlB1bGxSZXF1ZXN0NDYyMTIwNzU4

add DoQA dataset (ACL 2020)

closed

CONTRIBUTOR

add DoQA dataset (ACL 2020) http://ixa.eus/node/12931

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MDExOlB1bGxSZXF1ZXN0NDYyMTE1MjA4

add crd3 dataset

closed

CONTRIBUTOR

opening new PR for CRD3 dataset (ACL2020) to fix the circle CI problems

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MDExOlB1bGxSZXF1ZXN0NDYyMTExNTU1

add reuters21578 dataset

closed

CONTRIBUTOR

new PR to add the reuters21578 dataset and fix the circle CI problems. Fix partially: - #353 Subsequent PR after: - #449

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MDExOlB1bGxSZXF1ZXN0NDYyMDc0MzQ0

Adding IWSLT 2017 dataset.

closed

CONTRIBUTOR

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 handle bilingual vs multilingual. Given `nlp` architecture a Config option seems to be the way to go, however, it might be a bit confusing to have different language pairs with different option. Using just language pairs is not viable as English to German exists in both. Any opinion on how that should be done ? EDIT: I decided to just omit de-en from multilingual as it's only a subset of the bilingual one. That way only language pairs exist. EDIT : Could be interesting for #438

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MDU6SXNzdWU2NzE4NzY5NjM=

invalid data type 'str' at _convert_outputs in arrow_dataset.py

closed

NONE

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 'str' I'm using pyarrow 1.0.0. And I have simple custom data set with Text and Integer Label. Ex: Data Text , Label #Column Header I'm facing an Network issue, 1 I forgot my password, 2 Error StackTrace: File "C:\**\transformers\trainer.py", line 492, in train for step, inputs in enumerate(epoch_iterator): File "C:\**\tqdm\std.py", line 1104, in __iter__ for obj in iterable: File "C:\**\torch\utils\data\dataloader.py", line 345, in __next__ data = self._next_data() File "C:\**\torch\utils\data\dataloader.py", line 385, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\**\torch\utils\data\_utils\fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\**\nlp\arrow_dataset.py", line 414, in __getitem__ output_all_columns=self._output_all_columns, File "C:\**\nlp\arrow_dataset.py", line 403, in _getitem outputs, format_type=format_type, format_columns=format_columns, output_all_columns=output_all_columns File "C:\**\nlp\arrow_dataset.py", line 343, in _convert_outputs v = command(v) TypeError: new(): invalid data type 'str'

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MDU6SXNzdWU2NzE2MjI0NDE=

UnicodeDecodeError while loading PAN-X task of XTREME dataset

closed

MEMBER

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-input-5-1d61f439b843> in <module> ----> 1 dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') /usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 528 ignore_verifications = ignore_verifications or save_infos 529 # Download/copy dataset processing script --> 530 module_path, hash = prepare_module(path, download_config=download_config, dataset=True) 531 532 # Get dataset builder class from the processing script /usr/local/lib/python3.6/dist-packages/nlp/load.py in prepare_module(path, download_config, dataset, force_local_path, **download_kwargs) 265 266 # Download external imports if needed --> 267 imports = get_imports(local_path) 268 local_imports = [] 269 library_imports = [] /usr/local/lib/python3.6/dist-packages/nlp/load.py in get_imports(file_path) 156 lines = [] 157 with open(file_path, mode="r") as f: --> 158 lines.extend(f.readlines()) 159 160 logger.info("Checking %s for additional imports.", file_path) /usr/lib/python3.6/encodings/ascii.py in decode(self, input, final) 24 class IncrementalDecoder(codecs.IncrementalDecoder): 25 def decode(self, input, final=False): ---> 26 return codecs.ascii_decode(input, self.errors)[0] 27 28 class StreamWriter(Codec,codecs.StreamWriter): UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 111: ordinal not in range(128) ``` ## Steps to reproduce Install from nlp's master branch ```python pip install git+https://github.com/huggingface/nlp.git ``` then run ```python from nlp import load_dataset # AmazonPhotos.zip is located in data/ dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data') ``` ## OS / platform details - `nlp` version: latest from master - Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.6.9 - PyTorch version (GPU?): 1.4.0 (True) - Tensorflow version (GPU?): 2.1.0 (True) - Using GPU in script?: True - Using distributed or parallel set-up in script?: False ## Proposed solution Either change [line 762](https://github.com/huggingface/nlp/blob/7ada00b1d62f94eee22a7df38c6b01e3f27194b7/datasets/xtreme/xtreme.py#L762) in `xtreme.py` to include UTF-8 encoding: ``` # old with open(filepath) as f # new with open(filepath, encoding='utf-8') as f ``` or raise a warning that suggests setting the locale explicitly, e.g. ```python import locale locale.setlocale(locale.LC_ALL, 'C.UTF-8') ``` I have a preference for the first solution. Let me know if you agree and I'll be happy to implement the simple fix!

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MDExOlB1bGxSZXF1ZXN0NDYxNzgwMzUy

DOCS: Fix typo

closed

CONTRIBUTOR

Fix typo from dictionnary -> dictionary

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MDExOlB1bGxSZXF1ZXN0NDYxMDEzOTM0

[METRICS] Various improvements on metrics

closed

MEMBER

- 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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MDExOlB1bGxSZXF1ZXN0NDYwMjEwODYw

Keep features after transform

closed

MEMBER

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) can be updated with inferred features if their type don't match. `map` transform sets `update_features=True` when writing to cache file or buffer. Features won't change by default in `map`. - added the `with_metadata` parameter. If `True`, the `features` (after update) will be written inside the metadata of the schema in this format: ``` { "huggingface": {"features" : <serialized Features exactly like dataset_info.json>} } ``` Then, once a dataset is instantiated without info/features, these metadata are used to set the features of the dataset.

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MDExOlB1bGxSZXF1ZXN0NDYwMTAxNDYz

Add rename, remove and cast in-place operations

closed

MEMBER

Add a bunch of in-place operation leveraging the Arrow back-end to rename and remove columns and cast to new features without using the more expensive `map` method. 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 the new pattern with a trailing underscore indicating in-place methods.

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MDExOlB1bGxSZXF1ZXN0NDYwMDcyNjQ1

Add dataset/mlsum

closed

CONTRIBUTOR

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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MDExOlB1bGxSZXF1ZXN0NDYwMDU0NDgz

add DoQA (ACL 2020) dataset

closed

CONTRIBUTOR

adds DoQA (ACL 2020) dataset

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MDExOlB1bGxSZXF1ZXN0NDYwMDQzNDY5

Doqa

closed

CONTRIBUTOR

add DoQA (ACL 2020) dataset

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MDExOlB1bGxSZXF1ZXN0NDU5OTM2OTU2

Fix KeyboardInterrupt in map and bad indices in select

closed

MEMBER

If you interrupted a map function while it was writing, the cached file was not discarded. 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 is finished.

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MDExOlB1bGxSZXF1ZXN0NDU5OTAxMjEy

[Breaking] Update Dataset and DatasetDict API

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MEMBER

This PR contains a few breaking changes so it's probably good to keep it for the next (major) release: - 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 should keep the convention of having a trailing underscore for methods which have an in-place effet. I also adopt the conversion of not returning the (self) dataset for these methods. This is different than what PyTorch does for instance (`model.to()` is in-place but return the self model) but I feel like it's a safer approach in terms of UX. - remove the `dataset.columns` property which returns a low-level Apache Arrow object and should not be used by users. Similarly, remove `dataset. nbytes` which we don't really want to expose in this bare-bone format. - add a few more properties and methods to `DatasetDict`

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MDExOlB1bGxSZXF1ZXN0NDU5Mzk5ODg2

Install CoVal metric from github

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MEMBER

Changed the import statements in `coval.py` to direct the user to install the original package from github if it's not already installed (the warning will only display properly after merging [PR455](https://github.com/huggingface/nlp/pull/455)) Also changed the function call to use named rather than positional arguments.

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MDExOlB1bGxSZXF1ZXN0NDU5MzMyOTM1

add set_format to DatasetDict + tests

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MEMBER

Add the `set_format` and `formated_as` and `reset_format` to `DatasetDict`. Add tests to these for `Dataset` and `DatasetDict`. Fix some bugs uncovered by the tests for `pandas` formating.

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MDExOlB1bGxSZXF1ZXN0NDU5MTc1MTY0

add crd3(ACL 2020) dataset

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CONTRIBUTOR

This PR adds the **Critical Role Dungeons and Dragons Dataset** published at ACL 2020

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MDExOlB1bGxSZXF1ZXN0NDU4NTk4NTUw

Add bleurt

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MEMBER

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 using `bleurt-base-128` instead. I think it's safer to have our users have a functioning metric when they call the default behavior, we'll address discrepancies in the issues/discussions if it comes up. In addition to the BLEURT file, `load.py` was changed so we can ask users to pip install the required packages from git when they have a `setup.py` but are not on PyPL cc @ankparikh @tsellam

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MDExOlB1bGxSZXF1ZXN0NDU4NTc3MzA3

Create SECURITY.md

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NONE

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MDExOlB1bGxSZXF1ZXN0NDU4MzQwNzky

add builder tests

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MEMBER

I added `as_dataset` and `download_and_prepare` to the tests

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