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https://api.github.com/repos/huggingface/datasets/issues/218

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https://github.com/huggingface/datasets/pull/218

MDExOlB1bGxSZXF1ZXN0NDI1MDI2NzEz

Add Natual Questions and C4 scripts

closed

MEMBER

Scripts are ready ! However they are not processed nor directly available from gcp yet.

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MDU6SXNzdWU2MjcxMjg0MDM=

Multi-task dataset mixing

open

CONTRIBUTOR

It seems like many of the best performing models on the GLUE benchmark make some use of multitask learning (simultaneous training on multiple tasks). The [T5 paper](https://arxiv.org/pdf/1910.10683.pdf) highlights multiple ways of mixing the tasks together during finetuning: - **Examples-proportional mixing** - sample from tasks proportionally to their dataset size - **Equal mixing** - sample uniformly from each task - **Temperature-scaled mixing** - The generalized approach used by multilingual BERT which uses a temperature T, where the mixing rate of each task is raised to the power 1/T and renormalized. When T=1 this is equivalent to equal mixing, and becomes closer to equal mixing with increasing T. Following this discussion https://github.com/huggingface/transformers/issues/4340 in [transformers](https://github.com/huggingface/transformers), @enzoampil suggested that the `nlp` library might be a better place for this functionality. Some method for combining datasets could be implemented ,e.g. ``` dataset = nlp.load_multitask(['squad','imdb','cnn_dm'], temperature=2.0, ...) ``` We would need a few additions: - Method of identifying the tasks - how can we support adding a string to each task as an identifier: e.g. 'summarisation: '? - Method of combining the metrics - a standard approach is to use the specific metric for each task and add them together for a combined score. It would be great to support common use cases such as pretraining on the GLUE benchmark before fine-tuning on each GLUE task in turn. I'm willing to write bits/most of this I just need some guidance on the interface and other library details so I can integrate it properly.

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https://github.com/huggingface/datasets/issues/216

MDU6SXNzdWU2MjY4OTY4OTA=

❓ How to get ROUGE-2 with the ROUGE metric ?

closed

NONE

I'm trying to use ROUGE metric, but I don't know how to get the ROUGE-2 metric. --- I compute scores with : ```python import nlp rouge = nlp.load_metric('rouge') with open("pred.txt") as p, open("ref.txt") as g: for lp, lg in zip(p, g): rouge.add([lp], [lg]) score = rouge.compute() ``` then : _(print only the F-score for readability)_ ```python for k, s in score.items(): print(k, s.mid.fmeasure) ``` It gives : >rouge1 0.7915168355671788 rougeL 0.7915168355671788 --- **How can I get the ROUGE-2 score ?** Also, it's seems weird that ROUGE-1 and ROUGE-L scores are the same. Did I made a mistake ? @lhoestq

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MDU6SXNzdWU2MjY4Njc4Nzk=

NonMatchingSplitsSizesError when loading blog_authorship_corpus

closed

NONE

Getting this error when i run `nlp.load_dataset('blog_authorship_corpus')`. ``` raise NonMatchingSplitsSizesError(str(bad_splits)) nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=616473500, num_examples=536323, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=30786661, num_examples=27766, dataset_name='blog_authorship_corpus')}] ``` Upon checking it seems like there is a disparity between the information in `datasets/blog_authorship_corpus/dataset_infos.json` and what was downloaded. Although I can get away with this by passing `ignore_verifications=True` in `load_dataset`, I'm thinking doing so might give problems later on.

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https://github.com/huggingface/datasets/pull/214

MDExOlB1bGxSZXF1ZXN0NDI0NTk1NjIx

[arrow_dataset.py] add new filter function

closed

MEMBER

The `.map()` function is super useful, but can IMO a bit tedious when filtering certain examples. I think, filtering out examples is also a very common operation people would like to perform on datasets. This PR is a proposal to add a `.filter()` function in the same spirit than the `.map()` function. Here is a sample code you can play around with: ```python ds = nlp.load_dataset("squad", split="validation[:10%]") def remove_under_idx_5(example, idx): return idx < 5 def only_keep_examples_with_is_in_context(example): return "is" in example["context"] result_keep_only_first_5 = ds.filter(remove_under_idx_5, with_indices=True, load_from_cache_file=False) result_keep_examples_with_is_in_context = ds.filter(only_keep_examples_with_is_in_context, load_from_cache_file=False) print("Original number of examples: {}".format(len(ds))) print("First five examples number of examples: {}".format(len(result_keep_only_first_5))) print("Is in context examples number of examples: {}".format(len(result_keep_examples_with_is_in_context))) ```

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https://github.com/huggingface/datasets/pull/213

MDExOlB1bGxSZXF1ZXN0NDI0NTUxODE3

better message if missing beam options

closed

MEMBER

WDYT @yjernite ? For example: ```python dataset = nlp.load_dataset('wikipedia', '20200501.aa') ``` Raises: ``` MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20200501.aa', beam_runner='DirectRunner')` ```

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https://github.com/huggingface/datasets/pull/212

MDExOlB1bGxSZXF1ZXN0NDI0NTQ1NjAy

have 'add' and 'add_batch' for metrics

closed

MEMBER

This should fix #116 Previously the `.add` method of metrics expected a batch of examples. Now `.add` expects one prediction/reference and `.add_batch` expects a batch. I think it is more coherent with the way the ArrowWriter works.

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MDU6SXNzdWU2MjY1NjU5OTQ=

[Arrow writer, Trivia_qa] Could not convert TagMe with type str: converting to null type

closed

MEMBER

Running the following code ``` import nlp ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards... ds.map(lambda x: x, load_from_cache_file=False) ``` triggers a `ArrowInvalid: Could not convert TagMe with type str: converting to null type` error. On the other hand if we remove a certain column of `trivia_qa` which seems responsible for the bug, it works: ``` import nlp ds = nlp.load_dataset("trivia_qa", "rc", split="validation[:1%]") # this might take 2.3 min to download but it's cached afterwards... ds.map(lambda x: x, remove_columns=["entity_pages"], load_from_cache_file=False) ``` . Seems quite hard to debug what's going on here... @lhoestq @thomwolf - do you have a good first guess what the problem could be? **Note** BTW: I think this could be a good test to check that the datasets work correctly: Take a tiny portion of the dataset and check that it can be written correctly.

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https://github.com/huggingface/datasets/pull/210

MDExOlB1bGxSZXF1ZXN0NDI0NDgyNDgz

fix xnli metric kwargs description

closed

MEMBER

The text was wrong as noticed in #202

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https://github.com/huggingface/datasets/pull/209

MDExOlB1bGxSZXF1ZXN0NDI0NDAwOTc4

Add a Google Drive exception for small files

closed

CONTRIBUTOR

I tried to use the ``nlp`` library to load personnal datasets. I mainly copy-paste the code for ``multi-news`` dataset because my files are stored on Google Drive. One of my dataset is small (< 25Mo) so it can be verified by Drive without asking the authorization to the user. This makes the download starts directly. Currently the ``nlp`` raises a error: ``ConnectionError: Couldn't reach https://drive.google.com/uc?export=download&id=1DGnbUY9zwiThTdgUvVTSAvSVHoloCgun`` while the url is working. So I just add a new exception as you have already done for ``firebasestorage.googleapis.com`` : ``` elif (response.status_code == 400 and "firebasestorage.googleapis.com" in url) or (response.status_code == 405 and "drive.google.com" in url) ``` I make an example of the error that you can run on [](https://colab.research.google.com/drive/1ae_JJ9uvUt-9GBh0uGZhjbF5aXkl-BPv?usp=sharing) I avoid the error by adding an exception but there is maybe a proper way to do it. Many thanks :hugs: Best,

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MDExOlB1bGxSZXF1ZXN0NDI0Mzk0ODIx

[Dummy data] insert config name instead of config

closed

MEMBER

Thanks @yjernite for letting me know. in the dummy data command the config name shuold be passed to the dataset builder and not the config itself. Also, @lhoestq fixed small import bug introduced by beam command I think.

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MDU6SXNzdWU2MjU5MzIyMDA=

Remove test set from NLP viewer

closed

NONE

While the new [NLP viewer](https://huggingface.co/nlp/viewer/) is a great tool, I think it would be best to outright remove the option of looking at the test sets. At the very least, a warning should be displayed to users before showing the test set. Newcomers to the field might not be aware of best practices, and small things like this can help increase awareness.

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MDU6SXNzdWU2MjU4NDI5ODk=

[Question] Combine 2 datasets which have the same columns

closed

CONTRIBUTOR

Hi, I am using ``nlp`` to load personal datasets. I created summarization datasets in multi-languages based on wikinews. I have one dataset for english and one for german (french is getting to be ready as well). I want to keep these datasets independent because they need different pre-processing (add different task-specific prefixes for T5 : *summarize:* for english and *zusammenfassen:* for german) My issue is that I want to train T5 on the combined english and german datasets to see if it improves results. So I would like to combine 2 datasets (which have the same columns) to make one and train T5 on it. I was wondering if there is a proper way to do it? I assume that it can be done by combining all examples of each dataset but maybe you have a better solution. Hoping this is clear enough, Thanks a lot 😊 Best

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https://github.com/huggingface/datasets/pull/205

MDExOlB1bGxSZXF1ZXN0NDIzOTY2ODE1

Better arrow dataset iter

closed

MEMBER

I tried to play around with `tf.data.Dataset.from_generator` and I found out that the `__iter__` that we have for `nlp.arrow_dataset.Dataset` ignores the format that has been set (torch or tensorflow). With these changes I should be able to come up with a `tf.data.Dataset` that uses lazy loading, as asked in #193.

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MDExOlB1bGxSZXF1ZXN0NDIzODE5MTQw

Add Dataflow support + Wikipedia + Wiki40b

closed

MEMBER

# Add Dataflow support + Wikipedia + Wiki40b ## Support datasets processing with Apache Beam Some datasets are too big to be processed on a single machine, for example: wikipedia, wiki40b, etc. Apache Beam allows to process datasets on many execution engines like Dataflow, Spark, Flink, etc. To process such datasets with Beam, I added a command to run beam pipelines `nlp-cli run_beam path/to/dataset/script`. Then I used it to process the english + french wikipedia, and the english of wiki40b. The processed arrow files are on GCS and are the result of a Dataflow job. I added a markdown documentation file in `docs` that explains how to use it properly. ## Load already processed datasets Now that we have those datasets already processed, I made it possible to load datasets that are already processed. You can do `load_dataset('wikipedia', '20200501.en')` and it will download the processed files from the Hugging Face GCS directly into the user's cache and be ready to use ! The Wikipedia dataset was already asked in #187 and this PR should soon allow to add Natural Questions as asked in #129 ## Other changes in the code To make things work, I had to do a few adjustments: - add a `ship_files_with_pipeline` method to the `DownloadManager`. This is because beam pipelines can be run in the cloud and therefore need to have access to your downloaded data. I used it in the wikipedia script: ```python if not pipeline.is_local(): downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline) ``` - add parquet to arrow conversion. This is because the output of beam pipelines are parquet files so we need to convert them to arrow and have the arrow files on GCS - add a test script with a dummy beam dataset - minor adjustments to allow read/write operations on remote files using `apache_beam.io.filesystems.FileSystems` if we want (it can be connected to gcp, s3, hdfs, etc...)

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MDExOlB1bGxSZXF1ZXN0NDIzNzEyMTQ3

Raise an error if no config name for datasets like glue

closed

MEMBER

Some datasets like glue (see #130) and scientific_papers (see #197) have many configs. For example for glue there are cola, sst2, mrpc etc. Currently if a user does `load_dataset('glue')`, then Cola is loaded by default and it can be confusing. Instead, we should raise an error to let the user know that he has to pick one of the available configs (as proposed in #152). For example for glue, the message looks like: ``` ValueError: Config name is missing. Please pick one among the available configs: ['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax'] Example of usage: `load_dataset('glue', 'cola')` ``` The error is raised if the config name is missing and if there are >=2 possible configs.

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MDU6SXNzdWU2MjU0OTM5ODM=

Mistaken `_KWARGS_DESCRIPTION` for XNLI metric

closed

NONE

Hi! The [`_KWARGS_DESCRIPTION`](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/xnli/xnli.py#L45) for the XNLI metric uses `Args` and `Returns` text from [BLEU](https://github.com/huggingface/nlp/blob/7d0fa58641f3f462fb2861dcdd6ce7f0da3f6a56/metrics/bleu/bleu.py#L58) metric: ``` _KWARGS_DESCRIPTION = """ Computes XNLI score which is just simple accuracy. Args: predictions: list of translations to score. Each translation should be tokenized into a list of tokens. references: list of lists of references for each translation. Each reference should be tokenized into a list of tokens. max_order: Maximum n-gram order to use when computing BLEU score. smooth: Whether or not to apply Lin et al. 2004 smoothing. Returns: 'bleu': bleu score, 'precisions': geometric mean of n-gram precisions, 'brevity_penalty': brevity penalty, 'length_ratio': ratio of lengths, 'translation_length': translation_length, 'reference_length': reference_length """ ``` But it should be something like: ``` _KWARGS_DESCRIPTION = """ Computes XNLI score which is just simple accuracy. Args: predictions: Predicted labels. references: Ground truth labels. Returns: 'accuracy': accuracy ```

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https://github.com/huggingface/datasets/pull/201

MDExOlB1bGxSZXF1ZXN0NDIzNDkzNTMw

Fix typo in README

closed

MEMBER

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MDExOlB1bGxSZXF1ZXN0NDIzNDg2NTM0

[ArrowWriter] Set schema at first write example

closed

MEMBER

Right now if the schema was not specified when instantiating `ArrowWriter`, then it could be set with the first `write_table` for example (it calls `self._build_writer()` to do so). I noticed that it was not done if the first example is added via `.write`, so I added it for coherence.

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https://github.com/huggingface/datasets/pull/199

MDExOlB1bGxSZXF1ZXN0NDIzNDc4ODIx

Fix GermEval 2014 dataset infos

closed

CONTRIBUTOR

Hi, this PR just removes the `dataset_info.json` file and adds a newly generated `dataset_infos.json` file.

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https://github.com/huggingface/datasets/issues/198

MDU6SXNzdWU2MjUyMDA2Mjc=

Index outside of table length

closed

NONE

The offset input box warns of numbers larger than a limit (like 2000) but then the errors start at a smaller value than that limit (like 1955). > ValueError: Index (2000) outside of table length (2000). > Traceback: > File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script > exec(code, module.__dict__) > File "/home/sasha/nlp_viewer/run.py", line 116, in <module> > v = d[item][k] > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__ > output_all_columns=self._output_all_columns, > File "/home/sasha/.local/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 290, in _getitem > raise ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")

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MDU6SXNzdWU2MjQ5NjY5MDQ=

Scientific Papers only downloading Pubmed

closed

NONE

Hi! I have been playing around with this module, and I am a bit confused about the `scientific_papers` dataset. I thought that it would download two separate datasets, arxiv and pubmed. But when I run the following: ``` dataset = nlp.load_dataset('scientific_papers', data_dir='.', cache_dir='.') Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.05k/5.05k [00:00<00:00, 2.66MB/s] Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.90k/4.90k [00:00<00:00, 2.42MB/s] Downloading and preparing dataset scientific_papers/pubmed (download: 4.20 GiB, generated: 2.33 GiB, total: 6.53 GiB) to ./scientific_papers/pubmed/1.1.1... Downloading: 3.62GB [00:40, 90.5MB/s] Downloading: 880MB [00:08, 101MB/s] Dataset scientific_papers downloaded and prepared to ./scientific_papers/pubmed/1.1.1. Subsequent calls will reuse this data. ``` only a pubmed folder is created. There doesn't seem to be something for arxiv. Are these two datasets merged? Or have I misunderstood something? Thanks!

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https://github.com/huggingface/datasets/pull/196

MDExOlB1bGxSZXF1ZXN0NDIzMjIwMjIw

Check invalid config name

closed

MEMBER

As said in #194, we should raise an error if the config name has bad characters. Bad characters are those that are not allowed for directory names on windows.

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MDExOlB1bGxSZXF1ZXN0NDIzMTg1NTAy

[Dummy data command] add new case to command

closed

MEMBER

Qanta: #194 introduces a case that was not noticed before. This change in code helps community users to have an easier time creating the dummy data.

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MDExOlB1bGxSZXF1ZXN0NDIzMTgyNDM5

Add Dataset: Qanta

closed

MEMBER

Fixes dummy data for #169 @EntilZha

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MDU6SXNzdWU2MjQ2NTU1NTg=

[Tensorflow] Use something else than `from_tensor_slices()`

closed

NONE

In the example notebook, the TF Dataset is built using `from_tensor_slices()` : ```python columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions'] train_tf_dataset.set_format(type='tensorflow', columns=columns) features = {x: train_tf_dataset[x] for x in columns[:3]} labels = {"output_1": train_tf_dataset["start_positions"]} labels["output_2"] = train_tf_dataset["end_positions"] tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8) ``` But according to [official tensorflow documentation](https://www.tensorflow.org/guide/data#consuming_numpy_arrays), this will load the entire dataset to memory. **This defeats one purpose of this library, which is lazy loading.** Is there any other way to load the `nlp` dataset into TF dataset lazily ? --- For example, is it possible to use [Arrow dataset](https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset) ? If yes, is there any code example ?

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MDU6SXNzdWU2MjQzOTc1OTI=

[Question] Create Apache Arrow dataset from raw text file

closed

CONTRIBUTOR

Hi guys, I have gathered and preprocessed about 2GB of COVID papers from CORD dataset @ Kggle. I have seen you have a text dataset as "Crime and punishment" in Apache arrow format. Do you have any script to do it from a raw txt file (preprocessed as for BERT like) or any guide? Is the worth of send it to you and add it to the NLP library? Thanks, Manu

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MDExOlB1bGxSZXF1ZXN0NDIyODI3MDMy

[Squad es] add dataset_infos

closed

MEMBER

@mariamabarham - was still about to upload this. Should have waited with my comment a bit more :D

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MDExOlB1bGxSZXF1ZXN0NDIyNjA4NzAw

add squad Spanish v1 and v2

closed

CONTRIBUTOR

This PR add the Spanish Squad versions 1 and 2 datasets. Fixes #164

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MDU6SXNzdWU2MjQwNDg4ODE=

[Question] BERT-style multiple choice formatting

closed

NONE

Hello, I am wondering what the equivalent formatting of a dataset should be to allow for multiple-choice answering prediction, BERT-style. Previously, this was done by passing a list of `InputFeatures` to the dataloader instead of a list of `InputFeature`, where `InputFeatures` contained lists of length equal to the number of answer choices in the MCQ instead of single items. I'm a bit confused on what the output of my feature conversion function should be when using `dataset.map()` to ensure similar behavior. Thanks!

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MDU6SXNzdWU2MjM4OTA0MzA=

When will the remaining math_dataset modules be added as dataset objects

closed

NONE

Currently only the algebra_linear_1d is supported. Is there a timeline for making the other modules supported. If no timeline is established, how can I help?

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[Question] How to load wikipedia ? Beam runner ?

closed

CONTRIBUTOR

When `nlp.load_dataset('wikipedia')`, I got * `WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used.` * `AttributeError: 'NoneType' object has no attribute 'size'` Could somebody tell me what should I do ? # Env On Colab, ``` git clone https://github.com/huggingface/nlp cd nlp pip install -q . ``` ``` %pip install -q apache_beam mwparserfromhell -> ERROR: pydrive 1.3.1 has requirement oauth2client>=4.0.0, but you'll have oauth2client 3.0.0 which is incompatible. ERROR: google-api-python-client 1.7.12 has requirement httplib2<1dev,>=0.17.0, but you'll have httplib2 0.12.0 which is incompatible. ERROR: chainer 6.5.0 has requirement typing-extensions<=3.6.6, but you'll have typing-extensions 3.7.4.2 which is incompatible. ``` ``` pip install -q apache-beam[interactive] ERROR: google-colab 1.0.0 has requirement ipython~=5.5.0, but you'll have ipython 5.10.0 which is incompatible. ``` # The whole message ``` WARNING:nlp.builder:Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided. Please pass a nlp.DownloadConfig(beam_runner=...) object to the builder.download_and_prepare(download_config=...) method. Default values will be used. Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/wikipedia/20200501.aa/1.0.0... --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process() 44 frames /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window() /usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result) 1081 writer.write(e) -> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)] 1083 /usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self) 422 def close(self): --> 423 self.sink.close(self.temp_handle) 424 return self.temp_shard_path /usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer) 537 if len(self._buffer[0]) > 0: --> 538 self._flush_buffer() 539 if self._record_batches_byte_size > 0: /usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self) 569 for b in x.buffers(): --> 570 size = size + b.size 571 self._record_batches_byte_size = self._record_batches_byte_size + size AttributeError: 'NoneType' object has no attribute 'size' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last) <ipython-input-9-340aabccefff> in <module>() ----> 1 dset = nlp.load_dataset('wikipedia') /usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs) 370 verify_infos = not save_infos and not ignore_verifications 371 self._download_and_prepare( --> 372 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 373 ) 374 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 770 with beam.Pipeline(runner=beam_runner, options=beam_options,) as pipeline: 771 super(BeamBasedBuilder, self)._download_and_prepare( --> 772 dl_manager, pipeline=pipeline, verify_infos=False 773 ) # TODO{beam} verify infos 774 /usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb) 501 def __exit__(self, exc_type, exc_val, exc_tb): 502 if not exc_type: --> 503 self.run().wait_until_finish() 504 505 def visit(self, visitor): /usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api) 481 return Pipeline.from_runner_api( 482 self.to_runner_api(use_fake_coders=True), self.runner, --> 483 self._options).run(False) 484 485 if self._options.view_as(TypeOptions).runtime_type_check: /usr/local/lib/python3.6/dist-packages/apache_beam/pipeline.py in run(self, test_runner_api) 494 finally: 495 shutil.rmtree(tmpdir) --> 496 return self.runner.run_pipeline(self, self._options) 497 498 def __enter__(self): /usr/local/lib/python3.6/dist-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options) 128 runner = BundleBasedDirectRunner() 129 --> 130 return runner.run_pipeline(pipeline, options) 131 132 /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_pipeline(self, pipeline, options) 553 554 self._latest_run_result = self.run_via_runner_api( --> 555 pipeline.to_runner_api(default_environment=self._default_environment)) 556 return self._latest_run_result 557 /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_via_runner_api(self, pipeline_proto) 563 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to 564 # the teststream (if any), and all the stages). --> 565 return self.run_stages(stage_context, stages) 566 567 @contextlib.contextmanager /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in run_stages(self, stage_context, stages) 704 stage, 705 pcoll_buffers, --> 706 stage_context.safe_coders) 707 metrics_by_stage[stage.name] = stage_results.process_bundle.metrics 708 monitoring_infos_by_stage[stage.name] = ( /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in _run_stage(self, worker_handler_factory, pipeline_components, stage, pcoll_buffers, safe_coders) 1071 cache_token_generator=cache_token_generator) 1072 -> 1073 result, splits = bundle_manager.process_bundle(data_input, data_output) 1074 1075 def input_for(transform_id, input_id): /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs) 2332 2333 with UnboundedThreadPoolExecutor() as executor: -> 2334 for result, split_result in executor.map(execute, part_inputs): 2335 2336 split_result_list += split_result /usr/lib/python3.6/concurrent/futures/_base.py in result_iterator() 584 # Careful not to keep a reference to the popped future 585 if timeout is None: --> 586 yield fs.pop().result() 587 else: 588 yield fs.pop().result(end_time - time.monotonic()) /usr/lib/python3.6/concurrent/futures/_base.py in result(self, timeout) 430 raise CancelledError() 431 elif self._state == FINISHED: --> 432 return self.__get_result() 433 else: 434 raise TimeoutError() /usr/lib/python3.6/concurrent/futures/_base.py in __get_result(self) 382 def __get_result(self): 383 if self._exception: --> 384 raise self._exception 385 else: 386 return self._result /usr/local/lib/python3.6/dist-packages/apache_beam/utils/thread_pool_executor.py in run(self) 42 # If the future wasn't cancelled, then attempt to execute it. 43 try: ---> 44 self._future.set_result(self._fn(*self._fn_args, **self._fn_kwargs)) 45 except BaseException as exc: 46 # Even though Python 2 futures library has #set_exection(), /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in execute(part_map) 2329 self._registered, 2330 cache_token_generator=self._cache_token_generator) -> 2331 return bundle_manager.process_bundle(part_map, expected_outputs) 2332 2333 with UnboundedThreadPoolExecutor() as executor: /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in process_bundle(self, inputs, expected_outputs) 2243 process_bundle_descriptor_id=self._bundle_descriptor.id, 2244 cache_tokens=[next(self._cache_token_generator)])) -> 2245 result_future = self._worker_handler.control_conn.push(process_bundle_req) 2246 2247 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse] /usr/local/lib/python3.6/dist-packages/apache_beam/runners/portability/fn_api_runner.py in push(self, request) 1557 self._uid_counter += 1 1558 request.instruction_id = 'control_%s' % self._uid_counter -> 1559 response = self.worker.do_instruction(request) 1560 return ControlFuture(request.instruction_id, response) 1561 /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request) 413 # E.g. if register is set, this will call self.register(request.register)) 414 return getattr(self, request_type)( --> 415 getattr(request, request_type), request.instruction_id) 416 else: 417 raise NotImplementedError /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id) 448 with self.maybe_profile(instruction_id): 449 delayed_applications, requests_finalization = ( --> 450 bundle_processor.process_bundle(instruction_id)) 451 monitoring_infos = bundle_processor.monitoring_infos() 452 monitoring_infos.extend(self.state_cache_metrics_fn()) /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id) 837 for data in data_channel.input_elements(instruction_id, 838 expected_transforms): --> 839 input_op_by_transform_id[data.transform_id].process_encoded(data.data) 840 841 # Finish all operations. /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values) 214 decoded_value = self.windowed_coder_impl.decode_from_stream( 215 input_stream, True) --> 216 self.output(decoded_value) 217 218 def try_split(self, fraction_of_remainder, total_buffer_size): /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/worker/operations.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented() /usr/local/lib/python3.6/dist-packages/future/utils/__init__.py in raise_with_traceback(exc, traceback) 417 if traceback == Ellipsis: 418 _, _, traceback = sys.exc_info() --> 419 raise exc.with_traceback(traceback) 420 421 else: /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process() /usr/local/lib/python3.6/dist-packages/apache_beam/runners/common.cpython-36m-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window() /usr/local/lib/python3.6/dist-packages/apache_beam/io/iobase.py in process(self, element, init_result) 1080 for e in bundle[1]: # values 1081 writer.write(e) -> 1082 return [window.TimestampedValue(writer.close(), timestamp.MAX_TIMESTAMP)] 1083 1084 /usr/local/lib/python3.6/dist-packages/apache_beam/io/filebasedsink.py in close(self) 421 422 def close(self): --> 423 self.sink.close(self.temp_handle) 424 return self.temp_shard_path /usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in close(self, writer) 536 def close(self, writer): 537 if len(self._buffer[0]) > 0: --> 538 self._flush_buffer() 539 if self._record_batches_byte_size > 0: 540 self._write_batches(writer) /usr/local/lib/python3.6/dist-packages/apache_beam/io/parquetio.py in _flush_buffer(self) 568 for x in arrays: 569 for b in x.buffers(): --> 570 size = size + b.size 571 self._record_batches_byte_size = self._record_batches_byte_size + size AttributeError: 'NoneType' object has no attribute 'size' [while running 'train/Save to parquet/Write/WriteImpl/WriteBundles'] ```

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MDU6SXNzdWU2MjM1OTUxODA=

Weird-ish: Not creating unique caches for different phases

closed

NONE

Sample code: ```python import nlp dataset = nlp.load_dataset('boolq') def func1(x): return x def func2(x): return None train_output = dataset["train"].map(func1) valid_output = dataset["validation"].map(func1) print() print(len(train_output), len(valid_output)) # Output: 9427 9427 ``` The map method in both cases seem to be pointing to the same cache, so the latter call based on the validation data will return the processed train data cache. What's weird is that the following doesn't seem to be an issue: ```python train_output = dataset["train"].map(func2) valid_output = dataset["validation"].map(func2) print() print(len(train_output), len(valid_output)) # 9427 3270 ```

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MDExOlB1bGxSZXF1ZXN0NDIxODkxNjY2

[Commands] In-detail instructions to create dummy data folder

closed

MEMBER

### Dummy data command This PR adds a new command `python nlp-cli dummy_data <path_to_dataset_folder>` that gives in-detail instructions on how to add the dummy data files. It would be great if you can try it out by moving the current dummy_data folder of any dataset in `./datasets` with `mv datasets/<dataset_script>/dummy_data datasets/<dataset_name>/dummy_data_copy` and running the command `python nlp-cli dummy_data ./datasets/<dataset_name>` to see if you like the instructions. ### CONTRIBUTING.md Also the CONTRIBUTING.md is made cleaner including a new section on "How to add a dataset". ### Current PRs It would be nice if we can try out if this command helps current PRs, *e.g.* #169 to add a dataset. I comment on those PRs.

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MDExOlB1bGxSZXF1ZXN0NDIxODQ5MTQ3

Use IndexError instead of ValueError when index out of range

closed

CONTRIBUTOR

**`default __iter__ needs IndexError`**. When I want to create a wrapper of arrow dataset to adapt to fastai, I don't know how to initialize it, so I didn't use inheritance but use object composition. I wrote sth like this. ``` clas HF_dataset(): def __init__(self, arrow_dataset): self.dset = arrow_dataset def __getitem__(self, i): return self.my_get_item(self.dset) ``` But `for sample in my_dataset:` gave me `ValueError(f"Index ({key}) outside of table length ({self._data.num_rows}).")` . This is because default `__iter__` will stop when it catched `IndexError`. You can also see my [work](https://github.com/richardyy1188/Pretrain-MLM-and-finetune-on-GLUE-with-fastai/blob/master/GLUE_with_fastai.ipynb) that uses fastai2 to show/load batches from huggingface/nlp GLUE datasets So I hope we can use `IndexError` instead to let other people who want to wrap it for any purpose won't be caught by this caveat. BTW, I super appreciate your work, both transformers and nlp save my life. 💖💖💖💖💖💖💖

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MDU6SXNzdWU2MjMwNTQyNzA=

[Bug] labels of glue/ax are all -1

closed

CONTRIBUTOR

``` ax = nlp.load_dataset('glue', 'ax') for i in range(30): print(ax['test'][i]['label'], end=', ') ``` ``` -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, ```

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MDExOlB1bGxSZXF1ZXN0NDIxNDcxMjg4

Update newsroom.py

closed

CONTRIBUTOR

Updated the URL for Newsroom download so it's more robust to future changes.

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MDU6SXNzdWU2MjI2MzQ0MjA=

Cannot upload my own dataset

closed

NONE

I look into `nlp-cli` and `user.py` to learn how to upload my own data. It is supposed to work like this - Register to get username, password at huggingface.co - `nlp-cli login` and type username, passworld - I have a single file to upload at `./ttc/ttc_freq_extra.csv` - `nlp-cli upload ttc/ttc_freq_extra.csv` But I got this error. ``` 2020-05-21 16:33:52.722464: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 About to upload file /content/ttc/ttc_freq_extra.csv to S3 under filename ttc/ttc_freq_extra.csv and namespace korakot Proceed? [Y/n] y Uploading... This might take a while if files are large Traceback (most recent call last): File "/usr/local/bin/nlp-cli", line 33, in <module> service.run() File "/usr/local/lib/python3.6/dist-packages/nlp/commands/user.py", line 234, in run token=token, filename=filename, filepath=filepath, organization=self.args.organization File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 141, in presign_and_upload urls = self.presign(token, filename=filename, organization=organization) File "/usr/local/lib/python3.6/dist-packages/nlp/hf_api.py", line 132, in presign return PresignedUrl(**d) TypeError: __init__() got an unexpected keyword argument 'cdn' ```

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MDExOlB1bGxSZXF1ZXN0NDIxMzk5Nzg2

Add hall of fame

closed

MEMBER

powered by https://github.com/sourcerer-io/hall-of-fame

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MDU6SXNzdWU2MjI1MjU0MTA=

[Feature request] separate split name and split instructions

closed

MEMBER

Currently, the name of an nlp.NamedSplit is parsed in arrow_reader.py and used as the instruction. This makes it impossible to have several training sets, which can occur when: - A dataset corresponds to a collection of sub-datasets - A dataset was built in stages, adding new examples at each stage Would it be possible to have two separate fields in the Split class, a name /instruction and a unique ID that is used as the key in the builder's split_dict ?

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MDExOlB1bGxSZXF1ZXN0NDIwOTMyMDI5

[Manual data] improve error message for manual data in general

closed

MEMBER

`nlp.load("xsum")` now leads to the following error message:  I guess the manual download instructions for `xsum` can also be improved.

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MDExOlB1bGxSZXF1ZXN0NDIwOTI4MzE0

Xsum manual download instruction

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NDIwODkzNDky

[Tests] Refactor MockDownloadManager

closed

MEMBER

Clean mock download manager class. The print function was not of much help I think. We should think about adding a command that creates the dummy folder structure for the user.

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MDU6SXNzdWU2MjE5Mjk0Mjg=

[Manual data dir] Error message: nlp.load_dataset('xsum') -> TypeError

closed

CONTRIBUTOR

v 0.1.0 from pip ```python import nlp xsum = nlp.load_dataset('xsum') ``` Issue is `dl_manager.manual_dir`is `None` ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-42-8a32f066f3bd> in <module> ----> 1 xsum = nlp.load_dataset('xsum') ~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 515 download_mode=download_mode, 516 ignore_verifications=ignore_verifications, --> 517 save_infos=save_infos, 518 ) 519 ~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs) 361 verify_infos = not save_infos and not ignore_verifications 362 self._download_and_prepare( --> 363 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 364 ) 365 # Sync info ~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 397 split_dict = SplitDict(dataset_name=self.name) 398 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 399 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 400 # Checksums verification 401 if verify_infos: ~/miniconda3/envs/nb/lib/python3.7/site-packages/nlp/datasets/xsum/5c5fca23aaaa469b7a1c6f095cf12f90d7ab99bcc0d86f689a74fd62634a1472/xsum.py in _split_generators(self, dl_manager) 102 with open(dl_path, "r") as json_file: 103 split_ids = json.load(json_file) --> 104 downloaded_path = os.path.join(dl_manager.manual_dir, "xsum-extracts-from-downloads") 105 return [ 106 nlp.SplitGenerator( ~/miniconda3/envs/nb/lib/python3.7/posixpath.py in join(a, *p) 78 will be discarded. An empty last part will result in a path that 79 ends with a separator.""" ---> 80 a = os.fspath(a) 81 sep = _get_sep(a) 82 path = a TypeError: expected str, bytes or os.PathLike object, not NoneType ```

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MDU6SXNzdWU2MjE5Mjg0MDM=

nlp.load_dataset('xsum') -> TypeError

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NDIwNzUyNzQy

Rm extracted test dirs

closed

MEMBER

All the dummy data used for tests were duplicated. For each dataset, we had one zip file but also its extracted directory. I removed all these directories Furthermore instead of extracting next to the dummy_data.zip file, we extract in the temp `cached_dir` used for tests, so that all the extracted directories get removed after testing. Finally there was a bug in the `mock_download_manager` that would let it create directories with invalid names, as in #172. I fixed that by encoding url arguments. I had to rename the dummy data for `scientific_papers` and `cnn_dailymail` (the aws tests don't pass for those 2 in this PR, but they will once aws will be synced, as the local ones do) Let me know if it sounds good to you @patrickvonplaten . I'm still not entirely familiar with the mock downloader

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MDU6SXNzdWU2MjEzNzczODY=

Clone not working on Windows environment

closed

CONTRIBUTOR

Cloning in a windows environment is not working because of use of special character '?' in folder name .. Please consider changing the folder name .... Reference to folder - nlp/datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs/dailymail/stories/ error log: fatal: cannot create directory at 'datasets/cnn_dailymail/dummy/3.0.0/3.0.0/dummy_data-zip-extracted/dummy_data/uc?export=download&id=0BwmD_VLjROrfM1BxdkxVaTY2bWs': Invalid argument

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MDExOlB1bGxSZXF1ZXN0NDIwMjk0ODM0

fix squad metric format

closed

MEMBER

The format of the squad metric was wrong. This should fix #143 I tested with ```python3 predictions = [ {'id': '56be4db0acb8001400a502ec', 'prediction_text': 'Denver Broncos'} ] references = [ {'answers': [{'text': 'Denver Broncos'}], 'id': '56be4db0acb8001400a502ec'} ] ```

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MDExOlB1bGxSZXF1ZXN0NDIwMjMwMDIx

Rename anli dataset

closed

MEMBER

What we have now as the `anli` dataset is actually the αNLI dataset from the ART challenge dataset. This name is confusing because `anli` is also the name of adversarial NLI (see [https://github.com/facebookresearch/anli](https://github.com/facebookresearch/anli)). I renamed the current `anli` dataset by `art`.

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MDExOlB1bGxSZXF1ZXN0NDIwMjE1NDkw

Adding Qanta (Quizbowl) Dataset

closed

CONTRIBUTOR

This PR adds the qanta question answering datasets from [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792) and [Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples](https://www.aclweb.org/anthology/Q19-1029/) (adversarial fold) This partially continues a discussion around fixing dummy data from https://github.com/huggingface/nlp/issues/161 I ran the following code to double check that it works and did some sanity checks on the output. The majority of the code itself is from our `allennlp` version of the dataset reader. ```python import nlp # Default is full question data = nlp.load_dataset('./datasets/qanta') # Four configs # Primarily useful for training data = nlp.load_dataset('./datasets/qanta', 'mode=sentences,char_skip=25') # Primarily used in evaluation data = nlp.load_dataset('./datasets/qanta', 'mode=first,char_skip=25') data = nlp.load_dataset('./datasets/qanta', 'mode=full,char_skip=25') # Primarily useful in evaluation and "live" play data = nlp.load_dataset('./datasets/qanta', 'mode=runs,char_skip=25') ```

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MDU6SXNzdWU2MjA5NTk4MTk=

Loading 'wikitext' dataset fails

closed

NONE

Loading the 'wikitext' dataset fails with Attribute error: Code to reproduce (From example notebook): import nlp wikitext_dataset = nlp.load_dataset('wikitext') Error: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-17-d5d9df94b13c> in <module>() 11 12 # Load a dataset and print the first examples in the training set ---> 13 wikitext_dataset = nlp.load_dataset('wikitext') 14 print(wikitext_dataset['train'][0]) 6 frames /usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs) 518 download_mode=download_mode, 519 ignore_verifications=ignore_verifications, --> 520 save_infos=save_infos, 521 ) 522 /usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, dl_manager, **download_and_prepare_kwargs) 363 verify_infos = not save_infos and not ignore_verifications 364 self._download_and_prepare( --> 365 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 366 ) 367 # Sync info /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 416 try: 417 # Prepare split will record examples associated to the split --> 418 self._prepare_split(split_generator, **prepare_split_kwargs) 419 except OSError: 420 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or "")) /usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator) 594 example = self.info.features.encode_example(record) 595 writer.write(example) --> 596 num_examples, num_bytes = writer.finalize() 597 598 assert num_examples == num_examples, f"Expected to write {split_info.num_examples} but wrote {num_examples}" /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in finalize(self, close_stream) 173 def finalize(self, close_stream=True): 174 if self.pa_writer is not None: --> 175 self.write_on_file() 176 self.pa_writer.close() 177 if close_stream: /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self) 124 else: 125 # All good --> 126 self._write_array_on_file(pa_array) 127 self.current_rows = [] 128 /usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array) 93 def _write_array_on_file(self, pa_array): 94 """Write a PyArrow Array""" ---> 95 pa_batch = pa.RecordBatch.from_struct_array(pa_array) 96 self._num_bytes += pa_array.nbytes 97 self.pa_writer.write_batch(pa_batch) AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'

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MDExOlB1bGxSZXF1ZXN0NDIwMDY0NDMw

[Tests] refactor tests

closed

MEMBER

This PR separates AWS and Local tests to remove these ugly statements in the script: ```python if "/" not in dataset_name: logging.info("Skip {} because it is a canonical dataset") return ``` To run a `aws` test, one should now run the following command: ```python pytest -s tests/test_dataset_common.py::AWSDatasetTest::test_builder_class_wmt14 ``` The same `local` test, can be run with: ```python pytest -s tests/test_dataset_common.py::LocalDatasetTest::test_builder_class_wmt14 ```

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MDU6SXNzdWU2MjA4NTAyMTg=

Add a method to shuffle a dataset

closed

MEMBER

Could maybe be a `dataset.shuffle(generator=None, seed=None)` signature method. Also, we could maybe have a clear indication of which method modify in-place and which methods return/cache a modified dataset. I kinda like torch conversion of having an underscore suffix for all the methods which modify a dataset in-place. What do you think?

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MDU6SXNzdWU2MjA3NTgyMjE=

ANLI

closed

NONE

Can I recommend the following: For ANLI, use https://github.com/facebookresearch/anli. As that paper says, "Our dataset is not to be confused with abductive NLI (Bhagavatula et al., 2019), which calls itself αNLI, or ART.". Indeed, the paper cited under what is currently called anli says in the abstract "We introduce a challenge dataset, ART". The current naming will confuse people :)

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MDU6SXNzdWU2MjA1NDAyNTA=

Add Spanish POR and NER Datasets

closed

CONTRIBUTOR

Hi guys, In order to cover multilingual support a little step could be adding standard Datasets used for Spanish NER and POS tasks. I can provide it in raw and preprocessed formats.

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MDU6SXNzdWU2MjA1MzQzMDc=

[Feature request] Add cos-e v1.0

closed

NONE

I noticed the second release of cos-e (v1.11) is included in this repo. I wanted to request inclusion of v1.0, since this is the version on which results are reported on in [the paper](https://www.aclweb.org/anthology/P19-1487/), and v1.11 has noted [annotation](https://github.com/salesforce/cos-e/issues/2) [issues](https://arxiv.org/pdf/2004.14546.pdf).

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MDExOlB1bGxSZXF1ZXN0NDE5NzQ4Mzky

fix prev files hash in map

closed

MEMBER

Fix the `.map` issue in #160. This makes sure it takes the previous files when computing the hash.

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MDU6SXNzdWU2MjA0ODc1MzU=

Discussion on version identifier & MockDataLoaderManager for test data

open

CONTRIBUTOR

Hi, I'm working on adding a dataset and ran into an error due to `download` not being defined on `MockDataLoaderManager`, but being defined in `nlp/utils/download_manager.py`. The readme step running this: `RUN_SLOW=1 pytest tests/test_dataset_common.py::DatasetTest::test_load_real_dataset_localmydatasetname` triggers the error. If I can get something to work, I can include it in my data PR once I'm done.

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MDU6SXNzdWU2MjA0NDgyMzY=

caching in map causes same result to be returned for train, validation and test

closed

NONE

hello, I am working on a program that uses the `nlp` library with the `SST2` dataset. The rough outline of the program is: ``` import nlp as nlp_datasets ... parser.add_argument('--dataset', help='HuggingFace Datasets id', default=['glue', 'sst2'], nargs='+') ... dataset = nlp_datasets.load_dataset(*args.dataset) ... # Create feature vocabs vocabs = create_vocabs(dataset.values(), vectorizers) ... # Create a function to vectorize based on vectorizers and vocabs: print('TS', train_set.num_rows) print('VS', valid_set.num_rows) print('ES', test_set.num_rows) # factory method to create a `convert_to_features` function based on vocabs convert_to_features = create_featurizer(vectorizers, vocabs) train_set = train_set.map(convert_to_features, batched=True) train_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batchsz) valid_set = valid_set.map(convert_to_features, batched=True) valid_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) valid_loader = torch.utils.data.DataLoader(valid_set, batch_size=args.batchsz) test_set = test_set.map(convert_to_features, batched=True) test_set.set_format(type='torch', columns=list(vectorizers.keys()) + ['y', 'lengths']) test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batchsz) print('TS', train_set.num_rows) print('VS', valid_set.num_rows) print('ES', test_set.num_rows) ``` Im not sure if Im using it incorrectly, but the results are not what I expect. Namely, the `.map()` seems to grab the datset from the cache and then loses track of what the specific dataset is, instead using my training data for all datasets: ``` TS 67349 VS 872 ES 1821 TS 67349 VS 67349 ES 67349 ``` The behavior changes if I turn off the caching but then the results fail: ``` train_set = train_set.map(convert_to_features, batched=True, load_from_cache_file=False) ... valid_set = valid_set.map(convert_to_features, batched=True, load_from_cache_file=False) ... test_set = test_set.map(convert_to_features, batched=True, load_from_cache_file=False) ``` Now I get the right set of features back... ``` TS 67349 VS 872 ES 1821 100%|██████████| 68/68 [00:00<00:00, 92.78it/s] 100%|██████████| 1/1 [00:00<00:00, 75.47it/s] 0%| | 0/2 [00:00<?, ?it/s]TS 67349 VS 872 ES 1821 100%|██████████| 2/2 [00:00<00:00, 77.19it/s] ``` but I think its losing track of the original training set: ``` Traceback (most recent call last): File "/home/dpressel/dev/work/baseline/api-examples/layers-classify-hf-datasets.py", line 148, in <module> for x in train_loader: File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 345, in __next__ data = self._next_data() File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 385, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dpressel/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 338, in __getitem__ output_all_columns=self._output_all_columns, File "/home/dpressel/anaconda3/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 294, in _getitem outputs = self._unnest(self._data.slice(key, 1).to_pydict()) File "pyarrow/table.pxi", line 1211, in pyarrow.lib.Table.slice File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 3: In chunk 0: Invalid: Length spanned by list offsets (15859698) larger than values array (length 100000) Process finished with exit code 1 ``` The full-example program (minus the print stmts) is here: https://github.com/dpressel/mead-baseline/pull/620/files

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MDU6SXNzdWU2MjA0MjA3MDA=

How can we add more datasets to nlp library?

closed

NONE

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MDExOlB1bGxSZXF1ZXN0NDE5NjUyNTQy

add Toronto Books Corpus

closed

CONTRIBUTOR

This PR adds the Toronto Books Corpus. . It on consider TMX and plain text files (Moses) defined in the table **Statistics and TMX/Moses Downloads** [here](http://opus.nlpl.eu/Books.php )

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MDU6SXNzdWU2MjAzNTY1NDI=

nlp.load_dataset() gives "TypeError: list_() takes exactly one argument (2 given)"

closed

NONE

I'm trying to load datasets from nlp but there seems to have error saying "TypeError: list_() takes exactly one argument (2 given)" gist can be found here https://gist.github.com/saahiluppal/c4b878f330b10b9ab9762bc0776c0a6a

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MDU6SXNzdWU2MjAyNjM2ODc=

SyntaxError with WMT datasets

closed

NONE

The following snippet produces a syntax error: ``` import nlp dataset = nlp.load_dataset('wmt14') print(dataset['train'][0]) ``` ``` Traceback (most recent call last): File "/home/tom/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-8-3206959998b9>", line 3, in <module> dataset = nlp.load_dataset('wmt14') File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 505, in load_dataset builder_cls = import_main_class(module_path, dataset=True) File "/home/tom/.local/lib/python3.6/site-packages/nlp/load.py", line 56, in import_main_class module = importlib.import_module(module_path) File "/usr/lib/python3.6/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 994, in _gcd_import File "<frozen importlib._bootstrap>", line 971, in _find_and_load File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 665, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 678, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt14.py", line 21, in <module> from .wmt_utils import Wmt, WmtConfig File "/home/tom/.local/lib/python3.6/site-packages/nlp/datasets/wmt14/c258d646f4f5870b0245f783b7aa0af85c7117e06aacf1e0340bd81935094de2/wmt_utils.py", line 659 <<<<<<< HEAD ^ SyntaxError: invalid syntax ``` Python version: `3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]` Running on Ubuntu 18.04, via a Jupyter notebook

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MDExOlB1bGxSZXF1ZXN0NDE5Mzg1ODM0

Include more links in README, fix typos

closed

CONTRIBUTOR

Include more links and fix typos in README

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MDExOlB1bGxSZXF1ZXN0NDE5Mzc4Mzgw

add Ubuntu Dialogs Corpus datasets

closed

CONTRIBUTOR

This PR adds the Ubuntu Dialog Corpus datasets version 2.0.

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MDU6SXNzdWU2MTk5NzIyNDY=

Meta-datasets (GLUE/XTREME/...) – Special care to attributions and citations

open

MEMBER

Meta-datasets are interesting in terms of standardized benchmarks but they also have specific behaviors, in particular in terms of attribution and authorship. It's very important that each specific dataset inside a meta dataset is properly referenced and the citation/specific homepage/etc are very visible and accessible and not only the generic citation of the meta-dataset itself. Let's take GLUE as an example: The configuration has the citation for each dataset included (e.g. [here](https://github.com/huggingface/nlp/blob/master/datasets/glue/glue.py#L154-L161)) but it should be copied inside the dataset info so that, when people access `dataset.info.citation` they get both the citation for GLUE and the citation for the specific datasets inside GLUE that they have loaded.

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MDExOlB1bGxSZXF1ZXN0NDE5MzA4OTE2

Add GLUE config name check

closed

CONTRIBUTOR

Fixes #130 by adding a name check to the Glue class

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MDExOlB1bGxSZXF1ZXN0NDE5MzA2MTYz

Fix JSON tests.

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NDE5MTgyODU4

Add WNUT 17 NER dataset

closed

CONTRIBUTOR

Hi, this PR adds the WNUT 17 dataset to `nlp`. > Emerging and Rare entity recognition > This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarisation), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so.. kktny in 30 mins?” - even human experts find entity kktny hard to detect and resolve. This task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text. > > The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. More information about the dataset can be found on the [shared task page](https://noisy-text.github.io/2017/emerging-rare-entities.html). Dataset is taken is taken from their [GitHub repository](https://github.com/leondz/emerging_entities_17), because the data provided in this repository contains minor fixes in the dataset format. ## Usage Then the WNUT 17 dataset can be used in `nlp` like this: ```python import nlp wnut_17 = nlp.load_dataset("./datasets/wnut_17/wnut_17.py") print(wnut_17) ``` This outputs: ```txt 'train': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 3394) 'validation': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1009) 'test': Dataset(schema: {'id': 'string', 'tokens': 'list<item: string>', 'labels': 'list<item: string>'}, num_rows: 1287) ``` Number are identical with the ones in [this paper](https://www.ijcai.org/Proceedings/2019/0702.pdf) and are the same as using the `dataset` reader in Flair. ## Features The following feature format is used to represent a sentence in the WNUT 17 dataset: | Feature | Example | Description | ---- | ---- | ----------------- | `id` | `0` | Number (id) of current sentence | `tokens` | `["AHFA", "extends", "deadline"]` | List of tokens (strings) for a sentence | `labels` | `["B-group", "O", "O"]` | List of labels (outer span) The following labels are used in WNUT 17: ```txt O B-corporation I-corporation B-location I-location B-product I-product B-person I-person B-group I-group B-creative-work I-creative-work ```

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MDU6SXNzdWU2MTk3MzU3Mzk=

[Feature request] Add Ubuntu Dialogue Corpus dataset

closed

NONE

https://github.com/rkadlec/ubuntu-ranking-dataset-creator or http://dataset.cs.mcgill.ca/ubuntu-corpus-1.0/

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