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MDExOlB1bGxSZXF1ZXN0NTkxNTA4MTQz

Fix: Wikipedia - save memory by replacing root.clear with elem.clear

closed

CONTRIBUTOR

see: https://github.com/huggingface/datasets/issues/2031 What I did: - replace root.clear with elem.clear - remove lines to get root element - $ make style - $ make test - some tests required some pip packages, I installed them. test results on origin/master and my branch are same. I think it's not related on my modification, isn't it? ``` ==================================================================================== short test summary info ==================================================================================== FAILED tests/test_arrow_writer.py::TypedSequenceTest::test_catch_overflow - AssertionError: OverflowError not raised ============================================================= 1 failed, 2332 passed, 5138 skipped, 70 warnings in 91.75s (0:01:31) ============================================================== make: *** [Makefile:19: test] Error 1 ``` Is there anything else I should do?

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https://github.com/huggingface/datasets/issues/2036

MDU6SXNzdWU4Mjk5MDkyNTg=

Cannot load wikitext

closed

NONE

when I execute these codes ``` >>> from datasets import load_dataset >>> test_dataset = load_dataset("wikitext") ``` I got an error,any help? ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module local_path = cached_path(file_path, download_config=download_config) File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path use_etag=download_config.use_etag, File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 487, in get_from_cache raise ConnectionError("Couldn't reach {}".format(url)) ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/wikitext/wikitext.py ```

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MDU6SXNzdWU4Mjk0NzU1NDQ=

wiki40b/wikipedia for almost all languages cannot be downloaded

open

NONE

Hi I am trying to download the data as below: ``` from datasets import load_dataset dataset = load_dataset("wiki40b", "cs") print(dataset) ``` I am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error. I really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources. thank you very much. ``` (fast) dara@vgne046:/user/dara/dev/codes/seq2seq$ python test_data.py Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp/dara/cache_home_2/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f... Traceback (most recent call last): File "test_data.py", line 3, in <module> dataset = load_dataset("wiki40b", "cs") File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/load.py", line 746, in load_dataset use_auth_token=use_auth_token, File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 579, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 1105, in _download_and_prepare import apache_beam as beam File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/__init__.py", line 96, in <module> from apache_beam import io File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/__init__.py", line 23, in <module> from apache_beam.io.avroio import * File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/avroio.py", line 55, in <module> import avro 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 668, in _load_unlocked File "<frozen importlib._bootstrap>", line 638, in _load_backward_compatible File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 34, in <module> File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 30, in LoadResource NotADirectoryError: [Errno 20] Not a directory: '/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/VERSION.txt' ```

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https://github.com/huggingface/datasets/pull/2034

MDExOlB1bGxSZXF1ZXN0NTkxMDU2MTEw

Fix typo

closed

CONTRIBUTOR

Change `ENV_XDG_CACHE_HOME ` to `XDG_CACHE_HOME `

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https://github.com/huggingface/datasets/pull/2033

MDExOlB1bGxSZXF1ZXN0NTkwOTgzMDAy

Raise an error for outdated sacrebleu versions

closed

MEMBER

The `sacrebleu` metric seem to only work for sacrecleu>=1.4.12 For example using sacrebleu==1.2.10, an error is raised (from metric/sacrebleu/sacrebleu.py): ```python def _compute( self, predictions, references, smooth_method="exp", smooth_value=None, force=False, lowercase=False, tokenize=scb.DEFAULT_TOKENIZER, use_effective_order=False, ): references_per_prediction = len(references[0]) if any(len(refs) != references_per_prediction for refs in references): raise ValueError("Sacrebleu requires the same number of references for each prediction") transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)] > output = scb.corpus_bleu( sys_stream=predictions, ref_streams=transformed_references, smooth_method=smooth_method, smooth_value=smooth_value, force=force, lowercase=lowercase, tokenize=tokenize, use_effective_order=use_effective_order, ) E TypeError: corpus_bleu() got an unexpected keyword argument 'smooth_method' /mnt/cache/modules/datasets_modules/metrics/sacrebleu/b390045b3d1dd4abf6a95c4a2a11ee3bcc2b7620b076204d0ddc353fa649fd86/sacrebleu.py:114: TypeError ``` I improved the error message when users have an outdated version of sacrebleu. The new error message tells the user to update sacrebleu. cc @LysandreJik

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MDU6SXNzdWU4MjkyNTA5MTI=

Use Arrow filtering instead of writing a new arrow file for Dataset.filter

closed

MEMBER

Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time. Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker. I think there are two cases: - if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)` - if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)` The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table. The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask. Feel free to discuss this idea in this thread :) One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle. cc @theo-m @gchhablani related issues: #1796 #1949

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MDU6SXNzdWU4MjkxMjI3Nzg=

wikipedia.py generator that extracts XML doesn't release memory

closed

CONTRIBUTOR

I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe. I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop. https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502 `root.clear()` intend to clear memory, but it doesn't. https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490 https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494 I replaced them with `elem.clear()`, then it seems to work correctly. here is the notebook to reproduce it. https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb

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https://github.com/huggingface/datasets/pull/2030

MDExOlB1bGxSZXF1ZXN0NTkwODI4NzQ4

Implement Dataset from text

closed

MEMBER

Implement `Dataset.from_text`. Analogue to #1943, #1946.

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MDU6SXNzdWU4MjkwOTcyOTA=

Loading a faiss index KeyError

closed

NONE

I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation. The basic steps are: 1. Create a dataset (dataset1) 2. Create an embeddings column using DPR 3. Add a faiss index to the dataset 4. Save faiss index to a file 5. Create a new dataset (dataset2) with the same text and label information as dataset1 6. Try to load the faiss index from file to dataset2 7. Get `KeyError: "Column embeddings not in the dataset"` I've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU. https://colab.research.google.com/drive/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing Ubuntu Version VERSION="18.04.5 LTS (Bionic Beaver)" datasets==1.4.1 faiss==1.5.3 faiss-gpu==1.7.0 torch==1.8.0+cu101 transformers==4.3.3 NVIDIA-SMI 460.56 Driver Version: 460.32.03 CUDA Version: 11.2 Tesla K80 I was basically following the steps here: https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index I included the exact code from the documentation at the end of the notebook to show that they don't work either.

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MDExOlB1bGxSZXF1ZXN0NTkwNDk1NzEx

Adding PersiNLU reading-comprehension

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTkwMjkzNDA1

Update format columns in Dataset.rename_columns

closed

CONTRIBUTOR

Fixes #2026

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MDU6SXNzdWU4MjgxOTQ0Njc=

KeyError on using map after renaming a column

closed

CONTRIBUTOR

Hi, I'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function. Here is what I try: ```python transform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])]) def prepare_features(examples): images = [] labels = [] print(examples) for example_idx, example in enumerate(examples["image"]): if transform is not None: images.append(transform(examples["image"][example_idx].permute(2,0,1))) else: images.append(examples["image"][example_idx].permute(2,0,1)) labels.append(examples["label"][example_idx]) output = {"label":labels, "image":images} return output raw_dataset = load_dataset('cifar10') raw_dataset.set_format('torch',columns=['img','label']) raw_dataset = raw_dataset.rename_column('img','image') features = datasets.Features({ "image": datasets.Array3D(shape=(3,32,32),dtype="float32"), "label": datasets.features.ClassLabel(names=[ "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck", ]), }) train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000) ``` The error: ```python --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-54-bf29672c53ee> in <module>() 14 ]), 15 }) ---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000) 2 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint) 1287 test_inputs = self[:2] if batched else self[0] 1288 test_indices = [0, 1] if batched else 0 -> 1289 update_data = does_function_return_dict(test_inputs, test_indices) 1290 logger.info("Testing finished, running the mapping function on the dataset") 1291 /usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices) 1258 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns] 1259 processed_inputs = ( -> 1260 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1261 ) 1262 does_return_dict = isinstance(processed_inputs, Mapping) <ipython-input-52-b4dccbafb70d> in prepare_features(examples) 3 labels = [] 4 print(examples) ----> 5 for example_idx, example in enumerate(examples["image"]): 6 if transform is not None: 7 images.append(transform(examples["image"][example_idx].permute(2,0,1))) KeyError: 'image' ``` The print statement inside returns this: ```python {'label': tensor([6, 9])} ``` Apparently, both `img` and `image` do not exist after renaming. Note that this code works fine with `img` everywhere. Notebook: https://colab.research.google.com/drive/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing

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MDExOlB1bGxSZXF1ZXN0NTg5ODk2NjMz

[Refactor] Use in-memory/memory-mapped/concatenation tables in Dataset

closed

MEMBER

## Intro Currently there is one assumption that we need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk with memory mapping (using the dataset._data_files). This assumption is used for pickling for example: - in-memory dataset can just be pickled/unpickled in-memory - on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling ## Issues Because of this assumption, we can't easily implement methods like `Dataset.add_item` to append more rows to a dataset, or `dataset.add_column` to add a column, since we can't mix data from memory and data from the disk. Moreover, `concatenate_datasets` doesn't work if the datasets to concatenate are not all from memory, or all form the disk. ## Solution provided in this PR I changed this by allowing several types of Table to be used in the Dataset object. More specifically I added three pyarrow Table wrappers: InMemoryTable, MemoryMappedTable and ConcatenationTable. The in-memory and memory-mapped tables implement the pickling behavior described above. The ConcatenationTable can be made from several tables (either in-memory or memory mapped) called "blocks". Pickling a ConcatenationTable simply pickles the underlying blocks. ## Implementation details The three tables classes mentioned above all inherit from a `Table` class defined in `table.py`, which is a wrapper of a pyarrow table. The `Table` wrapper implements all the attributes and methods of the underlying pyarrow table. Regarding the MemoryMappedTable: Reloading a pyarrow table from the disk makes you lose all the changes you may have applied (slice, rename_columns, drop, cast etc.). Therefore the MemoryMappedTable implements a "replay" mechanism to re-apply the changes when reloading the pyarrow table from the disk. ## Checklist - [x] add InMemoryTable - [x] add MemoryMappedTable - [x] add ConcatenationTable - [x] Update the ArrowReader to use these new tables depending on the `in_memory` parameter - [x] Update Dataset.from_xxx methods - [x] Update load_from_disk and save_to_disk - [x] Backward compatibility of load_from_disk - [x] Add tests for the new tables - [x] Update current tests - [ ] Documentation ---------- I would be happy to discuss the design of this PR :) Close #1877

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MDExOlB1bGxSZXF1ZXN0NTg5NzEzNDAy

Remove print statement from mnist.py

closed

CONTRIBUTOR

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MDExOlB1bGxSZXF1ZXN0NTg5NjkyNDU2

Add Romanian to XQuAD

closed

CONTRIBUTOR

On Jan 18, XQuAD was updated with a new Romanian validation file ([xquad commit link](https://github.com/deepmind/xquad/commit/60cac411649156efb6aab9dd4c9cde787a2c0345))

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MDU6SXNzdWU4Mjc0MzUwMzM=

ValueError when rename_column on splitted dataset

closed

NONE

Hi there, I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so: ```python split = { 'train': ReadInstruction('train', to=90, unit='%'), 'test': ReadInstruction('train', from_=-10, unit='%') } dataset = load_dataset( path='csv', # use 'text' loading script to load from local txt-files delimiter='\t', # xxx data_files=text_files, # list of paths to local text files split=split, # xxx ) dataset ``` Part of output: ```python DatasetDict({ train: Dataset({ features: ['sentence', 'sentiment'], num_rows: 900 }) test: Dataset({ features: ['sentence', 'sentiment'], num_rows: 100 }) }) ``` Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however: ```python dataset['train'].rename_column('sentence', 'text') ``` ```python /usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name) 353 for split_name in split_names_from_instruction: 354 if not re.match(_split_re, split_name): --> 355 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.") 356 357 def __str__(self): ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('. ``` In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split. Thanks in advance! :)

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MDU6SXNzdWU4MjY5ODgwMTY=

Interactively doing save_to_disk and load_from_disk corrupts the datasets object?

closed

NONE

dataset_info.json file saved after using save_to_disk gets corrupted as follows.  Is there a way to disable the cache that will save to /tmp/huggiface/datastes ? I have a feeling there is a serious issue with cashing.

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MDExOlB1bGxSZXF1ZXN0NTg4OTE3MjYx

Remove unnecessary docstart check in conll-like datasets

closed

CONTRIBUTOR

Related to this PR: #1998 Additionally, this PR adds the docstart note to the conll2002 dataset card ([link](https://raw.githubusercontent.com/teropa/nlp/master/resources/corpora/conll2002/ned.train) to the raw data with `DOCSTART` lines).

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MDExOlB1bGxSZXF1ZXN0NTg4NjEyODgy

Replace print with logging in dataset scripts

closed

CONTRIBUTOR

Replaces `print(...)` in the dataset scripts with the library logger.

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MDExOlB1bGxSZXF1ZXN0NTg4NDc0NTQz

Md gender card update

closed

CONTRIBUTOR

I updated the descriptions of the datasets as they appear in the HF repo and the descriptions of the source datasets according to what I could find from the paper and the references. I'm still a little unclear about some of the fields of the different configs, and there was little info on the word list and name list. I'll contact the authors to see if they have any additional information or suggested changes.

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MDExOlB1bGxSZXF1ZXN0NTg4NDMyNDc2

Add TF-based Features to handle different modes of data

closed

CONTRIBUTOR

Hi, I am creating this draft PR to work on add features similar to [TF datasets](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/core/features). I'll be starting with `Tensor` and `FeatureConnector` classes, and build upon them to add other features as well. This is a work in progress.

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MDExOlB1bGxSZXF1ZXN0NTg4MDA5NjEz

Not all languages have 2 digit codes.

closed

CONTRIBUTOR

.

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MDExOlB1bGxSZXF1ZXN0NTg3OTg4NTQ0

Fix ipython function creation in tests

closed

MEMBER

The test at `tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function` was failing in python 3.8 because the ipython function was not properly created. Fix #2010

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MDExOlB1bGxSZXF1ZXN0NTg3OTY1NDg3

more explicit method parameters

closed

CONTRIBUTOR

re: #2009 not super convinced this is better, and while I usually fight against kwargs here it seems to me that it better conveys the relationship to the `_split_generator` method.

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MDExOlB1bGxSZXF1ZXN0NTg3NzYzMTgx

Add Cryptonite dataset

closed

CONTRIBUTOR

cc @aviaefrat who's the original author of the dataset & paper, see https://github.com/aviaefrat/cryptonite

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MDU6SXNzdWU4MjU2MzQwNjQ=

No upstream branch

closed

CONTRIBUTOR

Feels like the documentation on adding a new dataset is outdated? https://github.com/huggingface/datasets/blob/987df6b4e9e20fc0c92bc9df48137d170756fd7b/ADD_NEW_DATASET.md#L49-L54 There is no upstream branch on remote.

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MDExOlB1bGxSZXF1ZXN0NTg3Njk4MTAx

Add RoSent Dataset

closed

CONTRIBUTOR

This PR adds a Romanian sentiment analysis dataset. This PR also closes pending PR #1529. I had to add an `original_id` feature because the dataset files have repeated IDs. I can remove them if needed. I have also added `id` which is unique. Let me know in case of any issues.

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MDU6SXNzdWU4MjU1Njc2MzU=

Local testing fails

closed

CONTRIBUTOR

I'm following the CI setup as described in https://github.com/huggingface/datasets/blob/8eee4fa9e133fe873a7993ba746d32ca2b687551/.circleci/config.yml#L16-L19 in a new conda environment, at commit https://github.com/huggingface/datasets/commit/4de6dbf84e93dad97e1000120d6628c88954e5d4 and getting ``` FAILED tests/test_caching.py::RecurseDumpTest::test_dump_ipython_function - TypeError: an integer is required (got type bytes) 1 failed, 2321 passed, 5109 skipped, 10 warnings in 124.32s (0:02:04) ``` Seems like a discrepancy with CI, perhaps a lib version that's not controlled? Tried with `pyarrow=={1.0.0,0.17.1,2.0.0}`

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MDU6SXNzdWU4MjU1NDEzNjY=

Ambiguous documentation

closed

CONTRIBUTOR

https://github.com/huggingface/datasets/blob/2ac9a0d24a091989f869af55f9f6411b37ff5188/templates/new_dataset_script.py#L156-L158 Looking at the template, I find this documentation line to be confusing, the method parameters don't include the `gen_kwargs` so I'm unclear where they're coming from. Happy to push a PR with a clearer statement when I understand the meaning.

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MDExOlB1bGxSZXF1ZXN0NTg3Mjc1Njk4

Fix various typos/grammer in the docs

closed

CONTRIBUTOR

This PR: * fixes various typos/grammer I came across while reading the docs * adds the "Install with conda" installation instructions Closes #1959

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MDU6SXNzdWU4MjQ1MTgxNTg=

How to not load huggingface datasets into memory

closed

NONE

Hi I am running this example from transformers library version 4.3.3: (Here is the full documentation https://github.com/huggingface/transformers/issues/8771 but the running command should work out of the box) USE_TF=0 deepspeed run_seq2seq.py --model_name_or_path google/mt5-base --dataset_name wmt16 --dataset_config_name ro-en --source_prefix "translate English to Romanian: " --task translation_en_to_ro --output_dir /test/test_large --do_train --do_eval --predict_with_generate --max_train_samples 500 --max_val_samples 500 --max_source_length 128 --max_target_length 128 --sortish_sampler --per_device_train_batch_size 8 --val_max_target_length 128 --deepspeed ds_config.json --num_train_epochs 1 --eval_steps 25000 --warmup_steps 500 --overwrite_output_dir (Here please find the script: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/run_seq2seq.py) If you do not pass max_train_samples in above command to load the full dataset, then I get memory issue on a gpu with 24 GigBytes of memory. I need to train large-scale mt5 model on large-scale datasets of wikipedia (multiple of them concatenated or other datasets in multiple languages like OPUS), could you help me how I can avoid loading the full data into memory? to make the scripts not related to data size? In above example, I was hoping the script could work without relying on dataset size, so I can still train the model without subsampling training set. thank you so much @lhoestq for your great help in advance

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MDExOlB1bGxSZXF1ZXN0NTg2Njg5Nzk2

Don't gitignore dvc.lock

closed

MEMBER

The benchmarks runs are [failing](https://github.com/huggingface/datasets/runs/2055534629?check_suite_focus=true) because of ``` ERROR: 'dvc.lock' is git-ignored. ``` I removed the dvc.lock file from the gitignore to fix that

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MDU6SXNzdWU4MjQyNzUwMzU=

Setting to torch format not working with torchvision and MNIST

closed

CONTRIBUTOR

Hi I am trying to use `torchvision.transforms` to handle the transformation of the image data in the `mnist` dataset. Assume I have a `transform` variable which contains the `torchvision.transforms` object. A snippet of what I am trying to do: ```python def prepare_features(examples): images = [] labels = [] for example_idx, example in enumerate(examples["image"]): if transform is not None: images.append(transform( np.array(examples["image"][example_idx], dtype=np.uint8) )) else: images.append(torch.tensor(np.array(examples["image"][example_idx], dtype=np.uint8))) labels.append(torch.tensor(examples["label"][example_idx])) output = {"label":labels, "image":images} return output raw_dataset = load_dataset('mnist') train_dataset = raw_dataset.map(prepare_features, batched=True, batch_size=10000) train_dataset.set_format("torch",columns=["image","label"]) ``` After this, I check the type of the following: ```python print(type(train_dataset["train"]["label"])) print(type(train_dataset["train"]["image"][0])) ``` This leads to the following output: ```python <class 'torch.Tensor'> <class 'list'> ``` I use `torch.utils.DataLoader` for batches, the type of `batch["train"]["image"]` is also `<class 'list'>`. I don't understand why only the `label` is converted to a torch tensor, why does the image not get converted? How can I fix this issue? Thanks, Gunjan EDIT: I just checked the shapes, and the types, `batch[image]` is a actually a list of list of tensors. Shape is (1,28,2,28), where `batch_size` is 2. I don't understand why this is happening. Ideally it should be a tensor of shape (2,1,28,28). EDIT 2: Inside `prepare_train_features`, the shape of `images[0]` is `torch.Size([1,28,28])`, the conversion is working. However, the output of the `map` is a list of list of list of list.

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MDExOlB1bGxSZXF1ZXN0NTg2MzcyODY1

LaRoSeDa

closed

CONTRIBUTOR

Add LaRoSeDa to huggingface datasets.

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MDU6SXNzdWU4MjQwMzQ2Nzg=

Messages are being printed to the `stdout`

closed

NONE

In this code segment, we can see some messages are being printed to the `stdout`. https://github.com/huggingface/datasets/blob/7e60bb509b595e8edc60a87f32b2bacfc065d607/src/datasets/builder.py#L545-L554 According to the comment, it is done intentionally, but I don't really understand why don't we log it with a higher level or print it directly to the `stderr`. In my opinion, this kind of messages should never printed to the stdout. At least some configuration/flag should make it possible to provide in order to explicitly prevent the package to contaminate the stdout.

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MDExOlB1bGxSZXF1ZXN0NTg2MjgwNzE3

MOROCO

closed

CONTRIBUTOR

Add MOROCO to huggingface datasets.

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MDU6SXNzdWU4MjM5NDY3MDY=

Empty evidence document ("provenance") in KILT ELI5 dataset

closed

NONE

In the original KILT benchmark(https://github.com/facebookresearch/KILT), all samples has its evidence document (i.e. wikipedia page id) for prediction. For example, a sample in ELI5 dataset has the format including provenance (=evidence document) like this `{"id": "1kiwfx", "input": "In Trading Places (1983, Akroyd/Murphy) how does the scheme at the end of the movie work? Why would buying a lot of OJ at a high price ruin the Duke Brothers?", "output": [{"answer": "I feel so old. People have been askinbg what happened at the end of this movie for what must be the last 15 years of my life. It never stops. Every year/month/fortnight, I see someone asking what happened, and someone explaining. Andf it will keep on happening, until I am 90yrs old, in a home, with nothing but the Internet and my bladder to keep me going. And there it will be: \"what happens at the end of Trading Places?\""}, {"provenance": [{"wikipedia_id": "242855", "title": "Futures contract", "section": "Section::::Abstract.", "start_paragraph_id": 1, "start_character": 14, "end_paragraph_id": 1, "end_character": 612, "bleu_score": 0.9232808519770748}]}], "meta": {"partial_evidence": [{"wikipedia_id": "520990", "title": "Trading Places", "section": "Section::::Plot.\n", "start_paragraph_id": 7, "end_paragraph_id": 7, "meta": {"evidence_span": ["On television, they learn that Clarence Beeks is transporting a secret USDA report on orange crop forecasts.", "On television, they learn that Clarence Beeks is transporting a secret USDA report on orange crop forecasts. Winthorpe and Valentine recall large payments made to Beeks by the Dukes and realize that the Dukes plan to obtain the report to corner the market on frozen orange juice.", "Winthorpe and Valentine recall large payments made to Beeks by the Dukes and realize that the Dukes plan to obtain the report to corner the market on frozen orange juice."]}}]}}` However, KILT ELI5 dataset from huggingface datasets library only contain empty list of provenance. `{'id': '1oy5tc', 'input': 'in football whats the point of wasting the first two plays with a rush - up the middle - not regular rush plays i get those', 'meta': {'left_context': '', 'mention': '', 'obj_surface': [], 'partial_evidence': [], 'right_context': '', 'sub_surface': [], 'subj_aliases': [], 'template_questions': []}, 'output': [{'answer': 'In most cases the O-Line is supposed to make a hole for the running back to go through. If you run too many plays to the outside/throws the defense will catch on.\n\nAlso, 2 5 yard plays gets you a new set of downs.', 'meta': {'score': 2}, 'provenance': []}, {'answer': "I you don't like those type of plays, watch CFL. We only get 3 downs so you can't afford to waste one. Lots more passing.", 'meta': {'score': 2}, 'provenance': []}]} ` should i perform other procedure to obtain evidence documents?

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MDU6SXNzdWU4MjM4OTk5MTA=

Windows Permission Error (most recent version of datasets)

closed

NONE

Hi everyone, Can anyone help me with why the dataset loading script below raises a Windows Permission Error? I stuck quite closely to https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py , only I want to load the data from three local three-column tsv-files (id\ttokens\tpos_tags\n). I am using the most recent version of datasets. Thank you in advance! Luisa My script: ``` import datasets import csv logger = datasets.logging.get_logger(__name__) class SampleConfig(datasets.BuilderConfig): def __init__(self, **kwargs): super(SampleConfig, self).__init__(**kwargs) class Sample(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ SampleConfig(name="conll2003", version=datasets.Version("1.0.0"), description="Conll2003 dataset"), ] def _info(self): return datasets.DatasetInfo( description="Dataset with words and their POS-Tags", features=datasets.Features( { "id": datasets.Value("string"), "tokens": datasets.Sequence(datasets.Value("string")), "pos_tags": datasets.Sequence( datasets.features.ClassLabel( names=[ "''", ",", "-LRB-", "-RRB-", ".", ":", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "MD", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WRB", "``" ] ) ), } ), supervised_keys=None, homepage="https://catalog.ldc.upenn.edu/LDC2011T03", citation="Weischedel, Ralph, et al. OntoNotes Release 4.0 LDC2011T03. Web Download. Philadelphia: Linguistic Data Consortium, 2011.", ) def _split_generators(self, dl_manager): loaded_files = dl_manager.download_and_extract(self.config.data_files) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": loaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": loaded_files["test"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": loaded_files["val"]}) ] def _generate_examples(self, filepath): logger.info("generating examples from = %s", filepath) with open(filepath, encoding="cp1252") as f: data = csv.reader(f, delimiter="\t") ids = list() tokens = list() pos_tags = list() for id_, line in enumerate(data): #print(line) if len(line) == 1: if tokens: yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags} ids = list() tokens = list() pos_tags = list() else: ids.append(line[0]) tokens.append(line[1]) pos_tags.append(line[2]) # last example yield id_, {"id": ids, "tokens": tokens, "pos_tags": pos_tags} def main(): dataset = datasets.load_dataset( "data_loading.py", data_files={ "train": "train.tsv", "test": "test.tsv", "val": "val.tsv" } ) #print(dataset) if __name__=="__main__": main() ```

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MDExOlB1bGxSZXF1ZXN0NTg2MTM5ODMy

Add FashionMNIST dataset

closed

CONTRIBUTOR

This PR adds [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset.

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MDExOlB1bGxSZXF1ZXN0NTg2MTE4NTQ4

Add -DOCSTART- note to dataset card of conll-like datasets

closed

CONTRIBUTOR

Closes #1983

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MDU6SXNzdWU4MjM2Nzk0NjU=

from datasets import MoleculeDataset, GEOMDataset

closed

NONE

I met the ImportError: cannot import name 'MoleculeDataset' from 'datasets'. Have anyone met the similar issues? Thanks!

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MDU6SXNzdWU4MjM1NzM0MTA=

Error when exploring `arabic_speech_corpus`

closed

NONE

Navigate to https://huggingface.co/datasets/viewer/?dataset=arabic_speech_corpus Error: ``` ImportError: To be able to use this dataset, you need to install the following dependencies['soundfile'] using 'pip install soundfile' for instance' Traceback: File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/script_runner.py", line 332, in _run_script exec(code, module.__dict__) File "/home/sasha/nlp-viewer/run.py", line 233, in <module> configs = get_confs(option) File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 604, in wrapped_func return get_or_create_cached_value() File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/streamlit/caching.py", line 588, in get_or_create_cached_value return_value = func(*args, **kwargs) File "/home/sasha/nlp-viewer/run.py", line 145, in get_confs module_path = nlp.load.prepare_module(path, dataset=True File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/datasets/load.py", line 342, in prepare_module f"To be able to use this {module_type}, you need to install the following dependencies" ```

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MDExOlB1bGxSZXF1ZXN0NTg1NDI5NTg0

[Timit_asr] Make sure not only the first sample is used

closed

MEMBER

When playing around with timit I noticed that only the first sample is used for all indices. I corrected this typo so that the dataset is correctly loaded.

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MDU6SXNzdWU4MjI4NzEyMzg=

not being able to get wikipedia es language

open

NONE

Hi I am trying to run a code with wikipedia of config 20200501.es, getting: Traceback (most recent call last): File "run_mlm_t5.py", line 608, in <module> main() File "run_mlm_t5.py", line 359, in main datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/load.py", line 612, in load_dataset ignore_verifications=ignore_verifications, File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 527, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/dara/libs/anaconda3/envs/success432/lib/python3.7/site-packages/datasets-1.2.1-py3.7.egg/datasets/builder.py", line 1050, in _download_and_prepare "\n\t`{}`".format(usage_example) datasets.builder.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.es', beam_runner='DirectRunner')` thanks @lhoestq for any suggestion/help

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MDU6SXNzdWU4MjI3NTgzODc=

How to load a dataset with load_from disk and save it again after doing transformations without changing the original?

closed

NONE

I am using the latest datasets library. In my work, I first use **load_from_disk** to load a data set that contains 3.8Gb information. Then during my training process, I update that dataset object and add new elements and save it in a different place. When I save the dataset with **save_to_disk**, the original dataset which is already in the disk also gets updated. I do not want to update it. How to prevent from this?

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`datasets.map` multi processing much slower than single processing

open

NONE

Hi, thank you for the great library. I've been using datasets to pretrain language models, and it often involves datasets as large as ~70G. My data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer. I noticed that `map` function with `num_proc=mp.cpu_count() //2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running. What could be the reason? I would be happy to provide information necessary to spot the reason. p.s. I was experiencing the imbalance issue mentioned in [here](https://github.com/huggingface/datasets/issues/610#issuecomment-705177036) when I was using multi processing. p.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. 

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MDExOlB1bGxSZXF1ZXN0NTg1MTYwNDkx

Adding the conllpp dataset

closed

CONTRIBUTOR

Adding the conllpp dataset, is a revision from https://github.com/huggingface/datasets/pull/1910.

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MDU6SXNzdWU4MjIzODQ1MDI=

OSError: Memory mapping file failed: Cannot allocate memory

closed

NONE

Hi, I am trying to run a code with a wikipedia dataset, here is the command to reproduce the error. You can find the codes for run_mlm.py in huggingface repo here: https://github.com/huggingface/transformers/blob/v4.3.2/examples/language-modeling/run_mlm.py ``` python run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.en --do_train --do_eval --output_dir /dara/test --max_seq_length 128 ``` I am using transformer version: 4.3.2 But I got memory erorr using this dataset, is there a way I could save on memory with dataset library with wikipedia dataset? Specially I need to train a model with multiple of wikipedia datasets concatenated. thank you very much @lhoestq for your help and suggestions: ``` File "run_mlm.py", line 441, in <module> main() File "run_mlm.py", line 233, in main split=f"train[{data_args.validation_split_percentage}%:]", File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/load.py", line 750, in load_dataset ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 740, in as_dataset map_tuple=True, File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/utils/py_utils.py", line 225, in map_nested return function(data_struct) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 757, in _build_single_dataset in_memory=in_memory, File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 829, in _as_dataset in_memory=in_memory, File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 215, in read return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 236, in read_files pa_table = self._read_files(files, in_memory=in_memory) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 171, in _read_files pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename pa_table = ArrowReader.read_table(filename, in_memory=in_memory) File "/dara/libs/anaconda3/envs/code/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/arrow_reader.py", line 322, in read_table stream = stream_from(filename) File "pyarrow/io.pxi", line 782, in pyarrow.lib.memory_map File "pyarrow/io.pxi", line 743, in pyarrow.lib.MemoryMappedFile._open File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: Memory mapping file failed: Cannot allocate memory ```

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MDU6SXNzdWU4MjIzMjgxNDc=

Question/problem with dataset labels

closed

NONE

Hi, I'm using a dataset with two labels "nurse" and "not nurse". For whatever reason (that I don't understand), I get an error that I think comes from the datasets package (using csv). Everything works fine if the labels are "nurse" and "surgeon". This is the trace I get: ``` File "../../../models/tr-4.3.2/run_puppets.py", line 523, in <module> main() File "../../../models/tr-4.3.2/run_puppets.py", line 249, in main datasets = load_dataset("csv", data_files=data_files) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 572, in download_and_prepare self._download_and_prepare( File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 650, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/builder.py", line 1028, in _prepare_split writer.write_table(table) File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/datasets/arrow_writer.py", line 292, in write_table pa_table = pa_table.cast(self._schema) File "pyarrow/table.pxi", line 1311, in pyarrow.lib.Table.cast File "pyarrow/table.pxi", line 265, in pyarrow.lib.ChunkedArray.cast File "/dccstor/redrug_ier/envs/last-tr/lib/python3.8/site-packages/pyarrow/compute.py", line 87, in cast return call_function("cast", [arr], options) File "pyarrow/_compute.pyx", line 298, in pyarrow._compute.call_function File "pyarrow/_compute.pyx", line 192, in pyarrow._compute.Function.call 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: Failed to parse string: not nurse ``` Any ideas how to fix this? For now, I'll probably make them numeric.

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MDU6SXNzdWU4MjIzMjQ2MDU=

Readme.md is misleading about kinds of datasets?

closed

NONE

Hi! At the README.MD, you say: "efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. " But here: https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py#L82-L117 You mention other kinds of datasets, with images and so on. I'm confused. Is it possible to use it to store, say, imagenet locally?

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MDU6SXNzdWU4MjIzMDg5NTY=

wmt15 is broken

closed

CONTRIBUTOR

While testing the hotfix, I tried a random other wmt release and found wmt15 to be broken: ``` python -c 'from datasets import load_dataset; load_dataset("wmt15", "de-en")' Downloading: 2.91kB [00:00, 818kB/s] Downloading: 3.02kB [00:00, 897kB/s] Downloading: 41.1kB [00:00, 19.1MB/s] Downloading and preparing dataset wmt15/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/stas/.cache/huggingface/datasets/wmt15/de-en/1.0.0/39ad5f9262a0910a8ad7028ad432731ad23fdf91f2cebbbf2ba4776b9859e87f... Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/builder.py", line 578, in download_and_prepare self._download_and_prepare( File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/builder.py", line 634, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt15/39ad5f9262a0910a8ad7028ad432731ad23fdf91f2cebbbf2ba4776b9859e87f/wmt_utils.py", line 757, in _split_generators downloaded_files = dl_manager.download_and_extract(urls_to_download) File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 283, in download_and_extract return self.extract(self.download(url_or_urls)) File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 191, in download downloaded_path_or_paths = map_nested( File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 203, in map_nested mapped = [ File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 204, in <listcomp> _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm) File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 160, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 160, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested return function(data_struct) File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 214, in _download return cached_path(url_or_filename, download_config=download_config) File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 274, in cached_path output_path = get_from_cache( File "/home/stas/anaconda3/envs/main-38/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 614, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/wmt/wmt15/resolve/main/training-parallel-nc-v10.tgz ```

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MDU6SXNzdWU4MjIxNzYyOTA=

wmt datasets fail to load

closed

NONE

~\.cache\huggingface\modules\datasets_modules\datasets\wmt14\43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e\wmt_utils.py in _split_generators(self, dl_manager) 758 # Extract manually downloaded files. 759 manual_files = dl_manager.extract(manual_paths_dict) --> 760 extraction_map = dict(downloaded_files, **manual_files) 761 762 for language in self.config.language_pair: TypeError: type object argument after ** must be a mapping, not list

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https://github.com/huggingface/datasets/pull/1985

MDExOlB1bGxSZXF1ZXN0NTg0ODM4NjIw

Optimize int precision

closed

MEMBER

Optimize int precision to reduce dataset file size. Close #1973, close #1825, close #861.

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MDU6SXNzdWU4MjE4MTY1ODg=

Add tests for WMT datasets

closed

MEMBER

As requested in #1981, we need tests for WMT datasets, using dummy data.

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MDU6SXNzdWU4MjE3NDYwMDg=

The size of CoNLL-2003 is not consistant with the official release.

closed

NONE

Thanks for the dataset sharing! But when I use conll-2003, I meet some questions. The statistics of conll-2003 in this repo is : \#train 14041 \#dev 3250 \#test 3453 While the official statistics is: \#train 14987 \#dev 3466 \#test 3684 Wish for your reply~

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MDExOlB1bGxSZXF1ZXN0NTg0MjM2NzQ0

Fix NestedDataStructure.data for empty dict

closed

MEMBER

Fix #1981

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MDU6SXNzdWU4MjE0MTExMDk=

wmt datasets fail to load

closed

CONTRIBUTOR

on master: ``` python -c 'from datasets import load_dataset; load_dataset("wmt14", "de-en")' Downloading and preparing dataset wmt14/de-en (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/stas/.cache/huggingface/datasets/wmt14/de-en/1.0.0/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e... Traceback (most recent call last): File "<string>", line 1, in <module> File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset builder_instance.download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 578, in download_and_prepare self._download_and_prepare( File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 634, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt14/43e717d978d2261502b0194999583acb874ba73b0f4aed0ada2889d1bb00f36e/wmt_utils.py", line 760, in _split_generators extraction_map = dict(downloaded_files, **manual_files) ``` it worked fine recently. same problem if I try wmt16. git bisect points to this commit from Feb 25 as the culprit https://github.com/huggingface/datasets/commit/792f1d9bb1c5361908f73e2ef7f0181b2be409fa @albertvillanova

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MDExOlB1bGxSZXF1ZXN0NTg0MTI1OTUy

Loading all answers from drop

closed

CONTRIBUTOR

Hello all, I propose this change to the DROP loading script so that all answers are loaded no matter their type. Currently, only "span" answers are loaded, which excludes a significant amount of answers from drop (i.e. "number" and "date"). I updated the script with the version I use for my work. However, I couldn't find a way to verify that all is working when integrated with the datasets repo, since the `load_dataset` method seems to always download the script from github and not local files. Note that 9 items from the train set have no answers, as well as 1 from the validation set. The script I propose simply do not load them. Let me know if there is anything else I can do, Clément

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MDExOlB1bGxSZXF1ZXN0NTgzODQ3MTk3

Add article_id and process test set template for semeval 2020 task 11…

closed

CONTRIBUTOR

… dataset - `article_id` is needed to create the submission file for the task at https://propaganda.qcri.org/semeval2020-task11/ - The `technique classification` task provides the span indices in a template for the test set that is necessary to complete the task. This PR implements processing of that template for the dataset.

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MDExOlB1bGxSZXF1ZXN0NTgzODI5Njgz

Adding ro sts dataset

closed

CONTRIBUTOR

Adding [RO-STS](https://github.com/dumitrescustefan/RO-STS) dataset

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MDU6SXNzdWU4MjAzMTIwMjI=

ModuleNotFoundError: No module named 'apache_beam' for wikipedia datasets

open

NONE

Hi I am trying to run run_mlm.py code [1] of huggingface with following "wikipedia"/ "20200501.aa" dataset: `python run_mlm.py --model_name_or_path bert-base-multilingual-cased --dataset_name wikipedia --dataset_config_name 20200501.aa --do_train --do_eval --output_dir /tmp/test-mlm --max_seq_length 256 ` I am getting this error, but as per documentation, huggingface dataset provide processed version of this dataset and users can load it without requiring setup extra settings for apache-beam. could you help me please to load this dataset? Do you think I can run run_ml.py with this dataset? or anyway I could subsample and train the model? I greatly appreciate providing the processed version of all languages for this dataset, which allow the user to use them without setting up apache-beam,. thanks I really appreciate your help. @lhoestq thanks. [1] https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py error I get: ``` >>> import datasets >>> datasets.load_dataset("wikipedia", "20200501.aa") Downloading and preparing dataset wikipedia/20200501.aa (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /dara/temp/cache_home_2/datasets/wikipedia/20200501.aa/1.0.0/4021357e28509391eab2f8300d9b689e7e8f3a877ebb3d354b01577d497ebc63... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/load.py", line 746, in load_dataset use_auth_token=use_auth_token, File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 573, in download_and_prepare dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs File "/dara/temp/libs/anaconda3/envs/codes/lib/python3.7/site-packages/datasets-1.3.0-py3.7.egg/datasets/builder.py", line 1099, in _download_and_prepare import apache_beam as beam ModuleNotFoundError: No module named 'apache_beam' ```

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MDExOlB1bGxSZXF1ZXN0NTgzMjA3NDI4

Add datasets full offline mode with HF_DATASETS_OFFLINE

closed

MEMBER

Add the HF_DATASETS_OFFLINE environment variable for users who want to use `datasets` offline without having to wait for the network timeouts/retries to happen. This was requested in https://github.com/huggingface/datasets/issues/1939 cc @stas00

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MDExOlB1bGxSZXF1ZXN0NTgzMTg4NjM3

Fix flake8

closed

MEMBER

Fix flake8 style.

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MDExOlB1bGxSZXF1ZXN0NTgzMTE5MDI0

feat(docs): navigate with left/right arrow keys

closed

NONE

Enables docs navigation with left/right arrow keys. It can be useful for the ones who navigate with keyboard a lot. More info : https://github.com/sphinx-doc/sphinx/pull/2064 You can try here : https://29353-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html

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MDU6SXNzdWU4MjAwNzczMTI=

Question: what gets stored in the datasets cache and why is it so huge?

closed

NONE

I'm running several training jobs (around 10) with a relatively large dataset (3M samples). The datasets cache reached 178G and it seems really large. What is it stored in there and why is it so large? I don't think I noticed this problem before and seems to be related to the new version of the datasets library. Any insight? Thank you!

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MDU6SXNzdWU4MTk3NTI3NjE=

'Dataset' object has no attribute 'rename_column'

closed

NONE

'Dataset' object has no attribute 'rename_column'

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MDExOlB1bGxSZXF1ZXN0NTgyNzgyNTU0

Fix ArrowWriter closes stream at exit

closed

MEMBER

Current implementation of ArrowWriter does not properly release the `stream` resource (by closing it) if its `finalize()` method is not called and/or an Exception is raised before/during the call to its `finalize()` method. Therefore, ArrowWriter should be used as a context manager that properly closes its `stream` resource at exit.

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MDExOlB1bGxSZXF1ZXN0NTgyNjAzMzEw

Fixing the URL filtering for bad MLSUM examples in GEM

closed

MEMBER

This updates the code and metadata to use the updated `gem_mlsum_bad_ids_fixed.json` file provided by @juand-r cc @sebastianGehrmann

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MDExOlB1bGxSZXF1ZXN0NTgyMjc5OTEx

Add Turkish News Category Dataset - 270K - Lite Version

closed

CONTRIBUTOR

This PR adds the Turkish News Categories Dataset (270K - Lite Version) dataset which is a text classification dataset by me, @basakbuluz and @serdarakyol. This dataset contains the same news from the current [interpress_news_category_tr dataset](https://huggingface.co/datasets/interpress_news_category_tr) but contains less information, OCR errors are reduced, can be easily separated, and can be divided into 10 classes ("kültürsanat", "ekonomi", "siyaset", "eğitim", "dünya", "spor", "teknoloji", "magazin", "sağlık", "gündem") were rearranged.

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MDExOlB1bGxSZXF1ZXN0NTgyMjU2MzE0

Fix metrics collision in separate multiprocessed experiments

closed

MEMBER

As noticed in #1942 , there's a issue with locks if you run multiple separate evaluation experiments in a multiprocessed setup. Indeed there is a time span in Metric._finalize() where the process 0 loses its lock before re-acquiring it. This is bad since the lock of the process 0 tells the other process that the corresponding cache file is available for writing/reading/deleting: we end up having one metric cache that collides with another one. This can raise FileNotFound errors when a metric tries to read the cache file and if the second conflicting metric deleted it. To fix that I made sure that the lock file of the process 0 stays acquired from the cache file creation to the end of the metric computation. This way the other metrics can simply sample a new hashing name in order to avoid the collision. Finally I added missing tests for separate experiments in distributed setup.

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