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MDU6SXNzdWU3MDAyMjU4MjY=

load_dataset for text files not working

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Trying the following snippet, I get different problems on Linux and Windows. ```python dataset = load_dataset("text", data_files="data.txt") # or dataset = load_dataset("text", data_files=["data.txt"]) ``` (ps [This example](https://huggingface.co/docs/datasets/loading_datasets.html#json-files) shows that you can use a string as input for data_files, but the signature is `Union[Dict, List]`.) The problem on Linux is that the script crashes with a CSV error (even though it isn't a CSV file). On Windows the script just seems to freeze or get stuck after loading the config file. Linux stack trace: ``` PyTorch version 1.6.0+cu101 available. Checking /home/bram/.cache/huggingface/datasets/b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports. Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7 Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/dataset_infos.json Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at /home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.json Using custom data configuration default Generating dataset text (/home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7) Downloading and preparing dataset text/default-0907112cc6cd2a38 (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/bram/.cache/huggingface/datasets/text/default-0907112cc6cd2a38/0.0.0/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7... Dataset not on Hf google storage. Downloading and preparing it from source Downloading took 0.0 min Checksum Computation took 0.0 min Unable to verify checksums. Generating split train Traceback (most recent call last): File "/home/bram/Python/projects/dutch-simplification/utils.py", line 45, in prepare_data dataset = load_dataset("text", data_files=dataset_f) File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/load.py", line 608, in load_dataset builder_instance.download_and_prepare( File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 468, in download_and_prepare self._download_and_prepare( File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 546, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/datasets/builder.py", line 888, in _prepare_split for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose): File "/home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/tqdm/std.py", line 1130, in __iter__ for obj in iterable: File "/home/bram/.cache/huggingface/modules/datasets_modules/datasets/text/7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7/text.py", line 100, in _generate_tables pa_table = pac.read_csv( File "pyarrow/_csv.pyx", line 714, in pyarrow._csv.read_csv File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: CSV parse error: Expected 1 columns, got 2 ``` Windows just seems to get stuck. Even with a tiny dataset of 10 lines, it has been stuck for 15 minutes already at this message: ``` Checking C:\Users\bramv\.cache\huggingface\datasets\b1d50a0e74da9a7b9822cea8ff4e4f217dd892e09eb14f6274a2169e5436e2ea.30c25842cda32b0540d88b7195147decf9671ee442f4bc2fb6ad74016852978e.py for additional imports. Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7 Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py to C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.py Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text\dataset_infos.json Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.1/datasets/text/text.py at C:\Users\bramv\.cache\huggingface\modules\datasets_modules\datasets\text\7e13bc0fa76783d4ef197f079dc8acfe54c3efda980f2c9adfab046ede2f0ff7\text.json Using custom data configuration default ```

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MDExOlB1bGxSZXF1ZXN0NDg1ODQ3ODYz

[docs] Index: The native emoji looks kinda ugly in large size

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MDU6SXNzdWU2OTk4MTUxMzU=

map/filter multiprocessing raises errors and corrupts datasets

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After upgrading to the 1.0 started seeing errors in my data loading script after enabling multiprocessing. ```python ... ner_ds_dict = ner_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed) ner_ds_dict["validation"] = ner_ds_dict["test"] rel_ds_dict = rel_ds.train_test_split(test_size=test_pct, shuffle=True, seed=seed) rel_ds_dict["validation"] = rel_ds_dict["test"] return ner_ds_dict, rel_ds_dict ``` The first train_test_split, `ner_ds`/`ner_ds_dict`, returns a `train` and `test` split that are iterable. The second, `rel_ds`/`rel_ds_dict` in this case, returns a Dataset dict that has rows but if selected from or sliced into into returns an empty dictionary. eg `rel_ds_dict['train'][0] == {}` and `rel_ds_dict['train'][0:100] == {}`. Ok I think I know the problem -- the rel_ds was mapped though a mapper with `num_proc=12`. If I remove `num_proc`. The dataset loads. I also see errors with other map and filter functions when `num_proc` is set. ``` Done writing 67 indices in 536 bytes . Done writing 67 indices in 536 bytes . Fatal Python error: PyCOND_WAIT(gil_cond) failed ```

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MDU6SXNzdWU2OTk3MzM2MTI=

Mistakes in MLQA features names

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CONTRIBUTOR

I think the following features in MLQA shouldn't be named the way they are: 1. `questions` (should be `question`) 2. `ids` (should be `id`) 3. `start` (should be `answer_start`) The reasons I'm suggesting these features be renamed are: * To make them consistent with other QA datasets like SQuAD, XQuAD, TyDiQA etc. and hence make it easier to concatenate multiple QA datasets. * The features names are not the same as the ones provided in the original MLQA datasets (it uses the names I suggested). I know these columns can be renamed using using `Dataset.rename_column_`, `questions` and `ids` can be easily renamed but `start` on the other hand is annoying to rename since it's nested inside the feature `answers`.

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MDExOlB1bGxSZXF1ZXN0NDg1NDAxMzI5

sync logging utils with transformers

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CONTRIBUTOR

sync the docs/code with the recent changes in transformers' `logging` utils: 1. change the default level to `WARNING` 2. add `DATASETS_VERBOSITY` env var 3. expand docs

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MDU6SXNzdWU2OTk0NzI1OTY=

Compare different Rouge implementations

closed

NONE

I used RougeL implementation provided in `datasets` [here](https://github.com/huggingface/datasets/blob/master/metrics/rouge/rouge.py) and it gives numbers that match those reported in the pegasus paper but very different from those reported in other papers, [this](https://arxiv.org/pdf/1909.03186.pdf) for example. Can you make sure the google-research implementation you are using matches the official perl implementation? There are a couple of python wrappers around the perl implementation, [this](https://pypi.org/project/pyrouge/) has been commonly used, and [this](https://github.com/pltrdy/files2rouge) is used in fairseq). There's also a python reimplementation [here](https://github.com/pltrdy/rouge) but its RougeL numbers are way off.

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MDU6SXNzdWU2OTk0NjIyOTM=

UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors

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I am trying out the library and want to load in pickled data with `from_dict`. In that dict, one column `text` should be tokenized and the other (an embedding vector) should be retained. All other columns should be removed. When I eventually try to set the format for the columns with `set_format` I am getting this strange Userwarning without a stack trace: > Set __getitem__(key) output type to torch for ['input_ids', 'sembedding'] columns (when key is int or slice) and don't output other (un-formatted) columns. > C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\datasets\arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:141.) > return torch.tensor(x, **format_kwargs) The first one might not be related to the warning, but it is odd that it is shown, too. It is unclear whether that is something that I should do or something that that the program is doing at that moment. Snippet: ``` dataset = Dataset.from_dict(torch.load("data/dummy.pt.pt")) print(dataset) tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") keys_to_retain = {"input_ids", "sembedding"} dataset = dataset.map(lambda example: tokenizer(example["text"], padding='max_length'), batched=True) dataset.remove_columns_(set(dataset.column_names) - keys_to_retain) dataset.set_format(type="torch", columns=["input_ids", "sembedding"]) dataloader = torch.utils.data.DataLoader(dataset, batch_size=2) print(next(iter(dataloader))) ``` PS: the input type for `remove_columns_` should probably be an Iterable rather than just a List.

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MDU6SXNzdWU2OTk0MTA3NzM=

Offset overflow when slicing a big dataset with an array of indices in Pyarrow >= 1.0.0

closed

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How to reproduce: ```python from datasets import load_dataset wiki = load_dataset("wikipedia", "20200501.en", split="train") wiki[[0]] --------------------------------------------------------------------------- ArrowInvalid Traceback (most recent call last) <ipython-input-13-381aedc9811b> in <module> ----> 1 wikipedia[[0]] ~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in __getitem__(self, key) 1069 format_columns=self._format_columns, 1070 output_all_columns=self._output_all_columns, -> 1071 format_kwargs=self._format_kwargs, 1072 ) 1073 ~/Desktop/hf/nlp/src/datasets/arrow_dataset.py in _getitem(self, key, format_type, format_columns, output_all_columns, format_kwargs) 1037 ) 1038 else: -> 1039 data_subset = self._data.take(indices_array) 1040 1041 if format_type is not None: ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.take() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/compute.py in take(data, indices, boundscheck) 266 """ 267 options = TakeOptions(boundscheck) --> 268 return call_function('take', [data, indices], options) 269 270 ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowInvalid: offset overflow while concatenating arrays ``` It seems to work fine with small datasets or with pyarrow 0.17.1

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MDExOlB1bGxSZXF1ZXN0NDg0OTQ2MzA1

[doc] Update deploy.sh

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MDExOlB1bGxSZXF1ZXN0NDg0ODkyMTUx

Add CoNLL-2003 shared task dataset

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Please consider adding CoNLL-2003 shared task dataset as it's beneficial for token classification tasks. The motivation behind this PR is the [PR](https://github.com/huggingface/transformers/pull/7041) in the transformers project. This dataset would be not only useful for the usual run-of-the-mill NER tasks but also for syntactic chunking and part-of-speech (POS) tagging.

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MDExOlB1bGxSZXF1ZXN0NDg0Nzk2Mjg5

add multi-proc to dataset dict

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Add multi-proc to `DatasetDict`

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MDU6SXNzdWU2OTg4NjM5ODg=

ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648

closed

NONE

Hi, I'm trying to load a dataset from Dataframe, but I get the error: ```bash --------------------------------------------------------------------------- ArrowCapacityError Traceback (most recent call last) <ipython-input-7-146b6b495963> in <module> ----> 1 dataset = Dataset.from_pandas(emb) ~/miniconda3/envs/dev/lib/python3.7/site-packages/nlp/arrow_dataset.py in from_pandas(cls, df, features, info, split) 223 info.features = features 224 pa_table: pa.Table = pa.Table.from_pandas( --> 225 df=df, schema=pa.schema(features.type) if features is not None else None 226 ) 227 return cls(pa_table, info=info, split=split) ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.from_pandas() ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in dataframe_to_arrays(df, schema, preserve_index, nthreads, columns, safe) 591 for i, maybe_fut in enumerate(arrays): 592 if isinstance(maybe_fut, futures.Future): --> 593 arrays[i] = maybe_fut.result() 594 595 types = [x.type for x in arrays] ~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/_base.py in result(self, timeout) 426 raise CancelledError() 427 elif self._state == FINISHED: --> 428 return self.__get_result() 429 430 self._condition.wait(timeout) ~/miniconda3/envs/dev/lib/python3.7/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 ~/miniconda3/envs/dev/lib/python3.7/concurrent/futures/thread.py in run(self) 55 56 try: ---> 57 result = self.fn(*self.args, **self.kwargs) 58 except BaseException as exc: 59 self.future.set_exception(exc) ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/pandas_compat.py in convert_column(col, field) 557 558 try: --> 559 result = pa.array(col, type=type_, from_pandas=True, safe=safe) 560 except (pa.ArrowInvalid, 561 pa.ArrowNotImplementedError, ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.array() ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._ndarray_to_array() ~/miniconda3/envs/dev/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowCapacityError: List array cannot contain more than 2147483646 child elements, have 2147483648 ``` My code is : ```python from nlp import Dataset dataset = Dataset.from_pandas(emb) ```

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MDU6SXNzdWU2OTgzNDkzODg=

Load text file for RoBERTa pre-training.

closed

NONE

I migrate my question from https://github.com/huggingface/transformers/pull/4009#issuecomment-690039444 I tried to train a Roberta from scratch using transformers. But I got OOM issues with loading a large text file. According to the suggestion from @thomwolf , I tried to implement `datasets` to load my text file. This test.txt is a simple sample where each line is a sentence. ``` from datasets import load_dataset dataset = load_dataset('text', data_files='test.txt',cache_dir="./") dataset.set_format(type='torch',columns=["text"]) dataloader = torch.utils.data.DataLoader(dataset, batch_size=8) next(iter(dataloader)) ``` But dataload cannot yield sample and error is: ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-12-388aca337e2f> in <module> ----> 1 next(iter(dataloader)) /Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in __next__(self) 361 362 def __next__(self): --> 363 data = self._next_data() 364 self._num_yielded += 1 365 if self._dataset_kind == _DatasetKind.Iterable and \ /Library/Python/3.7/site-packages/torch/utils/data/dataloader.py in _next_data(self) 401 def _next_data(self): 402 index = self._next_index() # may raise StopIteration --> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 404 if self._pin_memory: 405 data = _utils.pin_memory.pin_memory(data) /Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 42 def fetch(self, possibly_batched_index): 43 if self.auto_collation: ---> 44 data = [self.dataset[idx] for idx in possibly_batched_index] 45 else: 46 data = self.dataset[possibly_batched_index] /Library/Python/3.7/site-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0) 42 def fetch(self, possibly_batched_index): 43 if self.auto_collation: ---> 44 data = [self.dataset[idx] for idx in possibly_batched_index] 45 else: 46 data = self.dataset[possibly_batched_index] KeyError: 0 ``` `dataset.set_format(type='torch',columns=["text"])` returns a log says: ``` Set __getitem__(key) output type to torch for ['text'] columns (when key is int or slice) and don't output other (un-formatted) columns. ``` I noticed the dataset is `DatasetDict({'train': Dataset(features: {'text': Value(dtype='string', id=None)}, num_rows: 44)})`. Each sample can be accessed by `dataset["train"]["text"]` instead of `dataset["text"]`. Could you please give me any suggestions on how to modify this code to load the text file? Versions: Python version 3.7.3 PyTorch version 1.6.0 TensorFlow version 2.3.0 datasets version: 1.0.1

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https://github.com/huggingface/datasets/pull/609

MDExOlB1bGxSZXF1ZXN0NDg0MTc4Nzky

Update GLUE URLs (now hosted on FB)

closed

CONTRIBUTOR

NYU is switching dataset hosting from Google to FB. This PR closes https://github.com/huggingface/datasets/issues/608 and is necessary for https://github.com/jiant-dev/jiant/issues/161. This PR updates the data URLs based on changes made in https://github.com/nyu-mll/jiant/pull/1112.

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https://github.com/huggingface/datasets/issues/608

MDU6SXNzdWU2OTgyOTExNTY=

Don't use the old NYU GLUE dataset URLs

closed

CONTRIBUTOR

NYU is switching dataset hosting from Google to FB. Initial changes to `datasets` are in https://github.com/jeswan/nlp/commit/b7d4a071d432592ded971e30ef73330529de25ce. What tests do you suggest I run before opening a PR? See: https://github.com/jiant-dev/jiant/issues/161 and https://github.com/nyu-mll/jiant/pull/1112

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https://github.com/huggingface/datasets/pull/607

MDExOlB1bGxSZXF1ZXN0NDgzOTcyMDg4

Add transmit_format wrapper and tests

closed

MEMBER

Same as #605 but using a decorator on-top of dataset transforms that are not in place

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MDExOlB1bGxSZXF1ZXN0NDgzOTMzMDA1

Quick fix :)

closed

MEMBER

`nlp` => `datasets`

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https://github.com/huggingface/datasets/pull/605

MDExOlB1bGxSZXF1ZXN0NDgzNzg1Mjc1

[Datasets] Transmit format to children

closed

MEMBER

Transmit format to children obtained when processing a dataset. Added a test. When concatenating datasets, if the formats are disparate, the concatenated dataset has a format reset to defaults.

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https://github.com/huggingface/datasets/pull/604

MDExOlB1bGxSZXF1ZXN0NDgzNjgxNTc0

Update bucket prefix

closed

MEMBER

cc @julien-c

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https://github.com/huggingface/datasets/pull/603

MDExOlB1bGxSZXF1ZXN0NDgzNjY2ODk5

Set scripts version to master

closed

MEMBER

By default the scripts version is master, so that if the library is installed with ``` pip install git+http://github.com/huggingface/nlp.git ``` or ``` git clone http://github.com/huggingface/nlp.git pip install -e ./nlp ``` will use the latest scripts, and not the ones from the previous version.

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https://github.com/huggingface/datasets/pull/602

MDExOlB1bGxSZXF1ZXN0NDgzNTU3NDM0

apply offset to indices in multiprocessed map

closed

MEMBER

Fix #597 I fixed the indices by applying an offset. I added the case to our tests to make sure it doesn't happen again. I also added the message proposed by @thomwolf in #597 ```python >>> d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2, load_from_cache_file=False) Done writing 10 indices in 80 bytes . Testing the mapped function outputs [0, 1] Testing finished, running the mapping function on the dataset Done writing 5 indices in 41 bytes . Done writing 5 indices in 41 bytes . Spawning 2 processes [0, 1, 2, 3, 4] [5, 6, 7, 8, 9] #0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 377.90ba/s] #1: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 378.92ba/s] Concatenating 2 shards from multiprocessing # Dataset(features: {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}, num_rows: 10) ```

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MDExOlB1bGxSZXF1ZXN0NDgzNTAzMjAw

check if trasnformers has PreTrainedTokenizerBase

closed

MEMBER

Fix #598

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MDU6SXNzdWU2OTc0OTY5MTM=

Pickling error when loading dataset

closed

NONE

Hi, I modified line 136 in the original [run_language_modeling.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) as: ``` # line 136: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size) dataset = load_dataset("text", data_files=file_path, split="train") dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True) dataset.set_format(type='torch', columns=['input_ids']) return dataset ``` When I run this with transformers (3.1.0) and nlp (0.4.0), I get the following error: ``` Traceback (most recent call last): File "src/run_language_modeling.py", line 319, in <module> main() File "src/run_language_modeling.py", line 248, in main get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None File "src/run_language_modeling.py", line 139, in get_dataset dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True) File "/data/nlp/src/nlp/arrow_dataset.py", line 1136, in map new_fingerprint=new_fingerprint, File "/data/nlp/src/nlp/fingerprint.py", line 158, in wrapper self._fingerprint, transform, kwargs_for_fingerprint File "/data/nlp/src/nlp/fingerprint.py", line 105, in update_fingerprint hasher.update(transform_args[key]) File "/data/nlp/src/nlp/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/data/nlp/src/nlp/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/data/nlp/src/nlp/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/data/nlp/src/nlp/utils/py_utils.py", line 362, in dumps dump(obj, file) File "/data/nlp/src/nlp/utils/py_utils.py", line 339, in dump Pickler(file, recurse=True).dump(obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 446, in dump StockPickler.dump(self, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 409, in dump self.save(obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1438, in save_function obj.__dict__, fkwdefaults), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1170, in save_cell pickler.save_reduce(_create_cell, (f,), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 736, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 521, in save self.save_reduce(obj=obj, *rv) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 605, in save_reduce save(cls) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 1365, in save_type obj.__bases__, _dict), obj=obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 610, in save_reduce save(args) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 751, in save_tuple save(element) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 476, in save f(self, obj) # Call unbound method with explicit self File "/root/miniconda3/envs/py3.6/lib/python3.6/site-packages/dill/_dill.py", line 933, in save_module_dict StockPickler.save_dict(pickler, obj) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 821, in save_dict self._batch_setitems(obj.items()) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 847, in _batch_setitems save(v) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 507, in save self.save_global(obj, rv) File "/root/miniconda3/envs/py3.6/lib/python3.6/pickle.py", line 927, in save_global (obj, module_name, name)) _pickle.PicklingError: Can't pickle typing.Union[str, NoneType]: it's not the same object as typing.Union ```

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https://github.com/huggingface/datasets/pull/599

MDExOlB1bGxSZXF1ZXN0NDgzMzI3ODQ5

Add MATINF dataset

closed

CONTRIBUTOR

@lhoestq The command to create metadata failed. I guess it's because the zip is not downloaded from a remote address? How to solve that? Also the CI fails and I don't know how to fix that :(

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MDU6SXNzdWU2OTcxNTY1MDE=

The current version of the package on github has an error when loading dataset

closed

NONE

Instead of downloading the package from pip, downloading the version from source will result in an error when loading dataset (the pip version is completely fine): To recreate the error: First, installing nlp directly from source: ``` git clone https://github.com/huggingface/nlp.git cd nlp pip install -e . ``` Then run: ``` from nlp import load_dataset dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') ``` will give error: ``` >>> dataset = load_dataset('wikitext', 'wikitext-2-v1',split = 'train') Checking /home/zeyuy/.cache/huggingface/datasets/84a754b488511b109e2904672d809c041008416ae74e38f9ee0c80a8dffa1383.2e21f48d63b5572d19c97e441fbb802257cf6a4c03fbc5ed8fae3d2c2273f59e.py for additional imports. Found main folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d Found script file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.py Found dataset infos file from https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/dataset_infos.json to /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/dataset_infos.json Found metadata file for dataset https://raw.githubusercontent.com/huggingface/nlp/0.4.0/datasets/wikitext/wikitext.py at /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d/wikitext.json Loading Dataset Infos from /home/zeyuy/.cache/huggingface/modules/nlp_modules/datasets/wikitext/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d Overwrite dataset info from restored data version. Loading Dataset info from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d Reusing dataset wikitext (/home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d) Constructing Dataset for split train, from /home/zeyuy/.cache/huggingface/datasets/wikitext/wikitext-2-v1/1.0.0/5de6e79516446f747fcccc09aa2614fa159053b75909594d28d262395f72d89d Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/load.py", line 600, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 611, in as_dataset datasets = utils.map_nested( File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 216, in map_nested return function(data_struct) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 631, in _build_single_dataset ds = self._as_dataset( File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/builder.py", line 704, in _as_dataset return Dataset(**dataset_kwargs) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/arrow_dataset.py", line 188, in __init__ self._fingerprint = generate_fingerprint(self) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 91, in generate_fingerprint hasher.update(key) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 57, in update self.m.update(self.hash(value).encode("utf-8")) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 53, in hash return cls.hash_default(value) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/fingerprint.py", line 46, in hash_default return cls.hash_bytes(dumps(value)) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 361, in dumps with _no_cache_fields(obj): File "/home/zeyuy/miniconda3/lib/python3.8/contextlib.py", line 113, in __enter__ return next(self.gen) File "/home/zeyuy/transformers/examples/language-modeling/nlp/src/nlp/utils/py_utils.py", line 348, in _no_cache_fields if isinstance(obj, tr.PreTrainedTokenizerBase) and hasattr(obj, "cache") and isinstance(obj.cache, dict): AttributeError: module 'transformers' has no attribute 'PreTrainedTokenizerBase' ```

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MDU6SXNzdWU2OTcxMTIwMjk=

Indices incorrect with multiprocessing

closed

CONTRIBUTOR

When `num_proc` > 1, the indices argument passed to the map function is incorrect: ```python d = load_dataset('imdb', split='test[:1%]') def fn(x, inds): print(inds) return x d.select(range(10)).map(fn, with_indices=True, batched=True) # [0, 1] # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] d.select(range(10)).map(fn, with_indices=True, batched=True, num_proc=2) # [0, 1] # [0, 1] # [0, 1, 2, 3, 4] # [0, 1, 2, 3, 4] ``` As you can see, the subset passed to each thread is indexed from 0 to N which doesn't reflect their positions in `d`.

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MDExOlB1bGxSZXF1ZXN0NDgyOTM5MTgw

[style/quality] Moving to isort 5.0.0 + style/quality on datasets and metrics

closed

MEMBER

Move the repo to isort 5.0.0. Also start testing style/quality on datasets and metrics. Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies. Maybe we could add this in datasets but while cleaning this I've seen many example of really unused imports in dataset so maybe it's better to have it as a line-by-line nova instead of a general rule like in metrics.

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MDU6SXNzdWU2OTY4OTIzMDQ=

`Dataset`/`DatasetDict` has no attribute 'save_to_disk'

closed

NONE

Hi, As the title indicates, both `Dataset` and `DatasetDict` classes don't seem to have the `save_to_disk` method. While the file [`arrow_dataset.py`](https://github.com/huggingface/nlp/blob/34bf0b03bfe03e7f77b8fec1cd48f5452c4fc7c1/src/nlp/arrow_dataset.py) in the repo here has the method, the file `arrow_dataset.py` which is saved after `pip install nlp -U` in my `conda` environment DOES NOT contain the `save_to_disk` method. I even tried `pip install git+https://github.com/huggingface/nlp.git ` and still no luck. Do I need to install the library in another way?

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MDExOlB1bGxSZXF1ZXN0NDgyODQ1OTc5

Fix germeval url

closed

MEMBER

Continuation of #593 but without the dummy data hack

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MDExOlB1bGxSZXF1ZXN0NDgyNzI5NTgw

GermEval 2014: new download urls

closed

CONTRIBUTOR

Hi, unfortunately, the download links for the GermEval 2014 dataset have changed: they're now located on a Google Drive. I changed the URLs and bump version from 1.0.0 to 2.0.0.

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MDExOlB1bGxSZXF1ZXN0NDgyNjc4MDkw

Test in memory and on disk

closed

MEMBER

I added test parameters to do every test both in memory and on disk. I also found a bug in concatenate_dataset thanks to the new tests and fixed it.

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MDExOlB1bGxSZXF1ZXN0NDgyNjAxMzc1

fix #589 (backward compat)

closed

MEMBER

Fix #589

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MDU6SXNzdWU2OTY1MDE4Mjc=

The process cannot access the file because it is being used by another process (windows)

closed

NONE

Hi, I consistently get the following error when developing in my PC (windows 10): ``` train_dataset = train_dataset.map(convert_to_features, batched=True) File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\site-packages\nlp\arrow_dataset.py", line 970, in map shutil.move(tmp_file.name, cache_file_name) File "C:\Users\saareliad\AppData\Local\Continuum\miniconda3\envs\py38\lib\shutil.py", line 803, in move os.unlink(src) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\saareliad\\.cache\\huggingface\\datasets\\squad\\plain_text\\1.0.0\\408a8fa46a1e2805445b793f1022e743428ca739a34809fce872f0c7f17b44ab\\tmpsau1bep1' ```

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MDU6SXNzdWU2OTY0ODg0NDc=

Cannot use nlp.load_dataset text, AttributeError: module 'nlp.utils' has no attribute 'logging'

closed

NONE

``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 533, in load_dataset builder_cls = import_main_class(module_path, dataset=True) File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/load.py", line 61, in import_main_class module = importlib.import_module(module_path) File "/root/anaconda3/envs/pytorch/lib/python3.7/importlib/__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1006, in _gcd_import File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "/root/anaconda3/envs/pytorch/lib/python3.7/site-packages/nlp/datasets/text/5dc629379536c4037d9c2063e1caa829a1676cf795f8e030cd90a537eba20c08/text.py", line 9, in <module> logger = nlp.utils.logging.get_logger(__name__) AttributeError: module 'nlp.utils' has no attribute 'logging' ``` Occurs on the following code, or any code including the load_dataset('text'): ``` dataset = load_dataset("text", data_files=file_path, split="train") dataset = dataset.map(lambda ex: tokenizer(ex["text"], add_special_tokens=True, truncation=True, max_length=args.block_size), batched=True) dataset.set_format(type='torch', columns=['input_ids']) return dataset ```

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MDExOlB1bGxSZXF1ZXN0NDgxNTE5NzQx

Support pathlike obj in load dataset

closed

MEMBER

Fix #582 (I recreated the PR, I got an issue with git)

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MDExOlB1bGxSZXF1ZXN0NDgxNTE2Mzkx

Support pathlike obj in load dataset

closed

MEMBER

Fix #582

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MDExOlB1bGxSZXF1ZXN0NDgxNTA5MzU1

Better message when data files is empty

closed

MEMBER

Fix #581

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MDExOlB1bGxSZXF1ZXN0NDgxNDY4NTM4

Fix select for pyarrow < 1.0.0

closed

MEMBER

Fix #583

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MDExOlB1bGxSZXF1ZXN0NDgxNDY0NjEz

Use github versioning

closed

MEMBER

Right now dataset scripts and metrics are downloaded from S3 which is in sync with master. It means that it's not currently possible to pin the dataset/metric script version. To fix that I changed the download url from S3 to github, and adding a `version` parameter in `load_dataset` and `load_metric` to pin a certain version of the lib, as in #562

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MDU6SXNzdWU2OTUxNjYyNjU=

ArrowIndexError on Dataset.select

closed

MEMBER

If the indices table consists in several chunks, then `dataset.select` results in an `ArrowIndexError` error for pyarrow < 1.0.0 Example: ```python from nlp import load_dataset mnli = load_dataset("glue", "mnli", split="train") shuffled = mnli.shuffle(seed=42) mnli.select(list(range(len(mnli)))) ``` raises: ```python --------------------------------------------------------------------------- ArrowIndexError Traceback (most recent call last) <ipython-input-64-006a5d38d418> in <module> ----> 1 mnli.shuffle(seed=42).select(list(range(len(mnli)))) ~/Desktop/hf/nlp/src/nlp/fingerprint.py in wrapper(*args, **kwargs) 161 # Call actual function 162 --> 163 out = func(self, *args, **kwargs) 164 165 # Update fingerprint of in-place transforms + update in-place history of transforms ~/Desktop/hf/nlp/src/nlp/arrow_dataset.py in select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 1653 if self._indices is not None: 1654 if PYARROW_V0: -> 1655 indices_array = self._indices.column(0).chunk(0).take(indices_array) 1656 else: 1657 indices_array = self._indices.column(0).take(indices_array) ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib.Array.take() ~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowIndexError: take index out of bounds ``` This is because the `take` method is only done on the first chunk which only contains 1000 elements by default (mnli has ~400 000 elements). Shall we change that to use ```python pa.concat_tables(self._indices._indices.slice(i, 1) for i in indices_array) ``` instead of `take` ? @thomwolf

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MDU6SXNzdWU2OTUxMjY0NTY=

Allow for PathLike objects

closed

CONTRIBUTOR

Using PathLike objects as input for `load_dataset` does not seem to work. The following will throw an error. ```python files = list(Path(r"D:\corpora\yourcorpus").glob("*.txt")) dataset = load_dataset("text", data_files=files) ``` Traceback: ``` Traceback (most recent call last): File "C:/dev/python/dutch-simplification/main.py", line 7, in <module> dataset = load_dataset("text", data_files=files) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset builder_instance.download_and_prepare( File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare self._save_info() File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 564, in _save_info self.info.write_to_directory(self._cache_dir) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 149, in write_to_directory self._dump_info(f) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\info.py", line 156, in _dump_info file.write(json.dumps(asdict(self)).encode("utf-8")) File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\__init__.py", line 231, in dumps return _default_encoder.encode(obj) File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 199, in encode chunks = self.iterencode(o, _one_shot=True) File "c:\users\bramv\appdata\local\programs\python\python38\lib\json\encoder.py", line 257, in iterencode return _iterencode(o, 0) TypeError: keys must be str, int, float, bool or None, not WindowsPath ``` We have to cast to a string explicitly to make this work. It would be nicer if we could actually use PathLike objects. ```python files = [str(f) for f in Path(r"D:\corpora\wablieft").glob("*.txt")] ```

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MDU6SXNzdWU2OTUxMjA1MTc=

Better error message when input file does not exist

closed

CONTRIBUTOR

In the following scenario, when `data_files` is an empty list, the stack trace and error message could be improved. This can probably be solved by checking for each file whether it actually exists and/or whether the argument is not false-y. ```python dataset = load_dataset("text", data_files=[]) ``` Example error trace. ``` Using custom data configuration default Downloading and preparing dataset text/default-d18f9b6611eb8e16 (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to C:\Users\bramv\.cache\huggingface\datasets\text\default-d18f9b6611eb8e16\0.0.0\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b... Traceback (most recent call last): File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 424, in incomplete_dir yield tmp_dir File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 462, in download_and_prepare self._download_and_prepare( File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 537, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 813, in _prepare_split num_examples, num_bytes = writer.finalize() File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\arrow_writer.py", line 217, in finalize self.pa_writer.close() AttributeError: 'NoneType' object has no attribute 'close' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:/dev/python/dutch-simplification/main.py", line 7, in <module> dataset = load_dataset("text", data_files=files) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\load.py", line 548, in load_dataset builder_instance.download_and_prepare( File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 470, in download_and_prepare self._save_info() File "c:\users\bramv\appdata\local\programs\python\python38\lib\contextlib.py", line 131, in __exit__ self.gen.throw(type, value, traceback) File "C:\Users\bramv\.virtualenvs\dutch-simplification-nbNdqK9u\lib\site-packages\nlp\builder.py", line 430, in incomplete_dir shutil.rmtree(tmp_dir) File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 737, in rmtree return _rmtree_unsafe(path, onerror) File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 615, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "c:\users\bramv\appdata\local\programs\python\python38\lib\shutil.py", line 613, in _rmtree_unsafe os.unlink(fullname) PermissionError: [WinError 32] The process cannot access the file because it is being used by another process: 'C:\\Users\\bramv\\.cache\\huggingface\\datasets\\text\\default-d18f9b6611eb8e16\\0.0.0\\3a79870d85f1982d6a2af884fde86a71c771747b4b161fd302d28ad22adf985b.incomplete\\text-train.arrow' ```

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MDU6SXNzdWU2OTQ5NTQ1NTE=

nlp re-creates already-there caches when using a script, but not within a shell

closed

CONTRIBUTOR

`nlp` keeps creating new caches for the same file when launching `filter` from a script, and behaves correctly from within the shell. Example: try running ``` import nlp hans_easy_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 0) hans_hard_data = nlp.load_dataset('hans', split="validation").filter(lambda x: x['label'] == 1) ``` twice. If launched from a `file.py` script, the cache will be re-created the second time. If launched as 3 shell/`ipython` commands, `nlp` will correctly re-use the cache. As observed with @lhoestq.

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MDExOlB1bGxSZXF1ZXN0NDgxMjU1OTI5

Doc metrics

closed

MEMBER

Adding documentation on metrics loading/using/sharing

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MDExOlB1bGxSZXF1ZXN0NDgxMTczNDE0

Add CommonGen Dataset

closed

CONTRIBUTOR

CC Authors: @yuchenlin @MichaelZhouwang

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MDU6SXNzdWU2OTQ2MDcxNDg=

Some languages in wikipedia dataset are not loading

closed

CONTRIBUTOR

Hi, I am working with the `wikipedia` dataset and I have a script that goes over 92 of the available languages in that dataset. So far I have detected that `ar`, `af`, `an` are not loading. Other languages like `fr` and `en` are working fine. Here's how I am loading them: ``` import nlp langs = ['ar'. 'af', 'an'] for lang in langs: data = nlp.load_dataset('wikipedia', f'20200501.{lang}', beam_runner='DirectRunner', split='train') print(lang, len(data)) ``` Here's what I see for 'ar' (it gets stuck there): ``` Downloading and preparing dataset wikipedia/20200501.ar (download: Unknown size, generated: Unknown size, post-processed: Unknown sizetotal: Unknown size) to /home/gaguilar/.cache/huggingface/datasets/wikipedia/20200501.ar/1.0.0/7be7f4324255faf70687be8692de57cf79197afdc33ff08d6a04ed602df32d50... ``` Note that those languages are indeed in the list of expected languages. Any suggestions on how to work around this? Thanks!

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https://github.com/huggingface/datasets/pull/576

MDExOlB1bGxSZXF1ZXN0NDgwNzM3NDQ1

Fix the code block in doc

closed

CONTRIBUTOR

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MDU6SXNzdWU2OTM2OTE2MTE=

Couldn't reach certain URLs and for the ones that can be reached, code just blocks after downloading.

closed

NONE

Hi, I'm following the [quick tour](https://huggingface.co/nlp/quicktour.html) and tried to load the glue dataset: ``` >>> from nlp import load_dataset >>> dataset = load_dataset('glue', 'mrpc', split='train') ``` However, this ran into a `ConnectionError` saying it could not reach the URL (just pasting the last few lines): ``` /net/vaosl01/opt/NFS/su0/miniconda3/envs/hf/lib/python3.7/site-packages/nlp/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only) 354 " to False." 355 ) --> 356 raise ConnectionError("Couldn't reach {}".format(url)) 357 358 # From now on, connected is True. ConnectionError: Couldn't reach https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc ``` I tried glue with cola and sst2. I got the same error, just instead of mrpc in the URL, it was replaced with cola and sst2. Since this was not working, I thought I'll try another dataset. So I tried downloading the imdb dataset: ``` ds = load_dataset('imdb', split='train') ``` This downloads the data, but it just blocks after that: ``` Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4.56k/4.56k [00:00<00:00, 1.38MB/s] Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.07k/2.07k [00:00<00:00, 1.15MB/s] Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.06 MiB, post-processed: Unknown sizetotal: 207.28 MiB) to /net/vaosl01/opt/NFS/su0/huggingface/datasets/imdb/plain_text/1.0.0/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743... Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 84.1M/84.1M [00:07<00:00, 11.1MB/s] ``` I checked the folder `$HF_HOME/datasets/downloads/extracted/<id>/aclImdb`. This folder is constantly growing in size. When I navigated to the train folder within, there was no file. However, the test folder seemed to be populating. The last time I checked it was 327M. I thought the Imdb dataset was smaller than that. My questions are: 1. Why is it still blocking? Is it still downloading? 2. I specified split as train, so why is the test folder being populated? 3. I read somewhere that after downloading, `nlp` converts the text files into some sort of `arrow` files, which will also take a while. Is this also happening here? Thanks.

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MDExOlB1bGxSZXF1ZXN0NDc5ODU5NzQy

Add modules cache

closed

MEMBER

As discusses in #554 , we should use a module cache directory outside of the python packages directory since we may not have write permissions. I added a new HF_MODULES_PATH directory that is added to the python path when doing `import nlp`. In this directory, a module `nlp_modules` is created so that datasets can be added to `nlp_modules.datasets` and metrics to `nlp_modules.metrics`. `nlp_modules` doesn't exist on Pypi. If someone using cloudpickle still wants to have the downloaded dataset/metrics scripts to be inside the nlp directory, it is still possible to change the environment variable HF_MODULES_CACHE to be a path inside the nlp lib.

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MDExOlB1bGxSZXF1ZXN0NDc5NjE4Mzc2

Faster caching for text dataset

closed

MEMBER

As mentioned in #546 and #548 , hashing `data_files` contents to get the cache directory name for a text dataset can take a long time. To make it faster I changed the hashing so that it takes into account the `path` and the `last modified timestamp` of each data file, instead of iterating through the content of each file to get a hash.

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MDExOlB1bGxSZXF1ZXN0NDc5MTgyNDU3

Add CLUE Benchmark (11 datasets)

closed

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Add 11 tasks of [CLUE](https://github.com/CLUEbenchmark/CLUE).

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MDExOlB1bGxSZXF1ZXN0NDc4NzQ2MjMz

Serialization

closed

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I added `save` and `load` method to serialize/deserialize a dataset object in a folder. It moves the arrow files there (or write them if the tables were in memory), and saves the pickle state in a json file `state.json`, except the info that are in a separate file `dataset_info.json`. Example: ```python import nlp squad = nlp.load_dataset("squad", split="train") squad.save("tmp/squad") squad = nlp.Dataset.load("tmp/squad") ``` `ls tmp/squad` ``` dataset_info.json squad-train.arrow state.json ``` `cat tmp/squad/state.json` ```json { "_data": null, "_data_files": [ { "filename": "squad-train.arrow", "skip": 0, "take": 87599 } ], "_fingerprint": "61f452797a686bc1", "_format_columns": null, "_format_kwargs": {}, "_format_type": null, "_indexes": {}, "_indices": null, "_indices_data_files": [], "_inplace_history": [ { "transforms": [] } ], "_output_all_columns": false, "_split": "train" } ``` `cat tmp/squad/dataset_info.json` ```json { "builder_name": "squad", "citation": "@article{2016arXiv160605250R,\n author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},\n Konstantin and {Liang}, Percy},\n title = \"{SQuAD: 100,000+ Questions for Machine Comprehension of Text}\",\n journal = {arXiv e-prints},\n year = 2016,\n eid = {arXiv:1606.05250},\n pages = {arXiv:1606.05250},\narchivePrefix = {arXiv},\n eprint = {1606.05250},\n}\n", "config_name": "plain_text", "dataset_size": 89789763, "description": "Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.\n", "download_checksums": { "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json": { "checksum": "95aa6a52d5d6a735563366753ca50492a658031da74f301ac5238b03966972c9", "num_bytes": 4854279 }, "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json": { "checksum": "3527663986b8295af4f7fcdff1ba1ff3f72d07d61a20f487cb238a6ef92fd955", "num_bytes": 30288272 } }, "download_size": 35142551, "features": { "answers": { "_type": "Sequence", "feature": { "answer_start": { "_type": "Value", "dtype": "int32", "id": null }, "text": { "_type": "Value", "dtype": "string", "id": null } }, "id": null, "length": -1 }, "context": { "_type": "Value", "dtype": "string", "id": null }, "id": { "_type": "Value", "dtype": "string", "id": null }, "question": { "_type": "Value", "dtype": "string", "id": null }, "title": { "_type": "Value", "dtype": "string", "id": null } }, "homepage": "https://rajpurkar.github.io/SQuAD-explorer/", "license": "", "post_processed": { "features": null, "resources_checksums": { "train": {}, "train[:10%]": {} } }, "post_processing_size": 0, "size_in_bytes": 124932314, "splits": { "train": { "dataset_name": "squad", "name": "train", "num_bytes": 79317110, "num_examples": 87599 }, "validation": { "dataset_name": "squad", "name": "validation", "num_bytes": 10472653, "num_examples": 10570 } }, "supervised_keys": null, "version": { "description": "New split API (https://tensorflow.org/datasets/splits)", "major": 1, "minor": 0, "nlp_version_to_prepare": null, "patch": 0, "version_str": "1.0.0" } } ```

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MDExOlB1bGxSZXF1ZXN0NDc4NTI3OTQz

add reuters21578 dataset

closed

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Reopen a PR this the merge.

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MDExOlB1bGxSZXF1ZXN0NDc4NTE2Mzc2

Revert "add reuters21578 dataset"

closed

CONTRIBUTOR

Reverts huggingface/nlp#471

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MDU6SXNzdWU2OTE2Mzg2NTY=

`metric.compute` throws `ArrowInvalid` error

closed

NONE

I get the following error with `rouge.compute`. It happens only with distributed training, and it occurs randomly I can't easily reproduce it. This is using `nlp==0.4.0` ``` File "/home/beltagy/trainer.py", line 92, in validation_step rouge_scores = rouge.compute(predictions=generated_str, references=gold_str, rouge_types=['rouge2', 'rouge1', 'rougeL']) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 224, in compute self.finalize(timeout=timeout) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/metric.py", line 213, in finalize self.data = Dataset(**reader.read_files(node_files)) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 217, in read_files dataset_kwargs = self._read_files(files=files, info=self._info, original_instructions=original_instructions) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 162, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/nlp/arrow_reader.py", line 276, in _get_dataset_from_filename f = pa.ipc.open_stream(mmap) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 173, in open_stream return RecordBatchStreamReader(source) File "/home/beltagy/miniconda3/envs/allennlp/lib/python3.7/site-packages/pyarrow/ipc.py", line 64, in __init__ self._open(source) File "pyarrow/ipc.pxi", line 469, in pyarrow.lib._RecordBatchStreamReader._open File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Tried reading schema message, was null or length 0 ```

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MDExOlB1bGxSZXF1ZXN0NDc4MTc2Njgx

Fix BLEURT metrics for backward compatibility

closed

MEMBER

Fix #565

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MDExOlB1bGxSZXF1ZXN0NDc3OTM2NTIz

Remove logger pickling to fix gg colab issues

closed

MEMBER

A `logger` objects are not picklable in google colab, contrary to `logger` objects in jupyter notebooks or in python shells. It creates some issues in google colab right now. Indeed by calling any `Dataset` method, the fingerprint update pickles the transform function, and as the logger comes with it, it results in an error (full stacktrace [here](http://pastebin.fr/64330)): ```python /usr/local/lib/python3.6/dist-packages/zmq/backend/cython/socket.cpython-36m-x86_64-linux-gnu.so in zmq.backend.cython.socket.Socket.__reduce_cython__() TypeError: no default __reduce__ due to non-trivial __cinit__ ``` To fix that I no longer dump the transform (`_map_single`, `select`, etc.), but the full name only (`nlp.arrow_dataset.Dataset._map_single`, `nlp.arrow_dataset.Dataset.select`, etc.)

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MDU6SXNzdWU2OTEwMzkxMjE=

No module named 'nlp.logging'

closed

NONE

Hi, I am using nlp version 0.4.0. Trying to use bleurt as an eval metric, however, the bleurt script imports nlp.logging which creates the following error. What am I missing? ``` >>> import nlp 2020-09-02 13:47:09.210310: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 >>> bleurt = nlp.load_metric("bleurt") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 443, in load_metric metric_cls = import_main_class(module_path, dataset=False) File "/home/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/load.py", line 61, in import_main_class module = importlib.import_module(module_path) File "/home/melody/anaconda3/envs/transformers/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/melody/anaconda3/envs/transformers/lib/python3.6/site-packages/nlp/metrics/bleurt/43448cf2959ea81d3ae0e71c5c8ee31dc15eed9932f197f5f50673cbcecff2b5/bleurt.py", line 20, in <module> from nlp.logging import get_logger ModuleNotFoundError: No module named 'nlp.logging' ``` Just to show once again that I can't import the logging module: ``` >>> import nlp 2020-09-02 13:48:38.190621: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 >>> nlp.__version__ '0.4.0' >>> from nlp.logging import get_logger Traceback (most recent call last): File "<stdin>", line 1, in <module> ModuleNotFoundError: No module named 'nlp.logging' ```

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MDExOlB1bGxSZXF1ZXN0NDc3ODAyMTk2

Wait for writing in distributed metrics

closed

MEMBER

There were CI bugs where a distributed metric would try to read all the files in process 0 while the other processes haven't started writing. To fix that I added a custom locking mechanism that waits for the file to exist before trying to read it

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MDExOlB1bGxSZXF1ZXN0NDc3NzI2MTEz

[Large datasets] Speed up download and processing

closed

MEMBER

Various improvements to speed-up creation and processing of large scale datasets. Currently: - distributed downloads - remove etag from datafiles hashes to spare a request when restarting a failed download

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MDExOlB1bGxSZXF1ZXN0NDc3NzI1MjMx

[Reproductibility] Allow to pin versions of datasets/metrics

closed

MEMBER

Repurpose the `version` attribute in datasets and metrics to let the user pin a specific version of datasets and metric scripts: ``` dataset = nlp.load_dataset('squad', version='1.0.0') metric = nlp.load_metric('squad', version='1.0.0') ``` Notes: - version number are the release version of the library - currently only possible for canonical datasets/metrics, ie. integrated in the GitHub repo of the library

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MDExOlB1bGxSZXF1ZXN0NDc3Njk1NDQy

Made `share_dataset` more readable

closed

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MDU6SXNzdWU2OTA0ODg3NjQ=

Using custom DownloadConfig results in an error

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## Version / Environment Ubuntu 18.04 Python 3.6.8 nlp 0.4.0 ## Description Loading `imdb` dataset works fine when when I don't specify any `download_config` argument. When I create a custom `DownloadConfig` object and pass it to the `nlp.load_dataset` function, this results in an error. ## How to reproduce ### Example without DownloadConfig --> works ```python import os os.environ["HF_HOME"] = "/data/hf-test-without-dl-config-01/" import logging import nlp logging.basicConfig(level=logging.INFO) if __name__ == "__main__": imdb = nlp.load_dataset(path="imdb") ``` ### Example with DownloadConfig --> doesn't work ```python import os os.environ["HF_HOME"] = "/data/hf-test-with-dl-config-01/" import logging import nlp from nlp.utils import DownloadConfig logging.basicConfig(level=logging.INFO) if __name__ == "__main__": download_config = DownloadConfig() imdb = nlp.load_dataset(path="imdb", download_config=download_config) ``` Error traceback: ``` Traceback (most recent call last): File "/.../example_with_dl_config.py", line 13, in <module> imdb = nlp.load_dataset(path="imdb", download_config=download_config) File "/.../python3.6/python3.6/site-packages/nlp/load.py", line 549, in load_dataset download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications, File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 463, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/.../python3.6/python3.6/site-packages/nlp/builder.py", line 518, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/.../python3.6/python3.6/site-packages/nlp/datasets/imdb/76cdbd7249ea3548c928bbf304258dab44d09cd3638d9da8d42480d1d1be3743/imdb.py", line 86, in _split_generators arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL) File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 220, in download_and_extract return self.extract(self.download(url_or_urls)) File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 158, in download self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) File "/.../python3.6/python3.6/site-packages/nlp/utils/download_manager.py", line 108, in _record_sizes_checksums self._recorded_sizes_checksums[url] = get_size_checksum_dict(path) File "/.../python3.6/python3.6/site-packages/nlp/utils/info_utils.py", line 79, in get_size_checksum_dict with open(path, "rb") as f: IsADirectoryError: [Errno 21] Is a directory: '/data/hf-test-with-dl-config-01/datasets/extracted/b6802c5b61824b2c1f7dbf7cda6696b5f2e22214e18d171ce1ed3be90c931ce5' ```

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MDExOlB1bGxSZXF1ZXN0NDc3MzAzOTM2

Adding the KILT knowledge source and tasks

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This adds Wikipedia pre-processed for KILT, as well as the task data. Only the question IDs are provided for TriviaQA, but they can easily be mapped back with: ``` import nlp kilt_wikipedia = nlp.load_dataset('kilt_wikipedia') kilt_tasks = nlp.load_dataset('kilt_tasks') triviaqa = nlp.load_dataset('trivia_qa', 'unfiltered.nocontext') triviaqa_map = {} for k in ['train', 'validation', 'test']: triviaqa_map = dict([(q_id, i) for i, q_id in enumerate(triviaqa[k]['question_id'])]) kilt_tasks[k + '_triviaqa'] = kilt_tasks[k + '_triviaqa'].filter(lambda x: x['id'] in triviaqa_map) kilt_tasks[k + '_triviaqa'].map(lambda x: {'input': triviaqa[split][triviaqa_map[x['id']]]['question']}) ``` It would be great to have the dataset by Monday, which is when the paper should land on Arxiv and @fabiopetroni is planning on tweeting about the paper and `facebookresearch` repository for the datasett

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MDExOlB1bGxSZXF1ZXN0NDc3MjI2ODA0

Rerun pip install -e

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Hopefully it fixes the github actions

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MDExOlB1bGxSZXF1ZXN0NDc3MTQ1NjAx

Fix a few typos

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MDExOlB1bGxSZXF1ZXN0NDc3MTQ0MTky

Add DailyDialog

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http://yanran.li/dailydialog.html https://arxiv.org/pdf/1710.03957.pdf

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MDExOlB1bGxSZXF1ZXN0NDc3MTI2OTIy

Upgrade pip in benchmark github action

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It looks like it fixes the `import nlp` issue we have

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MDU6SXNzdWU2OTAxNzMyMTQ=

nlp downloads to its module path

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I am trying to package `nlp` for Nix, because it is now an optional dependency for `transformers`. The problem that I encounter is that the `nlp` library downloads to the module path, which is typically not writable in most package management systems: ```>>> import nlp >>> squad_dataset = nlp.load_dataset('squad') Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 530, in load_dataset module_path, hash = prepare_module(path, download_config=download_config, dataset=True) File "/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/load.py", line 329, in prepare_module os.makedirs(main_folder_path, exist_ok=True) File "/nix/store/685kq8pyhrvajah1hdsfn4q7gm3j4yd4-python3-3.8.5/lib/python3.8/os.py", line 223, in makedirs mkdir(name, mode) OSError: [Errno 30] Read-only file system: '/nix/store/2yhik0hhqayksmkkfb0ylqp8cf5wa5wp-python3-3.8.5-env/lib/python3.8/site-packages/nlp/datasets/squad' ``` Do you have any suggested workaround for this issue? Perhaps overriding the default value for `force_local_path` of `prepare_module`?

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MDExOlB1bGxSZXF1ZXN0NDc3MDgxNTg2

[Fix GitHub Actions] test adding tmate

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MDExOlB1bGxSZXF1ZXN0NDc3MDI4MzMx