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MDU6SXNzdWU4NDM2NjI2MTM=

TypeError when using save_to_disk in a dataset loaded with ReadInstruction split

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Hi, Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`. Here is the minimal reproducible example: ```python from datasets import load_dataset from datasets import ReadInstruction data_1 = load_dataset( "wikiann", "en", split="validation", ) data_1.save_to_disk("temporary_path_1") print("Save with regular split works.") data_2 = load_dataset( "wikiann", "en", split=ReadInstruction("validation", to=50, unit="%"), ) data_2.save_to_disk("temporary_path_2") ``` and the corresponding output: ``` Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Save with regular split works. Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9) Traceback (most recent call last): File "bug.py", line 20, in <module> data_2.save_to_disk("temporary_path_2") File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk json.dump(state, state_file, indent=2, sort_keys=True) File "/usr/lib/python3.7/json/__init__.py", line 179, in dump for chunk in iterable: File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode yield from _iterencode_dict(o, _current_indent_level) File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict yield from chunks File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode o = _default(o) File "/usr/lib/python3.7/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.__class__.__name__} ' TypeError: Object of type ReadInstruction is not JSON serializable ``` Let me know if there is some misuse from my end. Thanks in advance.

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MDExOlB1bGxSZXF1ZXN0NjAyODc4NzU2

Add CER metric

closed

CONTRIBUTOR

Add Character Error Rate (CER) metric that is used in evaluation in ASR. I also have written unittests (hopefully thorough enough) but I'm not sure how to integrate them into the existed codebase. ```python from cer import CER cer = CER() class TestCER(unittest.TestCase): def test_cer_case_senstive(self): refs = ['White House'] preds = ['white house'] # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.1818181818) < 1e-6) def test_cer_whitespace(self): refs = ['were wolf'] preds = ['werewolf'] # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.1111111) < 1e-6) refs = ['werewolf'] preds = ['weae wolf'] # S = 1, D = 1, I = 0, N = 8, CER = 0.25 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.25) < 1e-6) # consecutive whitespaces case 1 refs = ['were wolf'] preds = ['were wolf'] # S = 0, D = 0, I = 0, N = 9, CER = 0 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.0) < 1e-6) # consecutive whitespaces case 2 refs = ['were wolf'] preds = ['were wolf'] # S = 0, D = 0, I = 0, N = 9, CER = 0 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.0) < 1e-6) def test_cer_sub(self): refs = ['werewolf'] preds = ['weaewolf'] # S = 1, D = 0, I = 0, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_del(self): refs = ['werewolf'] preds = ['wereawolf'] # S = 0, D = 1, I = 0, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_insert(self): refs = ['werewolf'] preds = ['wereolf'] # S = 0, D = 0, I = 1, N = 8, CER = 0.125 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.125) < 1e-6) def test_cer_equal(self): refs = ['werewolf'] char_error_rate = cer.compute(predictions=refs, references=refs) self.assertEqual(char_error_rate, 0.0) def test_cer_list_of_seqs(self): refs = ['werewolf', 'I am your father'] char_error_rate = cer.compute(predictions=refs, references=refs) self.assertEqual(char_error_rate, 0.0) refs = ['werewolf', 'I am your father', 'doge'] preds = ['werxwolf', 'I am your father', 'doge'] # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28 char_error_rate = cer.compute(predictions=preds, references=refs) self.assertTrue(abs(char_error_rate - 0.03571428) < 1e-6) def test_cer_unicode(self): ref = [u'我能吞下玻璃而不伤身体'] pred = [u' 能吞虾玻璃而 不霜身体啦'] # S = 3, D = 2, I = 0, N = 11 # CER = 5 / 11 char_error_rate = cer.compute(predictions=pred, references=ref) self.assertTrue(abs(char_error_rate - 0.4545454545) < 1e-6) ref = [u'我能吞', u'下玻璃而不伤身体'] pred = [u'我 能 吞 下 玻 璃', u'而不伤身体'] # S = 0, D = 5, I = 0, N = 11 # CER = 5 / 11 char_error_rate = cer.compute(predictions=pred, references=ref) self.assertTrue(abs(char_error_rate - 0.454545454545) < 1e-6) ref = [u'我能吞下玻璃而不伤身体'] char_error_rate = cer.compute(predictions=ref, references=ref) self.assertFalse(char_error_rate, 0.0) def test_cer_empty(self): ref = '' pred = 'Hypothesis' with self.assertRaises(ValueError): char_error_rate = cer.compute(predictions=pred, references=ref) if __name__ == '__main__': unittest.main() ```

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MDExOlB1bGxSZXF1ZXN0NjAyODc0MDYw

Fix missing infos from concurrent dataset loading

closed

MEMBER

This should fix issue #2131 When calling `load_dataset` at the same time from 2 workers, one of the worker could have missing split infos when reloading the dataset from the cache.

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MDExOlB1bGxSZXF1ZXN0NjAyODY0ODY5

fix dialogue action slot name and value

closed

CONTRIBUTOR

fix #2128

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MDU6SXNzdWU4NDMyNDYzNDQ=

en language data from MLQA dataset is missing

closed

CONTRIBUTOR

Hi I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue.

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MDU6SXNzdWU4NDMyNDI4NDk=

Saving large in-memory datasets with save_to_disk crashes because of pickling

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NONE

Using Datasets 1.5.0 on Python 3.7. Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library. So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method. When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB). ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 80, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 75, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify contexts_dataset.save_to_disk(chunked_path) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk self = pickle.loads(pickle.dumps(self)) OverflowError: cannot serialize a bytes object larger than 4 GiB ``` From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository. To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk. Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that. ``` Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last): File "./tokenize_and_chunkify_in_memory.py", line 94, in <module> main() File "./tokenize_and_chunkify_in_memory.py", line 89, in main tokenize_and_chunkify(config) File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp> transformed_shards = [r.get() for r in results] File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get raise self._value File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks put(task) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send self._send_bytes(_ForkingPickler.dumps(obj)) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps cls(buf, protocol, *args, **kwds).dump(obj) File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump StockPickler.dump(self, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump self.save(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce save(state) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict StockPickler.save_dict(pickler, obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict self._batch_setitems(obj.items()) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems save(v) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends save(tmp[0]) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list self._batch_appends(obj) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends save(x) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save self.save_reduce(obj=obj, *rv) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce save(args) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple save(element) File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save f(self, obj) # Call unbound method with explicit self File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes self._write_large_bytes(BINBYTES + pack("<I", n), obj) struct.error: 'I' format requires 0 <= number <= 4294967295 ```

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MDU6SXNzdWU4NDMxNDk2ODA=

bug in mlqa dataset

closed

NONE

Hi Looking into MLQA dataset for langauge "ar": ``` "question": [ "\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?", "\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?", "\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?" ] ``` the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq

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MDU6SXNzdWU4NDMxNDI4MjI=

TydiQA dataset is mixed and is not split per language

open

NONE

Hi @lhoestq Currently TydiQA is mixed and user can only access the whole training set of all languages: https://www.tensorflow.org/datasets/catalog/tydi_qa for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this. Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot

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MDU6SXNzdWU4NDMxMzMxMTI=

When training with Multi-Node Multi-GPU the worker 2 has TypeError: 'NoneType' object

closed

NONE

version: 1.5.0 met a very strange error, I am training large scale language model, and need train on 2 machines(workers). And sometimes I will get this error `TypeError: 'NoneType' object is not iterable` This is traceback ``` 71 |   | Traceback (most recent call last): -- | -- | -- 72 |   | File "run_gpt.py", line 316, in <module> 73 |   | main() 74 |   | File "run_gpt.py", line 222, in main 75 |   | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"]) 76 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset 77 |   | use_auth_token=use_auth_token, 78 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare 79 |   | self.download_post_processing_resources(dl_manager) 80 |   | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources 81 |   | for split in self.info.splits: 82 |   | TypeError: 'NoneType' object is not iterable 83 |   | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2) 84 |   | Traceback (most recent call last): 85 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main 86 |   | "__main__", mod_spec) 87 |   | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code 88 |   | exec(code, run_globals) 89 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module> 90 |   | main() 91 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main 92 |   | sigkill_handler(signal.SIGTERM, None) # not coming back 93 |   | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler 94 |   | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd) ``` On worker 1 it loads the dataset well, however on worker 2 will get this error. And I will meet this error from time to time, sometimes it just goes well.

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MDU6SXNzdWU4NDMxMTE5MzY=

wikiann dataset is missing columns

closed

NONE

Hi Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq

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MDU6SXNzdWU4NDMwMzM2NTY=

How to train BERT model with next sentence prediction?

closed

NONE

Hello. I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?

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MDU6SXNzdWU4NDMwMjM5MTA=

Dialogue action slot name and value are reversed in MultiWoZ 2.2

closed

CONTRIBUTOR

Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial! I spot an error that the order of Dialogue action slot names and values are reversed. https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262

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MDExOlB1bGxSZXF1ZXN0NjAyNDYxMzc3

make documentation more clear to use different cloud storage

closed

MEMBER

This PR extends the cloud storage documentation. To show you can use a different `fsspec` implementation.

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MDExOlB1bGxSZXF1ZXN0NjAyMjcyMjg4

Replace legacy torch.Tensor constructor with torch.tensor

closed

CONTRIBUTOR

The title says it all (motivated by [this issue](https://github.com/pytorch/pytorch/issues/53146) in the pytorch repo).

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MDU6SXNzdWU4NDI2OTA1NzA=

Is dataset timit_asr broken?

closed

NONE

Using `timit_asr` dataset, I saw all records are the same. ``` python from datasets import load_dataset, load_metric timit = load_dataset("timit_asr") from datasets import ClassLabel import random import pandas as pd from IPython.display import display, HTML def show_random_elements(dataset, num_examples=10): assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset." picks = [] for _ in range(num_examples): pick = random.randint(0, len(dataset)-1) while pick in picks: pick = random.randint(0, len(dataset)-1) picks.append(pick) df = pd.DataFrame(dataset[picks]) display(HTML(df.to_html())) show_random_elements(timit['train'].remove_columns(["file", "phonetic_detail", "word_detail", "dialect_region", "id", "sentence_type", "speaker_id"]), num_examples=20) ``` `output` <img width="312" alt="Screen Shot 2021-03-28 at 17 29 04" src="https://user-images.githubusercontent.com/42398050/112746646-21acee80-8feb-11eb-84f3-dbb5d4269724.png"> I double-checked it [here](https://huggingface.co/datasets/viewer/), and met the same problem. <img width="1374" alt="Screen Shot 2021-03-28 at 17 32 07" src="https://user-images.githubusercontent.com/42398050/112746698-9bdd7300-8feb-11eb-97ed-5babead385f4.png">

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MDU6SXNzdWU4NDI2Mjc3Mjk=

Adding ScaNN library to do MIPS?

open

NONE

@lhoestq Hi I am thinking of adding this new google library to do the MIPS similar to **add_faiss_idex**. As the paper suggests, it is really fast when it comes to retrieving the nearest neighbors. https://github.com/google-research/google-research/tree/master/scann 

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MDU6SXNzdWU4NDI1NzcyODU=

Problem downloading GEM wiki_auto_asset_turk dataset

closed

NONE

@yjernite ### Summary I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code. ### Steps to reproduce Code snippet: from datasets import load_dataset #dataset = load_dataset('gem', 'web_nlg_en') dataset = load_dataset('gem', 'wiki_auto_asset_turk') ``` **Expected behavior:** I expect the dataset to start downloading (download bar appears and progresses toward 100%) **Actual behavior:** Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more: Downloading: 36.6kB [00:00, 37.2MB/s] Downloading: 41.7kB [00:00, ?B/s] Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d... ### Is this a regression? No, it was the first time I was trying to download this dataset (same for the other ones). ### Debug info - Python version: Python 3.8.2 - OS version: Windows 10 Family

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MDExOlB1bGxSZXF1ZXN0NjAxODE3MjI0

Fast table queries with interpolation search

closed

MEMBER

## Intro This should fix issue #1803 Currently querying examples in a dataset is O(n) because of the underlying pyarrow ChunkedArrays implementation. To fix this I implemented interpolation search that is pretty effective since datasets usually verifies the condition of evenly distributed chunks (the default chunk size is fixed). ## Benchmark Here is a [benchmark](https://pastebin.com/utEXUqsR) I did on bookcorpus (74M rows): for the current implementation ```python >>> python speed.py Loaded dataset 'bookcorpus', len=74004228, nbytes=4835358766 ========================= Querying unshuffled bookcorpus ========================= Avg access time key=1 : 0.018ms Avg access time key=74004227 : 0.215ms Avg access time key=range(74003204, 74004228) : 1.416ms Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 92.532ms ========================== Querying shuffled bookcorpus ========================== Avg access time key=1 : 0.187ms Avg access time key=74004227 : 6.642ms Avg access time key=range(74003204, 74004228) : 90.941ms Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 3448.456ms ``` for the new one using interpolation search: ```python >>> python speed.py Loaded dataset 'bookcorpus', len=74004228, nbytes=4835358766 ========================= Querying unshuffled bookcorpus ========================= Avg access time key=1 : 0.076ms Avg access time key=74004227 : 0.056ms Avg access time key=range(74003204, 74004228) : 1.807ms Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 24.028ms ========================== Querying shuffled bookcorpus ========================== Avg access time key=1 : 0.061ms Avg access time key=74004227 : 0.058ms Avg access time key=range(74003204, 74004228) : 22.166ms Avg access time key=RandIter(low=0, high=74004228, size=1024, seed=42): 42.757ms ``` The RandIter class is just an iterable of 1024 random indices from 0 to 74004228. Here is also a plot showing the speed improvement depending on the dataset size:  ## Implementation details: - `datasets.table.Table` objects implement interpolation search for the `slice` method - The interpolation search requires to store the offsets of all the chunks of a table. The offsets are stored when the `Table` is initialized. - `datasets.table.Table.slice` returns a `datasets.table.Table` using interpolation search - `datasets.table.Table.fast_slice` returns a `pyarrow.Table` object using interpolation search. This is useful to get a part of a dataset if we don't need the indexing structure for future computations. For example it's used when querying an example as a dictionary. - Now a `Dataset` object is always backed by a `datasets.table.Table` object. If one passes a `pyarrow.Table` to initialize a `Dataset`, then it's converted to a `datasets.table.Table` ## Checklist: - [x] implement interpolation search - [x] use `datasets.table.Table` in `Dataset` objects - [x] update current tests - [x] add tests for interpolation search - [x] comments and docstring - [x] add the benchmark to the CI Fix #1803.

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MDExOlB1bGxSZXF1ZXN0NjAxNzc4NDc4

Add Validation For README

closed

CONTRIBUTOR

Hi @lhoestq, @yjernite This is a simple Readme parser. All classes specific to different sections can inherit `Section` class, and we can define more attributes in each. Let me know if this is going in the right direction :) Currently the output looks like this, for `to_dict()` on `FashionMNIST` `README.md`: ```json { "name": "./datasets/fashion_mnist/README.md", "attributes": "", "subsections": [ { "name": "Dataset Card for FashionMNIST", "attributes": "", "subsections": [ { "name": "Table of Contents", "attributes": "- [Dataset Description](#dataset-description)\n - [Dataset Summary](#dataset-summary)\n - [Supported Tasks](#supported-tasks-and-leaderboards)\n - [Languages](#languages)\n- [Dataset Structure](#dataset-structure)\n - [Data Instances](#data-instances)\n - [Data Fields](#data-instances)\n - [Data Splits](#data-instances)\n- [Dataset Creation](#dataset-creation)\n - [Curation Rationale](#curation-rationale)\n - [Source Data](#source-data)\n - [Annotations](#annotations)\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\n- [Considerations for Using the Data](#considerations-for-using-the-data)\n - [Social Impact of Dataset](#social-impact-of-dataset)\n - [Discussion of Biases](#discussion-of-biases)\n - [Other Known Limitations](#other-known-limitations)\n- [Additional Information](#additional-information)\n - [Dataset Curators](#dataset-curators)\n - [Licensing Information](#licensing-information)\n - [Citation Information](#citation-information)\n - [Contributions](#contributions)", "subsections": [] }, { "name": "Dataset Description", "attributes": "- **Homepage:** [GitHub](https://github.com/zalandoresearch/fashion-mnist)\n- **Repository:** [GitHub](https://github.com/zalandoresearch/fashion-mnist)\n- **Paper:** [arXiv](https://arxiv.org/pdf/1708.07747.pdf)\n- **Leaderboard:**\n- **Point of Contact:**", "subsections": [ { "name": "Dataset Summary", "attributes": "Fashion-MNIST is a dataset of Zalando's article images\u2014consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.", "subsections": [] }, { "name": "Supported Tasks and Leaderboards", "attributes": "[More Information Needed]", "subsections": [] }, { "name": "Languages", "attributes": "[More Information Needed]", "subsections": [] } ] }, { "name": "Dataset Structure", "attributes": "", "subsections": [ { "name": "Data Instances", "attributes": "A data point comprises an image and its label.", "subsections": [] }, { "name": "Data Fields", "attributes": "- `image`: a 2d array of integers representing the 28x28 image.\n- `label`: an integer between 0 and 9 representing the classes with the following mapping:\n | Label | Description |\n | --- | --- |\n | 0 | T-shirt/top |\n | 1 | Trouser |\n | 2 | Pullover |\n | 3 | Dress |\n | 4 | Coat |\n | 5 | Sandal |\n | 6 | Shirt |\n | 7 | Sneaker |\n | 8 | Bag |\n | 9 | Ankle boot |", "subsections": [] }, { "name": "Data Splits", "attributes": "The data is split into training and test set. The training set contains 60,000 images and the test set 10,000 images.", "subsections": [] } ] }, { "name": "Dataset Creation", "attributes": "", "subsections": [ { "name": "Curation Rationale", "attributes": "**From the arXiv paper:**\nThe original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. \"If it doesn't work on MNIST, it won't work at all\", they said. \"Well, if it does work on MNIST, it may still fail on others.\"\nHere are some good reasons:\n- MNIST is too easy. Convolutional nets can achieve 99.7% on MNIST. Classic machine learning algorithms can also achieve 97% easily. Check out our side-by-side benchmark for Fashion-MNIST vs. MNIST, and read \"Most pairs of MNIST digits can be distinguished pretty well by just one pixel.\"\n- MNIST is overused. In this April 2017 Twitter thread, Google Brain research scientist and deep learning expert Ian Goodfellow calls for people to move away from MNIST.\n- MNIST can not represent modern CV tasks, as noted in this April 2017 Twitter thread, deep learning expert/Keras author Fran\u00e7ois Chollet.", "subsections": [] }, { "name": "Source Data", "attributes": "", "subsections": [ { "name": "Initial Data Collection and Normalization", "attributes": "**From the arXiv paper:**\nFashion-MNIST is based on the assortment on Zalando\u2019s website. Every fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit. The original picture has a light-gray background (hexadecimal color: #fdfdfd) and stored in 762 \u00d7 1000 JPEG format. For efficiently serving different frontend components, the original picture is resampled with multiple resolutions, e.g. large, medium, small, thumbnail and tiny.\nWe use the front look thumbnail images of 70,000 unique products to build Fashion-MNIST. Those products come from different gender groups: men, women, kids and neutral. In particular, whitecolor products are not included in the dataset as they have low contrast to the background. The thumbnails (51 \u00d7 73) are then fed into the following conversion pipeline:\n1. Converting the input to a PNG image.\n2. Trimming any edges that are close to the color of the corner pixels. The \u201ccloseness\u201d is defined by the distance within 5% of the maximum possible intensity in RGB space.\n3. Resizing the longest edge of the image to 28 by subsampling the pixels, i.e. some rows and columns are skipped over.\n4. Sharpening pixels using a Gaussian operator of the radius and standard deviation of 1.0, with increasing effect near outlines.\n5. Extending the shortest edge to 28 and put the image to the center of the canvas.\n6. Negating the intensities of the image.\n7. Converting the image to 8-bit grayscale pixels.", "subsections": [] }, { "name": "Who are the source image producers?", "attributes": "**From the arXiv paper:**\nEvery fashion product on Zalando has a set of pictures shot by professional photographers, demonstrating different aspects of the product, i.e. front and back looks, details, looks with model and in an outfit.", "subsections": [] } ] }, { "name": "Annotations", "attributes": "", "subsections": [ { "name": "Annotation process", "attributes": "**From the arXiv paper:**\nFor the class labels, they use the silhouette code of the product. The silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando. Each product Zalando is the Europe\u2019s largest online fashion platform. Each product contains only one silhouette code.", "subsections": [] }, { "name": "Who are the annotators?", "attributes": "**From the arXiv paper:**\nThe silhouette code is manually labeled by the in-house fashion experts and reviewed by a separate team at Zalando.", "subsections": [] } ] }, { "name": "Personal and Sensitive Information", "attributes": "[More Information Needed]", "subsections": [] } ] }, { "name": "Considerations for Using the Data", "attributes": "", "subsections": [ { "name": "Social Impact of Dataset", "attributes": "[More Information Needed]", "subsections": [] }, { "name": "Discussion of Biases", "attributes": "[More Information Needed]", "subsections": [] }, { "name": "Other Known Limitations", "attributes": "[More Information Needed]", "subsections": [] } ] }, { "name": "Additional Information", "attributes": "", "subsections": [ { "name": "Dataset Curators", "attributes": "Han Xiao and Kashif Rasul and Roland Vollgraf", "subsections": [] }, { "name": "Licensing Information", "attributes": "MIT Licence", "subsections": [] }, { "name": "Citation Information", "attributes": "@article{DBLP:journals/corr/abs-1708-07747,\n author = {Han Xiao and\n Kashif Rasul and\n Roland Vollgraf},\n title = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning\n Algorithms},\n journal = {CoRR},\n volume = {abs/1708.07747},\n year = {2017},\n url = {http://arxiv.org/abs/1708.07747},\n archivePrefix = {arXiv},\n eprint = {1708.07747},\n timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},\n biburl = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}", "subsections": [] }, { "name": "Contributions", "attributes": "Thanks to [@gchhablani](https://github.com/gchablani) for adding this dataset.", "subsections": [] } ] } ] } ] } ``` Thanks, Gunjan

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MDU6SXNzdWU4NDE5NTQ1MjE=

dataset viewer does not work anymore

closed

NONE

Hi I normally use this link to see all datasets and how I can load them https://huggingface.co/datasets/viewer/ Now I am getting 502 Bad Gateway nginx/1.18.0 (Ubuntu) could you bring this webpage back ? this was very helpful @lhoestq thanks for your help

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MDExOlB1bGxSZXF1ZXN0NjAxMjg2MjIy

copy.deepcopy os.environ instead of copy

closed

CONTRIBUTOR

Fixes: https://github.com/huggingface/datasets/issues/2115 - bug fix: using envrion.copy() returns a dict. - using deepcopy(environ) returns an `_environ` object - Changing the datatype of the _environ object can break code, if subsequent libraries perform operations using apis exclusive to the environ object, like `environ.getenv()` for example. Testing: Tested the change on my terminal: ``` >>> import os >>> x = deepcopy(os.environ) >>> y = os.environ >>> x is y False >>> isinstance(x, type(os.environ)) True >>> z = os.environ.copy() >>> isinstance(z, type(os.environ)) False >>> isinstance(z, dict) True ```

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MDExOlB1bGxSZXF1ZXN0NjAxMjgzMDUx

Remove os.environ.copy in Dataset.map

closed

CONTRIBUTOR

Replace `os.environ.copy` with in-place modification Fixes #2115

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MDU6SXNzdWU4NDE1MzUyODM=

load_metric from local "glue.py" meet error 'NoneType' object is not callable

closed

NONE

actual_task = "mnli" if task == "mnli-mm" else task dataset = load_dataset(path='/home/glue.py', name=actual_task) metric = load_metric(path='/home/glue.py', name=actual_task) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-8-7ab77a465d81> in <module> 1 actual_task = "mnli" if task == "mnli-mm" else task 2 dataset = load_dataset(path='/home/jcli/glue.py', name=actual_task) ----> 3 metric = load_metric(path='/home/jcli/glue.py', name=actual_task) ~/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs) 508 keep_in_memory=keep_in_memory, 509 experiment_id=experiment_id, --> 510 **metric_init_kwargs, 511 ) 512 TypeError: 'NoneType' object is not callable Please help

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MDU6SXNzdWU4NDE0ODEyOTI=

Creating custom dataset results in error while calling the map() function

closed

NONE

calling `map()` of `datasets` library results into an error while defining a Custom dataset. Reproducible example: ``` import datasets class MyDataset(datasets.Dataset): def __init__(self, sentences): "Initialization" self.samples = sentences def __len__(self): "Denotes the total number of samples" return len(self.samples) def __getitem__(self, index): "Generates one sample of data" # Select sample # Load data and get label samples = self.samples[index] return samples def preprocess_function_train(examples): inputs = examples labels = [example+tokenizer.eos_token for example in examples ] inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True) labels = tokenizer(labels, max_length=30, padding=True, truncation=True) model_inputs = inputs model_inputs["labels"] = labels["input_ids"] print("about to return") return model_inputs ##train["sentence"] is dataframe column train_dataset = MyDataset(train['sentence'].values.tolist()) train_dataset = train_dataset.map( preprocess_function, batched = True, batch_size=32 ) ``` Stack trace of error: ``` Traceback (most recent call last): File "dir/train_generate.py", line 362, in <module> main() File "dir/train_generate.py", line 245, in main train_dataset = train_dataset.map( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map return self._map_single( File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper unformatted_columns = set(self.column_names) - set(self._format_columns or []) File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names return self._data.column_names AttributeError: 'MyDataset' object has no attribute '_data' ```

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MDU6SXNzdWU4NDEyODM5NzQ=

The datasets.map() implementation modifies the datatype of os.environ object

closed

NONE

In our testing, we noticed that the datasets.map() implementation is modifying the datatype of python os.environ object from '_Environ' to 'dict'. This causes following function calls to fail as follows: ` x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None) TypeError: get() takes no keyword arguments ` It looks like the following line in datasets.map implementation introduced this functionality. https://github.com/huggingface/datasets/blob/0cb1ac06acb0df44a1cf4128d03a01865faa2504/src/datasets/arrow_dataset.py#L1421 Here is the test script to reproduce this error. ``` from datasets import load_dataset from transformers import AutoTokenizer import os def test_train(): model_checkpoint = "distilgpt2" datasets = load_dataset('wikitext', 'wikitext-2-raw-v1') tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True) tokenizer.pad_token = tokenizer.eos_token def tokenize_function(examples): y = tokenizer(examples['text'], truncation=True, max_length=64) return y x = os.environ.get("TEST_ENV_VARIABLE_BEFORE_dataset_map", default=None) print(f"Testing environment variable: TEST_ENV_VARIABLE_BEFORE_dataset_map {x}") print(f"Data type of os.environ before datasets.map = {os.environ.__class__.__name__}") datasets.map(tokenize_function, batched=True, num_proc=2, remove_columns=["text"]) print(f"Data type of os.environ after datasets.map = {os.environ.__class__.__name__}") x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None) print(f"Testing environment variable: TEST_ENV_VARIABLE_AFTER_dataset_map {x}") if __name__ == "__main__": test_train() ```

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MDExOlB1bGxSZXF1ZXN0NjAwOTc1MTA3

Support for legal NLP datasets (EURLEX, ECtHR cases and EU-REG-IR)

closed

CONTRIBUTOR

Add support for two legal NLP datasets: - EURLEX (https://www.aclweb.org/anthology/P19-1636/) - ECtHR cases (https://arxiv.org/abs/2103.13084) - EU-REG-IR (https://arxiv.org/abs/2101.10726)

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MDExOlB1bGxSZXF1ZXN0NjAwOTYxMDEz

Implement Dataset as context manager

closed

MEMBER

When used as context manager, it would be safely deleted if some exception is raised. This will avoid > During handling of the above exception, another exception occurred:

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MDExOlB1bGxSZXF1ZXN0NjAwODgyMjA0

Support for legal NLP datasets (EURLEX and ECtHR cases)

closed

CONTRIBUTOR

Add support for two legal NLP datasets: - EURLEX (https://www.aclweb.org/anthology/P19-1636/) - ECtHR cases (https://arxiv.org/abs/2103.13084)

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MDExOlB1bGxSZXF1ZXN0NjAwODY4OTg5

Compute WER metric iteratively

closed

MEMBER

Compute WER metric iteratively to avoid MemoryError. Fix #2078.

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MDExOlB1bGxSZXF1ZXN0NjAwNjI1NDQ5

Fix incorrect assertion in builder.py

closed

CONTRIBUTOR

Fix incorrect num_examples comparison assertion in builder.py

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MDExOlB1bGxSZXF1ZXN0NjAwNTg1MzM5

Add more issue templates and customize issue template chooser

closed

MEMBER

When opening an issue, it is not evident for the users how to choose a blank issue template. There is a link at the bottom of all the other issue templates (`Don’t see your issue here? Open a blank issue.`), but this is not very visible for users. This is the reason why many users finally chose the `add-dataset` template instead (this is more visible) for issues that indeed are not requesting the addition of a new dataset. ~~With this PR, the default blank issue template would be as visible as the other templates (as the `add-dataset` template), thus making easier for the users to choose it.~~ With this PR: - more issue templates, besides `add-dataset`, are added: `bug-report` and `feature-request` - the issue template chooser is customized, so that it now includes a link to `Discussions` for questions

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MDU6SXNzdWU4NDAxODEwNTU=

Is there a way to use a GPU only when training an Index in the process of add_faisis_index?

open

NONE

Motivation - Some FAISS indexes like IVF consist of the training step that clusters the dataset into a given number of indexes. It would be nice if we can use a GPU to do the training step and covert the index back to CPU as mention in [this faiss example](https://gist.github.com/mdouze/46d6bbbaabca0b9778fca37ed2bcccf6).

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MDExOlB1bGxSZXF1ZXN0NTk5NTAxODE5

Metadata validation

closed

CONTRIBUTOR

- `pydantic` metadata schema with dedicated validators against our taxonomy - ci script to validate new changes against this schema and start a vertuous loop - soft validation on tasks ids since we expect the taxonomy to undergo some changes in the near future for reference with the current validation we have ~365~ 378 datasets with invalid metadata! full error report [_here_.](https://gist.github.com/theo-m/61b3c0c47fc6121d08d3174bd4c2a26b)

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MDU6SXNzdWU4MzkwODQyNjQ=

WMT19 Dataset for Kazakh-English is not formatted correctly

open

NONE

In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error. The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here: > Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді. > > Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды. > > Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code ``` import datasets from datasets import load_dataset dataset = load_dataset('wmt19', 'kk-en') for key in dataset['train']['translation']: if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']: print(key['en']) print(key['kk']) break ``` we get: > 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды. > The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one. Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface.

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MDU6SXNzdWU4MzkwNTkyMjY=

Request to remove S2ORC dataset

open

NONE

Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks!

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MDU6SXNzdWU4MzkwMjc4MzQ=

Trouble loading wiki_movies

closed

NONE

Hello, I am trying to load_dataset("wiki_movies") and it gives me this error - `FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py` Trying to do `python run_mlm.py \ --model_name_or_path roberta-base \ --dataset_name wiki_movies \` also gives the same error. Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago. Thank you!

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MDU6SXNzdWU4Mzg5NDY5MTY=

citation, homepage, and license fields of `dataset_info.json` are duplicated many times

closed

NONE

This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation. Example result: ``` "citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n ``` @lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times.

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MDExOlB1bGxSZXF1ZXN0NTk4OTEyNzUw

Move Dataset.to_csv to csv module

closed

MEMBER

Move the implementation of `Dataset.to_csv` to module `datasets.io.csv`.

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MDExOlB1bGxSZXF1ZXN0NTk4NzQzMDM4

MIAM dataset - new citation details

closed

CONTRIBUTOR

Hi @lhoestq, I have updated the citations to reference an OpenReview preprint.

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MDExOlB1bGxSZXF1ZXN0NTk4NzMzOTM0

Fix deprecated warning message and docstring

closed

MEMBER

Fix deprecated warnings: - Use deprecated Sphinx directive in docstring - Fix format of deprecated message - Raise FutureWarning

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MDU6SXNzdWU4Mzg1MjM4MTk=

load_from_disk takes a long time to load local dataset

closed

NONE

I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though). Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers? Tagging @lhoestq since you seem to be working on these issues and PRs :)

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MDU6SXNzdWU4Mzg0NDc5NTk=

SQuAD version

closed

NONE

Hi~ I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it.

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MDExOlB1bGxSZXF1ZXN0NTk4MzM4MTA3

fixes issue #1110 by descending further if `obj["_type"]` is a dict

closed

CONTRIBUTOR

Check metrics

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MDU6SXNzdWU4MzgwMzgzNzk=

CoNLL 2003 dataset not including German

closed

NONE

Hello, thanks for all the work on developing and maintaining this amazing platform, which I am enjoying working with! I was wondering if there is a reason why the German CoNLL 2003 dataset is not included in the [repository](https://github.com/huggingface/datasets/tree/master/datasets/conll2003), since a copy of it could be found in some places on the internet such as GitHub? I could help adding the German data to the hub, unless there are some copyright issues that I am unaware of... This is considering that many work use the union of CoNLL 2002 and 2003 datasets for comparing cross-lingual NER transfer performance in `en`, `de`, `es`, and `nl`. E.g., [XLM-R](https://www.aclweb.org/anthology/2020.acl-main.747.pdf). ## Adding a Dataset - **Name:** CoNLL 2003 German - **Paper:** https://www.aclweb.org/anthology/W03-0419/ - **Data:** https://github.com/huggingface/datasets/tree/master/datasets/conll2003

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MDExOlB1bGxSZXF1ZXN0NTk3NTgyNjUx

Fix: Allows a feature to be named "_type"

closed

CONTRIBUTOR

This PR tries to fix issue #1110. Sorry for taking so long to come back to this. It's a simple fix, but i am not sure if it works for all possible types of `obj`. Let me know what you think @lhoestq

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MDU6SXNzdWU4MzY5ODQwNDM=

How to disable making arrow tables in load_dataset ?

closed

NONE

Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ?

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MDExOlB1bGxSZXF1ZXN0NTk3Mjk4ODI3

Fix copy snippet in docs

closed

CONTRIBUTOR

With this change the lines starting with `...` in the code blocks can be properly copied to clipboard.

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MDExOlB1bGxSZXF1ZXN0NTk3MjgwNTEy

Add machine translated multilingual STS benchmark dataset

closed

CONTRIBUTOR

also see here https://github.com/PhilipMay/stsb-multi-mt

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MDU6SXNzdWU4MzY3ODgwMTk=

Add documentaton for dataset README.md files

closed

CONTRIBUTOR

Hi, the dataset README files have special headers. Somehow a documenation of the allowed values and tags is missing. Could you add that? Just to give some concrete questions that should be answered imo: - which values can be passted to multilinguality? - what should be passed to language_creators? - which values should licenses have? What do I say when it is a custom license? Should I add a link? - how should I choose size_categories ? What are valid ranges? - what are valid task_categories? Thanks Philip

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MDExOlB1bGxSZXF1ZXN0NTk3MjQ4Mzk1

change bibtex template to author instead of authors

closed

CONTRIBUTOR

Hi, IMO when using BibTex Author should be used instead of Authors. See here: http://www.bibtex.org/Using/de/ Thanks Philip

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MDExOlB1bGxSZXF1ZXN0NTk3MDg4NTk2

Update metadata if dataset features are modified

closed

CONTRIBUTOR

This PR adds a decorator that updates the dataset metadata if a previously executed transform modifies its features. Fixes #2083

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MDExOlB1bGxSZXF1ZXN0NTk2Nzg0Mjcz

change user permissions to -rw-r--r--

closed

CONTRIBUTOR

Fix for #2065

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MDExOlB1bGxSZXF1ZXN0NTk2NDYyOTc2

Fix max_wait_time in requests

closed

MEMBER

it was handled as a min time, not max cc @SBrandeis

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MDU6SXNzdWU4MzU3NTA2NzE=

CUAD - Contract Understanding Atticus Dataset

closed

CONTRIBUTOR

## Adding a Dataset - **Name:** CUAD - Contract Understanding Atticus Dataset - **Description:** As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community. - **Paper:** https://arxiv.org/abs/2103.06268 - **Data:** https://github.com/TheAtticusProject/cuad/ - **Motivation:** good domain specific datasets are valuable Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).

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MDU6SXNzdWU4MzU2OTU0MjU=

`concatenate_datasets` throws error when changing the order of datasets to concatenate

closed

MEMBER

Hey, I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO. Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing

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MDExOlB1bGxSZXF1ZXN0NTk2MDY1NTM0

Updated card using information from data statement and datasheet

closed

CONTRIBUTOR

I updated and clarified the REFreSD [data card](https://github.com/mcmillanmajora/datasets/blob/refresd_card/datasets/refresd/README.md) with information from the Eleftheria's [website](https://elbria.github.io/post/refresd/). I added brief descriptions where the initial card referred to the paper, and I also recreated some of the tables in the paper to show relevant dataset statistics. I'll email Eleftheria to see if she has any comments on the card.

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MDExOlB1bGxSZXF1ZXN0NTk1ODE3OTM4

Fix docstrings issues

closed

MEMBER

Fix docstring issues.

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MDU6SXNzdWU4MzUwMjMwMDA=

Multidimensional arrays in a Dataset

closed

NONE

Hi, I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row. The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`) ``` from datasets import Dataset import pandas as pd import numpy as np dataset = pd.DataFrame({ 'bbox': [ np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]), np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]) ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) ``` Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists. ``` import torch from datasets import Dataset import pandas as pd dataset = pd.DataFrame({ 'bbox': [ [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]], [[1,2,3,4],[1,2,3,4],[1,2,3,4]] ], 'input_ids': [1, 2, 3, 4] }) dataset = Dataset.from_pandas(dataset) def test(examples): return {'bbbox': torch.Tensor(examples['bbox'])} dataset = dataset.map(test) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) def test2(examples): return {'bbbox': torch.stack(examples['bbox'])} dataset = dataset.map(test2) print(dataset[0]['bbox']) print(dataset[0]['bbbox']) ``` Is is possible to support n-D arrays/tensors in datasets? It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263).

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MDExOlB1bGxSZXF1ZXN0NTk1NjU2MDQ5

Refactorize Metric.compute signature to force keyword arguments only

closed

MEMBER

Minor refactoring of Metric.compute signature to force the use of keyword arguments, by using the single star syntax.

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MDU6SXNzdWU4MzQ2OTQ4MTk=

MemoryError when computing WER metric

closed

NONE

Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation: ``` wer = load_metric("wer") print(wer.compute(predictions=result["predicted"], references=result["target"])) ``` However, I receive the following exception: `Traceback (most recent call last): File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module> print(wer.compute(predictions=result["predicted"], references=result["target"])) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute output = self._compute(predictions=predictions, references=references, **kwargs) File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute return wer(references, predictions) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer truth, hypothesis, truth_transform, hypothesis_transform, **kwargs File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures H, S, D, I = _get_operation_counts(truth, hypothesis) File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts editops = Levenshtein.editops(source_string, destination_string) MemoryError` My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.

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MDExOlB1bGxSZXF1ZXN0NTk1NDI0MTYw

Bump huggingface_hub version

closed

CONTRIBUTOR

`0.0.2 => 0.0.6`

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MDU6SXNzdWU4MzQ0NDUyOTY=

Issue: Dataset download error

open

NONE

The download link in `iwslt2017.py` file does not seem to work anymore. For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz` Would be nice if we could modify it script and use the new downloadable link?

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MDU6SXNzdWU4MzQzMDEyNDY=

ConnectionError: Couldn't reach common_voice.py

closed

NONE

When I run: from datasets import load_dataset, load_metric common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation") common_voice_test = load_dataset("common_voice", "zh-CN", split="test") Got: ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py Version: 1.4.1 Thanks! @lhoestq @LysandreJik @thomwolf

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MDExOlB1bGxSZXF1ZXN0NTk1MTIzMjYw

Fix size categories in YAML Tags

closed

CONTRIBUTOR

This PR fixes several `size_categories` in YAML tags and makes them consistent. Additionally, I have added a few more categories after `1M`, up to `1T`. I would like to add that to the streamlit app also. This PR also adds a couple of infos that I found missing. The code for generating this: ```python for dataset in sorted(os.listdir('./datasets/')): if '.' not in dataset and dataset not in ['c4', 'csv', 'downloads', 'cc100', 'ccaligned_multilingual', 'celeb_a', 'chr_en', 'emea', 'glue']: infos = {} stats = {} st = '' with open(f'datasets/{dataset}/README.md') as f: d = f.read() start_dash = d.find('---') + 3 end_dash = d[start_dash:].find('---') + 3 rest_text = d[end_dash + 3:] try: full_yaml = OmegaConf.create(d[start_dash:end_dash]) readme = OmegaConf.to_container(full_yaml['size_categories'], resolve=True) except Exception as e: print(e) continue try: with open(f'datasets/{dataset}/dataset_infos.json') as f: data = json.load(f) except Exception as e: print(e) continue # Skip those without infos. done_set = set([]) num_keys = len(data.keys()) for keys in data: # dataset = load_dataset('opus100', f'{dirs}') total = 0 for split in data[keys]['splits']: total = total + data[keys]['splits'][split]['num_examples'] if total < 1000: st += "- n<1K" + '\n' infos[keys] = ["n<1K"] elif total >= 1000 and total < 10000: infos[keys] = ["1K<n<10K"] elif total >= 10000 and total < 100000: infos[keys] = ["10K<n<100K"] elif total >= 100000 and total < 1000000: infos[keys] = ["100K<n<1M"] elif total >= 1000000 and total < 10000000: infos[keys] = ["1M<n<10M"] elif total >= 10000000 and total < 100000000: infos[keys] = ["10M<n<100M"] elif total >= 100000000 and total < 1000000000: infos[keys] = ["100M<n<1B"] elif total >= 1000000000 and total < 10000000000: infos[keys] = ["1B<n<10B"] elif total >= 10000000000 and total < 100000000000: infos[keys] = ["10B<n<100B"] elif total >= 100000000000 and total < 1000000000000: infos[keys] = ["100B<n<1T"] else: infos[keys] = ["n>1T"] done_set = done_set.union(infos[keys]) if (isinstance(readme, list) and list(infos.values())[0] != readme) or (isinstance(readme, dict) and readme != infos): print('-' * 30) print(done_set) print(f"Changing Full YAML for {dataset}") print(OmegaConf.to_yaml(full_yaml)) if len(done_set) == 1: full_yaml['size_categories'] = list(done_set) else: full_yaml['size_categories'] = dict([(k, v) for k, v in sorted(infos.items(), key=lambda x: x[0])]) full_yaml_string = OmegaConf.to_yaml(full_yaml) print('-' * 30) print(full_yaml_string) inp = input('Do you wish to continue?(Y/N)') if inp == 'Y': with open(f'./datasets/{dataset}/README.md', 'w') as f: f.write('---\n') f.write(full_yaml_string) f.write('---') f.write(rest_text) else: break ``` Note that the lower-bound is inclusive. I'm unsure if this is how it is done in the tagging app. EDIT: It would be great if there was a way to make the task categories consistent too. For this, the streamlit app can look into all the datasets and check for existing categories and show them in the list. This may add some consistency. EDIT: I understand this will not work for cases where only the infos for some of the configs are present, for example: `ccaligned_multingual` has only 5 out of several configs present, and infos has only information about them. Hence, I have skipped a few datasets in the code, if there are more such datasets, then I'll ignore them too.

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MDExOlB1bGxSZXF1ZXN0NTk1MDYyMzQ2

Fixes check of TF_AVAILABLE and TORCH_AVAILABLE

closed

MEMBER

# What is this PR doing This PR implements the checks if `Tensorflow` and `Pytorch` are available the same way as `transformers` does it. I added the additional checks for the different `Tensorflow` and `torch` versions. #2068

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MDExOlB1bGxSZXF1ZXN0NTk0OTQ5NjA4

Fix docstring issues

closed

MEMBER

Fix docstring issues.

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MDU6SXNzdWU4MzM5NTA4MjQ=

Multiprocessing is slower than single process

closed

CONTRIBUTOR

```python # benchmark_filter.py import logging import sys import time from datasets import load_dataset, set_caching_enabled if __name__ == "__main__": set_caching_enabled(False) logging.basicConfig(level=logging.DEBUG) bc = load_dataset("bookcorpus") now = time.time() try: bc["train"].filter(lambda x: len(x["text"]) < 64, num_proc=int(sys.argv[1])) except Exception as e: print(f"cancelled: {e}") elapsed = time.time() - now print(elapsed) ``` Running `python benchmark_filter.py 1` (20min+) is faster than `python benchmark_filter.py 2` (2hrs+)

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MDU6SXNzdWU4MzM3OTkwMzU=

ArrowInvalid issue for squad v2 dataset

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Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb). In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error: `ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178` My code is as follows: ``` def generate_candidate_questions(examples): val_questions = examples["question"] candididate_questions = random.sample(datasets["train"]["question"], len(val_questions)) candididate_questions = [x[:max_length] for x in candididate_questions] return candididate_questions def prepare_validation_features(examples, use_mixing=False): pad_on_right = tokenizer.padding_side == "right" tokenized_examples = tokenizer( examples["question" if pad_on_right else "context"], examples["context" if pad_on_right else "question"], truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) if use_mixing: candidate_questions = generate_candidate_questions(examples) tokenized_candidates = tokenizer( candidate_questions if pad_on_right else examples["context"], examples["context"] if pad_on_right else candidate_questions, truncation="only_second" if pad_on_right else "only_first", max_length=max_length, stride=doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length", ) sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") tokenized_examples["example_id"] = [] if use_mixing: tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"] tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"] tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"] for i in range(len(tokenized_examples["input_ids"])): sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples validation_features = datasets["validation"].map( lambda xs: prepare_validation_features(xs, True), batched=True, remove_columns=datasets["validation"].column_names ) ``` I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help!

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MDExOlB1bGxSZXF1ZXN0NTk0NzA5ODYw

Add and fix docstring for NamedSplit

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Add and fix docstring for `NamedSplit`, which was missing.

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MDU6SXNzdWU4MzM2MDI4MzI=

PyTorch not available error on SageMaker GPU docker though it is installed

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I get en error when running data loading using SageMaker SDK ``` File "main.py", line 34, in <module> run_training() File "main.py", line 25, in run_training dm.setup('fit') File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn return fn(*args, **kwargs) File "/opt/ml/code/data_module.py", line 103, in setup self.dataset[split].set_format(type="torch", columns=self.columns) File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper out = func(self, *args, **kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format _ = get_formatter(type, **format_kwargs) File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type] ValueError: PyTorch needs to be installed to be able to return PyTorch tensors. ``` when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines ``` self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns] self.dataset[split].set_format(type="torch", columns=self.columns) ``` The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 . By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`. Also as a first line in the data loading module I have ``` import os os.environ["USE_TF"] = "0" os.environ["USE_TORCH"] = "1" ```` But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack. Many Thanks!

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