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https://api.github.com/repos/huggingface/datasets/issues/2445
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911,577,578
MDExOlB1bGxSZXF1ZXN0NjYxODMzMTky
2,445
Fix broken URLs for bn_hate_speech and covid_tweets_japanese
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[ "Thanks ! To fix the CI you just have to rename the dummy data file in the dummy_data.zip files", "thanks for the tip with the dummy data - all fixed now!" ]
1,622,818,415,000
1,622,828,386,000
1,622,828,385,000
MEMBER
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Closes #2388
https://api.github.com/repos/huggingface/datasets/issues/2445/timeline
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911,297,139
MDU6SXNzdWU5MTEyOTcxMzk=
2,444
Sentence Boundaries missing in Dataset: xtreme / udpos
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[ "Hi,\r\n\r\nThis is a known issue. More info on this issue can be found in #2061. If you are looking for an open-source contribution, there are step-by-step instructions in the linked issue that you can follow to fix it.", "Closed by #2466." ]
1,622,797,826,000
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CONTRIBUTOR
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I was browsing through annotation guidelines, as suggested by the datasets introduction. The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments) But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter. PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array. You may compare in PAN-x.en and udpos.English in the explorer: https://huggingface.co/datasets/viewer/?dataset=xtreme
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909,983,574
MDU6SXNzdWU5MDk5ODM1NzQ=
2,443
Some tests hang on Windows
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[ "Hi ! That would be nice indeed to at least have a warning, since we don't handle the max path length limit.\r\nAlso if we could have an error instead of an infinite loop I'm sure windows users will appreciate that", "Unfortunately, I know this problem very well... ๐Ÿ˜… \r\n\r\nI remember having proposed to throw an error instead of hanging in an infinite loop #2220: 60c7d1b6b71469599a27147a08100f594e7a3f84, 8c8ab60018b00463edf1eca500e434ff061546fc \r\nbut @lhoestq told me:\r\n> Note that the filelock module comes from this project that hasn't changed in years - while still being used by ten of thousands of projects:\r\nhttps://github.com/benediktschmitt/py-filelock\r\n> \r\n> Unless we have proper tests for this, I wouldn't recommend to change it\r\n\r\nI opened an Issue requesting a warning/error at startup for that case: #2224", "@albertvillanova Thanks for additional info on this issue.\r\n\r\nYes, I think the best option is to throw an error instead of suppressing it in a loop. I've considered 2 more options, but I don't really like them:\r\n1. create a temporary file with a filename longer than 255 characters on import; if this fails, long paths are not enabled and raise a warning. I'm not sure about this approach because I don't like the idea of creating a temporary file on import for this purpose.\r\n2. check if long paths are enabled with [this code](https://stackoverflow.com/a/46546731/14095927). As mentioned in the comment, this code relies on an undocumented function and Win10-specific." ]
1,622,680,050,000
1,624,870,059,000
1,624,870,059,000
CONTRIBUTOR
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Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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909,677,029
MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1
2,442
add english language tags for ~100 datasets
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[ "Fixing the tags of all the datasets is out of scope for this PR so I'm merging even though the CI fails because of the missing tags" ]
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MEMBER
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As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs. Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English...
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908,554,713
MDU6SXNzdWU5MDg1NTQ3MTM=
2,441
DuplicatedKeysError on personal dataset
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[ "Hi ! In your dataset script you must be yielding examples like\r\n```python\r\nfor line in file:\r\n ...\r\n yield key, {...}\r\n```\r\n\r\nSince `datasets` 1.7.0 we enforce the keys to be unique.\r\nHowever it looks like your examples generator creates duplicate keys: at least two examples have key 0.\r\n\r\nYou can fix that by making sure that your keys are unique.\r\n\r\nFor example if you use a counter to define the key of each example, make sure that your counter is not reset to 0 in during examples generation (between two open files for examples).\r\n\r\nLet me know if you have other questions :)", "Yup, I indeed was generating duplicate keys. Fixed it and now it's working." ]
1,622,570,381,000
1,622,850,603,000
1,622,850,603,000
NONE
null
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## Describe the bug Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script. Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')` Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets. ## Steps to reproduce the bug I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested. ## Expected results For my data to be loaded. ## Actual results **DuplicatedKeysError** exception is raised ``` Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655... --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-c342ea0dae9d> in <module>() ----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, task, **config_kwargs) 749 try_from_hf_gcs=try_from_hf_gcs, 750 base_path=base_path, --> 751 use_auth_token=use_auth_token, 752 ) 753 /usr/local/lib/python3.7/dist-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 573 if not downloaded_from_gcs: 574 self._download_and_prepare( --> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 576 ) 577 # Sync info /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 650 try: 651 # Prepare split will record examples associated to the split --> 652 self._prepare_split(split_generator, **prepare_split_kwargs) 653 except OSError as e: 654 raise OSError( /usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator) 990 writer.write(example, key) 991 finally: --> 992 num_examples, num_bytes = writer.finalize() 993 994 split_generator.split_info.num_examples = num_examples /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream) 407 # In case current_examples < writer_batch_size, but user uses finalize() 408 if self._check_duplicates: --> 409 self.check_duplicate_keys() 410 # Re-intializing to empty list for next batch 411 self.hkey_record = [] /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 0 Keys should be unique and deterministic in nature ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.7.0 - Platform: Windows-10-10.0.19041-SP0 - Python version: 3.7.9 - PyArrow version: 3.0.0
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908,521,954
MDU6SXNzdWU5MDg1MjE5NTQ=
2,440
Remove `extended` field from dataset tagger
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[ "The tagger also doesn't insert the value for the `size_categories` field automatically, so this should be fixed too", "Thanks for reporting. Indeed the `extended` tag doesn't exist. Not sure why we had that in the tagger.\r\nThe repo of the tagger is here if someone wants to give this a try: https://github.com/huggingface/datasets-tagging\r\nOtherwise I can probably fix it next week", "I've opened a PR on `datasets-tagging` to fix the issue ๐Ÿš€ ", "thanks ! this is fixed now" ]
1,622,567,922,000
1,623,229,591,000
1,623,229,590,000
MEMBER
null
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## Describe the bug While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included: ``` dataset_name = 'arcd' @pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path)) def test_changed_dataset_card(dataset_name): card_path = repo_path / "datasets" / dataset_name / "README.md" assert card_path.exists() error_messages = [] try: ReadMe.from_readme(card_path) except Exception as readme_error: error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}") try: DatasetMetadata.from_readme(card_path) except Exception as metadata_error: error_messages.append( f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}" ) if error_messages: > raise ValueError("\n".join(error_messages)) E ValueError: The following issues have been found in the dataset cards: E YAML tags: E __init__() got an unexpected keyword argument 'extended' tests/test_dataset_cards.py:70: ValueError ``` Consider either removing this tag from the tagger or including it as part of the validation step in the CI. cc @yjernite
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908,511,983
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2,439
Better error message when trying to access elements of a DatasetDict without specifying the split
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As mentioned in #2437 it'd be nice to to have an indication to the users when they try to access an element of a DatasetDict without specifying the split name. cc @thomwolf
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Fix NQ features loading: reorder fields of features to match nested fields order in arrow data
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As mentioned in #2401, there is an issue when loading the features of `natural_questions` since the order of the nested fields in the features don't match. The order is important since it matters for the underlying arrow schema. To fix that I re-order the features based on the arrow schema: ```python inferred_features = Features.from_arrow_schema(arrow_table.schema) self.info.features = self.info.features.reorder_fields_as(inferred_features) assert self.info.features.type == inferred_features.type ``` The re-ordering is a recursive function. It takes into account that the `Sequence` feature type is a struct of list and not a list of struct. Now it's possible to load `natural_questions` again :)
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Better error message when using the wrong load_from_disk
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[ "We also have other cases where people are lost between Dataset and DatasetDict, maybe let's gather and solve them all here?\r\n\r\nFor instance, I remember that some people thought they would request a single element of a split but are calling this on a DatasetDict. Maybe here also a better error message when the split requested in not in the dict? pointing to the list of split and the fact that this is a datasetdict containing several datasets?", "Good idea, let me add a better error message for this case too", "As a digression from the topic of this PR, IMHO I think that the difference between Dataset and DatasetDict is an additional abstraction complexity that confuses \"typical\" end users. I think a user expects a \"Dataset\" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user.\r\n\r\nI don't know your opinion about this, but it might be worth discussing...\r\n\r\nFor example, I really like the line of the solution of using the function `load_from_disk`, which hides the previous mentioned complexity and handles under the hood whether Dataset/DatasetDict instances should be created...", "I totally agree, I just haven't found a solution that doesn't imply major breaking changes x)", "Yes I would also like to find a better solution. Do we have any solution actually? (even implying breaking changes)\r\n\r\nHere is a proposal for discussion and refined (and potential abandon if it's not good enough):\r\n- let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other\r\n- let's disallow the use of integers in split names (probably not a very big breaking change)\r\n- when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order)\r\n- when you index with strings/split name you have the same behavior as now (full backward compat)\r\n- let's then also have all the methods of a Dataset on the DatasetDict", "The end goal would be to merge both `Dataset` and `DatasetDict` object in a single object that would be (pretty much totally) backward compatible with both.", "I like the direction :) I think it can make sense to concatenate them.\r\n\r\nThere are a few things that I we could discuss if we want to merge Dataset and DatasetDict:\r\n1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature\r\n```python\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(...)\r\ndataset[\"train\"]\r\ndataset[\"input_ids\"]\r\n```\r\n2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss.\r\n\r\nMoreover regarding your points:\r\n- integers are not allowed as split names already\r\n- it's definitely doable to have all the methods. Maybe some of them like `train_test_split` that is currently only available for Dataset can be tweaked to work for a split dataset", "Instead of suggesting the use of `Dataset.load_from_disk` and `DatasetDict.load_from_disk`, the error message now suggests to use `datasets.load_from_disk` directly", "Merging the error message improvement, feel free to continue the discussion here or in a github issue" ]
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As mentioned in #2424, the error message when one tries to use `Dataset.load_from_disk` to load a DatasetDict object (or _vice versa_) can be improved. I added a suggestion in the error message to let users know that they should use the other one.
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Update DatasetMetadata and ReadMe
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This PR contains the changes discussed in #2395. **Edit**: In addition to those changes, I'll be updating the `ReadMe` as follows: Currently, `Section` has separate parsing and validation error lists. In `.validate()`, we add these lists to the final lists and throw errors. One way to make `ReadMe` consistent with `DatasetMetadata` and add a separate `.validate()` method is to throw separate parsing and validation errors. This way, we don't have to throw validation errors, but only parsing errors in `__init__ ()`. We can have an option in `__init__()` to suppress parsing errors so that an object is created for validation. Doing this will allow the user to get all the errors in one go. In `test_dataset_cards` , we are already catching error messages and appending to a list. This can be done for `ReadMe()` for parsing errors, and `ReadMe(...,suppress_errors=True); readme.validate()` for validation, separately. **Edit 2**: The only parsing issue we have as of now is multiple headings at the same level with the same name. I assume this will happen very rarely, but it is still better to throw an error than silently pick one of them. It should be okay to separate it this way. Wdyt @lhoestq ?
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Insert Extractive QA templates for SQuAD-like datasets
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[ "hi @lhoestq @SBrandeis i've now added the missing YAML tags, so this PR should be good to go :)", "urgh, the windows tests are failing because of encoding issues ๐Ÿ˜ข \r\n\r\n```\r\ndataset_name = 'squad_kor_v1'\r\n\r\n @pytest.mark.parametrize(\"dataset_name\", get_changed_datasets(repo_path))\r\n def test_changed_dataset_card(dataset_name):\r\n card_path = repo_path / \"datasets\" / dataset_name / \"README.md\"\r\n assert card_path.exists()\r\n error_messages = []\r\n try:\r\n ReadMe.from_readme(card_path)\r\n except Exception as readme_error:\r\n error_messages.append(f\"The following issues have been found in the dataset cards:\\nREADME:\\n{readme_error}\")\r\n try:\r\n DatasetMetadata.from_readme(card_path)\r\n except Exception as metadata_error:\r\n error_messages.append(\r\n f\"The following issues have been found in the dataset cards:\\nYAML tags:\\n{metadata_error}\"\r\n )\r\n \r\n if error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE README:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\nE The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE 'charmap' codec can't decode byte 0x90 in position 2283: character maps to <undefined>\r\n```", "Seems like the encoding issues on windows is also being tackled in #2418 - will see if this solves the problem in the current PR" ]
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This PR adds task templates for 9 SQuAD-like templates with the following properties: * 1 config * A schema that matches the `squad` one (i.e. same column names, especially for the nested `answers` column because the current implementation does not support casting with mismatched columns. see #2434) * Less than 20GB (my laptop can't handle more right now) The aim of this PR is to provide a few datasets to experiment with the task template integration in other libraries / services. PR #2429 should be merged before this one. cc @abhi1thakur
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Extend QuestionAnsweringExtractive template to handle nested columns
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[ "this is also the case for the following datasets and configurations:\r\n\r\n* `mlqa` with config `mlqa-translate-train.ar`\r\n\r\n" ]
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Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like: * `iapp_wiki_qa_squad` * `parsinlu_reading_comprehension` where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) <ipython-input-12-50e5b8f69c20> in <module> ----> 1 ds.prepare_for_task("question-answering-extractive")[0] ~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task) 1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__` 1437 dataset.info.task_templates = None -> 1438 dataset = dataset.cast(features=template.features) 1439 return dataset 1440 ~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc) 977 format = self.format 978 dataset = self.with_format("arrow") --> 979 dataset = dataset.map( 980 lambda t: t.cast(schema), 981 batched=True, ~/git/datasets/src/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, desc) 1600 1601 if num_proc is None or num_proc == 1: -> 1602 return self._map_single( 1603 function=function, 1604 with_indices=with_indices, ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 176 } 177 # apply actual function --> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 180 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 395 # Call actual function 396 --> 397 out = func(self, *args, **kwargs) 398 399 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(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, new_fingerprint, rank, offset, desc) 1940 ) # Something simpler? 1941 try: -> 1942 batch = apply_function_on_filtered_inputs( 1943 batch, 1944 indices, ~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset 1837 processed_inputs = ( -> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs) 1839 ) 1840 if update_data is None: ~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t) 978 dataset = self.with_format("arrow") 979 dataset = dataset.map( --> 980 lambda t: t.cast(schema), 981 batched=True, 982 batch_size=batch_size, ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe) 241 else: 242 options = CastOptions.unsafe(target_type) --> 243 return call_function("cast", [arr], options) 244 245 ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() ~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct ```
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Fix DuplicatedKeysError in adversarial_qa
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Fixes #2431
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Fix CI six installation on linux
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For some reason we end up with this error in the linux CI when running pip install .[tests] ``` pip._vendor.resolvelib.resolvers.InconsistentCandidate: Provided candidate AlreadyInstalledCandidate(six 1.16.0 (/usr/local/lib/python3.6/site-packages)) does not satisfy SpecifierRequirement('six>1.9'), SpecifierRequirement('six>1.9'), SpecifierRequirement('six>=1.11'), SpecifierRequirement('six~=1.15'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0'), SpecifierRequirement('six>=1.11.0'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.6.1'), SpecifierRequirement('six>=1.9'), SpecifierRequirement('six>=1.5'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six'), SpecifierRequirement('six'), SpecifierRequirement('six~=1.15.0'), SpecifierRequirement('six'), SpecifierRequirement('six<2.0,>=1.6.1'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0') ``` example CI failure here: https://app.circleci.com/pipelines/github/huggingface/datasets/6200/workflows/b64fdec9-f9e6-431c-acd7-e9f2c440c568/jobs/38247 The main version requirement comes from tensorflow: `six~=1.15.0` So I pinned the six version to this.
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DuplicatedKeysError when trying to load adversarial_qa
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[ "Thanks for reporting !\r\n#2433 fixed the issue, thanks @mariosasko :)\r\n\r\nWe'll do a patch release soon of the library.\r\nIn the meantime, you can use the fixed version of adversarial_qa by adding `script_version=\"master\"` in `load_dataset`" ]
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python dataset = load_dataset('adversarial_qa', 'adversarialQA') ``` ## Expected results The dataset should be loaded into memory ## Actual results >DuplicatedKeysError: FAILURE TO GENERATE DATASET ! >Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4 >Keys should be unique and deterministic in nature > > >During handling of the above exception, another exception occurred: > >DuplicatedKeysError Traceback (most recent call last) > >/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) > 347 for hash, key in self.hkey_record: > 348 if hash in tmp_record: >--> 349 raise DuplicatedKeysError(key) > 350 else: > 351 tmp_record.add(hash) > >DuplicatedKeysError: FAILURE TO GENERATE DATASET ! >Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4 >Keys should be unique and deterministic in nature ## Environment info - `datasets` version: 1.7.0 - Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyArrow version: 3.0.0
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Add version-specific BibTeX
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[ "Maybe we should only keep one citation ?\r\ncc @thomwolf @yjernite ", "For info:\r\n- The one automatically generated by Zenodo is version-specific, and a new one will be generated after each release.\r\n- Zenodo has also generated a project-specific DOI (they call it *Concept DOI* as opposed to *Version DOI*), but currently this only redirects to the DOI page of the latest version.\r\n- All the information automatically generated by Zenodo can be corrected/customized if necessary.\r\n - If we decide to correct/update metadata, take into account that there are the following fields (among others): Authors, Contributors, Title, Description, Keywords, Additional Notes, License,...\r\n\r\nAccording to Zenodo: https://help.zenodo.org/#versioning\r\n> **Which DOI should I use in citations?**\r\n> \r\n> You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility. By default, Zenodo uses the specific version to generate citations.\r\n> \r\n> You can use the Concept DOI representing all versions in citations when it is desirable to cite an evolving research artifact, without being specific about the version.", "Thanks for the details ! As zenodo says we should probably just show the versioned DOI. And we can remove the old citation.", "I have removed the old citation.\r\n\r\nWhat about the new one? Should we customize it? I have fixed some author names (replaced nickname with first and family names). Note that the list of authors is created automatically by Zenodo from this list: https://github.com/huggingface/datasets/graphs/contributors\r\nI do not know if this default automatic list of authors is what we want to show in the citation..." ]
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As pointed out by @lhoestq in #2411, after the creation of the Zenodo DOI for Datasets, a new BibTeX entry is created with each release. This PR adds a version-specific BibTeX entry, besides the existing one which is generic for the project. See version-specific BibTeX entry here: https://zenodo.org/record/4817769/export/hx#.YLSyd6j7RPY
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Rename QuestionAnswering template to QuestionAnsweringExtractive
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[ "> I like having \"extractive\" in the name to make things explicit. However this creates an inconsistency with transformers.\r\n> \r\n> See\r\n> https://huggingface.co/transformers/task_summary.html#extractive-question-answering\r\n> \r\n> But this is minor IMO and I'm ok with this renaming\r\n\r\nyes i chose this convention because it allows us to match the `QuestionAnsweringXxx` naming and i think it's better to have `task_name-subtask_name` should auto-complete ever become part of the Hub :)" ]
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Following the discussion with @thomwolf in #2255, this PR renames the QA template to distinguish extractive vs abstractive QA. The abstractive template will be added in a future PR.
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Add copyright info to MLSUM dataset
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Saving Graph/Structured Data in Datasets
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[ "It should probably work out of the box to save structured data. If you want to show an example we can help you.", "An example of a toy dataset is like:\r\n```json\r\n[\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n```\r\nWe can use the friendship relation to build a directional graph, and a user node can be represented using the articles written by himself. And the relationship between articles can be built when the article has read by the same user.\r\nThis dataset can be used to model the heterogeneous relationship between users and articles, and this graph can be used to build recommendation systems to recommend articles to the user, or potential friends to the user.", "Hi,\r\n\r\nyou can do the following to load this data into a `Dataset`:\r\n```python\r\nfrom datasets import Dataset\r\nexamples = [\r\n {\r\n \"name\": \"mike\",\r\n \"friends\": [\r\n \"tom\",\r\n \"lily\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"aaaaa\",\r\n \"reader\": [\r\n \"tom\",\r\n \"lucy\"\r\n ]\r\n }\r\n ]\r\n },\r\n {\r\n \"name\": \"tom\",\r\n \"friends\": [\r\n \"mike\",\r\n \"bbb\"\r\n ],\r\n \"articles\": [\r\n {\r\n \"title\": \"xxxxx\",\r\n \"reader\": [\r\n \"tom\",\r\n \"qqqq\"\r\n ]\r\n }\r\n ]\r\n }\r\n]\r\n\r\nkeys = examples[0].keys()\r\nvalues = [ex.values() for ex in examples]\r\ndataset = Dataset.from_dict({k: list(v) for k, v in zip(keys, zip(*values))})\r\n```\r\n\r\nLet us know if this works for you.", "Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset?", "If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https://huggingface.co/docs/datasets/master/loading_datasets.html#from-local-files) in the docs for more info.", "Nice! Thanks for your help." ]
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Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''. Although I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets. Thank you very much for your help.
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Fix Docstring Mistake: dataset vs. metric
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[ "IMO this PR is ready for review. I do not know why tests fail...", "The CI fail is unrelated to this PR, and it has been fixed on master, merging :)", "> I just have one comment: we use rouge, not rogue :p\r\n\r\nOops!", "rebased on master" ]
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PR to fix #2412
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load_from_disk and save_to_disk are not compatible with each other
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[ "Hi,\r\n\r\n`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk.", "Thanks it worked!", "Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussion place but feel free to open a new open if you prefer @lhoestq @albertvillanova ", "We should probably improve the error message indeed.\r\n\r\nAlso note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:\r\n\r\n\r\n```python\r\nfrom datasets import load_from_disk\r\n\r\ndataset_dict = load_from_disk(\"path/to/dataset/dict\")\r\nsingle_dataset = load_from_disk(\"path/to/single/dataset\")\r\n```", "I just opened #2437 to improve the error message", "Superseded by #2462 " ]
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## Describe the bug load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("art") dataset.save_to_disk("mydir") d = Dataset.load_from_disk("mydir") ``` ## Expected results It is expected that these two functions be the reverse of each other without more manipulation ## Actual results FileNotFoundError: [Errno 2] No such file or directory: 'mydir/art/state.json' ## Environment info - `datasets` version: 1.6.2 - Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.10 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>
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2,423
add `desc` in `map` for `DatasetDict` object
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[ "The CI error is unrelated to the PR, merging", "@lhoestq, can we release this feature if you guys are planning for any patch release for Datasets. It'll slow down [#11927](https://github.com/huggingface/transformers/pull/11927) otherwise :/ ", "Sure definitely, having a discrepancy between Dataset.map and DatasetDict.map is an issue that we should fix and include in a patch release. Will do it in the coming days" ]
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`desc` in `map` currently only works with `Dataset` objects. This PR adds support for `DatasetDict` objects as well
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Fix save_to_disk nested features order in dataset_info.json
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Fix issue https://github.com/huggingface/datasets/issues/2267 The order of the nested features matters (pyarrow limitation), but the save_to_disk method was saving the features types as JSON with `sort_keys=True`, which was breaking the order of the nested features.
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doc: fix typo HF_MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES
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MAX_MEMORY_DATASET_SIZE_IN_BYTES should be HF_MAX_MEMORY_DATASET_SIZE_IN_BYTES
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Updated Dataset Description
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Added Point of contact information and several other details about the dataset.
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adds license information for DailyDialog.
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[ "Thanks! Can you also add it as metadata in the YAML block at the top of the file?\r\n\r\nShould be in the form:\r\n\r\n```\r\nlicenses:\r\n- cc-by-sa-4.0\r\n```", "seems like we need to add all the other tags ? \r\n``` \r\nif error_messages:\r\n> raise ValueError(\"\\n\".join(error_messages))\r\nE ValueError: The following issues have been found in the dataset cards:\r\nE YAML tags:\r\nE __init__() missing 8 required positional arguments: 'annotations_creators', 'language_creators', 'languages', 'multilinguality', 'size_categories', 'source_datasets', 'task_categories', and 'task_ids'\r\n```", "I'll let @lhoestq or @yjernite chime in (and maybe complete/merge). Thanks!", "Looks like CircleCI has an incident. Let's wait for it to be working again and make sure the CI is green", "The remaining error is unrelated to this PR, merging" ]
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add utf-8 while reading README
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[ "Can you please add encoding to this line as well to fix the issue (and maybe replace `path.open(...)` with `open(path, ...)`)?\r\nhttps://github.com/huggingface/datasets/blob/7bee4be44706a59b084b9b69c4cd00f73ee72f76/src/datasets/utils/metadata.py#L58", "Sure, in fact even I was thinking of adding this in order to maintain the consistency!" ]
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It was causing tests to fail in Windows (see #2416). In Windows, the default encoding is CP1252 which is unable to decode the character byte 0x9d
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Make datasets PEP-561 compliant
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[ "This is super cool, I love that โค๏ธ " ]
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Allows to type-check datasets with `mypy` when imported as a third-party library PEP-561: https://www.python.org/dev/peps/pep-0561 MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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Add KLUE dataset
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[ "I'm not sure why I got error like below when I auto-generate dummy data \"mrc\" \r\n```\r\ndatasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\nFound duplicate Key: 0\r\nKeys should be unique and deterministic in nature\r\n```", "> I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> \r\n> ```\r\n> datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> Found duplicate Key: 0\r\n> Keys should be unique and deterministic in nature\r\n> ```\r\n\r\nPlease check out the suggestion below. I think it might be a cause.", "> > I'm not sure why I got error like below when I auto-generate dummy data \"mrc\"\r\n> > ```\r\n> > datasets.keyhash.DuplicatedKeysError: FAILURE TO GENERATE DATASET !\r\n> > Found duplicate Key: 0\r\n> > Keys should be unique and deterministic in nature\r\n> > ```\r\n> \r\n> Please check out the suggestion below. I think it might be a cause.\r\n\r\nThe problem was `id_` in mrc when yield was not unique. (I used index in `enumerate(paragraphs)` by mistake)\r\nI fixed it and update all the things", "To fix the CI you can just merge master into your branch and it should be all green hopefully :)", "@lhoestq\r\nThanks for reviewing!\r\n\r\nIt's harder than I thought to add dataset card. ๐Ÿ˜… \r\nI checked and updated your suggestion (script, readme details, dummy data). \r\n\r\ndummy data is little bit larger than expected because `ner` dataset is about 80 lines and `dp` dataset is about 25 lines to avoid 0 examples.\r\n\r\nI'm not sure why some CI keep fails, can u check for this?", "Thanks ! That makes sense for ner and dp\r\n\r\nFor mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?", "> Thanks ! That makes sense for ner and dp\r\n> \r\n> For mrc on the other hand there are still too many examples, maybe you can generate the dummy data for 5 examples for all tasks except ner and dp ?\r\n\r\nYes, I generate default lines in dataset-cli for other dataset except \"dp\" and \"ner\"\r\nI fixed mrc dataset, hope it's fine now :)\r\n\r\nthe reason CI failed was I forgot to merge master into my branch ๐Ÿ˜… " ]
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Add `KLUE (Korean Language Understanding Evaluation)` dataset released recently from [paper](https://arxiv.org/abs/2105.09680), [github](https://github.com/KLUE-benchmark/KLUE) and [webpage](https://klue-benchmark.com/tasks). Please let me know if there's anything missing in the code or README. Thanks!
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Cached dataset not loaded
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[ "It actually seems to happen all the time in above configuration:\r\n* the function `filter_by_duration` correctly loads cached processed dataset\r\n* the function `prepare_dataset` is always reexecuted\r\n\r\nI end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation here.", "Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:\r\n- the old fingerprint of the dataset\r\n- the hash of the function\r\n- the hash of the other parameters passed to `map`\r\n\r\nYou can compute the hash of your function (or any python object) with\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nmy_func = lambda x: x + 1\r\nprint(Hasher.hash(my_func))\r\n```\r\n\r\nIf `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.", "> If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.\r\n\r\nYes Iย think that was the issue.\r\n\r\nFor the hash of the function:\r\n* does it consider just the name or the actual code of the function\r\n* does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)", "> does it consider just the name or the actual code of the function\r\n\r\nIt looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.\r\nBasically the hash is computed using the pickle bytes of your function (computed using `dill` to support most python objects).\r\n\r\n> does it consider variables that are not passed explicitly as parameters to the functions (such as the processor here)\r\n\r\nYes it does thanks to recursive pickling.", "Thanks for these explanations. I'm closing the issue." ]
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## Describe the bug I have a large dataset (common_voice, english) where I use several map and filter functions. Sometimes my cached datasets after specific functions are not loaded. I always use the same arguments, same functions, no seedโ€ฆ ## Steps to reproduce the bug ```python def filter_by_duration(batch): return ( batch["duration"] <= 10 and batch["duration"] >= 1 and len(batch["target_text"]) > 5 ) def prepare_dataset(batch): batch["input_values"] = processor( batch["speech"], sampling_rate=batch["sampling_rate"][0] ).input_values with processor.as_target_processor(): batch["labels"] = processor(batch["target_text"]).input_ids return batch train_dataset = train_dataset.filter( filter_by_duration, remove_columns=["duration"], num_proc=data_args.preprocessing_num_workers, ) # PROBLEM HERE -> below function is reexecuted and cache is not loaded train_dataset = train_dataset.map( prepare_dataset, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) # Later in script set_caching_enabled(False) # apply map on trained model to eval/test sets ``` ## Expected results The cached dataset should always be reloaded. ## Actual results The function is reexecuted. I have access to cached files `cache-xxxxx.arrow`. Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)? ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.5 - PyTorch version (GPU?): 1.8.1+cu102 (True) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No
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Update README.md
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[ "Merging since the CI error is unrelated to this PR and has been fixed on master", "Thank you for taking a look at the CI error - I was a bit confused with that. Thanks!" ]
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Provides description of data instances and dataset features
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AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
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[ "Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.\r\n\r\nIdeally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code" ]
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## Describe the bug Hello, I'm trying to add dataset and contribute, but test keep fail with below cli. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>` ## Steps to reproduce the bug It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add. ` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>` ## Expected results All test passed ## Actual results ``` # check that dataset is not empty self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset)) for split in dataset_builder.info.splits.keys(): # check that loaded datset is not empty self.parent.assertTrue(len(dataset[split]) > 0) # check that we can cast features for each task template > task_templates = dataset_builder.info.task_templates E AttributeError: 'DatasetInfo' object has no attribute 'task_templates' tests/test_dataset_common.py:175: AttributeError ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Darwin-20.4.0-x86_64-i386-64bit - Python version: 3.7.7 - PyTorch version (GPU?): 1.7.0 (False) - Tensorflow version (GPU?): 2.3.0 (False) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No
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Docstring mistake: dataset vs. metric
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[ "> I can provide a PR l8er...\r\n\r\nSee #2425 " ]
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This: https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582 Should better be something like: `a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)` I can provide a PR l8er...
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Add DOI badge to README
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fix #2391 add original answers in kilt-TriviaQA
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[ "LGTM, but I'm not sure what's going on with the Unix tests @lhoestq ", "The CI error is unrelated to this PR, it's been fixed now on master.", "Thanks @PaulLerner !", "> #- [ ] - Hey![image](https://user-images.githubusercontent.com/71971234/121969638-00030e00-cd75-11eb-9512-25d32ac08051.jpeg)@fr[fr_fr**fr~~fr `fr```\nFR\n````~~**_]()", "Oh that was unexpected. I didn't know pokemons were into NLP" ]
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cc @yjernite is it ok like this?
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Add HF_ prefix to env var MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES
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[ "I thought the renaming was suggested only for the env var, and not for the config variable... As you think is better! ;)", "I think it's better if they match, so that users understand directly that they're directly connected", "Well, if you're not concerned about back-compat here, perhaps it could be renamed and shortened too ;)\r\n\r\nI'd suggest one of:\r\n\r\n* `HF_DATASETS_IN_MEMORY_MAX_SIZE`\r\n* `HF_DATASETS_MAX_IN_MEMORY_SIZE`\r\n\r\nthe itention is to:\r\n1. make it consistent with all the other `datasets` env vars which all start with `HF_DATASETS_`, e.g.:\r\n```\r\nHF_DATASETS_CACHE\r\nHF_DATASETS_OFFLINE \r\n```\r\n2. allow to recode in the future to support 1M, 4K, 1T and not just bytes - bytes is not a great choice for this type of variable since it will be at least X Mbytes for most reasonable uses.\r\n\r\nAnd I agree with @albertvillanova that the config variable name shouldn't have the HF prefix - it's preaching to the choir - the user already knows it's a local variable. \r\n\r\nThe only reason we prefix env vars, is because they are used outside of the software.\r\n\r\nBut I do see a good point of you trying to make things consistent too. How about this:\r\n\r\n`config.IN_MEMORY_MAX_SIZE` (or whatever the final env var will be minus `HF_DATASETS_` prefix).\r\n\r\nThis is of course just my opinion.\r\n\r\n", "Thanks for the comment :)\r\nI like both propositions, and I agree this would be better in order to allow support for 1M, 1T etc. \r\nRegarding the prefix of the variable in config.py I don't have a strong opinion. I just added it for consistency with the other variables that default to the env variables like HF_DATASETS_CACHE. However I agree this would be nice to have shorter names so I'm not against removing the prefix either. Since the feature is relatively new, I think we can still allow ourself to rename it", "Awesome, \r\n\r\nLet's use then:\r\n\r\n- `HF_DATASETS_IN_MEMORY_MAX_SIZE` for the env var\r\n- `config.IN_MEMORY_MAX_SIZE` for config.\r\n\r\nand for now bytes will be documented as the only option and down the road add support for K/M/G.\r\n\r\n@albertvillanova, does that sound good to you?", "Great!!! ๐Ÿค— ", "Did I miss a PR with this change?\r\n\r\nI want to make sure to add it to transformers tests to avoid the overheard of rebuilding the datasets.\r\n\r\nThank you!", "@stas00 I'm taking on this now that I have finally finished the collaborative training experiment. Sorry for the delay.", "Yes, of course! Thank you for taking care of it, @albertvillanova ", "Actually, why is this feature on by default? \r\n\r\nUsers are very unlikely to understand what is going on or to know where to look. Should it at the very least emit a warning that this was done w/o asking the user to do so and how to turn it off?\r\n\r\nIMHO, this feature should be enabled explicitly by those who want it and not be On by default. This is an optimization that benefits only select users and is a burden on the rest.\r\n\r\nIn my line of dev/debug work (multiple short runs that have to be very fast) now I have to remember to disable this feature explicitly on every machine I work :(\r\n", "Having the dataset in memory is nice to get the speed but I agree that the lack of caching for dataset in memory is an issue. By default we always had caching on.\r\nHere the issue is that in-memory datasets are still not able to use the cache - we should fix this asap IMO.\r\n\r\nHere is the PR that fixes this: https://github.com/huggingface/datasets/pull/2329", "But why do they have to be datasets in memory in the first place? Why not just have the default that all datasets are normal and are cached which seems to be working solidly. And only enable in memory datasets explicitly if the user chooses to and then it doesn't matter if it's cached on not for the majority of the users who will not make this choice.\r\n\r\nI mean the definition of in-memory-datasets is very arbitrary - why 250MB and not 5GB? It's very likely that the user will want to set this threshold based on their RAM availability. So while doing that they can enable the in-memory-datasets. Unless I'm missing something here.\r\n\r\nThe intention here is that things work well in general out of the box, and further performance optimizations are available to those who know what they are doing.\r\n", "This is just for speed improvements, especially for data exploration/experiments in notebooks. Ideally it shouldn't have changed anything regarding caching behavior in the first place (i.e. have the caching enabled by default).\r\n\r\nThe 250MB limit has also been chosen to not create unexpected high memory usage on small laptops.", "Won't it be more straight-forward to create a performance optimization doc and share all these optimizations there? That way the user will be in the knowing and will be able to get faster speeds if their RAM is large. \r\n\r\nIt is hard for me to tell the average size of a dataset an average user will have, but my gut feeling is that many NLP datasets are larger than 250MB. Please correct me if I'm wrong.\r\n\r\nBut at the same time what you're saying is that once https://github.com/huggingface/datasets/pull/2329 is completed and merged, the in-memory-datasets will be cached too. So if I wait long enough the whole issue will go away altogether, correct?" ]
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As mentioned in https://github.com/huggingface/datasets/pull/2399 the env var should be prefixed by HF_
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Fix head_qa keys
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There were duplicate in the keys, as mentioned in #2382
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.map() function got an unexpected keyword argument 'cache_file_name'
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[ "Hi @cindyxinyiwang,\r\nDid you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:\r\nhttps://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558", "Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?\r\n\r\nIf you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).\r\nIn this case, since there are several datasets in the dict, the `DatasetDict.map` method requires a `cache_file_names` argument (with an 's'), so that you can provide one file name per split.", "I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you!" ]
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## Describe the bug I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'". I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function. Here is the code I use ## Steps to reproduce the bug ```datasets = load_from_disk(dataset_path=my_path) [...] def tokenize_function(examples): return tokenizer(examples[text_column_name]) logger.info("Mapping dataset to tokenized dataset.") tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=True, cache_file_name="my_tokenized_file" ) ``` ## Actual results tokenized_datasets = datasets.map( TypeError: map() got an unexpected keyword argument 'cache_file_name' ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version:1.6.2 - Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10 - Python version:3.8.5 - PyArrow version:3.0.0
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Add guide on using task templates to documentation
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Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.
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Add dataset tags
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[ "Thanks!" ]
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The dataset tags were provided by Peter Clark following the guide.
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Paperswithcode dataset mapping
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[ "messed up my branch, repushing", "live mapping can be found at https://huggingface.co/api/pwc/datasets-mapping and will be kept up to date going forward" ]
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This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards. As discussed: - `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side. - I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though
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Free datasets with cache file in temp dir on exit
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This PR properly cleans up the memory-mapped tables that reference the cache files inside the temp dir. Since the built-in `_finalizer` of `TemporaryDirectory` can't be modified, this PR defines its own `TemporaryDirectory` class that accepts a custom clean-up function. Fixes #2402
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PermissionError on Windows when using temp dir for caching
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Currently, the following code raises a PermissionError on master if working on Windows: ```python # run as a script or call exit() in REPL to initiate the temp dir cleanup from datasets import * d = load_dataset("sst", split="train", keep_in_memory=False) set_caching_enabled(False) d.map(lambda ex: ex) ``` Error stack trace: ``` Traceback (most recent call last): File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc f() File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__ return info.func(*info.args, **(info.kwargs or {})) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup _shutil.rmtree(name) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree return _rmtree_unsafe(path, onerror) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe onerror(os.unlink, fullname, sys.exc_info()) File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe os.unlink(fullname) PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow' ```
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load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset"
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[ "I faced the similar problem. Downgrading datasets to 1.5.0 fixed it.", "Thanks for reporting, I'm looking into it", "I just opened #2438 to fix this :)", "Hi ! This has been fixed in the 1.8.0 release of `datasets`" ]
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## Describe the bug load_dataset('natural_questions') throws ValueError ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset('natural_questions', split='validation[:10]') ``` ## Expected results Call to load_dataset returns data. ## Actual results ``` Using custom data configuration default Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-d55ab8a8cc1c> in <module> ----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets') ~/miniconda3/lib/python3.8/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs) 756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 757 ) --> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory) 759 if save_infos: 760 builder_instance._save_infos() ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory) 735 736 # Create a dataset for each of the given splits --> 737 datasets = utils.map_nested( 738 partial( 739 self._build_single_dataset, ~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types) 193 # Singleton 194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 195 return function(data_struct) 196 197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO) ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory) 762 763 # Build base dataset --> 764 ds = self._as_dataset( 765 split=split, 766 in_memory=in_memory, ~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory) 838 in_memory=in_memory, 839 ) --> 840 return Dataset(**dataset_kwargs) 841 842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]: ~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object" 272 if self.info.features.type != inferred_features.type: --> 273 raise ValueError( 274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format( 275 self.info.features, self.info.features.type, inferred_features, inferred_features.type ValueError: External features info don't match the dataset: Got {'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>> but expected something like {'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}} with type struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2 - Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10 - Python version: 3.8.3 - PyTorch version (GPU?): 1.6.0 (False) - Tensorflow version (GPU?): not installed (NA) - Using GPU in script?: No - Using distributed or parallel set-up in script?: No
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Concatenate several datasets with removed columns is not working.
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[ "Hi,\r\n\r\ndid you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?\r\n\r\nThis code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error).", "@mariosasko you are right I was still on `1.5.0`. " ]
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## Describe the bug You can't concatenate datasets when you removed columns before. ## Steps to reproduce the bug ```python from datasets import load_dataset, concatenate_datasets wikiann= load_dataset("wikiann","en") wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"]) wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"]) assert wikiann["train"].features.type == wikiann["test"].features.type concate = concatenate_datasets([wikiann["train"],wikiann["test"]]) ``` ## Expected results Merged dataset ## Actual results ```python ValueError: External features info don't match the dataset: Got {'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>> but expected something like {'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} with type struct<ner_tags: list<item: int64>, tokens: list<item: string>> ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: ~1.6.2~ 1.5.0 - Platform: macos - Python version: 3.8.5 - PyArrow version: 3.0.0
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Add env variable for MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES
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[ "Thank you for clarifying the precedence, @albertvillanova \r\n\r\nIsn't it typically the case where env vars have the highest precedence? \r\n\r\nIn my understanding the point of env vars is to be able to override software w/o needing to touch the code. \r\n\r\nPlease correct me if this is not so in the general case.", "Hi @stas00, \r\n\r\nWell, I'm not an expert on this topic, but the precedence hierarchy I have normally found is from higher to lower:\r\n- command line parameters\r\n- env vars\r\n- config files\r\nSo yes, normally env vars have precedence over configuration files.\r\n\r\nAnyway, for Datasets, there are no configuration files. The _in-memory_ config is set from default values or env vars (which have precedence over default values). But this is done at import.\r\n\r\nHowever, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).", "In my limited experience env vars are typically above cmd line args, so that one can override scripts with cmd lines using env vars, but usually one then uses env vars inside cmd line args, so it's loud and clear.\r\n\r\nFor example specifying a specific gpu number on a command line will depend on `CUDA_VISIBLE_DEVICES` so gpu0 will be different if someone sets `CUDA_VISIBLE_DEVICES=2,3` vs `CUDA_VISIBLE_DEVICES=0,1`.\r\n\r\n> However, once the library is imported, the user can modify the in-memory config, and this will have precedence over the rest of mechanisms (which take place only at import).\r\n\r\nAnd this is exactly the problem we are trying to solve here. For a good reason HF examples don't want to use `keep_in_memory=False`, and they may choose to now set `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` and which means we again can't override it via env var.\r\n\r\n", "oops, sorry, didn't think earlier - do we need to prefix this with `HF_DATASETS` or `HF_` like all the other env vars? or is it long enough already to be unique - it's just not telling the user in the config file what projet this variable is for...", "You're right, I just opened https://github.com/huggingface/datasets/pull/2409" ]
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Add env variable for `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES`. This will allow to turn off default behavior: loading in memory (and not caching) small datasets. Fix #2387.
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News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
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I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi. ``` train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]') val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]') # filtering out examples that are not ar-en translations but ar-hi val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True) ``` * I'm fairly new to using datasets so I might be doing something wrong
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Fix number of classes in indic_glue sna.bn dataset
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[ "@lhoestq there are many things missing in the README.md file, but this correction is right despite not passing the validation tests...", "Yes indeed. We run the validation in all modified readme because we think that it is the time when contributors are the most likely to fix a dataset card - or it will never be done" ]
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As read in the [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.445.pdf), Table 11.
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strange datasets from OSCAR corpus
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[ "Hi ! Thanks for reporting\r\ncc @pjox is this an issue from the data ?\r\n\r\nAnyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere ", "Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https://arxiv.org/pdf/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https://github.com/oscar-corpus/oscar-website/issues) as well so that we can track it?" ]
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![image](https://user-images.githubusercontent.com/50871412/119260850-4f876b80-bc07-11eb-8894-124302600643.png) ![image](https://user-images.githubusercontent.com/50871412/119260875-675eef80-bc07-11eb-9da4-ee27567054ac.png) From the [official site ](https://oscar-corpus.com/), the Yue Chinese dataset should have 2.2KB data. 7 training instances is obviously not a right number. As I can read Yue Chinese, I call tell the last instance is definitely not something that would appear on Common Crawl. And even if you don't read Yue Chinese, you can tell the first six instance are problematic. (It is embarrassing, as the 7 training instances look exactly like something from a pornographic novel or flitting messages in a chat of a dating app) It might not be the problem of the huggingface/datasets implementation, because when I tried to download the dataset from the official site, I found out that the zip file is corrupted. I will try to inform the host of OSCAR corpus later. Awy a remake about this dataset in huggingface/datasets is needed, perhaps after the host of the dataset fixes the issue. > Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these problems with OSCAR were already discussed [here](https://arxiv.org/pdf/2103.12028.pdf) but we are working on explicitly documenting the problems by language on our website. In fact, could please you open an issue on [our repo](https://github.com/oscar-corpus/oscar-website/issues) as well so that we can track it? Thanks a lot, the new post is here: https://github.com/oscar-corpus/oscar-website/issues/11
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`pretty_name` for dataset in YAML tags
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[ "Initially I removed the ` - ` since there was only one `pretty_name` per config but turns out it was breaking here in `from_yaml_string`https://github.com/huggingface/datasets/blob/74751e3f98c74d22c48c6beb1fab0c13b5dfd075/src/datasets/utils/metadata.py#L197 in `/utils/metadata.py`", "@lhoestq I guess this will also need some validation?", "Looks like the parser doesn't allow things like\r\n```\r\npretty_name:\r\n config_name1: My awesome config number 1\r\n config_name2: My amazing config number 2\r\n```\r\ntherefore you had to use `-` and consider them as a list.\r\n\r\nI would be nice to add support for this case in the validator.\r\n\r\nThere's one thing though: the DatasetMetadata object currently corresponds to the yaml tags that are flattened: the config names are just ignored, and the lists are concatenated.\r\n\r\nTherefore I think we would potentially need to instantiate several `DatasetMetadata` objects: one per config. Otherwise we would end up with a list of several pretty_name while we actually need at most 1 per config.\r\n\r\nWhat do you think @gchhablani ?", "I was thinking of returning `metada_dict` (on line 193) whenever `load_dataset_card` is called (we can pass an extra parameter to `from_readme` or `from_yaml_string` for that to happen).\r\n\r\nOne just needs config_name as key for the dictionary inside `pretty_name` dict and for single config, there would be only one value to print. We can do this for other fields as well like `size_categories`, `languages` etc. This will obviate the need to flatten the YAML tags so that don't have to instantiate several DatasetMetadata objects. What do you guys think @lhoestq @gchhablani? \r\n\r\nUpdate: I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my `metadata_dict` before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.", "Hi @lhoestq @bhavitvyamalik \r\n\r\n@bhavitvyamalik, I'm not sure I understand your approach, can you please elaborate? The `metadata_dict` is flattened before instantiating the object, do you want to remove that? Still confused.\r\n\r\nFew things come to my mind after going through this PR. They might not be entirely relevant to the current task, but I'm just trying to think about possible cases and discuss them here.\r\n\r\n1. Instead of instantiating a new `DatasetMetadata` for each config with flattened tags, why can't we make it more flexible and validate only non-dict items? However, in that case, the types wouldn't be as strict for the class attributes. It would also not work for cases that are like `Dict[str,List[Dict[str,str]]`, but I guess that won't be needed anyway in the foreseeable future?\r\n\r\n Ideally, it would be something like - Check the metadata tag type (root), do a DFS, and find the non-dict objects (leaves), and validate them. Is this an overkill to handle the problem?\r\n2. For single-config datasets, there can be slightly different validation for `pretty_names`, than for multi-config. The user shouldn't need to provide a config name for single-config datasets, wdyt @bhavitvyamalik @lhoestq? Either way, for multi-config, the validation can use the dictionary keys in the path to that leaf node to verify `pretty_names: ... (config)` as well. This will check whether the config name is same as the key (might be unnecessary but prevents typos, so less work for the reviewer(s)). For future, however, it might be beneficial to have something like this.\r\n3. Should we have a default config name for single-config datasets? People use any string they feel like. I've seen `plain_text`, `default` and the dataset name. I've used `image` for a few datasets myself AFAIR. For smarter validation (again, a future case ;-;), it'd be easier for us to have a simple rule for naming configs in single-config datasets. Wdyt @lhoestq?", "Btw, `pretty_names` can also be used to handle this during validation :P \r\n\r\n```\r\n-# Dataset Card for [Dataset Name]\r\n+# Dataset Card for Allegro Reviews\r\n```\r\n\r\nThis is where `DatasetMetadata` and `ReadMe` should be combined. But there are very few overlaps, I guess.\r\n\r\n\n@bhavitvyamalik @lhoestq What about adding a pretty name across all configs, and then config-specific names?\n\nLike\n\n```yaml\npretty_names:\n all_configs: X (dataset_name)\n config_1: X1 (config_1_name)\n config_2: X2 (config_2_name)\n```\nThen, using the `metadata_dict`, the ReadMe header can be validated against `X`.\n\nSorry if I'm throwing too many ideas at once.", "@bhavitvyamalik\r\n\r\nNow, I think I better understand what you're saying. So you want to skip validation for the unflattened metadata and just return it? And let the validation run for the flattened version?", "Exactly! Validation is important but once the YAML tags are validated I feel we shouldn't do that again while calling `load_dataset_card`. +1 for default config name for single-config datasets.", "@bhavitvyamalik\r\nActually, I made the `ReadMe` validation similar to `DatasetMetadata` validation and the class was validating the metadata during the creation. \r\n\r\nMaybe we need to have a separate validation method instead of having it in `__post_init__`? Wdyt @lhoestq? \r\n\r\nI'm sensing too many things to look into. It'd be great to discuss these sometime. \r\n\r\nBut if this PR is urgent then @bhavitvyamalik's logic seems good to me. It doesn't need major modifications in validation.", "> Maybe we need to have a separate validation method instead of having it in __post_init__? Wdyt @lhoestq?\r\n\r\nWe can definitely have a `is_valid()` method instead of doing it in the post init.\r\n\r\n> What about adding a pretty name across all configs, and then config-specific names?\r\n\r\nLet's keep things simple to starts with. If we can allow both single-config and multi-config cases it would already be great :)\r\n\r\nfor single-config:\r\n```yaml\r\npretty_name: Allegro Reviews\r\n```\r\n\r\nfor multi-config:\r\n```yaml\r\npretty_name:\r\n mrpc: Microsoft Research Paraphrase Corpus (MRPC)\r\n sst2: Stanford Sentiment Treebank\r\n ...\r\n```\r\n\r\nTo support the multi-config case I see two options:\r\n1. Don't allow DatasetMetadata to have dictionaries but instead have separate DatasetMetadata objects per config\r\n2. allow DatasetMetadata to have dictionaries. It implies to remove the flattening step. Then we could get metadata for a specific config this way for example:\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\nglue_dataset_card = load_dataset_card(\"glue\")\r\nprint(glue_dataset_card.metadata)\r\n# DatasetMetatada object with dictionaries since there are many configs\r\nprint(glue_dataset_card.metadata.get_metadata_for_config(\"mrpc\"))\r\n# DatasetMetatada object with no dictionaries since there are only the mrpc tags\r\n```\r\n\r\nLet me know what you think or if you have other ideas.", "I think Option 2 is better.\n\nJust to clarify, will `get_metadata_for_config` also return common details, like language, say?", "> Just to clarify, will get_metadata_for_config also return common details, like language, say?\r\n\r\nYes that would be more convenient IMO. For example a dataset card like this\r\n```yaml\r\nlanguages:\r\n- en\r\npretty_name:\r\n config1: Pretty Name for Config 1\r\n config3: Pretty Name for Config 2\r\n```\r\n\r\nthen `metadat.get_metadata_for_config(\"config1\")` would return something like\r\n```python\r\nDatasetMetadata(languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\")\r\n```", "@lhoestq, should we do this post-processing in `load_dataset_card` by returning unflattened dictionary from `DatasetMetadata` or send this from `DatasetMetadata`? Since there isn't much to do I feel once we have the unflattened dictionary", "Not sure I understand the difference @bhavitvyamalik , could you elaborate please ?", "I was talking about this unflattened dictionary:\r\n\r\n> I was thinking of returning the whole dictionary before flattening so that user can access whatever they want with specific configs. Let's say [this](https://pastebin.com/eJ84314f) is my metadata_dict before flattening (the loaded YAML string), so instead of validating it and then returning the items individually we can return it just after loading the YAML string.\r\n\r\nPost-processing meant extracting config-specific fields from this dictionary and then return this `languages=[\"en\"], pretty_name=\"Pretty Name for Config 1\"`", "I still don't understand what you mean by \"returning unflattened dictionary from DatasetMetadata or send this from DatasetMetadata\", sorry. Can you give an example or rephrase this ?\r\n\r\nIMO load_dataset_card can return a dataset card object with a metadata field. If the metadata isn't flat (i.e. it has several configs), you can get the flat metadata of 1 specific config with `get_metadata_for_config`. But of course if you have better ideas or suggestions, we can discuss this", "@lhoestq, I think he is saying whatever `get_metadata_for_config` is doing can be done in `load_dataset_card` by taking the unflattened `metadata_dict` as input.\r\n\r\n@bhavitvyamalik, I think it'd be better to have this \"post-processing\" in `DatasetMetadata` instead of `load_dataset_card`, as @lhoestq has shown. I'll quickly get on that.\r\n\r\n---\r\nThree things that are to be changed in `DatasetMetadata`:\r\n1. Allow Non-flat elements and their validation.\r\n2. Create a method to get metadata by config name.\r\n3. Create a `validate()` method.\r\n\r\nOnce that is done, this PR can be updated and reviewed, wdys?", "Thanks @gchhablani for the help ! Now that https://github.com/huggingface/datasets/pull/2436 is merged you can remove the `-` in the pretty_name @bhavitvyamalik :)" ]
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I'm updating `pretty_name` for datasets in YAML tags as discussed with @lhoestq. Here are the first 10, please let me know if they're looking good. If dataset has 1 config, I've added `pretty_name` as `config_name: full_name_of_dataset` as config names were `plain_text`, `default`, `squad` etc (not so important in this case) whereas when dataset has >1 configs, I've added `config_name: full_name_of_dataset+config_name` so as to let user know about the `config` here.
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Update text classification template labels in DatasetInfo __post_init__
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[ "If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n```python\r\ndset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\ndset_tc = dset.prepare_for_task(\"text-classification\")\r\ndset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n```\r\nI see 2 options:\r\n1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid) ", "> If I'm not mistaken, one way to fix this would be to drop the task templates when copying the info by inserting `dataset.info.task_templates = None` before the `Dataset.cast` call in `Dataset.prepare_for_task`. Moreover, we should do this change independently of the KeyError being raised because currently the following is possible:\r\n> \r\n> ```python\r\n> dset = load_dataset(\"some_dataset\") # let's say 'some_dataset' supports text classification and question answering\r\n> dset_tc = dset.prepare_for_task(\"text-classification\")\r\n> dset_tc.preprare_for_task(\"question-answering\") # this should raise an error because the schema is no longer valid for this task; currently this fails on 'rename_columns'\r\n> ```\r\n> \r\n> I see 2 options:\r\n> \r\n> 1. to drop the task templates after the first `Dataset.prepare_for_task` call\r\n> 2. to save only the tasks compatible with the new schema after Dataset.prepare_for_task` (but then we have to update the column names of the compatible tasks to make sure the column mapping is still valid)\r\n\r\nthanks for the great idea @mariosasko and for spotting the problem with sequential task preparation! i am in favour of your option (1) since it is simple and saves us from having to keep track of the column mappings across multiple steps. \r\n\r\ni've implemented the change and refactored the tests to account for the new approach (including a new test that the templates are flushed after we call `prepare_for_task`). perhaps the slightly inelegant aspect here is that if we want to allow the user to set `labels` in the `TextClassification` template, then we have two places (`DatasetInfo.__post_init__` and `TextClassification.__post_init__`) where we need to update `label_schema`. \r\n\r\non the other hand, dropping `labels` from the `TextClassification` signature would have the nice effect that users only have to think about column names when defining their tasks.\r\n\r\nin any case, i think it would be a good idea to merge #2376 soon as the current PR is touching a lot of the same places in the codebase ๐Ÿ˜„ \r\n", "cc @SBrandeis who might also be interested in this feature :)", "Tests are failing only because the `emotion` dataset card doesn't pass our dataset card validator (tags are missing), you can ignore this since it's unrelated to this PR.", "@lhoestq @SBrandeis i've fixed the tests and think this is now in a good state for another review :)", "Maybe @SBrandeis you can also take a look to make sure you're fine with it ?" ]
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This PR implements the idea discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`. The main reason for doing so is so avoid duplicating the label definitions in both `DatasetInfo.features` and `DatasetInfo.task_templates`. To avoid storing state in `DatasetInfo.__post_init__`, the current implementation flushes `DatasetInfo.task_templates` before the features are cast in `Dataset.prepare_for_task` (thanks to @mariosasko for this idea!). Here is an example of the current workflow: ```python ds1 = load_dataset("./datasets/emotion/") # cast features and flush templates ds2 = ds1.prepare_for_task("text-classification") assert ds2.info.task_templates is None ``` Note that if users want to pass a `TextClassification` template to `prepare_for_task`, we require them to set `TextClassification.labels` to match the dataset's features corresponding to `label_column`: ```python ds1 = load_dataset("./datasets/emotion/") # TextClassification.labels is None by default => invalid template task = TextClassification(text_column="text", label_column="label") # Raises ValueError ds1.prepare_for_task(task) # Specifying the labels => valid template task = TextClassification(text_column="text", label_column="label", labels=['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']) ds1.prepare_for_task(task) ``` This PR also adds: * New tests + fixed some old tests that weren't testing `assertRaises` properly * A decorator to share docstrings across common functions. This allows us to document `DatasetDict.prepare_for_task` and `Dataset.prepare_for_task` in one place. * Fixes to avoid side-effects from in-place replacements of `DatasetInfo.task_templates` in `DatasetInfo.__post_init__`. Thanks to @lhoestq for figuring this out! * Removal of `FeaturesWithLazyClassLabel` since we now create a new instance of `TextClassification` in `DatasetInfo.__post_init__` and avoid the side-effects first pointed out by @mariosasko ### PR Description from original WIP Hi @yjernite and @lhoestq, here's a first stab at the suggestion discussed in #2389 to update the `labels` of the `TextClassification` template in the `DatasetInfo.__post_init__`. One problem I've spotted is that my current implementation introduces state into the `__post_init__`: * When we call `load_dataset`, `DatasetInfo.features` are the "raw" features without any casting so we can access the column names by the `label_column` specified in `TextClassification` * When we call `Dataset.prepare_for_task` we run into a problem because the `DatasetInfo.features` are first cast into the new schema which triggers a `KeyError` when we update the infos [here](https://github.com/huggingface/datasets/blob/8b2a78520828e0cc13c14a31f413a5395ef25110/src/datasets/arrow_dataset.py#L1959). Here's an explicit example of what I mean with the stack trace appended below: ```python from datasets import load_dataset # this works ds = load_dataset("emotion") # we can verify the task template is correctly set ds["train"].info.task_templates # returns [TextClassification(labels=('sadness', 'joy', 'love', 'anger', 'fear', 'surprise'), text_column='text', label_column='label')] # but this fails because the _post_init__ is looking for the original column names ds.prepare_for_task("text-classification") ``` ``` --------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-4-54a43019b319> in <module> ----> 1 ds.prepare_for_task("text-classification") ~/git/datasets/src/datasets/dataset_dict.py in prepare_for_task(self, task) 807 """ 808 self._check_values_type() --> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()}) ~/git/datasets/src/datasets/dataset_dict.py in <dictcomp>(.0) 807 """ 808 self._check_values_type() --> 809 return DatasetDict({k: dataset.prepare_for_task(task=task) for k, dataset in self.items()}) ~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task) 1421 dataset = self.remove_columns(columns_to_drop) 1422 dataset = dataset.rename_columns(column_mapping) -> 1423 dataset = dataset.cast(features=template.features) 1424 return dataset 1425 ~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc) 970 format = self.format 971 dataset = self.with_format("arrow") --> 972 dataset = dataset.map( 973 lambda t: t.cast(schema), 974 batched=True, ~/git/datasets/src/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) 1583 1584 if num_proc is None or num_proc == 1: -> 1585 return self._map_single( 1586 function=function, 1587 with_indices=with_indices, ~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 173 } 174 # apply actual function --> 175 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 176 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 177 # re-apply format to the output ~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs) 338 # Call actual function 339 --> 340 out = func(self, *args, **kwargs) 341 342 # Update fingerprint of in-place transforms + update in-place history of transforms ~/git/datasets/src/datasets/arrow_dataset.py in _map_single(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, new_fingerprint, rank, offset) 1959 if update_data: 1960 # Create new Dataset from buffer or file -> 1961 info = self.info.copy() 1962 info.features = writer._features 1963 if buf_writer is None: ~/git/datasets/src/datasets/info.py in copy(self) 274 275 def copy(self) -> "DatasetInfo": --> 276 return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) 277 278 ~/git/datasets/src/datasets/info.py in __init__(self, description, citation, homepage, license, features, post_processed, supervised_keys, task_templates, builder_name, config_name, version, splits, download_checksums, download_size, post_processing_size, dataset_size, size_in_bytes) ~/git/datasets/src/datasets/info.py in __post_init__(self) 174 # The reason is that Dataset.prepare_for_task calls Dataset.cast which converts the 175 # DatasetInfo.features to the new schema and thus template.label_column is no longer a valid key --> 176 object.__setattr__(template, "labels", tuple(self.features[template.label_column].names)) 177 template.label_schema["labels"] = ClassLabel(names=template.labels) 178 self.task_templates[idx] = template KeyError: 'label' ``` What do you think? I did this a bit quickly, so maybe I'm overlooking something obvious :) One thing would be to only update the labels of the task template on load, but this seems a bit hacky IMO
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Missing original answers in kilt-TriviaQA
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[ "That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) ", "I can open a PR but there is 2 details to fix:\r\n- the name for the corresponding key (e.g. `original_answer`)\r\n- how to implement it: Iโ€™m not sure what happens when you map `lambda x: {'input': ...}`ย as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['original_answer']`) I implemented it with a regular function (not lambda), see below\r\n\r\n```py\r\ndef add_original_answer(x, trivia_qa, triviaqa_map):\r\n i = triviaqa_map[x['id']]\r\n x['output']['original_answer'] = trivia_qa['validation'][i]['answer']['value']\r\n return x\r\n```" ]
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I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets ## Describe the bug The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative answer which are accepted for the question. However it'd be nice to know the original answer to the question (the only fields in `output` are `'answer', 'meta', 'provenance'`) ## How to fix It can be fixed by retrieving the original answer from the original TriviaQA (e.g. `trivia_qa['train'][0]['answer']['value']`), perhaps at the same place as here where one retrieves the questions https://github.com/huggingface/datasets/blob/master/datasets/kilt_tasks/README.md#loading-the-kilt-knowledge-source-and-task-data cc @yjernite who previously answered to an issue about KILT and TriviaQA :)
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Add check for task templates on dataset load
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[ "LGTM now, thank you =)" ]
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This PR adds a check that the features of a dataset match the schema of each compatible task template.
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Insert task templates for text classification
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[ "Update: found a few datasets that slipped through the net. Adding them shortly!", "You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?", "> You might have thought about this already, but would it make sense to use the `datasets.features.ClassLabel` values when possible instead of declaring the list once for the `feature` and once for the `template`?\r\n\r\nhi @yjernite, these code insertions are auto-generated so could certainly be improved :) \r\n\r\njust so i understand, your idea is that instead of doing something like\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n return datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n task_templates=[\r\n TextClassification(\r\n labels=(\"Business\", \"Sci/Tech\", \"Sports\", \"World\"),\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ],\r\n )\r\n```\r\n\r\nwe could do the following:\r\n\r\n```python\r\nclass AGNews(datasets.GeneratorBasedBuilder):\r\n \"\"\"AG News topic classification dataset.\"\"\"\r\n\r\n def _info(self):\r\n info = datasets.DatasetInfo(\r\n description=_DESCRIPTION,\r\n features=datasets.Features(\r\n {\r\n \"text\": datasets.Value(\"string\"),\r\n \"label\": datasets.features.ClassLabel(\r\n names=[\"World\", \"Sports\", \"Business\", \"Sci/Tech\"]\r\n ),\r\n }\r\n ),\r\n homepage=\"http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html\",\r\n citation=_CITATION,\r\n )\r\n\r\n info.task_templates = [\r\n TextClassification(\r\n labels=info.features.names,\r\n text_column=\"text\",\r\n label_column=\"label\",\r\n )\r\n ]\r\n return info\r\n```\r\n\r\n", "Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?", "> Or we could simply not specify the labels and update the template in the DatasetInfo postinit to give it the labels ?\r\n\r\nOh yes, that would be great! It does mean enforcing that people use the right feature type (sometimes people still use a `string` feature still because they don't want to enumerate the classes, but I guess you've been catching most of those in reviews @lhoestq )\r\n\r\nThere might be reasons where there should be a legitimate difference, but I can't really think of nay right now, and we can always duplicate the feature", "Let's ignore the CI fails since they are unrelated to your changes. They're about dataset cards issues" ]
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This PR inserts text-classification templates for datasets with the following properties: * Only one config * At most two features of `(Value, ClassLabel)` type Note that this misses datasets like `sentiment140` which only has `Value` type features - these will be handled in a separate PR
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Incorrect URLs for some datasets
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## Describe the bug It seems that the URLs for the following datasets are invalid: - [ ] `bn_hate_speech` has been renamed: https://github.com/rezacsedu/Bengali-Hate-Speech-Dataset/commit/c67ecfc4184911e12814f6b36901f9828df8a63a - [ ] `covid_tweets_japanese` has been renamed: http://www.db.info.gifu-u.ac.jp/covid-19-twitter-dataset/ As a result we can no longer load these datasets using `load_dataset`. The simple fix is to rename the URL in the dataset script - will do this asap. ## Steps to reproduce the bug ```python from datasets import load_dataset # pick one of the datasets from the list above ds = load_dataset("bn_hate_speech") ``` ## Expected results Dataset loads without error. ## Actual results ``` Downloading: 3.36kB [00:00, 1.07MB/s] Downloading: 2.03kB [00:00, 678kB/s] Using custom data configuration default Downloading and preparing dataset bn_hate_speech/default (download: 951.48 KiB, generated: 949.84 KiB, post-processed: Unknown size, total: 1.86 MiB) to /Users/lewtun/.cache/huggingface/datasets/bn_hate_speech/default/0.0.0/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c... Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/load.py", line 744, in load_dataset builder_instance.download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 574, in download_and_prepare self._download_and_prepare( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/builder.py", line 630, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/lewtun/.cache/huggingface/modules/datasets_modules/datasets/bn_hate_speech/a2dc726e511a2177523301bcad196af05d4d8a2cff30d2769ba8aacc1f5fdb5c/bn_hate_speech.py", line 76, in _split_generators train_path = dl_manager.download_and_extract(_URL) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 287, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 195, in download downloaded_path_or_paths = map_nested( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 195, in map_nested return function(data_struct) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/download_manager.py", line 218, in _download return cached_path(url_or_filename, download_config=download_config) File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 281, in cached_path output_path = get_from_cache( File "/Users/lewtun/miniconda3/envs/hf-hub_eval/lib/python3.8/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache raise FileNotFoundError("Couldn't find file at {}".format(url)) FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/rezacsedu/Bengali-Hate-Speech-Dataset/main/Bengali_%20Hate_Speech_Dataset_Subset.csv ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.2.dev0 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.8.8 - PyArrow version: 3.0.0
https://api.github.com/repos/huggingface/datasets/issues/2388/timeline
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datasets 1.6 ignores cache
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[ "Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:\r\n\r\n> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset(\"sst\", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them)\r\n\r\n", "Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on making the caching work for datasets in RAM.\r\n\r\nUntil then, I'd recommend passing `keep_in_memory=False` to the calls to `load_dataset` like here:\r\n\r\nhttps://github.com/huggingface/transformers/blob/223943872e8c9c3fc11db3c6e93da07f5177423f/examples/pytorch/language-modeling/run_clm.py#L233\r\n\r\nThis way you say explicitly that you want your dataset to stay on the disk, and it will be able to recover previously computed results from the cache.", "gotcha! thanks Quentin", "OK, It doesn't look like we can use the proposed workaround - see https://github.com/huggingface/transformers/issues/11801\r\n\r\nCould you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often and needs a quick startup time. Manual editing of standard scripts is not a practical option when one uses examples.\r\n\r\nThis could also be a problem for tests, which will be slower because of lack of cache, albeit usually we use tiny datasets there. I think we want caching for tests.\r\n\r\nThank you.", "Hi @stas00, \r\n\r\nYou are right: an env variable is needed to turn off this behavior. I am adding it.\r\n\r\nFor the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`\r\n\r\nYou can find this info in the docs:\r\n- in the docstring of the parameter `keep_in_memory` of the function [`load_datasets`](https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset):\r\n- in a Note in the docs about [Loading a Dataset](https://huggingface.co/docs/datasets/loading_datasets.html#from-the-huggingface-hub)\r\n\r\n> The default in ๐Ÿค—Datasets is to memory-map the dataset on drive if its size is larger than datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES (default 250 MiB); otherwise, the dataset is copied in-memory. This behavior can be disabled by setting datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None, and in this case the dataset is not loaded in memory.", "Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.\r\n\r\nMay be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in_memory=False` or `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0`?", "@stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.\r\n\r\nTell me if this is logical/convenient, or I should change it.", "In my PR, to turn off current default bahavior, you should set env variable to one of: `{\"\", \"OFF\", \"NO\", \"FALSE\"}`.\r\n\r\nFor example:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=\r\n```", "IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.\r\n\r\nAlso \"SIZE_IN_BYTES\" that can take one of `{\"\", \"OFF\", \"NO\", \"FALSE\"}` is also quite odd.\r\n\r\nI think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` that can accept any numerical value to match the name of the variable, requires minimal logic and is very straightforward. \r\n\r\nSo if you could adjust this logic - then `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` is all that's needed to not do in-memory datasets.\r\n\r\nDoes it make sense?", "I understand your point @stas00, as I am not very convinced with current implementation.\r\n\r\nMy concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this case.", "That's a good question, and again the normal bytes can be used for that:\r\n```\r\nMAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)\r\n```\r\nSince it's unlikely that anybody will have more than 1TB RAM.\r\n\r\nIt's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in this context.\r\n\r\nNot sure when it was added and if there are back-compat issues here, but perhaps it could be renamed `MAX_IN_MEMORY_DATASET_SIZE` and support 1M, 1G, 1T, etc. \r\n\r\nBut scientific notation is quite intuitive too, as each 000 zeros is the next M, G, T multiplier. Minus the discrepancy of 1024 vs 1000, which adds up. And it is easy to write down `1e12`, as compared to `1099511627776` (2**40). (`1.1e12` is more exact).\r\n", "Great! Thanks, @stas00.\r\n\r\nI am implementing your suggestion to turn off default value when set to `0`.\r\n\r\nFor the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation.", "Awesome! Thank you, @albertvillanova!!!\r\n\r\n" ]
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Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612 Quoting @VictorSanh: > > I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335): > > > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-c6aefe81ca4e5152.arrow'}], 'validation': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/3a8df094c671b4cb63ed0b41f40fb3bd855e9ce2e3765e5df50abcdfb5ec144b/cache-97cf4c813e6469c6.arrow'}]}` > > while the same command with the latest version of datasets (actually starting at `1.6.0`) gives: > > `{'train': [], 'validation': []}` > I also confirm that downgrading to `datasets==1.5.0` makes things fast again - i.e. cache is used. to reproduce: ``` USE_TF=0 python examples/pytorch/language-modeling/run_clm.py \ --model_name_or_path gpt2 \ --dataset_name "stas/openwebtext-10k" \ --output_dir output_dir \ --overwrite_output_dir \ --do_train \ --do_eval \ --max_train_samples 1000 \ --max_eval_samples 200 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --num_train_epochs 1 \ --warmup_steps 8 \ --block_size 64 \ --fp16 \ --report_to none ``` the first time the startup is slow and some 5 tqdm bars. It shouldn't do it on consequent runs. but with `datasets>1.5.0` it rebuilds on every run. @lhoestq
https://api.github.com/repos/huggingface/datasets/issues/2387/timeline
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Accessing Arrow dataset cache_files
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[ "Thanks @bhavitvyamalik for referencing the workaround. Setting `keep_in_memory=False` is working." ]
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## Describe the bug In datasets 1.5.0 the following code snippet would have printed the cache_files: ``` train_data = load_dataset('conll2003', split='train', cache_dir='data') print(train_data.cache_files[0]['filename']) ``` However, in the newest release (1.6.1), it prints an empty list. I also tried loading the dataset with `keep_in_memory=True` argument but still `cache_files` is empty. Was wondering if this is a bug or I need to pass additional arguments so I can access the cache_files.
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update citations
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To update citations for [Offenseval_dravidiain](https://huggingface.co/datasets/offenseval_dravidian)
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Add args description to DatasetInfo
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[ "Thanks for the suggestions! I've included them and made a few minor tweaks along the way", "Please merge master into this branch to fix the CI, I just fixed metadata validation tests." ]
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Closes #2354 I am not sure what `post_processed` and `post_processing_size` correspond to, so have left them empty for now. I also took a guess at some of the other fields like `dataset_size` vs `size_in_bytes`, so might have misunderstood their meaning.
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Improve example in rounding docs
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Improves the example in the rounding subsection of the Split API docs. With this change, it should more clear what's the difference between the `closest` and the `pct1_dropremainder` rounding.
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DuplicatedKeysError: FAILURE TO GENERATE DATASET ! load_dataset('head_qa', 'en')
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Hello everyone, I try to use head_qa dataset in [https://huggingface.co/datasets/viewer/?dataset=head_qa&config=en](url) ``` !pip install datasets from datasets import load_dataset dataset = load_dataset( 'head_qa', 'en') ``` When I write above load_dataset(.), it throws the following: ``` DuplicatedKeysError Traceback (most recent call last) <ipython-input-6-ea87002d32f0> in <module>() 2 from datasets import load_dataset 3 dataset = load_dataset( ----> 4 'head_qa', 'en') 5 frames /usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self) 347 for hash, key in self.hkey_record: 348 if hash in tmp_record: --> 349 raise DuplicatedKeysError(key) 350 else: 351 tmp_record.add(hash) DuplicatedKeysError: FAILURE TO GENERATE DATASET ! Found duplicate Key: 1 Keys should be unique and deterministic in nature ``` How can I fix the error? Thanks
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add dataset card title
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few of them were missed by me earlier which I've added now
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maintain YAML structure reading from README
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How YAML used be loaded earlier in the string (structure of YAML was affected because of this and YAML for datasets with multiple configs was not being loaded correctly): ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ``` How YAML is loaded in string now: ``` annotations_creators: labeled_final: - expert-generated labeled_swap: - expert-generated unlabeled_final: - machine-generated language_creators: - machine-generated languages: - en licenses: - other multilinguality: - monolingual size_categories: labeled_final: - 10K<n<100K labeled_swap: - 10K<n<100K unlabeled_final: - 100K<n<1M source_datasets: - original task_categories: - text-classification - text-scoring task_ids: - semantic-similarity-classification - semantic-similarity-scoring - text-scoring-other-paraphrase-identification ```
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Disallow duplicate keys in yaml tags
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Make sure that there's no duplidate keys in yaml tags. I added the check in the yaml tree constructor's method, so that the verification is done at every level in the yaml structure. cc @julien-c
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Add missing dataset_infos.json files
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Some of the datasets in `datasets` are missing a `dataset_infos.json` file, e.g. ``` [PosixPath('datasets/chr_en/chr_en.py'), PosixPath('datasets/chr_en/README.md')] [PosixPath('datasets/telugu_books/README.md'), PosixPath('datasets/telugu_books/telugu_books.py')] [PosixPath('datasets/reclor/README.md'), PosixPath('datasets/reclor/reclor.py')] [PosixPath('datasets/json/README.md')] [PosixPath('datasets/csv/README.md')] [PosixPath('datasets/wikihow/wikihow.py'), PosixPath('datasets/wikihow/README.md')] [PosixPath('datasets/c4/c4.py'), PosixPath('datasets/c4/README.md')] [PosixPath('datasets/text/README.md')] [PosixPath('datasets/lm1b/README.md'), PosixPath('datasets/lm1b/lm1b.py')] [PosixPath('datasets/pandas/README.md')] ``` For `json`, `text`, csv`, and `pandas` this is expected, but not for the others which should be fixed
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ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather
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[ "Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.\r\nMore info at #1933 " ]
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## Describe the bug A clear and concise description of what the bug is. ## Steps to reproduce the bug ```python from datasets import load_dataset from pyarrow import feather dataset = load_dataset('imdb', split='train') dataset.save_to_disk('dataset_dir') table = feather.read_table('dataset_dir/dataset.arrow') ``` ## Expected results I expect that the saved dataset can be read by the official Apache Arrow methods. ## Actual results ``` File "/usr/local/lib/python3.7/site-packages/pyarrow/feather.py", line 236, in read_table reader.open(source, use_memory_map=memory_map) File "pyarrow/feather.pxi", line 67, in pyarrow.lib.FeatherReader.open File "pyarrow/error.pxi", line 123, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Not a Feather V1 or Arrow IPC file ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: datasets-1.6.2 - Platform: Linux - Python version: 3.7 - PyArrow version: 0.17.1, also 2.0.0
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Improve task api code quality
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[ "Looks good thanks, what do you think @lewtun ?", "thanks for including the lazy `ClassLabel` class @mariosasko ! from my side this LGTM!" ]
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Improves the code quality of the `TaskTemplate` dataclasses. Changes: * replaces `return NotImplemented` with raise `NotImplementedError` * replaces `sorted` with `len` in the uniqueness check * defines `label2id` and `id2label` in the `TextClassification` template as properties * replaces the `object.__setattr__(self, attr, value)` syntax with (IMO nicer) `self.__dict__[attr] = value`
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Dataset Streaming
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# Dataset Streaming ## API Current API is ```python from datasets import load_dataset # Load an IterableDataset without downloading data snli = load_dataset("snli", streaming=True) # Access examples by streaming data print(next(iter(snli["train"]))) # {'premise': 'A person on a horse jumps over a broken down airplane.', # 'hypothesis': 'A person is training his horse for a competition.', # 'label': 1} ``` I already implemented a few methods: - IterableDataset.map: apply transforms on-the-fly to the examples - IterableDataset.shuffle: shuffle the data _a la_ TFDS, i.e. with a shuffling buffer - IterableDataset.with_format: set the format to `"torch"` to get a `torch.utils.data.IterableDataset` - merge_datasets: merge two iterable datasets by alternating one or the other (you can specify the probabilities) I would love to have your opinion on the API design :) ## Implementation details ### Streaming Data streaming is done using `fsspec` which has nice caching features. To make dataset streaming work I extend the `open` function of dataset scripts to support opening remote files without downloading them entirely. It also works with remote compressed archives (currently only zip is supported): ```python # Get a file-like object by streaming data from a remote file open("https://github.com/davidsbatista/NER-datasets/raw/master/CONLL2003/train.txt") # Get a file-like object by streaming data from a remote compressed archive by using the hop separator "::" open("zip://snli_1.0_train.txt::https://nlp.stanford.edu/projects/snli/snli_1.0.zip") ``` I also extend the `os.path.join` function to support navigation in remote compressed archives, since it has to deal with the `"::"` separator. This separator is used by `fsspec`. Finally I also added a retry mechanism in case the connection fails during data streaming. ### Transforms An IterableDataset wraps an ExamplesIterable instance. There are different subclasses depending on the transforms we want to apply: - ExamplesIterable: the basic one - MappedExamplesIterable: an iterable with a `map` function applied on the fly - BufferShuffledExamplesIterable: an iterable with a shuffling buffer - CyclingMultiSourcesExamplesIterable: alternates between several ExamplesIterable - RandomlyCyclingMultiSourcesExamplesIterable: randomly alternates between several ExamplesIterable ### DatasetBuilder I use the same builders as usual. I just added a new method `_get_examples_iterable_for_split` to get an ExamplesIterable for a given split. Currently only the GeneratorBasedBuilder and the ArrowBasedBuilder implement it. The BeamBasedBuilder doesn't implement it yet. It means that datasets like wikipedia and natural_questions can't be loaded as IterableDataset for now. ## Other details <S>I may have to do some changes in many dataset script to use `download` instead of `download_and_extract` when extraction is not needed. This will avoid errors for streaming.</s> EDIT: Actually I just check for the extension of the file to do extraction only if needed. EDIT2: It's not possible to stream from .tar.gz files without downloading the file completely. For now I raise an error if one want to get a streaming dataset based on .tar.gz files. ## TODO usual stuff: - [x] make streaming dependency "aiohttp" optional: `pip install datasets[streaming]` - [x] tests - [x] docs
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add `desc` to `tqdm` in `Dataset.map()`
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[ "Once this is merged, let's update `transformers` examples to use this new code. As currently all those tqdm bars are who knows what they are....\r\n\r\nhttps://github.com/huggingface/transformers/issues/11797", "Sure @stas00! Once this is merged let's discuss what all changes can be done on `transformers` side", "@bhavitvyamalik, as it has been merged would you like to tackle https://github.com/huggingface/transformers/issues/11797?\r\n", "Definitely @stas00. From what I could gather, you guys want more meaningful `.map` calls for all examples [here](https://github.com/huggingface/transformers/tree/master/examples/pytorch)?", "That's exactly right, @bhavitvyamalik \r\n\r\nPerhaps the best approach is to do one example, see that other maintainers agree on it. and then replicate to other." ]
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Fixes #2330. Please let me know if anything is also required in this
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Loading dataset from local path
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[ "Version below works, checked again in the docs, and data_files should be a path.\r\n```\r\nds = datasets.load_dataset('my_script.py', \r\n data_files='/data/dir/corpus.txt', \r\n cache_dir='.')\r\n```" ]
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I'm trying to load a local dataset with the code below ``` ds = datasets.load_dataset('my_script.py', data_files='corpus.txt', data_dir='/data/dir', cache_dir='.') ``` But internally a BuilderConfig is created, which tries to use getmtime on the data_files string, without using data_dir. Is this a bug or am I not using the load_dataset correctly? https://github.com/huggingface/datasets/blob/bc61954083f74e6460688202e9f77dde2475319c/src/datasets/builder.py#L153
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ConvQuestions benchmark added
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[ "Thanks for your helpful comments and suggestions! :)\r\nI integrated the additional fields, and extended some of the README/dataset card.\r\nAnd I actually realized that we had the cc-by-4.0 for the dataset, so this was also changed.", "I added the answers to the test set actually :)", "Oh great ! Let me revert my change then" ]
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Hello, I would like to integrate our dataset on conversational QA. The answers are grounded in the KG. The work was published in CIKM 2019 (https://dl.acm.org/doi/10.1145/3357384.3358016). We hope for further research on how to deal with the challenges of factoid conversational QA. Thanks! :)
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Align question answering tasks with sub-domains
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As pointed out by @thomwolf in #2255 we should consider breaking with the pipeline taxonomy of `transformers` to account for the various types of question-answering domains: > `question-answering` exists in two forms: abstractive and extractive question answering. > > we can keep a generic `question-answering` but then it will probably mean diferrent schema of input/output for both (abstractive will have text for both while extractive can use spans indication as well as text). > > Or we can also propose to use `abstractive-question-answering` and `extractive-question-answering` for instance. > Maybe we could have `question-answering-abstractive` and `question-answering-extractive` if somehow we can use a for a completion or search in the future (detail). > Actually I see that people are more organizing in terms of general and sub-tasks, for instance on paperwithcode: https://paperswithcode.com/area/natural-language-processing and on nlpprogress: https://github.com/sebastianruder/NLP-progress/blob/master/english/question_answering.md#squad > > Probably the best is to align with one of these in terms of denomination, PaperWithCode is probably the most active and maintained and we work with them as well. > Maybe you want to check with a few QA datasets that this schema make sense. Typically NaturalQuestions, TriviaQA and can be good second datasets to compare to and be sure of the generality of the schema. > > A good recent list of QA datasets to compare the schemas among, is for instance in the UnitedQA paper: https://arxiv.org/abs/2101.00178 Investigate which grouping of QA is best suited for `datasets` and adapt / extend the QA task template accordingly.
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Adding HendrycksTest dataset
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[ "@lhoestq Thank you for the review. I've made the suggested changes. There still might be some problems with dummy data though due to some csv loading issues (which I haven't found the cause to).", "I took a look at the dummy data and some csv lines were cropped. I fixed them :)" ]
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Adding Hendrycks test from https://arxiv.org/abs/2009.03300. I'm having a bit of trouble with dummy data creation because some lines in the csv files aren't being loaded properly (only the first entry loaded in a row of length 6). The dataset is loading just fine. Hope you can kindly help! Thank you!
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correct labels of conll2003
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# What does this PR It fixes/extends the `ner_tags` for conll2003 to include all. Paper reference https://arxiv.org/pdf/cs/0306050v1.pdf Model reference https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english/blob/main/config.json
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This PR allows "other-X" licenses during metadata validation. @lhoestq
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`Element.getchildren()` is now deprecated in the ElementTree library (I think in python 3.9, so it still passes the automated tests which are using 3.6. But for those of us on bleeding-edge distros it now fails). https://bugs.python.org/issue29209
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If you do ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then depending on the order of the features in the json data field it fails: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ValueError: Target schema's field names are not matching the table's field names: ['tokens', 'ner_tags'], ['ner_tags', 'tokens'] ``` This is because one must first re-order the columns of the table to match the `self.config.schema` before calling cast. One way to fix the `cast` would be to replace it with: ```python # reorder the arrays if necessary + cast to schema # we can't simply use .cast here because we may need to change the order of the columns pa_table = pa.Table.from_arrays([pa_table[name] for name in schema.names], schema=schema) ```
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Missing ClassLabel encoding in Json loader
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Currently if you want to load a json dataset this way ```python dataset = load_dataset("json", data_files=data_files, features=features) ``` Then if your features has ClassLabel types and if your json data needs class label encoding (i.e. if the labels in the json files are strings and not integers), then it would fail: ```python [...] ~/Desktop/hf/datasets/src/datasets/packaged_modules/json/json.py in _generate_tables(self, files) 94 if self.config.schema: 95 # Cast allows str <-> int/float, while parse_option explicit_schema does NOT ---> 96 pa_table = pa_table.cast(self.config.schema) 97 yield i, pa_table [...] ArrowInvalid: Failed to parse string: 'O' as a scalar of type int64 ``` This is because it just tries to cast the string data to integers, without applying the mapping str->int first The current workaround is to do instead ```python dataset = load_dataset("json", data_files=data_files) dataset = dataset.map(features.encode_example, features=features) ```
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[ "Regarding the license issue, I think we should allow it since it starts with `other-`. Cc @gchhablani what do you think ?", "@lhoestq I agree, I'll look into it." ]
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Closes #2275. Mentioned about -1 labels in MNLI, SNLI and how they should be removed before training. @lhoestq `check_code_quality` test might fail for MNLI as the license name `other-Open Portion of the American National Corpus` is not a registered tag for 'licenses'
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[ "also, I test the function on some little data , get the same message:\r\n\r\n```\r\nPython 3.8.5 (default, Jan 27 2021, 15:41:15)\r\n[GCC 9.3.0] on linux\r\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\r\n>>> from datasets import load_metric\r\n>>> metric = load_metric('accuracy')\r\n>>> metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\n2021-05-15 16:39:17.240991: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\r\n>>> metric.compute()\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py\", line 391, in compute\r\n self._finalize()\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py\", line 342, in _finalize\r\n self.writer.finalize()\r\n File \"/home/yshuang/.local/lib/python3.8/site-packages/datasets/arrow_writer.py\", line 370, in finalize\r\n self.stream.close()\r\n File \"pyarrow/io.pxi\", line 132, in pyarrow.lib.NativeFile.close\r\n File \"pyarrow/error.pxi\", line 112, in pyarrow.lib.check_status\r\nOSError: error closing file\r\n```", "Hi @hyusterr,\r\nIf you look at the example provided in `metrics/accuracy.py`, it only does `metric.compute()` to calculate the accuracy. Here's an example:\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.compute(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nprint(output['accuracy']) # 0.5\r\n```\r\n", "I thought I can use Metric to collect predictions and references, this follows the step from huggingface's sample colab.\r\nBTW, I fix the problem by setting other cache_dir in load_metric, but I'm still wondering about the mechanism.", "I tried this code on a colab notebook and it worked fine (with gpu enabled):\r\n```\r\nfrom datasets import load_metric\r\nmetric = load_metric('accuracy')\r\noutput = metric.add_batch(predictions=[1, 1, 1, 1], references=[1, 1, 0, 0])\r\nfinal_score = metric.compute()\r\nprint(final_score) # 0.5\r\n```\r\nAlso, in `load_metric`, I saw `cache_dir` is optional and it defaults to `~/.datasets/`", "Hi ! By default it caches the predictions and references used to compute the metric in `~/.cache/huggingface/datasets/metrics` (not `~/.datasets/`). Let me update the documentation @bhavitvyamalik .\r\n\r\nThe cache is used to store all the predictions and references passed to `add_batch` for example in order to compute the metric later when `compute` is called.\r\n\r\nI think the issue might come from the cache directory that is used by default. Can you check that you have the right permissions ? Otherwise feel free to set `cache_dir` to another location." ]
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I want to use metric.compute from load_metric('accuracy') to get training accuracy, but receive OSError. I am wondering what is the mechanism behind the metric calculation, why would it report an OSError? ```python 195 for epoch in range(num_train_epochs): 196 model.train() 197 for step, batch in enumerate(train_loader): 198 # print(batch['input_ids'].shape) 199 outputs = model(**batch) 200 201 loss = outputs.loss 202 loss /= gradient_accumulation_steps 203 accelerator.backward(loss) 204 205 predictions = outputs.logits.argmax(dim=-1) 206 metric.add_batch( 207 predictions=accelerator.gather(predictions), 208 references=accelerator.gather(batch['labels']) 209 ) 210 progress_bar.set_postfix({'loss': loss.item(), 'train batch acc.': train_metrics}) 211 212 if (step + 1) % 50 == 0 or step == len(train_loader) - 1: 213 train_metrics = metric.compute() ``` the error message is as below: ``` Traceback (most recent call last): File "run_multi.py", line 273, in <module> main() File "/home/yshuang/.local/lib/python3.8/site-packages/click/core.py", line 829, in __call__ return self.main(*args, **kwargs) File "/home/yshuang/.local/lib/python3.8/site-packages/click/core.py", line 782, in main rv = self.invoke(ctx) File "/home/yshuang/.local/lib/python3.8/site-packages/click/core.py", line 1066, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/yshuang/.local/lib/python3.8/site-packages/click/core.py", line 610, in invoke return callback(*args, **kwargs) File "run_multi.py", line 213, in main train_metrics = metric.compute() File "/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py", line 391, in compute self._finalize() File "/home/yshuang/.local/lib/python3.8/site-packages/datasets/metric.py", line 342, in _finalize self.writer.finalize() File "/home/yshuang/.local/lib/python3.8/site-packages/datasets/arrow_writer.py", line 370, in finalize self.stream.close() File "pyarrow/io.pxi", line 132, in pyarrow.lib.NativeFile.close File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status OSError: error closing file ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.6.1 - Platform: Linux NAME="Ubuntu" VERSION="20.04.1 LTS (Focal Fossa)" - Python version: python3.8.5 - PyArrow version: 4.0.0
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Fix web_nlg metadata
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[ "Hi ! `release_v2.1` and the others are dataset configuration names.\r\n\r\nThe configuration names are used to show the right code snippet in the UI to load the dataset.\r\nFor example if the parsing of the web_nlg tags worked correctly we would have:\r\n![image](https://user-images.githubusercontent.com/42851186/118475444-8d1e5d00-b70c-11eb-98e9-844d4daf6139.png)\r\n\r\nTherefore I don't think it's a good idea to rename the configurations from `release_v2.1` to `release_v2_1` as the code snippet would be wrong in this case.\r\n\r\nMoreover we can't really disallow dots in configuration names and rename the configurations since it would be a big breaking change. It's commonly used, especially with multilingual datasets. For example `load_dataset(\"indic_glue\", \"sna.bn\")`.\r\n\r\nIs this something that can be fixed on the moonlanding side instead ?", "> Is this something that can be fixed on the moonlanding side instead ?\r\n\r\nNot really unless we change database:)\r\n\r\nWe'll maybe try to find another workaround, but super low-prio given that it's the only dataset that has those dotted keys in the YAML metadata", "Ok, should we close this PR then ?" ]
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Our metadata storage system does not support `.` inside keys. cc @Pierrci
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Preserve dtype for numpy/torch/tf/jax arrays
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[ "Hi @lhoestq, \r\nIt turns out that pyarrow `ListArray` are not recognized as list-like when we get output from `numpy_to_pyarrow_listarray`. This might cause tests to fail. If possible can we convert that `ListArray` output to list inorder for tests to pass? Under the hood it'll maintain the dtype as that of numpy array passed during input only", "Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch` https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039 and `test_map_tf`https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056 \r\nthey're expecting `float64`. Shouldn't that be `float32` now?", "It's normal: pytorch and tensorflow use `float32` by default, unlike numpy which uses `float64`.\r\n\r\nI think that we should always keep the precision of the original tensor (torch/tf/numpy).\r\nIt means that as it is in this PR it's fine (the precision is conserved when doing the torch/tf -> numpy conversion).\r\n\r\nThis is a breaking change but in my opinion the fact that we had Value(\"float64\") for torch.float32 tensors was an issue already.\r\n\r\nLet me know what you think. Cc @albertvillanova if you have an opinion on this\r\n\r\nIf we agree on doing this breaking change, we can just change the test. ", "Hi @lhoestq, \r\nMerged master into this branch. Only changing the test is left for now (mentioned below) after which all tests should pass.\r\n\r\n> Brought down the failing tests from 7 to 4. Let me know if that part looks good. Failing tests are looking quite similar. In `test_map_torch`\r\n> \r\n> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1039\r\n> \r\n> and `test_map_tf`\r\n> https://github.com/huggingface/datasets/blob/3d46bc384f811435e59e3916faa3aa20a1cf87bc/tests/test_arrow_dataset.py#L1056\r\n> \r\n> \r\n> they're expecting `float64`. Shouldn't that be `float32` now?\r\n\r\n", "> they're expecting float64. Shouldn't that be float32 now?\r\n\r\nYes feel free to update those tests :)\r\n\r\nIt would be nice to have the same test for JAX as well", "Added same test for for JAX too. Also, I saw that I missed changing `test_cast_to_python_objects_jax` like I did for TF and PyTorch. Finished that as well" ]
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Fixes #625. This lets the user preserve the dtype of numpy array to pyarrow array which was getting lost due to conversion of numpy array -> list -> pyarrow array.
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Automatically detect datasets with compatible task schemas
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See description of #2255 for details.
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Allow model labels to be passed during task preparation
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Models have a config with label2id. And we have the same for datasets with the ClassLabel feature type. At one point either the model or the dataset must sync with the other. It would be great to do that on the dataset side. For example for sentiment classification on amazon reviews with you could have these labels: - "1 star", "2 stars", "3 stars", "4 stars", "5 stars" - "1", "2", "3", "4", "5" Some models may use the first set, while other models use the second set. Here in the `TextClassification` class, the user can only specify one set of labels, while many models could actually be compatible but have different sets of labels. Should we allow users to pass a list of compatible labels sets ? Then in terms of API, users could use `dataset.prepare_for_task("text-classification", labels=model.labels)` or something like that. The label set could also be the same but not in the same order. For NLI for example, some models use `["neutral", "entailment", "contradiction"]` and some others use `["neutral", "contradiction", "entailment"]`, so we should take care of updating the order of the labels in the dataset to match the labels order of the model. Let me know what you think ! This can be done in a future PR _Originally posted by @lhoestq in https://github.com/huggingface/datasets/pull/2255#discussion_r632412792_
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Roman Urdu Stopwords List
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[ "Hi ! Thanks for sharing :)\r\nI think the best place to share this is probably the `Languages at Hugging Face` section of the forum:\r\nhttps://discuss.huggingface.co/c/languages-at-hugging-face/15\r\n\r\nSince this is not a dataset, I'm closing this PR if you don't mind", "Thank you I will look into the link that you have shared with me.\n\n\n\n\nOn Mon, May 17, 2021 at 7:05 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> Closed #2358 <https://github.com/huggingface/datasets/pull/2358>.\n>\n> โ€”\n> You are receiving this because you authored the thread.\n> Reply to this email directly, view it on GitHub\n> <https://github.com/huggingface/datasets/pull/2358#event-4754836267>, or\n> unsubscribe\n> <https://github.com/notifications/unsubscribe-auth/AN7SJYJVY4C5XQRDNET743DTOEPC7ANCNFSM443AZ3MA>\n> .\n>\n" ]
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A list of most frequently used Roman Urdu words with different spellings and usages. This is a very basic effort to collect some basic stopwords for Roman Urdu to help efforts of analyzing text data in roman Urdu which makes up a huge part of daily internet interaction of Roman-Urdu users.
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Adding Microsoft CodeXGlue Datasets
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[ "Oh one other thing. Mentioned in the PR was that I would need to regenerate the dataset_infos.json once the camel casing was done. However, I am unsure why this is the case since there is no reference to any object names in the dataset_infos.json file.\r\n\r\nIf it needs to be reran, I can try it do it on my own machine, but I've had a memory issues with a previous dataset due to my compute constraints so I'd prefer to hopefully avoid it all together if not necessary to regenerate.", "Was just reviewing the `builder_name`s of each dataset and it seems like it is already following this format:\r\n\r\n`CodeXGlueCcCloneDetectionBigCloneBenchMain -> code_x_glue_cc_clone_detection_big_clone_bench_main` Is there a location I am missing?", "> Was just reviewing the `builder_name`s of each dataset and it seems like it is already following this format:\r\n> \r\n> `CodeXGlueCcCloneDetectionBigCloneBenchMain -> code_x_glue_cc_clone_detection_big_clone_bench_main` Is there a location I am missing?\r\n\r\nIf it's already in this format then it's fine thanks ! It's all good then\r\n\r\nTo fix the CI you just need to add the `encoding=` parameters to the `open()` calls", "@lhoestq I think everything should be good to go besides the code styling, which seem to be due to missing or unsupported metadata tags for the READMEs, is this something I should worry about since all the other datasets seem to be failing as well?", "Awesome! Just committed your changes and I will begin on adding the TOCs and filling in the content for the new sections/subsections.\r\n\r\nAlso, I see that we are having to only use the `code` tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.", "> Also, I see that we are having to only use the code tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.\r\n\r\nYes I agree. We'll be able to reuse the tags per programming language from this PR when we allow this feature\r\n\r\ncc @yjernite what do you think about extending our languages taxonomy to programming languages ?", "Hey @lhoestq, just finalizing the READMEs and testing them against the automated test. For the non, WIN tests, it seems like there is some dependency issue that doesn't have to do with the new datasets. For the WIN tests, it looks like some of the headings are mislabeled such as \"Supported Tasks and Leaderboards\" -> \"Supported Tasks\" in the TOC you posted. Should I base my TOC on the one you posted or on the one that the test script is using? Also, it throws errors for some of the fields being empty, such as \"Source Data\" in the `code_x_glue_tt_text_to_text` dataset. However, I am not familiar with this dataset, so I put the `[More Information Needed]` stub, similar to the other sections I couldn't easily answer. For some of the sections like \"Source Data\", is this info required?", "Yes you're right, it is `Supported Tasks and Leaderboards` that we need to use, sorry about that\r\n\r\nI also noticed the same for the splits section: we have to use `Data Splits` (not Data Splits Sample Size)\r\n", "Some subsections are also missing: `Initial Data Collection and Normalization`, `Who are the source language producers?`.\r\nIf you are interested you can fill those sections as well, or leave them empty for now.\r\nThis will also fix the error regarding \"Source Data\"\r\n\r\nYou can see the template of the readme here:\r\nhttps://github.com/huggingface/datasets/blob/9d8bf36fdb861d9b2922d7c782fb58f9f542997c/templates/README.md", "> > Also, I see that we are having to only use the code tag instead of individual langs and I get that is required for indexing or showing available tags on the datasets hub. However, as a future feature, it might be good to add tags for individual programming languages to make it easier to search.\r\n> \r\n> Yes I agree. We'll be able to reuse the tags per programming language from this PR when we allow this feature\r\n> \r\n> cc @yjernite what do you think about extending our languages taxonomy to programming languages ?\r\n\r\nSounds good, as long as they all share a prefix! maybe `code_cpp`, `code_java`, etc. ? \r\n\r\nI don't think we currently have `_` in language codes/names, but also don't see what it would break *a priori*", "We don't use `_` but there are some languages that use `-` though like `en-US`. Let's use `-` maybe, to match the same hierarchy pattern ?", "Hi guys, I just started working on https://github.com/huggingface/datasets/pull/997 this morning and I just realized that you were finishing it... You may want to get the dataset cards from https://github.com/madlag/datasets, and maybe some code too, as I did a few things like moving _CITATION and _DESCRIPTION to globals.\r\n\r\n", "I am renaming the main classes to match the dataset names, for example : CodeXGlueTcTextToCodeMain -> CodeXGlueTcTextToCode . And I am regenerating the dataset_infos.json accordingly.", "Thanks for renaming the classes and updating the dataset_infos.json ! This looks all clean now :)\r\n\r\nThis PR looks all good to me :) One just needs to merge master into this branch to make sure the CI is green with the latest changes. It should also fix the current CI issues that are not related to this PR", "Woot woot :rocket:! All green, looks like it is ready for showtime. Thank you both @lhoestq and especially @madlag, I think these datasets are going to be a great new addition to :hugs: datasets and I can't wait to use them in my research :nerd_face:.", "Thanks @ncoop57 for you contribution! It will be really cool to see those datasets used as soon as they are released !" ]
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Hi there, this is a new pull request to get the CodeXGlue datasets into the awesome HF datasets lib. Most of the work has been done in this PR #997 by the awesome @madlag. However, that PR has been stale for a while now and so I spoke with @lhoestq about finishing up the final mile and so he told me to open a new PR with the final changes :smile:. I believe I've met all of the changes still left in the old PR to do, except for the change to the languages. I believe the READMEs should include the different programming languages used rather than just using the tag "code" as when searching for datasets, SE researchers may specifically be looking only for what type of programming language and so being able to quickly filter will be very valuable. Let me know what you think of that or if you still believe it should be the "code" tag @lhoestq.
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[ "Hi ! sorry for the late response \r\n\r\nIt would be fantastic to have a guide for adding metrics as well ! Currently we only have this template here:\r\nhttps://github.com/huggingface/datasets/blob/master/templates/new_metric_script.py\r\n\r\nWe can also include test utilities for metrics in the guide.\r\n\r\nWe have a pytest suite with commands that you can use to make sure your metric works as expected.\r\nIt has two useful commands:\r\n\r\n1. This commands tests the code in the `Examples:` desction of the docstring of the metric:\r\n```\r\npytest tests/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\nThis will run this code for example:\r\n\r\nhttps://github.com/huggingface/datasets/blob/e0787aa2a781cc15a80f7597f56d1f12e23df4c9/metrics/accuracy/accuracy.py#L40-L45\r\n\r\nMoreover this test is meant to be fast so users are free to add patches to the metric to avoid intensive computations.\r\nAnd example of intensive call patch can be found here:\r\n\r\nhttps://github.com/huggingface/datasets/blob/e0787aa2a781cc15a80f7597f56d1f12e23df4c9/tests/test_metric_common.py#L138-L151\r\n\r\n2. This test runs the same thing as 1. except that it doesn't use patches (the real metric is used):\r\n```\r\nRUN_SLOW=1 pytest tests/test_metric_common.py::LocalMetricTest::test_load_metric_<metric_name>\r\n```\r\n\r\nFinally additional metric-specific tests can be added to `test_metric_common.py`.\r\n\r\nVoila :) Feel free to ping me if you have any question or if I can help\r\n" ]
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**Is your feature request related to a problem? Please describe.** Currently there is an absolutely fantastic guide for how to contribute a new dataset to the library. However, there isn't one for adding new metrics. **Describe the solution you'd like** I'd like for a guide in a similar style to the dataset guide for adding metrics. I believe many of the content in the dataset guide such as setup can be easily copied over with minimal changes. Also, from what I've seen with existing metrics, it shouldn't be as complicated, especially in documentation of the metric, mainly just citation and usage. The most complicated part I see would be in automated tests that run the new metrics, but y'all's test suite seem pretty comprehensive, so it might not be that hard. **Describe alternatives you've considered** One alternative would be just not having the metrics be community generated and so would not need a step by step guide. New metrics would just be proposed as issues and the internal team would take care of them. However, I think it makes more sense to have a step by step guide for contributors to follow. **Additional context** I'd be happy to help with creating this guide as I am very interested in adding software engineering metrics to the library :nerd_face:, the part I would need guidance on would be testing. P.S. Love the library and community y'all have built! :hugs:
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normalized TOCs and titles in data cards
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[ "Oh right! I'd be in favor of still having the same TOC across the board, we can either leave it as is or add a `[More Info Needed]` `Contributions` Section wherever it's currently missing, wdyt?", "(I thought those were programmatically updated based on git history :D )", "Merging for now to avoid conflict since there are so many changes but let's figure out the contributions section next ;) " ]
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I started fixing some of the READMEs that were failing the tests introduced by @gchhablani but then realized that there were some consistent differences between earlier and newer versions of some of the titles (e.g. Data Splits vs Data Splits Sample Size, Supported Tasks vs Supported Tasks and Leaderboards). We also had different versions of the Table of Content This PR normalizes all of them to the newer version
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Document DatasetInfo attributes
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**Is your feature request related to a problem? Please describe.** As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)
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Update README vallidation rules
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This PR allows unexpected subsections under third-level headings. All except `Contributions`. @lhoestq
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Set to_json default to JSON lines
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[ "This is perfect, @albertvillanova - thank you! Tested it to work.\r\n\r\nMight it be a good idea to document the args to `to_json`?\r\n\r\nand also even a very basic progress bar? took 10min for 8M large records for `openwebtext` so perhaps some indication of it's being alive every min or so?", "@lhoestq I added tests for both `lines` and `orient`." ]
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With this PR, the method `Dataset.to_json`: - is added to the docs - defaults to JSON lines
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simpllify faiss index save
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Fixes #2350 In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU. In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it. I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable.
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`FaissIndex.save` throws error on GPU
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[ "Just in case, this is a workaround that I use in my code and it seems to do the job.\r\n\r\n```python\r\nif use_gpu_index:\r\n data[\"train\"]._indexes[\"text_emb\"].faiss_index = faiss.index_gpu_to_cpu(data[\"train\"]._indexes[\"text_emb\"].faiss_index)\r\n```" ]
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## Describe the bug After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error. ``` File "index_wikipedia.py", line 119, in <module> data["train"].save_faiss_index("text_emb", index_save_path) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index index.save(file) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save faiss.write_index(index, str(file)) File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index return _swigfaiss.write_index(*args) RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index ``` ## Steps to reproduce the bug Any dataset will do, I just selected a familiar one. ```python import numpy as np import datasets INDEX_STR = "OPQ16_128,IVF512,PQ32" INDEX_SAVE_PATH = "will_not_save.faiss" data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]") def encode(item): return {"text_emb": np.random.randn(768).astype(np.float32)} data = data.map(encode) data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0) data.save_faiss_index("text_emb", INDEX_SAVE_PATH) ``` ## Expected results Saving the index ## Actual results Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index ## Environment info - `datasets` version: 1.6.2 - Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10 - Python version: 3.8.8 - PyTorch version (GPU?): 1.8.1+cu111 (True) - Tensorflow version (GPU?): 2.2.0 (False) - Using GPU in script?: Yes - Using distributed or parallel set-up in script?: No I will be proposing a fix in a couple of minutes
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Update task_ids for Ascent KB
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This "other-other-knowledge-base" task is better suited for the dataset.
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Add tests for dataset cards
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[ "@lhoestq\r\n\r\nShould I remove the scripts? or atleast remove running them from the CircleCI config?\r\n\r\nAlso, I hope it is okay that the combined method (metadata+content) is only a slow test, and for the Circle CI, I assume only non-slow tests are run? If yes, this would mean separate tests for content and metadata.", "Also feel free to remove the scripts from the CI and also remove the scripts files :)" ]
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Adding tests for dataset cards This PR will potentially remove the scripts being used for dataset tags and readme validation. Additionally, this will allow testing dataset readmes by providing the name as follows: ```bash pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist] ``` and ```bash pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist] ``` or a combined test as: ```bash pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist] ``` @lhoestq
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Add an API to access the language and pretty name of a dataset
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[ "Hi ! With @bhavitvyamalik we discussed about having something like\r\n```python\r\nfrom datasets import load_dataset_card\r\n\r\ndataset_card = load_dataset_card(\"squad\")\r\nprint(dataset_card.metadata.pretty_name)\r\n# Stanford Question Answering Dataset (SQuAD)\r\nprint(dataset_card.metadata.languages)\r\n# [\"en\"]\r\n\r\n```\r\nWhat do you think ?\r\n\r\nI don't know if you already have a way to load the model tags in `transformers` but we can agree on the API to have something consistent.\r\n\r\nAlso note that the pretty name would only be used to show users something prettier than a dataset id, but in the end the source of truth will stay the dataset id (here `squad`).", "That works for me!", "maybe use the hub-backed dataset_info method? (so there's only one parser of README.md metadata)?", "What dataset_info method are you talking about @julien-c ? In `huggingface_hub` I can only see `model_info`.", "hmm the equivalent method in `datasets` (which could go into `huggingface_hub` at some point)" ]
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It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.
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Add Qasper Dataset
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[ "I saw that the README [template](https://github.com/huggingface/datasets/blob/master/templates/README.md) changed while I was working on this ๐Ÿ˜… Some TOC titles may be different but I filled it to the best of my knowledge & readme quality check passes now.\r\nready for review @lhoestq " ]
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[Question Answering on Scientific Research Papers](https://allenai.org/project/qasper/home) Doing NLP on NLP papers to do NLP โ™ป๏ธ I had to add it~ - [x] Add README (just gotta fill out some more ) - [x] Dataloader code - [x] Make dummy dataset - [x] generate dataset infos - [x] Tests
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[Question] How to move and reuse preprocessed dataset?
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[ "@lhoestq @LysandreJik", "<s>Hi :) Can you share with us the code you used ?</s>\r\n\r\nEDIT: from https://github.com/huggingface/transformers/issues/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?\r\n", "Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same", "> Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same\r\n\r\nI only changed the `preprocessing_num_workers` maybe it is the problem~ I will try again~" ]
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Hi, I am training a gpt-2 from scratch using run_clm.py. I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess), I tried to : copy path_to_cache_dir/datasets to new_cache_dir/datasets set export HF_DATASETS_CACHE="new_cache_dir/" but the program still re-preprocess the whole dataset without loading cache. I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M. What is the proper way to do this?
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Is there a way to join multiple datasets in one?
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[ "Hi ! We don't have `join`/`merge` on a certain column as in pandas.\r\nMaybe you can just use the [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.\r\n" ]
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**Is your feature request related to a problem? Please describe.** I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2? **Describe the solution you'd like** Id like to join them with a merge or join method, just like pandas dataframes. **Additional context** If you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation.
https://api.github.com/repos/huggingface/datasets/issues/2344/timeline
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