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concatenate_datasets loads all the data into memory
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"Therefore, when I try to concatenate larger datasets (5x 35GB data sets) I also get an out of memory error, since over 90GB of swap space was used at the time of the crash:\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nMemoryError Traceback (most recent call last)\r\n<ipython-input-6-9766d77530b9> in <module>\r\n 20 print(file_name)\r\n 21 cv_batch = load_from_disk(file_name)\r\n---> 22 cv_sampled_train = concatenate_datasets([cv_sampled_train, cv_batch])\r\n 23 \r\n 24 print(\"Saving to disk!\")\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\arrow_dataset.py in concatenate_datasets(dsets, info, split, axis)\r\n 2891 \r\n 2892 # Concatenate tables\r\n-> 2893 table = concat_tables([dset._data for dset in dsets if len(dset._data) > 0], axis=axis)\r\n 2894 table = update_metadata_with_features(table, None)\r\n 2895 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in concat_tables(tables, axis)\r\n 837 if len(tables) == 1:\r\n 838 return tables[0]\r\n--> 839 return ConcatenationTable.from_tables(tables, axis=axis)\r\n 840 \r\n 841 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in from_tables(cls, tables, axis)\r\n 697 return result\r\n 698 \r\n--> 699 blocks = to_blocks(tables[0])\r\n 700 for table in tables[1:]:\r\n 701 table_blocks = to_blocks(table)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in to_blocks(table)\r\n 669 return [[InMemoryTable(table)]]\r\n 670 elif isinstance(table, ConcatenationTable):\r\n--> 671 return copy.deepcopy(table.blocks)\r\n 672 else:\r\n 673 return [[table]]\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 151 copier = getattr(x, \"__deepcopy__\", None)\r\n 152 if copier is not None:\r\n--> 153 y = copier(memo)\r\n 154 else:\r\n 155 reductor = dispatch_table.get(cls)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in __deepcopy__(self, memo)\r\n 143 # by adding it to the memo, self.table won't be copied\r\n 144 memo[id(self.table)] = self.table\r\n--> 145 return _deepcopy(self, memo)\r\n 146 \r\n 147 def __getstate__(self):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\datasets\\table.py in _deepcopy(x, memo)\r\n 62 memo[id(x)] = result\r\n 63 for k, v in x.__dict__.items():\r\n---> 64 setattr(result, k, copy.deepcopy(v, memo))\r\n 65 return result\r\n 66 \r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 170 y = x\r\n 171 else:\r\n--> 172 y = _reconstruct(x, memo, *rv)\r\n 173 \r\n 174 # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n 262 if deep and args:\r\n 263 args = (deepcopy(arg, memo) for arg in args)\r\n--> 264 y = func(*args)\r\n 265 if deep:\r\n 266 memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n 261 deep = memo is not None\r\n 262 if deep and args:\r\n--> 263 args = (deepcopy(arg, memo) for arg in args)\r\n 264 y = func(*args)\r\n 265 if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 170 y = x\r\n 171 else:\r\n--> 172 y = _reconstruct(x, memo, *rv)\r\n 173 \r\n 174 # If is its own copy, don't memoize.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)\r\n 262 if deep and args:\r\n 263 args = (deepcopy(arg, memo) for arg in args)\r\n--> 264 y = func(*args)\r\n 265 if deep:\r\n 266 memo[id(x)] = y\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <genexpr>(.0)\r\n 261 deep = memo is not None\r\n 262 if deep and args:\r\n--> 263 args = (deepcopy(arg, memo) for arg in args)\r\n 264 y = func(*args)\r\n 265 if deep:\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n 208 \r\n 209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210 y = [deepcopy(a, memo) for a in x]\r\n 211 # We're not going to put the tuple in the memo, but it's still important we\r\n 212 # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n 208 \r\n 209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210 y = [deepcopy(a, memo) for a in x]\r\n 211 # We're not going to put the tuple in the memo, but it's still important we\r\n 212 # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_tuple(x, memo, deepcopy)\r\n 208 \r\n 209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210 y = [deepcopy(a, memo) for a in x]\r\n 211 # We're not going to put the tuple in the memo, but it's still important we\r\n 212 # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in <listcomp>(.0)\r\n 208 \r\n 209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):\r\n--> 210 y = [deepcopy(a, memo) for a in x]\r\n 211 # We're not going to put the tuple in the memo, but it's still important we\r\n 212 # check for it, in case the tuple contains recursive mutable structures.\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 144 copier = _deepcopy_dispatch.get(cls)\r\n 145 if copier is not None:\r\n--> 146 y = copier(x, memo)\r\n 147 else:\r\n 148 if issubclass(cls, type):\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in _deepcopy_list(x, memo, deepcopy)\r\n 203 append = y.append\r\n 204 for a in x:\r\n--> 205 append(deepcopy(a, memo))\r\n 206 return y\r\n 207 d[list] = _deepcopy_list\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\copy.py in deepcopy(x, memo, _nil)\r\n 159 reductor = getattr(x, \"__reduce_ex__\", None)\r\n 160 if reductor is not None:\r\n--> 161 rv = reductor(4)\r\n 162 else:\r\n 163 reductor = getattr(x, \"__reduce__\", None)\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.__reduce_ex__()\r\n\r\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyarrow\\io.pxi in pyarrow.lib.Buffer.to_pybytes()\r\n\r\nMemoryError: \r\n\r\n```",
"Hi ! this looks like an important issue. Let me try to reproduce this.\r\nCc @samsontmr this might be related to the memory issue you have in #2134 ",
"@lhoestq Just went to open a similar issue.\r\n\r\nIt seems like deep copying (tested on master) the dataset object writes the table's record batches (`dset._data._batches`) into RAM.\r\n\r\nTo find the bug, I modified the `_deepcopy` function in `table.py` as follows:\r\n```python\r\ndef _deepcopy(x, memo: dict):\r\n \"\"\"deepcopy a regular class instance\"\"\"\r\n import psutil # pip install this package\r\n import time\r\n cls = x.__class__\r\n result = cls.__new__(cls)\r\n memo[id(x)] = result\r\n for k, v in x.__dict__.items():\r\n print(\"=\"* 50)\r\n print(\"Current memory:\", psutil.virtual_memory().percent)\r\n print(f\"Saving object {k} with value {v}\")\r\n setattr(result, k, copy.deepcopy(v, memo))\r\n time.sleep(5)\r\n print(\"Memory after copy:\", psutil.virtual_memory().percent)\r\n return result\r\n```\r\nTest script:\r\n```python\r\nimport copy\r\nfrom datasets import load_dataset\r\nbk = load_dataset(\"bookcorpus\", split=\"train\")\r\nbk_copy = copy.deepcopy(bk)\r\n```",
"Thanks for the insights @mariosasko ! I'm working on a fix.\r\nSince this is a big issue I'll make a patch release as soon as this is fixed",
"Hi @samsontmr @TaskManager91 the fix is on the master branch, feel free to install `datasets` from source and let us know if you still have issues",
"We just released `datasets` 1.6.2 that includes the fix :)",
"thanks it works like a charm! :)"
] | 2021-04-28T14:27:21Z
| 2021-05-03T08:41:55Z
| 2021-05-03T08:41:55Z
|
NONE
| null | null | null |
## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_from_disk
test_sampled_pro = load_from_disk("test_sampled_pro")
val_sampled_pro = load_from_disk("val_sampled_pro")
big_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])
# Loaded to memory
big_set.save_to_disk("big_set")
# Loaded to memory
big_set = concatenate_datasets([big_set, val_sampled_pro])
```
## Expected results
The data should be loaded into memory in batches and then saved directly to disk.
## Actual results
The entire data set is loaded into the memory and then saved to the hard disk.
## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.1
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
```
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PR_kwDODunzps4tOhRx
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|
Use template column_mapping to transmit_format instead of template features
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[
"Thanks for fixing!"
] | 2021-10-14T23:49:40Z
| 2021-10-15T14:40:05Z
| 2021-10-15T10:11:04Z
|
CONTRIBUTOR
| null | 0
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Use `template.column_mapping` to check for modified columns since `template.features` represent a generic template/column mapping.
Fix #3087
TODO:
- [x] Add a test
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PyTorch BatchSampler still loads from Dataset one-by-one
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"This change seems to come from a few months ago in the PyTorch side. That's good news and it means we may not need to pass a batch_sampler as soon as we add `Dataset.__getitems__` to get the optimal speed :)\r\n\r\nThanks for reporting ! Would you like to open a PR to add `__getitems__` and remove this outdated documentation ?",
"Yeah I figured this was the sort of thing that probably once worked. I can confirm that you no longer need the batch sampler, just `batch_size=n` in the `DataLoader`.\r\n\r\nI'll pass on the PR, I'm flat out right now, sorry."
] | 2023-02-06T01:14:55Z
| 2023-02-19T18:27:30Z
| 2023-02-19T18:27:30Z
|
NONE
| null | null | null |
### Describe the bug
In [the docs here](https://huggingface.co/docs/datasets/use_with_pytorch#use-a-batchsampler), it mentions the issue of the Dataset being read one-by-one, then states that using a BatchSampler resolves the issue.
I'm not sure if this is a mistake in the docs or the code, but it seems that the only way for a Dataset to be passed a list of indexes by PyTorch (instead of one index at a time) is to define a `__getitems__` method (note the plural) on the Dataset object, and since the HF Dataset doesn't have this, PyTorch executes [this line of code](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py#L58), reverting to fetching one-by-one.
### Steps to reproduce the bug
You can put a breakpoint in `Dataset.__getitem__()` or just print the args from there and see that it's called multiple times for a single `next(iter(dataloader))`, even when using the code from the docs:
```py
from torch.utils.data.sampler import BatchSampler, RandomSampler
batch_sampler = BatchSampler(RandomSampler(ds), batch_size=32, drop_last=False)
dataloader = DataLoader(ds, batch_sampler=batch_sampler)
```
### Expected behavior
The expected behaviour would be for it to fetch batches from the dataset, rather than one-by-one.
To demonstrate that there is room for improvement: once I have a HF dataset `ds`, if I just add this line:
```py
ds.__getitems__ = ds.__getitem__
```
...then the time taken to loop over the dataset improves considerably (for wikitext-103, from one minute to 13 seconds with batch size 32). Probably not a big deal in the grand scheme of things, but seems like an easy win.
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.31
- Python version: 3.10.8
- PyArrow version: 10.0.1
- Pandas version: 1.5.3
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IterableDataset returns duplicated data using PyTorch DDP
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"If you use huggingface trainer, you will find the trainer has wrapped a `IterableDatasetShard` to avoid duplication.\r\nSee:\r\nhttps://github.com/huggingface/transformers/blob/dfd818420dcbad68e05a502495cf666d338b2bfb/src/transformers/trainer.py#L835\r\n",
"If you want to support it by datasets natively, maybe we also need to change the code in `transformers` ?",
"Opened https://github.com/huggingface/transformers/issues/20770 to discuss this :)",
"Maybe something like this then ?\r\n```python\r\nfrom datasets.distributed import split_dataset_by_node\r\nds = split_dataset_by_node(ds, rank=rank, world_size=world_size)\r\n```\r\n\r\nFor map-style datasets the implementation is trivial (it can simply use `.shard()`).\r\n\r\nFor iterable datasets we would need to implement a new ExamplesIterable that would only iterate on a subset of the (possibly shuffled and re-shuffled after each epoch) list of shards, based on the rank and world size.",
"My plan is to skip examples by default to not end up with duplicates.\r\n\r\nAnd if a dataset has a number of shards that is a factor of the world size, then I'd make it more optimized by distributing the shards evenly across nodes instead.",
"Opened a PR here: https://github.com/huggingface/datasets/pull/5369\r\n\r\nfeel free to play with it and share your feedbacks :)",
"@lhoestq I add shuffle after split_dataset_by_node, duplicated data still exist. \r\nFor example, we have a directory named `mock_pretraining_data`, which has three files, `part-00000`, `part-00002`,`part-00002`. \r\nText in `part-00000` is like this: \r\n{\"id\": 0}\r\n{\"id\": 1}\r\n{\"id\": 2}\r\n{\"id\": 3}\r\n{\"id\": 4}\r\n{\"id\": 5}\r\n{\"id\": 6}\r\n{\"id\": 7}\r\n{\"id\": 8}\r\n{\"id\": 9}\r\n\r\nand `part-00001`\r\n{\"id\": 10}\r\n{\"id\": 11}\r\n{\"id\": 12}\r\n{\"id\": 13}\r\n{\"id\": 14}\r\n{\"id\": 15}\r\n{\"id\": 16}\r\n{\"id\": 17}\r\n{\"id\": 18}\r\n{\"id\": 19}\r\n\r\nand `part-00002`\r\n{\"id\": 20}\r\n{\"id\": 21}\r\n{\"id\": 22}\r\n{\"id\": 23}\r\n{\"id\": 24}\r\n{\"id\": 25}\r\n{\"id\": 26}\r\n{\"id\": 27}\r\n{\"id\": 28}\r\n{\"id\": 29}\r\n\r\nAnd code in `test_dist.py` like this,\r\n```python\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom datasets import load_dataset\r\nimport os\r\nfrom transformers import AutoTokenizer, NezhaForPreTraining\r\nfrom transformers import AdamW, get_linear_schedule_with_warmup\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport torch.distributed as dist\r\nfrom datasets.distributed import split_dataset_by_node\r\nfrom torch.nn.parallel import DistributedDataParallel as DDP\r\n\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '5,6,7'\r\n\r\ndist.init_process_group(\"nccl\")\r\nlocal_rank = int(os.environ['LOCAL_RANK'])\r\nworld_size = torch.distributed.get_world_size()\r\ndevice = torch.device('cuda', local_rank)\r\ndata_dir = './'\r\n\r\ndef load_trainset(train_path):\r\n dataset = load_dataset('json', data_dir=os.path.join(data_dir, train_path), split='train', streaming=True)\r\n return dataset\r\n\r\ndef collate_fn(examples):\r\n input_ids = []\r\n for example in examples:\r\n input_ids.append(example['id'])\r\n return torch.LongTensor(input_ids).to(device)\r\n\r\n\r\ndataset = load_trainset('mock_pretraining_data')\r\ndataset = split_dataset_by_node(dataset, rank=local_rank, world_size=world_size).shuffle(buffer_size=512)\r\n# train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\r\nbatch_size = 3\r\nprint('batch_size: {}'.format(batch_size))\r\ntrain_dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=collate_fn)\r\n\r\nfor x in train_dataloader:\r\n print({'rank': local_rank, 'id': x})\r\n```\r\nrun `python -m torch.distributed.launch --nproc_per_node=3 test_dist.py`\r\nThe output is\r\n```\r\n{'rank': 1, 'id': tensor([12, 15, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([16, 10, 18], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([17, 13, 19], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([11], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([0, 2, 9], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([4, 8, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([5, 3, 6], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([7], device='cuda:0')}\r\n{'rank': 2, 'id': tensor([13, 15, 14], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([19, 17, 18], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([12, 16, 11], device='cuda:2')}\r\n{'rank': 2, 'id': tensor([10], device='cuda:2')}\r\n```\r\n`part-00001` is loaded twice, `part-00002` isn't loaded.\r\n\r\nIf I run `python -m torch.distributed.launch --nproc_per_node=2 test_dist.py`\r\nThe output is weirder，many numbers appear twice\r\n```\r\n{'rank': 1, 'id': tensor([26, 8, 13], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([22, 19, 20], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([12, 28, 11], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([24, 2, 14], device='cuda:1')}\r\n{'rank': 1, 'id': tensor([ 6, 27, 3], device='cuda:1')}\r\n{'rank': 0, 'id': tensor([ 8, 25, 1], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([20, 4, 12], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([14, 29, 5], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([ 7, 18, 23], device='cuda:0')}\r\n{'rank': 0, 'id': tensor([19, 17, 11], device='cuda:0')}\r\n``` ",
"Hi ! Thanks for reporting, you need to pass `seed=` to `shuffle()` or the processes won't use the same seed to shuffle the shards order before assigning each shard to a node.\r\n\r\nThe issue is that the workers are not using the same seed to shuffle the shards before splitting the shards list by node.",
"Opened https://github.com/huggingface/datasets/issues/5696",
"I have the same issue\r\n```\r\nds['train'] = load_dataset(streaming=True)\r\nds['train'] = split_dataset_by_node(ds['train'], rank=int(os.environ[\"RANK\"]), world_size=int(os.environ[\"WORLD_SIZE\"]))\r\nvectorized_datasets = ds.map(\r\n prepare_dataset,\r\n remove_columns=raw_datasets_features,\r\n).with_format(\"torch\")\r\n\r\nvectorized_datasets[\"train\"] = vectorized_datasets[\"train\"].shuffle(\r\n buffer_size=500,\r\n seed=42,\r\n)\r\n\r\ndef prepare_dataset(batch):\r\n ....\r\n print(f\"sentence: {batch['sentence']}, target_text: {batch['target_text']}\")\r\n return batch\r\n```\r\nWhen using split_dataset_by_node(), the data being read is indeed different for each GPU ID.\r\n\r\n```\r\ntrainer = Trainer(\r\n model=model,\r\n data_collator=data_collator,\r\n args=training_args,\r\n compute_metrics=compute_metrics,\r\n train_dataset=vectorized_datasets[\"train\"] if training_args.do_train else None,\r\n eval_dataset=vectorized_datasets[\"eval\"] if training_args.do_eval else None,\r\n tokenizer=processor,\r\n callbacks=[ShuffleCallback()],\r\n )\r\n...\r\ntrain_result = trainer.train(resume_from_checkpoint=checkpoint)\r\n```\r\nHowever, when I execute trainer.train(), the data being read is different from what I expected.\r\nBecause I print the batch value in prepare_dataset() , I observe that the data is the same for each GPU ID.\r\n\r\nHow should I handle this issue?\r\n\r\n\r\n",
"There are two ways an iterable dataset can be split by node:\r\n1. if the number of shards is a factor of number of GPUs: in that case the shards are evenly distributed per GPU\r\n2. otherwise, each GPU iterate on the data and at the end keeps 1 sample out of n(GPUs) - skipping the others.\r\n\r\nIn case 2. it's therefore possible to have the same examples passed to `prepare_dataset` for each GPU.\r\n\r\nThis doesn't sound optimized though, because it runs the preprocessing on samples that won't be used in the end.\r\n\r\nCould you open a new issue so that we can discuss about this and find a solution ?"
] | 2022-12-14T16:06:19Z
| 2023-06-15T09:51:13Z
| 2023-01-16T13:33:33Z
|
MEMBER
| null | null | null |
As mentioned in https://github.com/huggingface/datasets/issues/3423, when using PyTorch DDP the dataset ends up with duplicated data. We already check for the PyTorch `worker_info` for single node, but we should also check for `torch.distributed.get_world_size()` and `torch.distributed.get_rank()`
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Support streaming datasets with pathlib.Path.with_suffix
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| 2022-11-29T07:06:33Z
| 2022-11-29T07:06:33Z
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MEMBER
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Extend support for streaming datasets that use `pathlib.Path.with_suffix`.
This feature will be useful e.g. for datasets containing text files and annotated files with the same name but different extension.
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6482). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I'm getting `AttributeError: module 'os' has no attribute 'statvfs'` on windows - reverting",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005294 / 0.011353 (-0.006059) | 0.003562 / 0.011008 (-0.007446) | 0.062030 / 0.038508 (0.023522) | 0.053335 / 0.023109 (0.030226) | 0.233303 / 0.275898 (-0.042595) | 0.252029 / 0.323480 (-0.071451) | 0.002835 / 0.007986 (-0.005151) | 0.002732 / 0.004328 (-0.001597) | 0.047973 / 0.004250 (0.043723) | 0.038380 / 0.037052 (0.001328) | 0.235028 / 0.258489 (-0.023461) | 0.265555 / 0.293841 (-0.028286) | 0.027136 / 0.128546 (-0.101410) | 0.010806 / 0.075646 (-0.064840) | 0.205040 / 0.419271 (-0.214231) | 0.035063 / 0.043533 (-0.008470) | 0.236351 / 0.255139 (-0.018788) | 0.254556 / 0.283200 (-0.028643) | 0.019528 / 0.141683 (-0.122155) | 1.099012 / 1.452155 (-0.353142) | 1.156250 / 1.492716 (-0.336466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093952 / 0.018006 (0.075946) | 0.304181 / 0.000490 (0.303692) | 0.000227 / 0.000200 (0.000027) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018568 / 0.037411 (-0.018844) | 0.060323 / 0.014526 (0.045798) | 0.073010 / 0.176557 (-0.103546) | 0.121723 / 0.737135 (-0.615412) | 0.075668 / 0.296338 (-0.220670) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288429 / 0.215209 (0.073220) | 2.797834 / 2.077655 (0.720180) | 1.480957 / 1.504120 (-0.023163) | 1.360872 / 1.541195 (-0.180323) | 1.406828 / 1.468490 (-0.061663) | 0.587596 / 4.584777 (-3.997181) | 2.533997 / 3.745712 (-1.211715) | 2.906697 / 5.269862 (-2.363164) | 1.801753 / 4.565676 (-2.763923) | 0.064360 / 0.424275 (-0.359915) | 0.005016 / 0.007607 (-0.002591) | 0.347334 / 0.226044 (0.121290) | 3.426344 / 2.268929 (1.157416) | 1.856014 / 55.444624 (-53.588610) | 1.581774 / 6.876477 (-5.294703) | 1.640036 / 2.142072 (-0.502037) | 0.656096 / 4.805227 (-4.149131) | 0.120212 / 6.500664 (-6.380452) | 0.044003 / 0.075469 (-0.031466) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.943933 / 1.841788 (-0.897855) | 11.846572 / 8.074308 (3.772263) | 10.330705 / 10.191392 (0.139313) | 0.129767 / 0.680424 (-0.550657) | 0.013508 / 0.534201 (-0.520693) | 0.289672 / 0.579283 (-0.289611) | 0.266427 / 0.434364 (-0.167937) | 0.342766 / 0.540337 (-0.197571) | 0.452068 / 1.386936 (-0.934868) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005308 / 0.011353 (-0.006045) | 0.003712 / 0.011008 (-0.007296) | 0.048848 / 0.038508 (0.010340) | 0.055156 / 0.023109 (0.032047) | 0.271942 / 0.275898 (-0.003956) | 0.293166 / 0.323480 (-0.030314) | 0.004056 / 0.007986 (-0.003930) | 0.002722 / 0.004328 (-0.001606) | 0.048418 / 0.004250 (0.044167) | 0.039320 / 0.037052 (0.002268) | 0.277184 / 0.258489 (0.018695) | 0.312398 / 0.293841 (0.018557) | 0.029392 / 0.128546 (-0.099155) | 0.011314 / 0.075646 (-0.064332) | 0.057883 / 0.419271 (-0.361389) | 0.032603 / 0.043533 (-0.010930) | 0.273025 / 0.255139 (0.017886) | 0.289265 / 0.283200 (0.006065) | 0.017553 / 0.141683 (-0.124129) | 1.127725 / 1.452155 (-0.324430) | 1.202293 / 1.492716 (-0.290423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097179 / 0.018006 (0.079173) | 0.309712 / 0.000490 (0.309222) | 0.000269 / 0.000200 (0.000069) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024742 / 0.037411 (-0.012670) | 0.070097 / 0.014526 (0.055571) | 0.082273 / 0.176557 (-0.094283) | 0.121696 / 0.737135 (-0.615439) | 0.082983 / 0.296338 (-0.213355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292688 / 0.215209 (0.077479) | 2.853436 / 2.077655 (0.775781) | 1.588999 / 1.504120 (0.084879) | 1.454547 / 1.541195 (-0.086648) | 1.476342 / 1.468490 (0.007852) | 0.559464 / 4.584777 (-4.025313) | 2.564597 / 3.745712 (-1.181115) | 2.900460 / 5.269862 (-2.369402) | 1.782156 / 4.565676 (-2.783520) | 0.061768 / 0.424275 (-0.362507) | 0.005042 / 0.007607 (-0.002565) | 0.345168 / 0.226044 (0.119124) | 3.412273 / 2.268929 (1.143344) | 1.953154 / 55.444624 (-53.491470) | 1.667347 / 6.876477 (-5.209130) | 1.685138 / 2.142072 (-0.456934) | 0.643270 / 4.805227 (-4.161958) | 0.115955 / 6.500664 (-6.384709) | 0.041090 / 0.075469 (-0.034379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976324 / 1.841788 (-0.865464) | 12.252294 / 8.074308 (4.177986) | 10.598062 / 10.191392 (0.406670) | 0.129779 / 0.680424 (-0.550644) | 0.015697 / 0.534201 (-0.518504) | 0.287241 / 0.579283 (-0.292042) | 0.287331 / 0.434364 (-0.147033) | 0.331710 / 0.540337 (-0.208628) | 0.574571 / 1.386936 (-0.812365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#702344140461b7a111139860c944d3dd0a2689e3 \"CML watermark\")\n"
] | 2023-12-08T13:39:30Z
| 2023-12-12T11:53:32Z
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reported in https://github.com/huggingface/datasets/pull/6482
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added real label for glue/mrpc to test set
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Added real label to `glue.py` `mrpc` task for test split.
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add lst20 with manual download
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"The pytest suite doesn't allow manual downloads so we just make sure that the `datasets-cli test` command to run without errors instead",
"@lhoestq Changes made. Thank you for the review. I've made some same mistakes for https://github.com/huggingface/datasets/pull/1253 too. Will fix them before review."
] | 2020-12-06T14:49:10Z
| 2020-12-09T16:33:10Z
| 2020-12-09T16:33:10Z
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passed on local:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_lst20
```
Not sure how to test:
```
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_lst20
```
```
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
```
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Remove links in docs to old dataset viewer
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-05-19T13:24:39Z
| 2022-05-20T15:24:28Z
| 2022-05-20T15:16:05Z
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Remove the links in the docs to the no longer maintained dataset viewer.
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load_dataset hang on file_lock
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[
"Can you try to upgrade to a more recent version of datasets?",
"Thank, upgrading to 1.1.3 resolved the issue.",
"Having the same issue with `datasets 1.1.3` of `1.5.0` (both tracebacks look the same) and `kilt_wikipedia`, Ubuntu 20.04\r\n\r\n```py\r\nIn [1]: from datasets import load_dataset \r\n\r\nIn [2]: wikipedia = load_dataset('kilt_wikipedia')['full'] \r\nDownloading: 7.37kB [00:00, 2.74MB/s] \r\nDownloading: 3.33kB [00:00, 1.44MB/s] \r\n^C---------------------------------------------------------------------------\r\nOSError Traceback (most recent call last)\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)\r\n 380 try:\r\n--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n 382 except (IOError, OSError):\r\n\r\nOSError: [Errno 37] No locks available\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nKeyboardInterrupt Traceback (most recent call last)\r\n<ipython-input-2-f412d3d46ec9> in <module>\r\n----> 1 wikipedia = load_dataset('kilt_wikipedia')['full']\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, sav\r\ne_infos, script_version, **config_kwargs)\r\n 601 hash=hash,\r\n 602 features=features,\r\n--> 603 **config_kwargs,\r\n 604 )\r\n 605 \r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, *args, **kwargs)\r\n 841 def __init__(self, *args, **kwargs):\r\n 842 self._writer_batch_size = kwargs.pop(\"writer_batch_size\", self._writer_batch_size)\r\n--> 843 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)\r\n 844 \r\n 845 @abc.abstractmethod\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)\r\n 174 os.makedirs(self._cache_dir_root, exist_ok=True)\r\n 175 lock_path = os.path.join(self._cache_dir_root, self._cache_dir.replace(os.sep, \"_\") + \".lock\")\r\n--> 176 with FileLock(lock_path):\r\n 177 if os.path.exists(self._cache_dir): # check if data exist\r\n 178 if len(os.listdir(self._cache_dir)) > 0:\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in __enter__(self)\r\n 312 \r\n 313 def __enter__(self):\r\n--> 314 self.acquire()\r\n 315 return self\r\n 316 \r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in acquire(self, timeout, poll_intervall)\r\n 261 if not self.is_locked:\r\n 262 logger().debug(\"Attempting to acquire lock %s on %s\", lock_id, lock_filename)\r\n--> 263 self._acquire()\r\n 264 \r\n 265 if self.is_locked:\r\n\r\n~/anaconda3/envs/transformers2/lib/python3.7/site-packages/datasets/utils/filelock.py in _acquire(self)\r\n 379 \r\n 380 try:\r\n--> 381 fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)\r\n 382 except (IOError, OSError):\r\n 383 os.close(fd)\r\n\r\nKeyboardInterrupt: \r\n\r\n```"
] | 2021-01-01T10:25:07Z
| 2021-03-31T16:24:13Z
| 2021-01-01T11:47:36Z
|
NONE
| null | null | null |
I am trying to load the squad dataset. Fails on Windows 10 but succeeds in Colab.
Transformers: 3.3.1
Datasets: 1.0.2
Windows 10 (also tested in WSL)
```
datasets.logging.set_verbosity_debug()
datasets.
train_dataset = load_dataset('squad', split='train')
valid_dataset = load_dataset('squad', split='validation')
train_dataset.features
```
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py not found in cache or force_download set to True, downloading to C:\Users\simpl\.cache\huggingface\datasets\tmpzj_o_6u7
Downloading:
5.24k/? [00:00<00:00, 134kB/s]
storing https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py in cache at C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
creating metadata file for C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py
Checking C:\Users\simpl\.cache\huggingface\datasets\f6877c8d2e01e8fcb60dc101be28b54a7522feac756deb9ac5c39c6d8ebef1ce.85f43de978b9b25921cb78d7a2f2b350c04acdbaedb9ecb5f7101cd7c0950e68.py for additional imports.
Found main folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad
Found specific version folder for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41
Found script file from https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py to C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.py
Couldn't find dataset infos file at https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad\dataset_infos.json
Found metadata file for dataset https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/squad.py at C:\Users\simpl\.cache\huggingface\modules\datasets_modules\datasets\squad\1244d044b266a5e4dbd4174d23cb995eead372fbca31a03edc3f8a132787af41\squad.json
No config specified, defaulting to first: squad/plain_text
```
Interrupting the jupyter kernel we are in a file lock.
In Google Colab the download is ok. In contrast to a local run in colab dataset_infos.json is downloaded
```
https://raw.githubusercontent.com/huggingface/datasets/1.0.2/datasets/squad/dataset_infos.json not found in cache or force_download set to True, downloading to /root/.cache/huggingface/datasets/tmptl9ha_ad
Downloading:
2.19k/? [00:00<00:00, 26.2kB/s]
```
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[docs] Index: The native emoji looks kinda ugly in large size
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PR_kwDODunzps5dnZM_
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Fix UnboundLocalError if preprocessing returns an empty list
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"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009286 / 0.011353 (-0.002067) | 0.005478 / 0.011008 (-0.005530) | 0.109768 / 0.038508 (0.071260) | 0.088460 / 0.023109 (0.065351) | 0.387664 / 0.275898 (0.111766) | 0.457379 / 0.323480 (0.133899) | 0.006517 / 0.007986 (-0.001469) | 0.004037 / 0.004328 (-0.000292) | 0.083911 / 0.004250 (0.079661) | 0.071658 / 0.037052 (0.034605) | 0.385065 / 0.258489 (0.126576) | 0.460928 / 0.293841 (0.167087) | 0.048062 / 0.128546 (-0.080484) | 0.016343 / 0.075646 (-0.059303) | 0.373675 / 0.419271 (-0.045597) | 0.067640 / 0.043533 (0.024108) | 0.391730 / 0.255139 (0.136591) | 0.432908 / 0.283200 (0.149708) | 0.035748 / 0.141683 (-0.105935) | 1.767625 / 1.452155 (0.315471) | 1.965606 / 1.492716 (0.472889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277405 / 0.018006 (0.259399) | 0.538448 / 0.000490 (0.537958) | 0.013795 / 0.000200 (0.013595) | 0.000518 / 0.000054 (0.000464) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043962 / 0.037411 (0.006550) | 0.115305 / 0.014526 (0.100780) | 0.117572 / 0.176557 (-0.058985) | 0.182168 / 0.737135 (-0.554968) | 0.114833 / 0.296338 (-0.181505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604209 / 0.215209 (0.389000) | 6.186113 / 2.077655 (4.108458) | 2.771067 / 1.504120 (1.266947) | 2.425420 / 1.541195 (0.884226) | 2.475200 / 1.468490 (1.006710) | 0.887096 / 4.584777 (-3.697681) | 5.214349 / 3.745712 (1.468637) | 4.989606 / 5.269862 (-0.280256) | 3.092135 / 4.565676 (-1.473541) | 0.104464 / 0.424275 (-0.319811) | 0.008994 / 0.007607 (0.001387) | 0.732819 / 0.226044 (0.506775) | 7.396007 / 2.268929 (5.127078) | 3.371167 / 55.444624 (-52.073457) | 2.645475 / 6.876477 (-4.231001) | 2.704215 / 2.142072 (0.562143) | 1.034724 / 4.805227 (-3.770504) | 0.219063 / 6.500664 (-6.281601) | 0.073863 / 0.075469 (-0.001606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625020 / 1.841788 (-0.216768) | 23.369980 / 8.074308 (15.295671) | 22.480951 / 10.191392 (12.289559) | 0.228219 / 0.680424 (-0.452204) | 0.026981 / 0.534201 (-0.507220) | 0.487670 / 0.579283 (-0.091613) | 0.582310 / 0.434364 (0.147946) | 0.539182 / 0.540337 (-0.001156) | 0.791962 / 1.386936 (-0.594974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008657 / 0.011353 (-0.002696) | 0.004971 / 0.011008 (-0.006037) | 0.089499 / 0.038508 (0.050991) | 0.075963 / 0.023109 (0.052854) | 0.497719 / 0.275898 (0.221821) | 0.507912 / 0.323480 (0.184432) | 0.006067 / 0.007986 (-0.001919) | 0.004118 / 0.004328 (-0.000210) | 0.079397 / 0.004250 (0.075146) | 0.059181 / 0.037052 (0.022129) | 0.501108 / 0.258489 (0.242619) | 0.565792 / 0.293841 (0.271951) | 0.048818 / 0.128546 (-0.079729) | 0.014813 / 0.075646 (-0.060833) | 0.093863 / 0.419271 (-0.325409) | 0.060824 / 0.043533 (0.017292) | 0.489289 / 0.255139 (0.234150) | 0.533624 / 0.283200 (0.250425) | 0.034997 / 0.141683 (-0.106685) | 1.770574 / 1.452155 (0.318419) | 1.837213 / 1.492716 (0.344496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237319 / 0.018006 (0.219313) | 0.594976 / 0.000490 (0.594486) | 0.008888 / 0.000200 (0.008688) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036955 / 0.037411 (-0.000456) | 0.097825 / 0.014526 (0.083299) | 0.111139 / 0.176557 (-0.065418) | 0.174776 / 0.737135 (-0.562359) | 0.117755 / 0.296338 (-0.178584) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606498 / 0.215209 (0.391289) | 6.089874 / 2.077655 (4.012219) | 2.811135 / 1.504120 (1.307015) | 2.428486 / 1.541195 (0.887292) | 2.399512 / 1.468490 (0.931022) | 0.823492 / 4.584777 (-3.761285) | 4.897107 / 3.745712 (1.151395) | 4.407589 / 5.269862 (-0.862272) | 2.868442 / 4.565676 (-1.697235) | 0.098774 / 0.424275 (-0.325502) | 0.007998 / 0.007607 (0.000391) | 0.699489 / 0.226044 (0.473445) | 7.139214 / 2.268929 (4.870285) | 3.511158 / 55.444624 (-51.933466) | 2.775459 / 6.876477 (-4.101018) | 2.951549 / 2.142072 (0.809477) | 1.006921 / 4.805227 (-3.798306) | 0.200105 / 6.500664 (-6.300559) | 0.071064 / 0.075469 (-0.004405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.680599 / 1.841788 (-0.161189) | 23.399777 / 8.074308 (15.325469) | 21.776357 / 10.191392 (11.584965) | 0.264697 / 0.680424 (-0.415726) | 0.034272 / 0.534201 (-0.499929) | 0.506984 / 0.579283 (-0.072299) | 0.609556 / 0.434364 (0.175192) | 0.599014 / 0.540337 (0.058677) | 0.824068 / 1.386936 (-0.562868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ab9de69420de8bd5d057579d71d07187b3a2c60 \"CML watermark\")\n"
] | 2023-10-24T08:38:43Z
| 2023-10-25T17:39:17Z
| 2023-10-25T16:36:38Z
|
CONTRIBUTOR
| null | 0
|
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If this tokenization function is used with IterableDatasets and no sample is as big as the context length, `input_batch` will be an empty list.
```
def tokenize(batch, tokenizer, context_length):
outputs = tokenizer(
batch["text"],
truncation=True,
max_length=context_length,
return_overflowing_tokens=True,
return_length=True
)
input_batch = []
for length, input_ids in zip(outputs["length"], outputs["input_ids"]):
if length == context_length:
input_batch.append(input_ids)
return {"input_ids": input_batch}
dataset.map(tokenize, batched=True, batch_size=batch_size, fn_kwargs={"context_length": context_length, "tokenizer": tokenizer}, remove_columns=dataset.column_names)
```
This will throw the following error: UnboundLocalError: local variable 'batch_idx' referenced before assignment, because the for loop was not executed a single time
```
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
yield new_key, example
current_idx += batch_idx + 1
```
Some of the possible solutions
```
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
yield new_key, example
try:
current_idx += batch_idx + 1
except:
current_idx += 1
```
or
```
batch_idx = 0
for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)):
yield new_key, example
current_idx += batch_idx + 1
```
|
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PR_kwDODunzps5XGChP
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Release: 2.14.3
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[
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007578 / 0.011353 (-0.003775) | 0.004271 / 0.011008 (-0.006738) | 0.086607 / 0.038508 (0.048098) | 0.063209 / 0.023109 (0.040099) | 0.351724 / 0.275898 (0.075826) | 0.399261 / 0.323480 (0.075781) | 0.004767 / 0.007986 (-0.003219) | 0.003487 / 0.004328 (-0.000842) | 0.071483 / 0.004250 (0.067233) | 0.051281 / 0.037052 (0.014229) | 0.387726 / 0.258489 (0.129237) | 0.408446 / 0.293841 (0.114605) | 0.041189 / 0.128546 (-0.087357) | 0.012446 / 0.075646 (-0.063200) | 0.331147 / 0.419271 (-0.088124) | 0.056721 / 0.043533 (0.013188) | 0.361306 / 0.255139 (0.106167) | 0.409651 / 0.283200 (0.126451) | 0.035485 / 0.141683 (-0.106198) | 1.461391 / 1.452155 (0.009236) | 1.554820 / 1.492716 (0.062104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237119 / 0.018006 (0.219113) | 0.518731 / 0.000490 (0.518241) | 0.004192 / 0.000200 (0.003992) | 0.000114 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024912 / 0.037411 (-0.012499) | 0.089420 / 0.014526 (0.074894) | 0.091209 / 0.176557 (-0.085347) | 0.152580 / 0.737135 (-0.584555) | 0.089660 / 0.296338 (-0.206678) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515223 / 0.215209 (0.300014) | 5.328359 / 2.077655 (3.250705) | 1.974326 / 1.504120 (0.470206) | 1.665216 / 1.541195 (0.124021) | 1.736040 / 1.468490 (0.267550) | 0.734746 / 4.584777 (-3.850031) | 4.186613 / 3.745712 (0.440901) | 3.535760 / 5.269862 (-1.734102) | 2.333247 / 4.565676 (-2.232429) | 0.071845 / 0.424275 (-0.352430) | 0.006147 / 0.007607 (-0.001460) | 0.546649 / 0.226044 (0.320605) | 5.452281 / 2.268929 (3.183353) | 2.512984 / 55.444624 (-52.931640) | 2.104210 / 6.876477 (-4.772267) | 2.409251 / 2.142072 (0.267178) | 0.822797 / 4.805227 (-3.982430) | 0.166648 / 6.500664 (-6.334016) | 0.056350 / 0.075469 (-0.019119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.397798 / 1.841788 (-0.443989) | 20.549399 / 8.074308 (12.475091) | 19.118168 / 10.191392 (8.926776) | 0.216361 / 0.680424 (-0.464063) | 0.027064 / 0.534201 (-0.507136) | 0.410762 / 0.579283 (-0.168521) | 0.559225 / 0.434364 (0.124861) | 0.468028 / 0.540337 (-0.072309) | 0.691520 / 1.386936 (-0.695416) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006463 / 0.011353 (-0.004890) | 0.003879 / 0.011008 (-0.007130) | 0.058723 / 0.038508 (0.020215) | 0.057202 / 0.023109 (0.034092) | 0.344397 / 0.275898 (0.068499) | 0.360388 / 0.323480 (0.036908) | 0.005502 / 0.007986 (-0.002483) | 0.004101 / 0.004328 (-0.000227) | 0.058168 / 0.004250 (0.053917) | 0.059112 / 0.037052 (0.022060) | 0.362206 / 0.258489 (0.103717) | 0.386444 / 0.293841 (0.092603) | 0.036613 / 0.128546 (-0.091934) | 0.010482 / 0.075646 (-0.065165) | 0.065850 / 0.419271 (-0.353421) | 0.046528 / 0.043533 (0.002995) | 0.349568 / 0.255139 (0.094429) | 0.360181 / 0.283200 (0.076981) | 0.029030 / 0.141683 (-0.112653) | 1.314569 / 1.452155 (-0.137586) | 1.422393 / 1.492716 (-0.070324) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.281554 / 0.018006 (0.263548) | 0.608018 / 0.000490 (0.607528) | 0.004568 / 0.000200 (0.004368) | 0.000182 / 0.000054 (0.000127) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023515 / 0.037411 (-0.013896) | 0.072994 / 0.014526 (0.058468) | 0.080688 / 0.176557 (-0.095868) | 0.125904 / 0.737135 (-0.611232) | 0.085457 / 0.296338 (-0.210882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471530 / 0.215209 (0.256321) | 4.796197 / 2.077655 (2.718542) | 2.189181 / 1.504120 (0.685061) | 1.886649 / 1.541195 (0.345454) | 1.871067 / 1.468490 (0.402577) | 0.661043 / 4.584777 (-3.923734) | 4.344027 / 3.745712 (0.598315) | 3.656967 / 5.269862 (-1.612895) | 2.286033 / 4.565676 (-2.279644) | 0.079146 / 0.424275 (-0.345129) | 0.006840 / 0.007607 (-0.000767) | 0.588750 / 0.226044 (0.362706) | 6.301286 / 2.268929 (4.032357) | 3.074702 / 55.444624 (-52.369923) | 2.398739 / 6.876477 (-4.477738) | 2.555057 / 2.142072 (0.412985) | 0.874189 / 4.805227 (-3.931038) | 0.191423 / 6.500664 (-6.309241) | 0.061227 / 0.075469 (-0.014242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.472763 / 1.841788 (-0.369024) | 19.441304 / 8.074308 (11.366996) | 15.974276 / 10.191392 (5.782884) | 0.172503 / 0.680424 (-0.507921) | 0.027016 / 0.534201 (-0.507185) | 0.356085 / 0.579283 (-0.223198) | 0.473251 / 0.434364 (0.038887) | 0.427949 / 0.540337 (-0.112388) | 0.588924 / 1.386936 (-0.798013) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0973da6e60ac7c1d24229ba6aa6881747b21858a \"CML watermark\")\n",
"_The documentation is not available anymore as the PR was closed or merged._",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006166 / 0.011353 (-0.005187) | 0.003558 / 0.011008 (-0.007450) | 0.080576 / 0.038508 (0.042068) | 0.066542 / 0.023109 (0.043432) | 0.323997 / 0.275898 (0.048099) | 0.369828 / 0.323480 (0.046348) | 0.004896 / 0.007986 (-0.003090) | 0.002909 / 0.004328 (-0.001419) | 0.062553 / 0.004250 (0.058302) | 0.049795 / 0.037052 (0.012742) | 0.321369 / 0.258489 (0.062880) | 0.422860 / 0.293841 (0.129019) | 0.027394 / 0.128546 (-0.101152) | 0.007954 / 0.075646 (-0.067693) | 0.264122 / 0.419271 (-0.155149) | 0.044881 / 0.043533 (0.001349) | 0.316702 / 0.255139 (0.061563) | 0.374718 / 0.283200 (0.091518) | 0.021728 / 0.141683 (-0.119955) | 1.394456 / 1.452155 (-0.057699) | 1.474936 / 1.492716 (-0.017780) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191902 / 0.018006 (0.173896) | 0.430468 / 0.000490 (0.429979) | 0.003790 / 0.000200 (0.003590) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024974 / 0.037411 (-0.012438) | 0.073053 / 0.014526 (0.058527) | 0.083801 / 0.176557 (-0.092756) | 0.143457 / 0.737135 (-0.593678) | 0.085099 / 0.296338 (-0.211240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428411 / 0.215209 (0.213202) | 4.278077 / 2.077655 (2.200422) | 2.230039 / 1.504120 (0.725919) | 2.057191 / 1.541195 (0.515996) | 2.120109 / 1.468490 (0.651619) | 0.495242 / 4.584777 (-4.089535) | 3.031299 / 3.745712 (-0.714413) | 2.802685 / 5.269862 (-2.467176) | 1.839828 / 4.565676 (-2.725849) | 0.056875 / 0.424275 (-0.367401) | 0.006446 / 0.007607 (-0.001161) | 0.498958 / 0.226044 (0.272913) | 4.980440 / 2.268929 (2.711511) | 2.659659 / 55.444624 (-52.784965) | 2.315174 / 6.876477 (-4.561303) | 2.475920 / 2.142072 (0.333848) | 0.586946 / 4.805227 (-4.218282) | 0.124291 / 6.500664 (-6.376373) | 0.060701 / 0.075469 (-0.014768) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245062 / 1.841788 (-0.596725) | 18.201444 / 8.074308 (10.127136) | 13.723271 / 10.191392 (3.531879) | 0.130203 / 0.680424 (-0.550221) | 0.016773 / 0.534201 (-0.517428) | 0.332909 / 0.579283 (-0.246374) | 0.347469 / 0.434364 (-0.086895) | 0.381364 / 0.540337 (-0.158973) | 0.541723 / 1.386936 (-0.845213) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005934 / 0.011353 (-0.005419) | 0.003573 / 0.011008 (-0.007435) | 0.062195 / 0.038508 (0.023687) | 0.059026 / 0.023109 (0.035917) | 0.413993 / 0.275898 (0.138095) | 0.459552 / 0.323480 (0.136072) | 0.004610 / 0.007986 (-0.003376) | 0.002907 / 0.004328 (-0.001421) | 0.062983 / 0.004250 (0.058733) | 0.047797 / 0.037052 (0.010745) | 0.415461 / 0.258489 (0.156972) | 0.417424 / 0.293841 (0.123583) | 0.027098 / 0.128546 (-0.101449) | 0.008106 / 0.075646 (-0.067540) | 0.067600 / 0.419271 (-0.351672) | 0.041432 / 0.043533 (-0.002101) | 0.407861 / 0.255139 (0.152722) | 0.430774 / 0.283200 (0.147575) | 0.020738 / 0.141683 (-0.120945) | 1.435127 / 1.452155 (-0.017028) | 1.486961 / 1.492716 (-0.005755) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231174 / 0.018006 (0.213168) | 0.421208 / 0.000490 (0.420718) | 0.005411 / 0.000200 (0.005211) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025362 / 0.037411 (-0.012049) | 0.078534 / 0.014526 (0.064008) | 0.085304 / 0.176557 (-0.091252) | 0.139048 / 0.737135 (-0.598087) | 0.087015 / 0.296338 (-0.209323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448506 / 0.215209 (0.233297) | 4.486694 / 2.077655 (2.409039) | 2.488022 / 1.504120 (0.983902) | 2.325321 / 1.541195 (0.784126) | 2.381311 / 1.468490 (0.912821) | 0.502102 / 4.584777 (-4.082675) | 3.018326 / 3.745712 (-0.727386) | 2.824922 / 5.269862 (-2.444940) | 1.857414 / 4.565676 (-2.708263) | 0.057514 / 0.424275 (-0.366761) | 0.006829 / 0.007607 (-0.000779) | 0.521939 / 0.226044 (0.295895) | 5.224393 / 2.268929 (2.955465) | 2.933132 / 55.444624 (-52.511492) | 2.661187 / 6.876477 (-4.215290) | 2.781950 / 2.142072 (0.639878) | 0.592927 / 4.805227 (-4.212300) | 0.126685 / 6.500664 (-6.373979) | 0.064188 / 0.075469 (-0.011281) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351107 / 1.841788 (-0.490681) | 18.344453 / 8.074308 (10.270145) | 13.838788 / 10.191392 (3.647396) | 0.157881 / 0.680424 (-0.522543) | 0.016636 / 0.534201 (-0.517565) | 0.331597 / 0.579283 (-0.247686) | 0.345573 / 0.434364 (-0.088791) | 0.397361 / 0.540337 (-0.142976) | 0.534289 / 1.386936 (-0.852647) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#582e722a76534904c0f3038d32ebb8db88ce9128 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006399 / 0.011353 (-0.004954) | 0.003872 / 0.011008 (-0.007136) | 0.083722 / 0.038508 (0.045214) | 0.068845 / 0.023109 (0.045736) | 0.329112 / 0.275898 (0.053214) | 0.343295 / 0.323480 (0.019815) | 0.005137 / 0.007986 (-0.002849) | 0.003303 / 0.004328 (-0.001026) | 0.064495 / 0.004250 (0.060245) | 0.051448 / 0.037052 (0.014395) | 0.322554 / 0.258489 (0.064065) | 0.361934 / 0.293841 (0.068093) | 0.030821 / 0.128546 (-0.097726) | 0.008482 / 0.075646 (-0.067164) | 0.288136 / 0.419271 (-0.131135) | 0.051935 / 0.043533 (0.008402) | 0.308283 / 0.255139 (0.053144) | 0.343421 / 0.283200 (0.060221) | 0.023639 / 0.141683 (-0.118044) | 1.485442 / 1.452155 (0.033288) | 1.533282 / 1.492716 (0.040565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218163 / 0.018006 (0.200157) | 0.464473 / 0.000490 (0.463983) | 0.003097 / 0.000200 (0.002897) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028650 / 0.037411 (-0.008761) | 0.083295 / 0.014526 (0.068769) | 0.096468 / 0.176557 (-0.080088) | 0.152086 / 0.737135 (-0.585050) | 0.102586 / 0.296338 (-0.193752) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393038 / 0.215209 (0.177829) | 3.925514 / 2.077655 (1.847859) | 1.938419 / 1.504120 (0.434300) | 1.760265 / 1.541195 (0.219071) | 1.810024 / 1.468490 (0.341534) | 0.486232 / 4.584777 (-4.098545) | 3.618747 / 3.745712 (-0.126965) | 3.206950 / 5.269862 (-2.062912) | 1.999240 / 4.565676 (-2.566436) | 0.056986 / 0.424275 (-0.367289) | 0.007193 / 0.007607 (-0.000415) | 0.469313 / 0.226044 (0.243269) | 4.688670 / 2.268929 (2.419741) | 2.400332 / 55.444624 (-53.044292) | 2.074197 / 6.876477 (-4.802279) | 2.290823 / 2.142072 (0.148751) | 0.582339 / 4.805227 (-4.222888) | 0.134127 / 6.500664 (-6.366537) | 0.061061 / 0.075469 (-0.014408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272782 / 1.841788 (-0.569006) | 19.463375 / 8.074308 (11.389067) | 14.306819 / 10.191392 (4.115427) | 0.164608 / 0.680424 (-0.515816) | 0.018626 / 0.534201 (-0.515575) | 0.395225 / 0.579283 (-0.184058) | 0.408984 / 0.434364 (-0.025380) | 0.463364 / 0.540337 (-0.076974) | 0.630425 / 1.386936 (-0.756511) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006465 / 0.011353 (-0.004888) | 0.003975 / 0.011008 (-0.007033) | 0.063643 / 0.038508 (0.025134) | 0.075214 / 0.023109 (0.052105) | 0.361734 / 0.275898 (0.085836) | 0.396664 / 0.323480 (0.073184) | 0.005251 / 0.007986 (-0.002735) | 0.003249 / 0.004328 (-0.001080) | 0.063841 / 0.004250 (0.059591) | 0.054504 / 0.037052 (0.017451) | 0.374791 / 0.258489 (0.116302) | 0.399205 / 0.293841 (0.105364) | 0.031355 / 0.128546 (-0.097192) | 0.008483 / 0.075646 (-0.067163) | 0.070234 / 0.419271 (-0.349037) | 0.048336 / 0.043533 (0.004803) | 0.373484 / 0.255139 (0.118345) | 0.382174 / 0.283200 (0.098974) | 0.022560 / 0.141683 (-0.119123) | 1.449799 / 1.452155 (-0.002355) | 1.525255 / 1.492716 (0.032539) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228350 / 0.018006 (0.210343) | 0.444344 / 0.000490 (0.443855) | 0.003699 / 0.000200 (0.003499) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030681 / 0.037411 (-0.006731) | 0.087340 / 0.014526 (0.072814) | 0.098636 / 0.176557 (-0.077920) | 0.151665 / 0.737135 (-0.585471) | 0.100840 / 0.296338 (-0.195498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417857 / 0.215209 (0.202648) | 4.168407 / 2.077655 (2.090752) | 2.201758 / 1.504120 (0.697638) | 1.997834 / 1.541195 (0.456639) | 2.127693 / 1.468490 (0.659202) | 0.486429 / 4.584777 (-4.098348) | 3.676335 / 3.745712 (-0.069378) | 3.226268 / 5.269862 (-2.043594) | 2.027255 / 4.565676 (-2.538422) | 0.056759 / 0.424275 (-0.367516) | 0.007628 / 0.007607 (0.000021) | 0.500482 / 0.226044 (0.274438) | 4.996236 / 2.268929 (2.727307) | 2.628884 / 55.444624 (-52.815740) | 2.347611 / 6.876477 (-4.528866) | 2.551328 / 2.142072 (0.409255) | 0.582449 / 4.805227 (-4.222778) | 0.132844 / 6.500664 (-6.367821) | 0.061791 / 0.075469 (-0.013678) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.373718 / 1.841788 (-0.468070) | 19.921217 / 8.074308 (11.846909) | 14.209642 / 10.191392 (4.018250) | 0.185334 / 0.680424 (-0.495090) | 0.018228 / 0.534201 (-0.515973) | 0.395549 / 0.579283 (-0.183734) | 0.404446 / 0.434364 (-0.029918) | 0.472456 / 0.540337 (-0.067882) | 0.622739 / 1.386936 (-0.764197) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006007 / 0.011353 (-0.005346) | 0.003588 / 0.011008 (-0.007420) | 0.080334 / 0.038508 (0.041826) | 0.058932 / 0.023109 (0.035823) | 0.404613 / 0.275898 (0.128715) | 0.438377 / 0.323480 (0.114897) | 0.003468 / 0.007986 (-0.004518) | 0.003702 / 0.004328 (-0.000627) | 0.062936 / 0.004250 (0.058686) | 0.047987 / 0.037052 (0.010934) | 0.411409 / 0.258489 (0.152920) | 0.450244 / 0.293841 (0.156403) | 0.027007 / 0.128546 (-0.101539) | 0.007932 / 0.075646 (-0.067714) | 0.261390 / 0.419271 (-0.157882) | 0.044992 / 0.043533 (0.001459) | 0.409730 / 0.255139 (0.154591) | 0.433331 / 0.283200 (0.150131) | 0.020446 / 0.141683 (-0.121237) | 1.425418 / 1.452155 (-0.026736) | 1.479242 / 1.492716 (-0.013475) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187375 / 0.018006 (0.169368) | 0.428532 / 0.000490 (0.428043) | 0.003406 / 0.000200 (0.003206) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024390 / 0.037411 (-0.013022) | 0.072571 / 0.014526 (0.058045) | 0.083513 / 0.176557 (-0.093044) | 0.144395 / 0.737135 (-0.592741) | 0.084813 / 0.296338 (-0.211526) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409176 / 0.215209 (0.193967) | 4.078082 / 2.077655 (2.000428) | 1.913596 / 1.504120 (0.409476) | 1.718470 / 1.541195 (0.177275) | 1.753106 / 1.468490 (0.284616) | 0.494167 / 4.584777 (-4.090610) | 3.029531 / 3.745712 (-0.716181) | 2.807331 / 5.269862 (-2.462531) | 1.839471 / 4.565676 (-2.726206) | 0.057169 / 0.424275 (-0.367106) | 0.006433 / 0.007607 (-0.001175) | 0.482666 / 0.226044 (0.256621) | 4.817601 / 2.268929 (2.548673) | 2.449967 / 55.444624 (-52.994658) | 2.113891 / 6.876477 (-4.762586) | 2.399293 / 2.142072 (0.257221) | 0.578903 / 4.805227 (-4.226324) | 0.124306 / 6.500664 (-6.376358) | 0.061572 / 0.075469 (-0.013897) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254692 / 1.841788 (-0.587096) | 18.414049 / 8.074308 (10.339741) | 13.992059 / 10.191392 (3.800667) | 0.146671 / 0.680424 (-0.533753) | 0.016925 / 0.534201 (-0.517275) | 0.333124 / 0.579283 (-0.246159) | 0.348007 / 0.434364 (-0.086357) | 0.378519 / 0.540337 (-0.161819) | 0.532540 / 1.386936 (-0.854396) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006050 / 0.011353 (-0.005303) | 0.003614 / 0.011008 (-0.007394) | 0.061707 / 0.038508 (0.023199) | 0.062874 / 0.023109 (0.039765) | 0.364760 / 0.275898 (0.088862) | 0.398136 / 0.323480 (0.074656) | 0.005598 / 0.007986 (-0.002388) | 0.002836 / 0.004328 (-0.001493) | 0.061880 / 0.004250 (0.057630) | 0.048165 / 0.037052 (0.011113) | 0.372656 / 0.258489 (0.114167) | 0.403967 / 0.293841 (0.110126) | 0.027046 / 0.128546 (-0.101501) | 0.008091 / 0.075646 (-0.067555) | 0.066783 / 0.419271 (-0.352489) | 0.041186 / 0.043533 (-0.002347) | 0.376009 / 0.255139 (0.120870) | 0.391769 / 0.283200 (0.108569) | 0.021020 / 0.141683 (-0.120663) | 1.514593 / 1.452155 (0.062438) | 1.548506 / 1.492716 (0.055790) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237610 / 0.018006 (0.219604) | 0.434274 / 0.000490 (0.433784) | 0.009720 / 0.000200 (0.009520) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025605 / 0.037411 (-0.011807) | 0.078971 / 0.014526 (0.064445) | 0.088154 / 0.176557 (-0.088403) | 0.139112 / 0.737135 (-0.598023) | 0.088890 / 0.296338 (-0.207449) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420027 / 0.215209 (0.204818) | 4.189493 / 2.077655 (2.111838) | 2.143907 / 1.504120 (0.639787) | 1.967032 / 1.541195 (0.425837) | 2.011845 / 1.468490 (0.543355) | 0.496692 / 4.584777 (-4.088085) | 3.025456 / 3.745712 (-0.720256) | 2.828436 / 5.269862 (-2.441426) | 1.860673 / 4.565676 (-2.705003) | 0.057199 / 0.424275 (-0.367076) | 0.006770 / 0.007607 (-0.000838) | 0.491281 / 0.226044 (0.265236) | 4.918065 / 2.268929 (2.649136) | 2.593172 / 55.444624 (-52.851452) | 2.250750 / 6.876477 (-4.625727) | 2.406235 / 2.142072 (0.264162) | 0.588648 / 4.805227 (-4.216579) | 0.125635 / 6.500664 (-6.375029) | 0.061697 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374065 / 1.841788 (-0.467722) | 18.439315 / 8.074308 (10.365007) | 14.031660 / 10.191392 (3.840268) | 0.153665 / 0.680424 (-0.526759) | 0.016980 / 0.534201 (-0.517221) | 0.331799 / 0.579283 (-0.247484) | 0.343201 / 0.434364 (-0.091163) | 0.392445 / 0.540337 (-0.147892) | 0.530387 / 1.386936 (-0.856549) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008189 / 0.011353 (-0.003164) | 0.004598 / 0.011008 (-0.006410) | 0.102199 / 0.038508 (0.063691) | 0.077961 / 0.023109 (0.054852) | 0.364936 / 0.275898 (0.089038) | 0.402606 / 0.323480 (0.079126) | 0.005522 / 0.007986 (-0.002464) | 0.004007 / 0.004328 (-0.000322) | 0.071560 / 0.004250 (0.067310) | 0.055818 / 0.037052 (0.018765) | 0.378394 / 0.258489 (0.119905) | 0.428990 / 0.293841 (0.135149) | 0.043142 / 0.128546 (-0.085404) | 0.013254 / 0.075646 (-0.062392) | 0.331102 / 0.419271 (-0.088170) | 0.061407 / 0.043533 (0.017875) | 0.387397 / 0.255139 (0.132258) | 0.416062 / 0.283200 (0.132862) | 0.036330 / 0.141683 (-0.105353) | 1.735352 / 1.452155 (0.283198) | 1.773329 / 1.492716 (0.280613) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188587 / 0.018006 (0.170581) | 0.519506 / 0.000490 (0.519016) | 0.004702 / 0.000200 (0.004502) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027152 / 0.037411 (-0.010260) | 0.094296 / 0.014526 (0.079770) | 0.098155 / 0.176557 (-0.078402) | 0.162541 / 0.737135 (-0.574595) | 0.112092 / 0.296338 (-0.184246) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.537555 / 0.215209 (0.322346) | 5.486821 / 2.077655 (3.409166) | 2.377127 / 1.504120 (0.873008) | 2.073205 / 1.541195 (0.532011) | 2.075130 / 1.468490 (0.606640) | 0.783779 / 4.584777 (-3.800998) | 5.029524 / 3.745712 (1.283812) | 4.382724 / 5.269862 (-0.887138) | 2.836180 / 4.565676 (-1.729496) | 0.108840 / 0.424275 (-0.315435) | 0.008123 / 0.007607 (0.000516) | 0.673460 / 0.226044 (0.447416) | 6.674030 / 2.268929 (4.405102) | 3.208922 / 55.444624 (-52.235702) | 2.464908 / 6.876477 (-4.411568) | 2.661929 / 2.142072 (0.519856) | 0.962529 / 4.805227 (-3.842698) | 0.197974 / 6.500664 (-6.302690) | 0.066656 / 0.075469 (-0.008813) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430373 / 1.841788 (-0.411415) | 21.180540 / 8.074308 (13.106232) | 19.027491 / 10.191392 (8.836099) | 0.217520 / 0.680424 (-0.462904) | 0.028038 / 0.534201 (-0.506163) | 0.435266 / 0.579283 (-0.144017) | 0.529510 / 0.434364 (0.095147) | 0.511011 / 0.540337 (-0.029327) | 0.728940 / 1.386936 (-0.657996) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007883 / 0.011353 (-0.003470) | 0.004448 / 0.011008 (-0.006560) | 0.071350 / 0.038508 (0.032842) | 0.075269 / 0.023109 (0.052160) | 0.396705 / 0.275898 (0.120807) | 0.457809 / 0.323480 (0.134329) | 0.005193 / 0.007986 (-0.002792) | 0.003695 / 0.004328 (-0.000633) | 0.078087 / 0.004250 (0.073836) | 0.054276 / 0.037052 (0.017224) | 0.412184 / 0.258489 (0.153695) | 0.452400 / 0.293841 (0.158559) | 0.049762 / 0.128546 (-0.078784) | 0.013206 / 0.075646 (-0.062440) | 0.085985 / 0.419271 (-0.333287) | 0.058837 / 0.043533 (0.015304) | 0.432481 / 0.255139 (0.177342) | 0.433260 / 0.283200 (0.150060) | 0.031190 / 0.141683 (-0.110493) | 1.582707 / 1.452155 (0.130552) | 1.664457 / 1.492716 (0.171741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223639 / 0.018006 (0.205633) | 0.524388 / 0.000490 (0.523899) | 0.005489 / 0.000200 (0.005289) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030182 / 0.037411 (-0.007230) | 0.089309 / 0.014526 (0.074783) | 0.103306 / 0.176557 (-0.073250) | 0.162624 / 0.737135 (-0.574511) | 0.108957 / 0.296338 (-0.187381) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.577423 / 0.215209 (0.362214) | 5.900154 / 2.077655 (3.822500) | 2.687369 / 1.504120 (1.183249) | 2.513061 / 1.541195 (0.971866) | 2.506453 / 1.468490 (1.037963) | 0.830838 / 4.584777 (-3.753939) | 5.032195 / 3.745712 (1.286483) | 4.396827 / 5.269862 (-0.873035) | 2.884230 / 4.565676 (-1.681447) | 0.102239 / 0.424275 (-0.322036) | 0.008178 / 0.007607 (0.000571) | 0.710027 / 0.226044 (0.483983) | 7.149626 / 2.268929 (4.880698) | 3.403605 / 55.444624 (-52.041019) | 2.661970 / 6.876477 (-4.214506) | 2.760227 / 2.142072 (0.618154) | 1.043981 / 4.805227 (-3.761246) | 0.195028 / 6.500664 (-6.305636) | 0.065211 / 0.075469 (-0.010258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.581265 / 1.841788 (-0.260522) | 21.640230 / 8.074308 (13.565922) | 19.031860 / 10.191392 (8.840468) | 0.196903 / 0.680424 (-0.483520) | 0.027061 / 0.534201 (-0.507140) | 0.444995 / 0.579283 (-0.134288) | 0.528195 / 0.434364 (0.093831) | 0.521540 / 0.540337 (-0.018797) | 0.730204 / 1.386936 (-0.656732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n"
] | 2023-08-03T10:18:32Z
| 2023-08-03T15:08:02Z
| 2023-08-03T10:24:57Z
|
MEMBER
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PR_kwDODunzps5AQemi
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refactor: replace AssertionError with more meaningful exceptions (#5074)
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-10-06T01:39:35Z
| 2022-10-07T14:35:43Z
| 2022-10-07T14:33:10Z
|
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Closes #5074
Replaces `AssertionError` in the following files with more descriptive exceptions:
- `src/datasets/arrow_reader.py`
- `src/datasets/builder.py`
- `src/datasets/utils/version.py`
The issue listed more files that needed to be fixed, but the rest of them were contained in the top-level `datasets` directory, which was removed when #4974 was merged
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MDExOlB1bGxSZXF1ZXN0NTU3MTM1MzM1
| 1,750
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Fix typo in README.md of cnn_dailymail
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"Good catch, thanks!",
"Thank you for merging!"
] | 2021-01-19T03:06:05Z
| 2021-01-19T11:07:29Z
| 2021-01-19T09:48:43Z
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When I read the README.md of `CNN/DailyMail Dataset`, there seems to be a typo `CCN`.
I am afraid this is a trivial matter, but I would like to make a suggestion for revision.
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Use auth to get parquet export
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6468). All of your documentation changes will be reflected on that endpoint.",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003510 / 0.011008 (-0.007499) | 0.062939 / 0.038508 (0.024431) | 0.049191 / 0.023109 (0.026082) | 0.259088 / 0.275898 (-0.016810) | 0.273523 / 0.323480 (-0.049957) | 0.003902 / 0.007986 (-0.004083) | 0.002699 / 0.004328 (-0.001630) | 0.049077 / 0.004250 (0.044827) | 0.037174 / 0.037052 (0.000121) | 0.256467 / 0.258489 (-0.002022) | 0.291235 / 0.293841 (-0.002606) | 0.028119 / 0.128546 (-0.100427) | 0.010404 / 0.075646 (-0.065243) | 0.205825 / 0.419271 (-0.213446) | 0.035741 / 0.043533 (-0.007792) | 0.253219 / 0.255139 (-0.001920) | 0.274986 / 0.283200 (-0.008214) | 0.018379 / 0.141683 (-0.123304) | 1.131139 / 1.452155 (-0.321016) | 1.175875 / 1.492716 (-0.316841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090717 / 0.018006 (0.072710) | 0.299285 / 0.000490 (0.298796) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018678 / 0.037411 (-0.018733) | 0.060558 / 0.014526 (0.046032) | 0.073828 / 0.176557 (-0.102728) | 0.119302 / 0.737135 (-0.617833) | 0.075261 / 0.296338 (-0.221078) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277018 / 0.215209 (0.061809) | 2.713255 / 2.077655 (0.635601) | 1.427512 / 1.504120 (-0.076608) | 1.311374 / 1.541195 (-0.229821) | 1.348756 / 1.468490 (-0.119734) | 0.561777 / 4.584777 (-4.023000) | 2.393578 / 3.745712 (-1.352134) | 2.798109 / 5.269862 (-2.471753) | 1.754808 / 4.565676 (-2.810869) | 0.062302 / 0.424275 (-0.361973) | 0.004948 / 0.007607 (-0.002659) | 0.328468 / 0.226044 (0.102423) | 3.246558 / 2.268929 (0.977629) | 1.786816 / 55.444624 (-53.657808) | 1.482937 / 6.876477 (-5.393540) | 1.516109 / 2.142072 (-0.625963) | 0.634457 / 4.805227 (-4.170770) | 0.116505 / 6.500664 (-6.384159) | 0.042162 / 0.075469 (-0.033308) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.935312 / 1.841788 (-0.906476) | 11.540599 / 8.074308 (3.466291) | 10.512593 / 10.191392 (0.321201) | 0.129638 / 0.680424 (-0.550786) | 0.013994 / 0.534201 (-0.520207) | 0.291490 / 0.579283 (-0.287793) | 0.263641 / 0.434364 (-0.170722) | 0.328718 / 0.540337 (-0.211619) | 0.437598 / 1.386936 (-0.949338) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005192 / 0.011353 (-0.006161) | 0.003454 / 0.011008 (-0.007554) | 0.049448 / 0.038508 (0.010940) | 0.050968 / 0.023109 (0.027859) | 0.273702 / 0.275898 (-0.002196) | 0.296934 / 0.323480 (-0.026545) | 0.004066 / 0.007986 (-0.003920) | 0.002611 / 0.004328 (-0.001718) | 0.048284 / 0.004250 (0.044034) | 0.041399 / 0.037052 (0.004346) | 0.283000 / 0.258489 (0.024511) | 0.302553 / 0.293841 (0.008712) | 0.029086 / 0.128546 (-0.099460) | 0.010510 / 0.075646 (-0.065137) | 0.058097 / 0.419271 (-0.361175) | 0.032992 / 0.043533 (-0.010541) | 0.271752 / 0.255139 (0.016613) | 0.293535 / 0.283200 (0.010335) | 0.016958 / 0.141683 (-0.124725) | 1.130126 / 1.452155 (-0.322028) | 1.187228 / 1.492716 (-0.305488) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092321 / 0.018006 (0.074315) | 0.302599 / 0.000490 (0.302109) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021837 / 0.037411 (-0.015574) | 0.071148 / 0.014526 (0.056622) | 0.082448 / 0.176557 (-0.094108) | 0.128083 / 0.737135 (-0.609053) | 0.090864 / 0.296338 (-0.205474) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296248 / 0.215209 (0.081039) | 2.881130 / 2.077655 (0.803476) | 1.580360 / 1.504120 (0.076240) | 1.454642 / 1.541195 (-0.086553) | 1.461453 / 1.468490 (-0.007037) | 0.567500 / 4.584777 (-4.017277) | 2.493708 / 3.745712 (-1.252004) | 2.756623 / 5.269862 (-2.513239) | 1.771319 / 4.565676 (-2.794358) | 0.062287 / 0.424275 (-0.361988) | 0.004917 / 0.007607 (-0.002691) | 0.348034 / 0.226044 (0.121990) | 3.426938 / 2.268929 (1.158010) | 1.954190 / 55.444624 (-53.490435) | 1.660870 / 6.876477 (-5.215607) | 1.675118 / 2.142072 (-0.466955) | 0.636843 / 4.805227 (-4.168384) | 0.115028 / 6.500664 (-6.385636) | 0.040702 / 0.075469 (-0.034767) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988076 / 1.841788 (-0.853711) | 11.890867 / 8.074308 (3.816559) | 10.621169 / 10.191392 (0.429777) | 0.131568 / 0.680424 (-0.548856) | 0.014994 / 0.534201 (-0.519207) | 0.288900 / 0.579283 (-0.290384) | 0.272092 / 0.434364 (-0.162272) | 0.329397 / 0.540337 (-0.210940) | 0.569337 / 1.386936 (-0.817599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae3b4a2268adc2f21568ff63891e9a83530c7e29 \"CML watermark\")\n"
] | 2023-12-04T11:18:27Z
| 2023-12-04T17:21:22Z
| 2023-12-04T17:15:11Z
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MEMBER
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added `token` to the `_datasets_server` functions
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| 100
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Add per type scores in seqeval metric
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[
"LGTM :-) Some small suggestions to shorten the code a bit :-) ",
"Can you put the kwargs as normal kwargs instead of a dict? (And add them to the kwargs description As well)",
"@thom Is-it what you meant?",
"Yes and there is a dynamically generated doc string in the metric script KWARGS DESCRIPTION"
] | 2020-05-14T09:37:52Z
| 2020-05-14T23:21:35Z
| 2020-05-14T23:21:34Z
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This PR add a bit more detail in the seqeval metric. Now the usage and output are:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
met.compute(predictions, references)
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
```
It is also possible to compute scores for non IOB notations, POS tagging for example hasn't this kind of notation. Add `suffix` parameter:
```python
import nlp
met = nlp.load_metric('metrics/seqeval')
references = [['O', 'O', 'O', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
predictions = [['O', 'O', 'MISC', 'MISC', 'MISC', 'MISC', 'O'], ['PER', 'PER', 'O']]
met.compute(predictions, references, metrics_kwargs={"suffix": True})
#Output: {'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.9}
```
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Add JAX device selection when formatting
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[
"The code below was throwing a warning:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_extension import Device\r\n \r\n self.device = (\r\n device if isinstance(device, Device) else jax.devices()[0]\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nWhen providing `device` via param:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nimport jax\r\n\r\nds = Dataset.from_dict({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\r\nds = ds.with_format(\"jax\", device=jax.devices()[0])\r\nprint(ds[0])\r\n```\r\n\r\nProducing the following warning:\r\n\r\n```\r\nWARNING:datasets.fingerprint:Parameter 'device'=TFRT_CPU_0 of the transform datasets.arrow_dataset.Dataset.set_format couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n```\r\n\r\nThat's why I decided to map all the available devices, and assign their string representation e.g. `TFRT_CPU_0` to `self.device` instead of `jaxlib.xla_extension.Device`, so that the value of the param `device` is washable. So on, the code that remains at the end is:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_client import Device\r\n\r\n self.device_mapping = self._map_devices_to_str()\r\n self.device = (\r\n device if isinstance(device, str) else str(device) if isinstance(device, Device) else str(jax.devices()[0])\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n def _map_devices_to_str(self) -> Mapping[str, \"jaxlib.xla_extension.Device\"]:\r\n import jax\r\n\r\n return {str(device): device for device in jax.devices()}\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device_mapping[self.device]):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nBut note that the latter also throws a warning if the provided `device` is not a string but a `jaxlib.xla_extension.Device`, so that's why it needs to be converted to string.",
"_The documentation is not available anymore as the PR was closed or merged._",
"After some investigation, it seems that when using `device=jaxlib.xla_extension.Device` instead of `device=string` it shows the warning so that later formats fail as that cannot be unpickled.\r\n\r\nSo I think we can either add that specifically in `use_with_jax.mdx` documentation entry I'm creating at #5535 so that the users know that they need to surroung the `jaxlib.xla_extension.Device` with `str()`, or find a workaround to override default `deepcopy` behavior with `def __deepcopy__(self)` so that the device param is converted to string if provided as a `jaxlib.xla_extension.Device`, but not sure if the latter works 😕 \r\n\r\nDo you think there's any other possible solution to this issue? Thanks, @lhoestq ",
"Cool ! Specifying the device is indeed super important.\r\n\r\n\r\nI think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable",
"> Cool ! Specifying the device is indeed super important.\r\n> \r\n> I think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable\r\n\r\nSure, then I'll restrict it to string for now! Also regarding the documentation update, should we wait until #5535 is merged so that I add this on top of that?",
"CI is failing due to missing `resampy` in `librosa` already being fixed by @lhoestq in https://github.com/huggingface/datasets/pull/5554",
"@lhoestq already moved to a global variable, I can confirm that the following now works:\r\n\r\n```python\r\nimport copy\r\nimport pickle\r\n\r\nimport jax\r\nimport pyarrow as pa\r\n\r\nfrom datasets.formatting import JaxFormatter\r\n\r\n\r\n_COL_A = [0, 1, 2]\r\n_COL_B = [\"foo\", \"bar\", \"foobar\"]\r\n_COL_C = [[[1.0, 0.0, 0.0]] * 2, [[0.0, 1.0, 0.0]] * 2, [[0.0, 0.0, 1.0]] * 2]\r\npa_table = pa.Table.from_pydict({\"a\": _COL_A, \"b\": _COL_B, \"c\": _COL_C})\r\n\r\ndevice = jax.devices()[0]\r\nformatter = JaxFormatter(device=str(device))\r\n\r\npickle.dumps(formatter)\r\ncopy.deepcopy(formatter)\r\n```",
"> Looks all good now thank you !\r\n> \r\n> Is there anything else you wanted to add ? Otherwise I think it's ready for merge\r\n\r\nNothing else to add, I've already applied your suggestions, so ready to merge! Thanks for your input/feedback @lhoestq :hugs:",
"<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009815 / 0.011353 (-0.001538) | 0.005443 / 0.011008 (-0.005565) | 0.101244 / 0.038508 (0.062736) | 0.036573 / 0.023109 (0.013464) | 0.304761 / 0.275898 (0.028863) | 0.365527 / 0.323480 (0.042047) | 0.008244 / 0.007986 (0.000258) | 0.004200 / 0.004328 (-0.000128) | 0.077471 / 0.004250 (0.073221) | 0.045266 / 0.037052 (0.008214) | 0.310213 / 0.258489 (0.051724) | 0.344247 / 0.293841 (0.050406) | 0.039530 / 0.128546 (-0.089016) | 0.012254 / 0.075646 (-0.063393) | 0.335039 / 0.419271 (-0.084233) | 0.049525 / 0.043533 (0.005992) | 0.298350 / 0.255139 (0.043211) | 0.312031 / 0.283200 (0.028832) | 0.108581 / 0.141683 (-0.033102) | 1.481178 / 1.452155 (0.029023) | 1.497662 / 1.492716 (0.004946) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014762 / 0.018006 (-0.003244) | 0.447099 / 0.000490 (0.446609) | 0.009074 / 0.000200 (0.008874) | 0.000688 / 0.000054 (0.000633) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027466 / 0.037411 (-0.009945) | 0.109715 / 0.014526 (0.095189) | 0.119062 / 0.176557 (-0.057495) | 0.188964 / 0.737135 (-0.548171) | 0.127057 / 0.296338 (-0.169282) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395092 / 0.215209 (0.179883) | 3.948091 / 2.077655 (1.870436) | 1.795160 / 1.504120 (0.291040) | 1.603704 / 1.541195 (0.062509) | 1.714491 / 1.468490 (0.246001) | 0.700489 / 4.584777 (-3.884288) | 3.767493 / 3.745712 (0.021781) | 3.288374 / 5.269862 (-1.981488) | 1.783711 / 4.565676 (-2.781965) | 0.085119 / 0.424275 (-0.339156) | 0.012349 / 0.007607 (0.004742) | 0.502135 / 0.226044 (0.276091) | 5.019321 / 2.268929 (2.750392) | 2.236469 / 55.444624 (-53.208155) | 1.914376 / 6.876477 (-4.962101) | 1.998579 / 2.142072 (-0.143494) | 0.847841 / 4.805227 (-3.957386) | 0.166035 / 6.500664 (-6.334629) | 0.062469 / 0.075469 (-0.013000) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245380 / 1.841788 (-0.596408) | 14.757872 / 8.074308 (6.683564) | 14.460373 / 10.191392 (4.268981) | 0.152981 / 0.680424 (-0.527443) | 0.029001 / 0.534201 (-0.505200) | 0.439597 / 0.579283 (-0.139686) | 0.437232 / 0.434364 (0.002868) | 0.532464 / 0.540337 (-0.007873) | 0.629225 / 1.386936 (-0.757711) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007165 / 0.011353 (-0.004188) | 0.005220 / 0.011008 (-0.005789) | 0.075849 / 0.038508 (0.037341) | 0.032717 / 0.023109 (0.009608) | 0.331205 / 0.275898 (0.055307) | 0.364955 / 0.323480 (0.041475) | 0.005518 / 0.007986 (-0.002468) | 0.004069 / 0.004328 (-0.000259) | 0.073900 / 0.004250 (0.069650) | 0.046346 / 0.037052 (0.009294) | 0.337473 / 0.258489 (0.078984) | 0.393062 / 0.293841 (0.099222) | 0.037533 / 0.128546 (-0.091013) | 0.012577 / 0.075646 (-0.063070) | 0.087975 / 0.419271 (-0.331297) | 0.049508 / 0.043533 (0.005975) | 0.333423 / 0.255139 (0.078284) | 0.354345 / 0.283200 (0.071145) | 0.099879 / 0.141683 (-0.041804) | 1.413304 / 1.452155 (-0.038851) | 1.494222 / 1.492716 (0.001506) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206835 / 0.018006 (0.188828) | 0.438246 / 0.000490 (0.437757) | 0.000410 / 0.000200 (0.000210) | 0.000059 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028186 / 0.037411 (-0.009225) | 0.109322 / 0.014526 (0.094797) | 0.119581 / 0.176557 (-0.056975) | 0.191784 / 0.737135 (-0.545351) | 0.125100 / 0.296338 (-0.171238) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419418 / 0.215209 (0.204209) | 4.167374 / 2.077655 (2.089720) | 1.995812 / 1.504120 (0.491693) | 1.804602 / 1.541195 (0.263407) | 1.869131 / 1.468490 (0.400641) | 0.709486 / 4.584777 (-3.875291) | 3.838019 / 3.745712 (0.092307) | 2.086206 / 5.269862 (-3.183656) | 1.323970 / 4.565676 (-3.241707) | 0.089477 / 0.424275 (-0.334798) | 0.012402 / 0.007607 (0.004795) | 0.519291 / 0.226044 (0.293246) | 5.194091 / 2.268929 (2.925162) | 2.487055 / 55.444624 (-52.957570) | 2.122495 / 6.876477 (-4.753982) | 2.194910 / 2.142072 (0.052837) | 0.842837 / 4.805227 (-3.962390) | 0.167229 / 6.500664 (-6.333435) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275931 / 1.841788 (-0.565857) | 14.577000 / 8.074308 (6.502692) | 13.633235 / 10.191392 (3.441843) | 0.184511 / 0.680424 (-0.495913) | 0.017439 / 0.534201 (-0.516762) | 0.424374 / 0.579283 (-0.154909) | 0.427803 / 0.434364 (-0.006561) | 0.527790 / 0.540337 (-0.012548) | 0.627301 / 1.386936 (-0.759635) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21c86d570faad32c3abbed4305bfd3698daa7fd0 \"CML watermark\")\n"
] | 2023-02-18T20:57:40Z
| 2023-02-21T16:10:55Z
| 2023-02-21T16:04:03Z
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## What's in this PR?
After exploring for a while the JAX integration in 🤗`datasets`, I found out that, even though JAX prioritizes the TPU and GPU as the default device when available, the `JaxFormatter` doesn't let you specify the device where you want to place the `jax.Array`s in case you don't want to rely on JAX's default array placement.
So on, I've included the `device` param in `JaxFormatter` but there are some things to take into consideration:
* A formatted `Dataset` is copied with `copy.deepcopy` which means that if one adds the param `device` in `JaxFormatter` as a `jaxlib.xla_extension.Device`, it "fails" because that object cannot be serialized (instead of serializing the param adds a random hash instead). That's the reason why I added a function `_map_devices_to_str` to basically create a mapping of strings to `jaxlib.xla_extension.Device`s so that `self.device` is a string and not a `jaxlib.xla_extension.Device`.
* To create a `jax.Array` in a device you need to either create it in the default device and then move it to the desired device with `jax.device_put` or directly create it in the device you want with `jax.default_device()` context manager.
* JAX will create an array by default in `jax.devices()[0]`
More information on JAX device management is available at https://jax.readthedocs.io/en/latest/faq.html#controlling-data-and-computation-placement-on-devices
## What's missing in this PR?
I've tested it both locally in CPU (Mac M2 and Mac M1, as no GPU support for Mac yet), and in GPU and TPU in Google Colab, let me know if you want me to provide you the Notebook for the latter.
But I did not implement any integration test as I wanted to get your feedback first.
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Latest v2.0.0 release of sacrebleu has broken some metrics
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[] | 2021-08-10T09:59:41Z
| 2021-08-10T11:16:07Z
| 2021-08-10T11:16:07Z
|
MEMBER
| null | null | null |
## Describe the bug
After `sacrebleu` v2.0.0 release (see changes here: https://github.com/mjpost/sacrebleu/pull/152/files#diff-2553a315bb1f7e68c9c1b00d56eaeb74f5205aeb3a189bc3e527b122c6078795L17-R15), some of `datasets` metrics are broken:
- Default tokenizer `sacrebleu.DEFAULT_TOKENIZER` no longer exists:
- #2739
- #2778
- Bleu tokenizers are no longer accessible with `sacrebleu.TOKENIZERS`:
- #2779
- `corpus_bleu` args have been renamed from `(sys_stream, ref_streams)` to `(hipotheses, references)`:
- #2782
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Remove YAML integer keys from class_label metadata
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"_The documentation is not available anymore as the PR was closed or merged._",
"Also note that this approach is valid when metadata keys are str, but also if they are int.\r\n- This will be helpful for any community dataset using old integer keys in their metadata",
"perfect !"
] | 2022-11-22T08:34:07Z
| 2022-11-22T13:58:26Z
| 2022-11-22T13:55:49Z
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Fix partially #5275.
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[
"Loading a local dataset also works the same way when `data_files` are not specified, so I agree we should make this info easier to discover \r\n\r\ncc @stevhliu ",
"Is this issue open? If so, I will self assign. ",
"@benjaminbrown038 Yes, it is. Maybe @stevhliu can give some pointers on improving this doc page's discoverability.",
"I think we can add a version of the [Main use-case](https://huggingface.co/docs/datasets/repository_structure#main-usecase) section to the [Share a dataset to the Hub](https://huggingface.co/docs/datasets/upload_dataset) tutorial. \r\n\r\nCurrently, it doesn't tell you *how* to structure the repository; it only tells you how to create it. So adding the \"main use-case\" will help bridge the gap and make it easier to find. We should also add a link to the [Structure your repository](https://huggingface.co/docs/datasets/repository_structure) guide for users who want to learn about the other options.",
"#self-assign"
] | 2023-06-21T08:26:44Z
| 2023-07-05T06:51:38Z
| null |
CONTRIBUTOR
| null | null | null |
The page https://huggingface.co/docs/datasets/repository_structure explains how to create a simple repository structure without a dataset script.
It's the simplest way to create a dataset and should be easier to find, particularly on the docs' first pages.
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Checksum mismatch for the reddit_tifu dataset
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[
"Thanks for reporting, @anna-kay. We are fixing it.",
"@albertvillanova Thank you for the fast response! However I am still getting the same error:\r\n\r\nDownloading: 2.23kB [00:00, ?B/s]\r\nTraceback (most recent call last):\r\n File \"C:\\Users\\Anna\\PycharmProjects\\summarization\\main.py\", line 17, in <module>\r\n dataset = load_dataset('reddit_tifu', 'long')\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\load.py\", line 1702, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 594, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\builder.py\", line 665, in _download_and_prepare\r\n verify_checksums(\r\n File \"C:\\Users\\Anna\\Desktop\\summarization\\summarization_env\\lib\\site-packages\\datasets\\utils\\info_utils.py\", line 40, in verify_checksums\r\n raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']\r\n\r\nI have cleaned the cache/huggingface/datasets & cache/huggingface/modules files and also tried on another machine with a fresh installation of trasnformers & datasets. \r\nThe reddit_tifu.py that gets downloaded still has the previous url on line 51, _URL = \"https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF\" ",
"Hi @anna-kay, I'm sorry I didn't clearly explain the details to you:\r\n- the error has been fixed in our `master` branch on GitHub: https://github.com/huggingface/datasets/commit/8ae21bf6a77175dc803ce2f1b93d18b8fbf45586\r\n- the fix will not be accessible to users in PyPI until our next release of the `datasets` library\r\n - our latest release (version 1.18.3) was made 23 days ago: https://github.com/huggingface/datasets/releases/tag/1.18.3\r\n- in the meantime, you can get the fix if you install datasets from our GitHub `master` branch:\r\n ```\r\n pip install git+https://github.com/huggingface/datasets#egg=datasets\r\n ```",
"@albertvillanova Ok great, makes sence. Thank you very much for the explanation!"
] | 2022-02-22T10:57:07Z
| 2022-02-25T19:27:49Z
| 2022-02-22T12:38:44Z
|
CONTRIBUTOR
| null | null | null |
## Describe the bug
A checksum occurs when downloading the reddit_tifu data (both long & short).
## Steps to reproduce the bug
reddit_tifu_dataset = load_dataset('reddit_tifu', 'long')
## Expected results
The expected result is for the dataset to be downloaded and cached locally.
## Actual results
File "/.../lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=1ffWfITKFMJeqjT8loC8aiCLRNJpc_XnF']
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31
- Python version: 3.9.7
- PyArrow version: 7.0.0
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"Thanks for reporting, @TJ-Solergibert.\r\n\r\nWe cannot access your Colab notebook: `There was an error loading this notebook. Ensure that the file is accessible and try again.`\r\nCould you please make it publicly accessible?\r\n",
"I swear it's public, I've checked the settings and I've been able to open it in incognito mode.\r\n\r\nNotebook: https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?usp=sharing\r\n\r\nAnyway, this is the code to reproduce the error:\r\n\r\n```python3\r\nfrom datasets import ClassLabel\r\nfrom datasets import load_dataset\r\n\r\neuroparl_ds = load_dataset(\"tj-solergibert/Europarl-ST\")\r\n\r\nsource_lang = \"nl\"\r\nlanguages = list(europarl_ds[\"train\"][0][\"transcriptions\"].keys())\r\nClassLabels = ClassLabel(num_classes = len(languages), names = languages)\r\n\r\ndef map_label2id(example):\r\n example['dest_lang'] = ClassLabels.str2int(example['dest_lang'])\r\n return example\r\n\r\ndef unfold_transcriptions(example):\r\n for lang in languages:\r\n example[lang] = example[\"transcriptions\"][lang]\r\n return example\r\n\r\ndef unroll(batch, src_lang, dest_langs):\r\n source_t, dest_t, dest_l = [], [], []\r\n for lang in dest_langs: \r\n source_t += batch[src_lang]\r\n dest_t += batch[lang]\r\n dest_l += [lang]\r\n return_dict = {\"source_text\": source_t, \"dest_text\": dest_t, \"dest_lang\": dest_l}\r\n return return_dict\r\n\r\ndef preprocess_split(ds_split, src_lang):\r\n dest_langs = [x for x in languages if x != src_lang]\r\n\r\n ds_split = ds_split.map(unroll, fn_kwargs= {\"src_lang\": src_lang, \"dest_langs\": dest_langs}, batched = True, batch_size = 1, remove_columns= list(languages))\r\n ds_split = ds_split.filter(lambda x: x[\"source_text\"] != None and x[\"dest_text\"] != None) # Remove incomplete translations\r\n ds_split = ds_split.filter(lambda x: x[\"source_text\"] != \"None\" and x[\"dest_text\"] != \"None\")\r\n ds_split = ds_split.map(map_label2id) \r\n ds_split = ds_split.cast_column(\"dest_lang\", ClassLabels)\r\n return ds_split\r\n\r\ndef reset_cortas(example):\r\n for lang in languages:\r\n if isinstance(example[lang], str):\r\n if example[lang].isnumeric () or len(example[lang]) <= 5:\r\n example[lang] = \"None\"\r\n return example\r\n\r\ndef clean_dataset(dataset):\r\n # Remove columns\r\n dataset = dataset.remove_columns([\"original_speech\", \"original_language\", \"audio_path\", \"segment_start\", \"segment_end\"])\r\n # Unfold\r\n dataset = dataset.map(unfold_transcriptions, remove_columns = [\"transcriptions\"])\r\n dataset = dataset.map(reset_cortas)\r\n return dataset\r\n\r\nprocessed_europarl = clean_dataset(europarl_ds[\"test\"])\r\nnew_train_ds = preprocess_split(processed_europarl, 'nl')\r\n```",
"Thanks, @TJ-Solergibert. I can access your notebook now. Maybe it was just a temporary issue.\r\n\r\nAt first sight, it seems something related to your data: maybe some of the examples do not have all the transcriptions for all the languages. Then, some of them are null when unrolled. And when trying to concatenate with the other rows containing strings, the cast issue is raised (the arrays to be concatenated have different types).\r\n\r\nDo you think this could be the case?",
"See, in this example, \"nl\" and \"ro\" transcripts are null:\r\n```python\r\n>>> europarl_ds[\"test\"][:1]\r\n{'original_speech': ['− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta'],\r\n 'original_language': ['es'],\r\n 'audio_path': ['es/audios/en.20081008.24.3-238.m4a'],\r\n 'segment_start': [0.6200000047683716],\r\n 'segment_end': [11.319999694824219],\r\n 'transcriptions': [{'de': '− Herr Präsident! Zunächst möchte ich Richard Seeber zu der von ihm geleisteten Arbeit gratulieren, denn sein Bericht greift viele der in diesem Haus zum Ausdruck gebrachten Anliegen',\r\n 'en': '− Mr President, firstly I would like to congratulate Mr Seeber on the work he has done, because his report picks up many of the concerns expressed in this',\r\n 'es': '− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta',\r\n 'fr': '− Monsieur le Président, je voudrais tout d ’ abord féliciter M. Seeber pour le travail qu ’ il a effectué, parce que son rapport reprend beaucoup des inquiétudes exprimées au sein de cette',\r\n 'it': \"− Signor Presidente, mi congratulo innanzi tutto con l'onorevole Seeber per il lavoro svolto, perché la sua relazione accoglie molti dei timori espressi da quest'Aula\",\r\n 'nl': None,\r\n 'pl': '− Panie przewodniczący! Po pierwsze chciałabym pogratulować panu posłowi Seeberowi wykonanej pracy, ponieważ jego sprawozdanie podejmuje szereg podnoszonych w tej Izbie',\r\n 'pt': '− Senhor Presidente, começo por felicitar o senhor deputado Seeber pelo trabalho que desenvolveu em torno deste relatório, que retoma muitas das preocupações expressas nesta',\r\n 'ro': None}]}\r\n```\r\n```python\r\n>>> processed_europarl[0]\r\n{'de': '− Herr Präsident! Zunächst möchte ich Richard Seeber zu der von ihm geleisteten Arbeit gratulieren, denn sein Bericht greift viele der in diesem Haus zum Ausdruck gebrachten Anliegen',\r\n 'en': '− Mr President, firstly I would like to congratulate Mr Seeber on the work he has done, because his report picks up many of the concerns expressed in this',\r\n 'es': '− Señor Presidente, en primer lugar, quisiera felicitar al señor Seeber por el trabajo realizado, porque en su informe se recogen muchas de las preocupaciones manifestadas en esta',\r\n 'fr': '− Monsieur le Président, je voudrais tout d ’ abord féliciter M. Seeber pour le travail qu ’ il a effectué, parce que son rapport reprend beaucoup des inquiétudes exprimées au sein de cette',\r\n 'it': \"− Signor Presidente, mi congratulo innanzi tutto con l'onorevole Seeber per il lavoro svolto, perché la sua relazione accoglie molti dei timori espressi da quest'Aula\",\r\n 'nl': None,\r\n 'pl': '− Panie przewodniczący! Po pierwsze chciałabym pogratulować panu posłowi Seeberowi wykonanej pracy, ponieważ jego sprawozdanie podejmuje szereg podnoszonych w tej Izbie',\r\n 'pt': '− Senhor Presidente, começo por felicitar o senhor deputado Seeber pelo trabalho que desenvolveu em torno deste relatório, que retoma muitas das preocupações expressas nesta',\r\n 'ro': None}\r\n```",
"You can fix this issue by forcing the cast of None to str by hand:\r\n- If you replace this line:\r\n```python\r\nsource_t += batch[src_lang]\r\n```\r\n- With this line (because the batch size is 1):\r\n```python\r\nsource_t += [str(batch[src_lang][0])]\r\n```\r\n- Or with this line (if the batch size were larger than 1):\r\n```python\r\nsource_t += [str(text) for text in batch[src_lang]]\r\n```",
"Problem solved! Thanks @albertvillanova, now I have even increased the batch size and it's crazy fast :rocket: !"
] | 2023-02-10T21:12:36Z
| 2023-02-14T17:41:08Z
| 2023-02-14T09:35:49Z
|
NONE
| null | null | null |
### Describe the bug
Processing a dataset I alredy uploaded to the Hub (https://huggingface.co/datasets/tj-solergibert/Europarl-ST) I found that for some splits and some languages (test split, source_lang = "nl") after applying a map function I get the mentioned error.
I alredy tried reseting the shorter strings (reset_cortas function). It only happends with NL, PL, RO and PT. It does not make sense since when processing the other languages I also use the corpus of those that fail and it does not cause any errors.
I suspect that the error may be in this direction:
We use cast_array_to_feature to support casting to custom types like Audio and Image # Also, when trying type "string", we don't want to convert integers or floats to "string". # We only do it if trying_type is False - since this is what the user asks for.
### Steps to reproduce the bug
Here I link a colab notebook to reproduce the error:
https://colab.research.google.com/drive/1JCrS7FlGfu_kFqChMrwKZ_bpabnIMqbP?authuser=1#scrollTo=FBAvlhMxIzpA
### Expected behavior
Data processing does not fail. A correct example can be seen here: https://huggingface.co/datasets/tj-solergibert/Europarl-ST-processed-mt-en
### Environment info
- `datasets` version: 2.9.0
- Platform: Linux-5.10.147+-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 9.0.0
- Pandas version: 1.3.5
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Adding dataset for proto_qa in huggingface datasets library
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[] | 2020-12-05T09:43:28Z
| 2020-12-05T18:53:10Z
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Added dataset for ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Followed all steps for adding a new dataset.
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[GEM] Add DART data-to-text generation dataset
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| 2020-10-27T17:34:21Z
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MEMBER
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## Adding a Dataset
- **Name:** DART
- **Description:** DART consists of 82,191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of the schema, annotated with sentence descriptions that cover all facts in the triple set.
- **Paper:** https://arxiv.org/abs/2007.02871v1
- **Data:** https://github.com/Yale-LILY/dart
- **Motivation:** It will likely be included in the GEM generation evaluation benchmark
Instructions to add a new dataset can be found [here](https://huggingface.co/docs/datasets/share_dataset.html).
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Change HTTP links to HTTPS
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I tested the links. I also fixed some typos.
Originally from #3489
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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 😄 \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 ?"
] | 2021-05-21T15:29:41Z
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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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I_kwDODunzps53sqDi
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Support one dataset loader per config when using YAML
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[] | 2023-11-23T13:03:07Z
| 2023-11-23T13:03:07Z
| null |
CONTRIBUTOR
| null | null | null |
### Feature request
See https://huggingface.co/datasets/datasets-examples/doc-unsupported-1
I would like to use CSV loader for the "csv" config, JSONL loader for the "jsonl" config, etc.
### Motivation
It would be more flexible for the users
### Your contribution
No specific contribution
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How to use generate this multitask dataset for SQUAD? I am getting a value error.
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[
"Hi! Replacing `nlp.<obj>` with `datasets.<obj>` in the script should fix the problem. `nlp` has been renamed to `datasets` more than a year ago, so please use `datasets` instead to avoid weird issues.",
"Thank You! Was able to solve with the help of this.",
"But I request you to please fix the same in the dataset hub explorer as well...",
"May I ask how to get this dataset?"
] | 2022-03-24T09:21:51Z
| 2022-03-26T09:48:21Z
| 2022-03-26T03:35:43Z
|
NONE
| null | null | null |
## Dataset viewer issue for 'squad_multitask*'
**Link:** https://huggingface.co/datasets/vershasaxena91/squad_multitask
*short description of the issue*
I am trying to generate the multitask dataset for squad dataset. However, gives the error in dataset explorer as well as my local machine.
I tried the command: dataset = load_dataset("vershasaxena91/squad_multitask", 'highlight_qg_format')
Error:
Status code: 400
Exception: TypeError
Message: argument of type 'Value' is not iterable
Kindly advice.
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Fix dataset_dict.shuffle with single seed
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| 2021-01-04T10:00:04Z
| 2021-01-04T10:00:03Z
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MEMBER
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Fix #1610
I added support for single integer used in `DatasetDict.shuffle`. Previously only a dictionary of seed was allowed.
Moreover I added the missing `seed` parameter. Previously only `seeds` was allowed.
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Our CI is broken: 3 failed, 12 errors
See: https://github.com/huggingface/datasets/actions/runs/6069947111/job/16465138041
```
=========================== short test summary info ============================
FAILED tests/test_load.py::ModuleFactoryTest::test_LocalDatasetModuleFactoryWithoutScript_with_data_dir - AssertionError: assert ({NamedSplit('train'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'], NamedSplit('test'): ['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/test.txt']} is not None and 2 == 1)
+ where 2 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_LocalDatasetModuleFactory2/data_dir2/subdir1/train.txt'])
FAILED tests/test_load.py::test_load_dataset_arrow[False] - AssertionError: assert 20 == 10
+ where 20 = Dataset({\n features: ['col_1'],\n num_rows: 20\n}).num_rows
FAILED tests/test_load.py::test_load_dataset_arrow[True] - assert 20 == 10
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_2/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_3/audiofolder_data_dir_with_metadata/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_4/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_audiofolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/audio_file2.wav', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_5/audiofolder_data_dir_with_metadata/train/metadata.csv'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_3/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[1-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_4/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_5/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_12/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[jsonl-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_13/imagefolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_folder_based_builder.py::test_data_files_with_metadata_and_splits[2-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/file2.txt', '/tmp/pytest-of-runner/pytest-0/popen-gw0/test_data_files_with_metadata_6/autofolder_data_dir_with_metadata_two_splits/train/metadata.jsonl'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-False] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_14/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
ERROR tests/packaged_modules/test_imagefolder.py::test_data_files_with_metadata_and_multiple_splits[csv-True] - AssertionError: assert 6 == 3
+ where 6 = len(['/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/image_rgb2.jpg', '/tmp/pytest-of-runner/pytest-0/popen-gw1/test_data_files_with_metadata_15/imagefolder_data_dir_with_metadata_two_splits/train/metadata.csv'])
= 3 failed, 2383 passed, 26 skipped, 9 warnings, 12 errors in 280.79s (0:04:40) =
```
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MDExOlB1bGxSZXF1ZXN0NTgwOTE4ODE2
| 1,951
|
Add cross-platform support for datasets-cli
|
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[] |
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[
"@mariosasko This is kinda cool! "
] | 2021-02-26T14:56:25Z
| 2021-03-11T02:18:26Z
| 2021-02-26T15:30:26Z
|
CONTRIBUTOR
| null | 0
|
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One thing I've noticed while going through the codebase is the usage of `scripts` in `setup.py`. This [answer](https://stackoverflow.com/a/28119736/14095927) on SO explains it nicely why it's better to use `entry_points` instead of `scripts`. To add cross-platform support to the CLI, this PR replaces `scripts` with `entry_points` in `setup.py` and moves datasets-cli to src/datasets/commands/datasets_cli.py. All *.md and *.rst files are updated accordingly. The same changes were made in the transformers repo to add cross-platform ([link to PR](https://github.com/huggingface/transformers/pull/4131)).
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MDExOlB1bGxSZXF1ZXN0NTA3MzAyNjE3
| 748
|
New version of CompGuessWhat?! with refined annotations
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[
"No worries. Always happy to help and thanks for your support in fixing the issue :)"
] | 2020-10-21T06:55:41Z
| 2020-10-21T08:52:42Z
| 2020-10-21T08:46:19Z
|
CONTRIBUTOR
| null | 0
|
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This pull request introduces a few fixes to the annotations for VisualGenome in the CompGuessWhat?! original split.
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I_kwDODunzps5FyXqG
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Missing languages in lvwerra/github-code dataset
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"Thanks for contacting @Eytan-S.\r\n\r\nI think @lvwerra could better answer this. ",
"That seems to be an oversight - I originally planned to include them in the dataset and for some reason they were in the list of languages but not in the query. Since there is an issue with the deduplication step I'll rerun the pipeline anyway and will double check the query.\r\n\r\nThanks for reporting this @Eytan-S!",
"Can confirm that the two languages are indeed missing from the dataset. Here are the file counts per language:\r\n```Python\r\n{'Assembly': 82847,\r\n 'Batchfile': 236755,\r\n 'C': 14127969,\r\n 'C#': 6793439,\r\n 'C++': 7368473,\r\n 'CMake': 175076,\r\n 'CSS': 1733625,\r\n 'Dockerfile': 331966,\r\n 'FORTRAN': 141963,\r\n 'GO': 2259363,\r\n 'Haskell': 340521,\r\n 'HTML': 11165464,\r\n 'Java': 19515696,\r\n 'JavaScript': 11829024,\r\n 'Julia': 58177,\r\n 'Lua': 576279,\r\n 'Makefile': 679338,\r\n 'Markdown': 8454049,\r\n 'PHP': 11181930,\r\n 'Perl': 497490,\r\n 'PowerShell': 136827,\r\n 'Python': 7203553,\r\n 'Ruby': 4479767,\r\n 'Rust': 321765,\r\n 'SQL': 655657,\r\n 'Scala': 0,\r\n 'Shell': 1382786,\r\n 'TypeScript': 0,\r\n 'TeX': 250764,\r\n 'Visual Basic': 155371}\r\n ```",
"@Eytan-S check out v1.1 of the `github-code` dataset where issue should be fixed:\r\n\r\n| | Language |File Count| Size (GB)|\r\n|---:|:-------------|---------:|-------:|\r\n| 0 | Java | 19548190 | 107.7 |\r\n| 1 | C | 14143113 | 183.83 |\r\n| 2 | JavaScript | 11839883 | 87.82 |\r\n| 3 | HTML | 11178557 | 118.12 |\r\n| 4 | PHP | 11177610 | 61.41 |\r\n| 5 | Markdown | 8464626 | 23.09 |\r\n| 6 | C++ | 7380520 | 87.73 |\r\n| 7 | Python | 7226626 | 52.03 |\r\n| 8 | C# | 6811652 | 36.83 |\r\n| 9 | Ruby | 4473331 | 10.95 |\r\n| 10 | GO | 2265436 | 19.28 |\r\n| 11 | TypeScript | 1940406 | 24.59 |\r\n| 12 | CSS | 1734406 | 22.67 |\r\n| 13 | Shell | 1385648 | 3.01 |\r\n| 14 | Scala | 835755 | 3.87 |\r\n| 15 | Makefile | 679430 | 2.92 |\r\n| 16 | SQL | 656671 | 5.67 |\r\n| 17 | Lua | 578554 | 2.81 |\r\n| 18 | Perl | 497949 | 4.7 |\r\n| 19 | Dockerfile | 366505 | 0.71 |\r\n| 20 | Haskell | 340623 | 1.85 |\r\n| 21 | Rust | 322431 | 2.68 |\r\n| 22 | TeX | 251015 | 2.15 |\r\n| 23 | Batchfile | 236945 | 0.7 |\r\n| 24 | CMake | 175282 | 0.54 |\r\n| 25 | Visual Basic | 155652 | 1.91 |\r\n| 26 | FORTRAN | 142038 | 1.62 |\r\n| 27 | PowerShell | 136846 | 0.69 |\r\n| 28 | Assembly | 82905 | 0.78 |\r\n| 29 | Julia | 58317 | 0.29 |",
"Thanks @lvwerra. "
] | 2022-03-16T10:32:03Z
| 2022-03-22T07:09:23Z
| 2022-03-21T14:50:47Z
|
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Hi,
I'm working with the github-code dataset. First of all, thank you for creating this amazing dataset!
I've noticed that two languages are missing from the dataset: TypeScript and Scala.
Looks like they're also omitted from the query you used to get the original code.
Are there any plans to add them in the future?
Thanks!
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Replace pa.OSFile by open
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It should fix #643
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add multi-proc in `to_json`
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[
"Thank you for working on this, @bhavitvyamalik \r\n\r\n10% is not solving the issue, we want 5-10x faster on a machine that has lots of resources, but limited processing time.\r\n\r\nSo let's benchmark it on an instance with many more cores, I can test with 12 on my dev box and 40 on JZ. \r\n\r\nCould you please share the test I could run with both versions?\r\n\r\nShould we also test the sharded version I shared in https://github.com/huggingface/datasets/issues/2663#issue-946552273 so optionally 3 versions to test.",
"Since I was facing `OSError: [Errno 12] Cannot allocate memory` in CircleCI tests, I've added `num_proc` option instead of always using full `cpu_count`. You can test both v1 and v2 through this branch (some redundancy needs to be removed). \r\n\r\nUpdate: I was able to convert into json which took 50% less time as compared to v1 on `ascent_kb` dataset. Will post the benchmarking script with results here.",
"Here are the benchmarks with the current branch for both v1 and v2 (dataset: `ascent_kb`, 8.9M samples):\r\n| batch_size | time (in sec) | time (in sec) |\r\n|------------|---------------|---------------|\r\n| | num_proc = 1 | num_proc = 4 |\r\n| 10k | 185.56 | 170.11 |\r\n| 50k | 175.79 | 86.84 |\r\n| **100k** | 191.09 | **78.35** |\r\n| 125k | 198.28 | 90.89 |\r\n\r\nIncreasing the batch size on my machine helped in making v2 around 50% faster as compared to v1. Timings may vary depending on the machine. I'm including the benchmarking script as well. CircleCI errors are unrelated (something related to `bertscore`)\r\n```\r\nimport time\r\nfrom datasets import load_dataset\r\nimport pathlib\r\nimport os\r\nfrom pathlib import Path\r\nimport shutil\r\nimport gc\r\n\r\nbatch_sizes = [10_000, 50_000, 100_000, 125_000]\r\nnum_procs = [1, 4] # change this according to your machine\r\n\r\nSAVE_LOC = \"./new_dataset.json\"\r\n\r\nfor batch in batch_sizes:\r\n for num in num_procs:\r\n dataset = load_dataset(\"ascent_kb\")\r\n\r\n local_start = time.time()\r\n ans = dataset['train'].to_json(SAVE_LOC, batch_size=batch, num_proc=num)\r\n local_end = time.time() - local_start\r\n\r\n print(f\"Time taken on {num} num_proc and {batch} batch_size: \", local_end)\r\n\r\n # remove that dataset and its contents from cache and newly generated json\r\n new_json = pathlib.Path(SAVE_LOC)\r\n new_json.unlink()\r\n\r\n try:\r\n shutil.rmtree(os.path.join(str(Path.home()), \".cache\", \"huggingface\"))\r\n except OSError as e:\r\n print(\"Error: %s - %s.\" % (e.filename, e.strerror))\r\n\r\n gc.collect()\r\n```\r\nThis will download the dataset in every iteration and run `to_json`. I didn't do multiple iterations here for `to_json` (for a specific batch_size and num_proc) and took average time as I found that v1 got faster after 1st iteration (maybe it's caching somewhere). Since you'll be doing this operation only once, I thought it'll be better to report how both v1 and v2 performed in single iteration only. \r\n\r\nImportant: Benchmarking script will delete the newly generated json and `~/.cache/huggingface/` after every iteration so that it doesn't end up using any cached data (just to be on a safe side)",
"Thank you for sharing the benchmark, @bhavitvyamalik. Your results look promising.\r\n\r\nBut if I remember correctly the sharded version at https://github.com/huggingface/datasets/issues/2663#issue-946552273 was much faster. So we probably should compare to it as well? And if it's faster than at least document that manual sharding version?\r\n\r\n-------\r\n\r\nThat's a dangerous benchmark as it'd wipe out many other HF things. Why not wipe out:\r\n```\r\n~/.cache/huggingface/datasets/ascent_kb/\r\n```\r\n\r\nRunning the benchmark now.",
"Weird, I tried to adapt your benchmark to using shards and the program no longer works. It instead quickly uses up all available RAM and hangs. Has something changed recently in `datasets`? You can try:\r\n\r\n```\r\nimport time\r\nfrom datasets import load_dataset\r\nimport pathlib\r\nimport os\r\nfrom pathlib import Path\r\nimport shutil\r\nimport gc\r\nfrom multiprocessing import cpu_count, Process, Queue\r\n\r\nbatch_sizes = [10_000, 50_000, 100_000, 125_000]\r\nnum_procs = [1, 8] # change this according to your machine\r\n\r\nDATASET_NAME = (\"ascent_kb\")\r\nnum_shards = [1, 8]\r\nfor batch in batch_sizes:\r\n for shards in num_shards:\r\n dataset = load_dataset(DATASET_NAME)[\"train\"]\r\n #print(dataset)\r\n\r\n def process_shard(idx):\r\n print(f\"Sharding {idx}\")\r\n ds_shard = dataset.shard(shards, idx, contiguous=True)\r\n # ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling\r\n print(f\"Saving {DATASET_NAME}-{idx}.jsonl\")\r\n ds_shard.to_json(f\"{DATASET_NAME}-{idx}.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\n local_start = time.time()\r\n queue = Queue()\r\n processes = [Process(target=process_shard, args=(idx,)) for idx in range(shards)]\r\n for p in processes:\r\n p.start()\r\n\r\n for p in processes:\r\n p.join()\r\n local_end = time.time() - local_start\r\n\r\n print(f\"Time taken on {shards} shards and {batch} batch_size: \", local_end)\r\n```\r\n\r\nJust careful, so that it won't crash your compute environment. As it almost crashed mine.",
"So this part seems to no longer work:\r\n```\r\n dataset = load_dataset(\"ascent_kb\")[\"train\"]\r\n ds_shard = dataset.shard(1, 0, contiguous=True)\r\n ds_shard.to_json(\"ascent_kb-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n```",
"If you are using `to_json` without any `num_proc`or `num_proc=1` then essentially it'll fall back to v1 only and I've kept it as it is (the tests were passing as well)\r\n\r\n> That's a dangerous benchmark as it'd wipe out many other HF things. Why not wipe out:\r\n\r\nThat's because some dataset related files were still left inside `~/.cache/huggingface/datasets` folder. You can wipe off datasets folder inside your cache maybe\r\n\r\n> dataset = load_dataset(\"ascent_kb\")[\"train\"]\r\n> ds_shard = dataset.shard(1, 0, contiguous=True)\r\n> ds_shard.to_json(\"ascent_kb-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)\r\n\r\nI tried this `lama` dataset (1.3M) and it worked fine. Trying it with `ascent_kb` currently, will update it here.",
"I don't think the issue has anything to do with your work, @bhavitvyamalik. I forgot to mention I tested to see the same problem with the latest datasets release.\r\n\r\nInteresting, I tried your suggestion. This:\r\n```\r\npython -c 'import datasets; ds=\"lama\"; dataset = datasets.load_dataset(ds)[\"train\"]; \\\r\ndataset.shard(1, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nworks fine and takes just a few GBs to complete.\r\n\r\nthis on the other hand blows up memory-wise:\r\n```\r\npython -c 'import datasets; ds=\"ascent_kb\"; dataset = datasets.load_dataset(ds)[\"train\"]; \\\r\ndataset.shard(1, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nand I have to kill it before it uses up all RAM. (I have 128GB of it, so it should be more than enough)",
"> That's because some dataset related files were still left inside ~/.cache/huggingface/datasets folder. You can wipe off datasets folder inside your cache maybe\r\n\r\nI think recent datasets added a method that will print out the path for all the different components for a given dataset, I can't recall the name though. It was when we were discussing a janitor program to clear up space selectively.",
"> and I have to kill it before it uses up all RAM. (I have 128GB of it, so it should be more than enough)\r\n\r\nSame thing just happened on my machine too. Memory leak somewhere maybe? Even if you were to load this dataset in your memory it shouldn't take more than 4GB. You were earlier doing this for `oscar` dataset. Is it working fine for that?",
"Hmm, looks like `datasets` has changed and won't accept my currently cached oscar-en (crashes), so I'd rather not download 0.5TB again. \r\n\r\nWere you able to reproduce the memory blow up with `ascent_kb`? It's should be a much quicker task to verify.\r\n\r\nBut yes, oscar worked just fine with `.shard()` which is what I used to process it fast.",
"What I tried is:\r\n```\r\nHF_DATASETS_OFFLINE=1 HF_DATASETS_CACHE=cache python -c 'import datasets; ds=\"oscar\"; \\\r\ndataset = datasets.load_dataset(ds, \"unshuffled_deduplicated_en\")[\"train\"]; \\\r\ndataset.shard(1000000, 0, contiguous=True).to_json(f\"{ds}-0.jsonl\", orient=\"records\", lines=True, force_ascii=False)'\r\n```\r\nand got:\r\n```\r\nUsing the latest cached version of the module from /gpfswork/rech/six/commun/modules/datasets_modules/datasets/oscar/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d (last modified on Fri Aug 6 01:52:35 2021) since it couldn't be found locally at oscar/oscar.py or remotely (OfflineModeIsEnabled).\r\nReusing dataset oscar (cache/oscar/unshuffled_deduplicated_en/1.0.0/e4f06cecc7ae02f7adf85640b4019bf476d44453f251a1d84aebae28b0f8d51d)\r\nTraceback (most recent call last):\r\n File \"<string>\", line 1, in <module>\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/load.py\", line 755, in load_dataset\r\n ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 737, in as_dataset\r\n datasets = utils.map_nested(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 203, in map_nested\r\n mapped = [\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 204, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/utils/py_utils.py\", line 142, in _single_map_nested\r\n return function(data_struct)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 764, in _build_single_dataset\r\n ds = self._as_dataset(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/builder.py\", line 834, in _as_dataset\r\n dataset_kwargs = ArrowReader(self._cache_dir, self.info).read(\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 217, in read\r\n return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 238, in read_files\r\n pa_table = self._read_files(files, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 173, in _read_files\r\n pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 308, in _get_table_from_filename\r\n table = ArrowReader.read_table(filename, in_memory=in_memory)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/arrow_reader.py\", line 327, in read_table\r\n return table_cls.from_file(filename)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/table.py\", line 450, in from_file\r\n table = _memory_mapped_arrow_table_from_file(filename)\r\n File \"/gpfswork/rech/six/commun/conda/stas/lib/python3.8/site-packages/datasets/table.py\", line 43, in _memory_mapped_arrow_table_from_file\r\n memory_mapped_stream = pa.memory_map(filename)\r\n File \"pyarrow/io.pxi\", line 782, in pyarrow.lib.memory_map\r\n File \"pyarrow/io.pxi\", line 743, in pyarrow.lib.MemoryMappedFile._open\r\n File \"pyarrow/error.pxi\", line 122, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 99, in pyarrow.lib.check_status\r\nOSError: Memory mapping file failed: Cannot allocate memory\r\n```",
"> Were you able to reproduce the memory blow up with ascent_kb? It's should be a much quicker task to verify.\r\n\r\nYes, this blows up memory-wise on my machine too. \r\n\r\nI found that a [similar error](https://discuss.huggingface.co/t/saving-memory-with-run-mlm-py-with-wikipedia-datasets/4160) was posted on the forum on 5th March. Since you already knew how much time [#2663 comment](https://github.com/huggingface/datasets/issues/2663#issue-946552273) took, can you try benchmarking v1 and v2 for now maybe until we have a fix for this memory blow up?",
"OK, so I benchmarked using \"lama\" though it's too small for this kind of test, since the sharding is much slower than one thread here.\r\n\r\nResults: https://gist.github.com/stas00/dc1597a1e245c5915cfeefa0eee6902c\r\n\r\nSo sharding does really bad there, and your json over procs is doing great!\r\n\r\nAny suggestions to a somewhat bigger dataset, but not too big? say 10 times of lama?",
"Looks great! I had a few questions/suggestions related to `benchmark-datasets-to_json.py`:\r\n \r\n1. You have used only 10_000 and 100_000 batch size. Including more batch sizes may help you find the perfect batch size for your machine and even give you some extra speed-up. \r\nFor eg, I found `load_dataset(\"cc100\", lang=\"eu\")` with batch size 125_000 took less time as compared to batch size 100_000 (71.16 sec v/s 67.26 sec) since this dataset has 2 fields only `['id', 'text']`, so that's why we can go for higher batch size here. \r\n \r\n2. Why have you used `num_procs` 1 and 4 only? \r\n\r\nYou can use:\r\n1. `dataset = load_dataset(\"cc100\", lang=\"af\")`. Even though it has only 2 fields but there are around 9.9 mil samples. (lama had around 1.3 mil samples)\r\n2. `dataset = load_dataset(\"cc100\", lang=\"eu\")` -> 16 mil samples. (if you want something more than 9.9 mil)\r\n3. `dataset = load_dataset(\"neural_code_search\", 'search_corpus')` -> 4.7 mil samples",
"Thank you, @bhavitvyamalik \r\n\r\nMy apologies, at the moment I have not found time to do more benchmark with the proposed other datasets. I will try to do it later, but I don't want it to hold your PR, it's definitely a great improvement based on the benchmarks I did run! And the comparison to sharded is really just of interest to me to see if it's on par or slower.\r\n\r\nSo if other reviewers are happy, this definitely looks like a great improvement to me and addresses the request I made in the first place.\r\n\r\n> Why have you used num_procs 1 and 4 only?\r\n\r\nOh, no particular reason, I was just comparing to 4 shards on my desktop. Typically it's sufficient to go from 1 to 2-4 to see whether the distributed approach is faster or not. Once hit larger numbers you often run into bottlenecks like IO, and then numbers can be less representative. I hope it makes sense.",
"Tested it with a larger dataset (`srwac`) and memory utilisation remained constant with no swap memory used. @lhoestq should I also add test for the same? Last time I tried this, I got `OSError: [Errno 12] Cannot allocate memory` in CircleCI tests"
] | 2021-08-03T08:30:13Z
| 2021-10-19T18:24:21Z
| 2021-09-13T13:56:37Z
|
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| null | 0
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Closes #2663. I've tried adding multiprocessing in `to_json`. Here's some benchmarking I did to compare the timings of current version (say v1) and multi-proc version (say v2). I did this with `cpu_count` 4 (2015 Macbook Air)
1. Dataset name: `ascent_kb` - 8.9M samples (all samples were used, reporting this for a single run)
v1- ~225 seconds for converting whole dataset to json
v2- ~200 seconds for converting whole dataset to json
2. Dataset name: `lama` - 1.3M samples (all samples were used, reporting this for 2 runs)
v1- ~26 seconds for converting whole dataset to json
v2- ~23.6 seconds for converting whole dataset to json
I think it's safe to say that v2 is 10% faster as compared to v1. Timings may improve further with better configuration.
The only bottleneck I feel is writing to file from the output list. If we can improve that aspect then timings may improve further.
Let me know if any changes/improvements can be done in this @stas00, @lhoestq, @albertvillanova. @lhoestq even suggested to extend this work with other export methods as well like `csv` or `parquet`.
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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."
] | 2021-05-20T23:57:43Z
| 2021-05-21T19:18:03Z
| 2021-05-21T19:18:03Z
|
NONE
| null | null | null |
## 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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Add Xitsonga Ner
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[
"looks like this PR includes changes about many files other than the ones related to xitsonga NER\r\n\r\ncould you create another branch and another PR please ?"
] | 2020-12-04T15:27:44Z
| 2020-12-06T18:31:35Z
| 2020-12-06T18:31:35Z
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Clean Xitsonga Ner PR
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Cannot push
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[
"Did you run `huggingface-cli lfs-enable-largefiles` before committing or before adding ? Maybe you can try before adding\r\n\r\nAnyway I'd encourage you to split your data into several TAR archives if possible, this way the dataset can loaded faster using multiprocessing (by giving each process a subset of shards to process)",
"@lhoestq \r\nThanks for the help!\r\n> Maybe you can try before adding\r\n\r\nIt did not help\r\n\r\nBut I totally got your point about split into multiple TAR archives. It really helped!"
] | 2022-11-09T15:32:05Z
| 2022-11-10T18:11:21Z
| 2022-11-10T18:11:11Z
|
NONE
| null | null | null |
### Describe the bug
I am facing the issue when I try to push the tar.gz file around 11G to HUB.
```
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ du -sh *
4.0K README.md
13G data
516K test.jsonl
18M train.jsonl
4.0K ulaanbal_v0.py
11G ulaanbal_v0.tar.gz
452K validation.jsonl
(venv) ╭─laptop@laptop~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git add ulaanbal_v0.tar.gz && git commit -m 'large version'
(venv) ╭─laptop@laptop ~/PersonalProjects/data/ulaanbal_v0 ‹main●›
╰─$ git push
EOFoading LFS objects: 0% (0/1), 0 B | 0 B/s
Uploading LFS objects: 0% (0/1), 0 B | 0 B/s, done.
error: failed to push some refs to 'https://huggingface.co/datasets/bayartsogt/ulaanbal_v0'
```
I have already tried pushing a small version of this and it was working fine. So my guess it is probably because of the big file.
Following I run before the commit:
```
╰─$ git lfs install
╰─$ huggingface-cli lfs-enable-largefiles .
```
### Steps to reproduce the bug
Create a private dataset on huggingface and push 12G tar.gz file
### Expected behavior
To be pushed with no issue
### Environment info
- `datasets` version: 2.6.1
- Platform: Darwin-21.6.0-x86_64-i386-64bit
- Python version: 3.7.11
- PyArrow version: 10.0.0
- Pandas version: 1.3.5
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Pass custom metadata filename to Image/Audio folders
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5635). All of your documentation changes will be reflected on that endpoint.",
"I'm not a big fan of this new param - I find assigning metadata files to splits via the `data_files` param cleaner. Also, assuming that the metadata filename is `metadata.json`/`metadata.csv` (I don't think we should allow other names), a user can do `load_dataset(\"imagefolder\", data_dir=\"data\")` to load a dataset with that structure.",
"@mariosasko I don't really like this change in it's current state either but passing specific files with `data_files` also looks not quite user-friendly to me. The idea of providing specific parameter for metadata filename seems natural to me but I don't see a way for implementing it without some ugly changes in `load.py` (passing the param to factories and creating metadata patterns on the fly). Why don't you like this parameter?\r\n\r\nFor context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with `data_files` approach.\r\n\r\nedit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)",
">For context: this PR emerged from the case where users wanted to use different metadata files with the same large set of images without copying directories on disk and it's not possible with data_files approach.\r\n>\r\n>edit: ah no, it's possible if one puts metadata files in different subdirs (so that the filenames can be left the same)\r\n\r\nSeems low prio, but one way to address this would be by allowing to pass \"exclude patterns\" to `data_files`"
] | 2023-03-14T15:08:16Z
| 2023-03-22T17:50:31Z
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This is a quick fix.
Now it requires to pass data via `data_files` parameters and include a required metadata file there and pass its filename as `metadata_filename` parameter.
For example, with the structure like:
```
data
images_dir/
im1.jpg
im2.jpg
...
metadata_dir/
meta_file1.jsonl
meta_file2.jsonl
...
```
to load data with `metadata_file1.jsonl` do:
```python
ds = load_dataset("imagefolder", data_files=["data/images_dir/**", "data/metadata_dir/meta_file1.jsonl"], metadata_filename="meta_file1.jsonl")
```
Note that if you have multiple splits, metadata file should be specified in each of them in `data_files`, smth like:
```python
data_files={
"train": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"],
"test": ["data/train/**", "data/metadata_dir/meta_file1.jsonl"]
}
```
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"_The documentation is not available anymore as the PR was closed or merged._",
"Hi @stevhliu, I've kept the `>>>` before all the in-line code comments as it was done like that in the default S3 example that was already there, I assume that it's done like that just for readiness, let me know whether we should remove the `>>>` in the Python blocks before the in-line code comments or keep them.\r\n\r\n\r\n",
"Comments are ignored by doctest, so I think we can remove the `>>>` :)",
"Cool I'll remove those now 👍🏻",
"Sure @lhoestq, I just kept that structure as that was the more similar one to the one that was already there, but we can go with that approach, just let me know whether I should change the headers so as to leave all those providers in the same level (`h2`). Thanks!"
] | 2022-06-16T11:46:09Z
| 2022-06-23T17:05:11Z
| 2022-06-23T16:54:59Z
|
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While I was going through the 🤗 Datasets documentation of the Cloud storage filesystems at https://huggingface.co/docs/datasets/filesystems, I realized that the Google Cloud Storage documentation could be improved e.g. bullet point says "Load your dataset" when the actual call was to "Save your dataset", in-line code comment was mentioning "s3 bucket" instead of "gcs bucket", and some more in-line comments could be included.
Also, I think that mixing Google Cloud Storage documentation with AWS S3's one was a little bit confusing, so I moved all those to the end of the document under an h2 tab named "Other filesystems", with an h3 for "Google Cloud Storage".
Besides that, I was currently working with Azure Blob Storage and found out that the URL to [adlfs](https://github.com/fsspec/adlfs) was common for both filesystems Azure Blob Storage and Azure DataLake Storage, as well as the URL, which was updated even though the redirect was working fine, so I decided to group those under the same row in the column of supported filesystems.
And took also the change to add a small documentation entry as for Google Cloud Storage but for Azure Blob Storage, as I assume that AWS S3, GCP Cloud Storage, and Azure Blob Storage, are the most used cloud storage providers.
Let me know if you're OK with these changes, or whether you want me to roll back some of those! :hugs:
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Add missing tags to XTREME
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Add missing tags to the XTREME benchmark for better discoverability.
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Viewing dataset card returns “502 Bad Gateway”
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"Can you try again? Maybe there was a minor outage.",
"Yes, it seems to be working now. In case it's helpful, the outage lasted several days. It was failing as late as yesterday morning. ",
"we fixed something on the server side, glad it's fixed now"
] | 2023-06-22T19:14:48Z
| 2023-06-27T08:38:19Z
| 2023-06-26T14:42:45Z
|
NONE
| null | null | null |
The url is: https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams
I am able to successfully view the “Files and versions” tab: [Confirm-Labs/pile_ngrams_trigrams at main](https://huggingface.co/datasets/Confirm-Labs/pile_ngrams_trigrams/tree/main)
Any help would be appreciated! Thanks! I hope this is the right place to report an issue like this.
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How to let `load_dataset` return a `Dataset` instead of `DatasetDict` in customized loading script
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"Hi @liyucheng09.\r\n\r\nUsers can pass the `split` parameter to `load_dataset`. For example, if your split name is \"train\",\r\n```python\r\nds = load_dataset(\"dataset_name\", split=\"train\")\r\n```\r\nwill return a `Dataset` instance.",
"@albertvillanova Thanks! I can't believe I didn't know this feature till now."
] | 2022-06-15T18:56:34Z
| 2022-06-16T10:40:08Z
| 2022-06-16T10:40:08Z
|
NONE
| null | null | null |
If the dataset does not need splits, i.e., no training and validation split, more like a table. How can I let the `load_dataset` function return a `Dataset` object directly rather than return a `DatasetDict` object with only one key-value pair.
Or I can paraphrase the question in the following way: how to skip `_split_generators` step in `DatasetBuilder` to let `as_dataset` gives a single `Dataset` rather than a list`[Dataset]`?
Many thanks for any help.
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Add ConLL-2000 dataset
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| 2020-09-17T10:38:10Z
| 2020-09-17T10:38:10Z
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Adds ConLL-2000 dataset used for text chunking. See https://www.clips.uantwerpen.be/conll2000/chunking/ for details and [motivation](https://github.com/huggingface/transformers/pull/7041#issuecomment-692710948) behind this PR
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Readme.md is misleading about kinds of datasets?
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[
"Hi ! Yes it's possible to use image data. There are already a few of them available (MNIST, CIFAR..)"
] | 2021-03-04T17:04:20Z
| 2021-08-04T18:05:23Z
| 2021-08-04T18:05:23Z
|
NONE
| null | null | null |
Hi!
At the README.MD, you say: "efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. "
But here:
https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py#L82-L117
You mention other kinds of datasets, with images and so on. I'm confused.
Is it possible to use it to store, say, imagenet locally?
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integration with imbalanced-learn
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[
"You can convert any dataset to pandas to be used with imbalanced-learn using `.to_pandas()`\r\n\r\nOtherwise if you want to keep a `Dataset` object and still use e.g. [make_imbalance](https://imbalanced-learn.org/stable/references/generated/imblearn.datasets.make_imbalance.html#imblearn.datasets.make_imbalance), you just need to pass the list of rows ids and labels:\r\n\r\n```python\r\nrow_indices = list(range(len(dataset)))\r\nresampled_row_indices, _ = make_imbalance(\r\n row_indices,\r\n dataset[\"label\"],\r\n sampling_strategy={0: 25, 1: 50, 2: 50},\r\n random_state=RANDOM_STATE,\r\n)\r\n\r\nresampled_dataset = dataset.select(resampled_row_indices)\r\n```"
] | 2023-03-22T11:05:17Z
| 2023-07-06T18:10:15Z
| 2023-07-06T18:10:15Z
|
NONE
| null | null | null |
### Feature request
Wouldn't it be great if the various class balancing operations from imbalanced-learn were available as part of datasets?
### Motivation
I'm trying to use imbalanced-learn to balance a dataset, but it's not clear how to get the two to interoperate - what would be great would be some examples. I've looked online, asked gpt-4, but so far not making much progress.
### Your contribution
If I can get this working myself I can submit a PR with example code to go in the docs
|
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Is `fs=` deprecated in `load_from_disk()` as well?
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[
"Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR? ",
"> Hi! Yes, we should deprecate the `fs` param here. Would you be interested in submitting a PR?\r\n\r\nYeah I can do that sometime next week. Should the storage_options be a new arg here? I’ll look around for anywhere else where fs is an arg.",
"Closed by #5393."
] | 2022-12-22T21:00:45Z
| 2023-01-23T10:50:05Z
| 2023-01-23T10:50:04Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
The `fs=` argument was deprecated from `Dataset.save_to_disk` and `Dataset.load_from_disk` in favor of automagically figuring it out via fsspec:
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/arrow_dataset.py#L1339-L1340
Is there a reason the same thing shouldn't also apply to `datasets.load.load_from_disk()` as well ?
https://github.com/huggingface/datasets/blob/9a7272cd4222383a5b932b0083a4cc173fda44e8/src/datasets/load.py#L1779
### Steps to reproduce the bug
n/a
### Expected behavior
n/a
### Environment info
n/a
|
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|
Unable to load XTREME dataset from disk
|
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[
"Hi @lewtun, you have to provide the full path to the downloaded file for example `/home/lewtum/..`",
"I was able to repro. Opening a PR to fix that.\r\nThanks for reporting this issue !",
"Thanks for the rapid fix @lhoestq!"
] | 2020-07-18T09:55:00Z
| 2020-07-21T08:15:44Z
| 2020-07-21T08:15:44Z
|
MEMBER
| null | null | null |
Hi 🤗 team!
## Description of the problem
Following the [docs](https://huggingface.co/nlp/loading_datasets.html?highlight=xtreme#manually-downloading-files) I'm trying to load the `PAN-X.fr` dataset from the [XTREME](https://github.com/google-research/xtreme) benchmark.
I have manually downloaded the `AmazonPhotos.zip` file from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1) and am running into a `FileNotFoundError` when I point to the location of the dataset.
As far as I can tell, the problem is that `AmazonPhotos.zip` decompresses to `panx_dataset` and `load_dataset()` is not looking in the correct path:
```
# path where load_dataset is looking for fr.tar.gz
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/
# path where it actually exists
/root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/panx_dataset/
```
## Steps to reproduce the problem
1. Manually download the XTREME benchmark from [here](https://www.amazon.com/clouddrive/share/d3KGCRCIYwhKJF0H3eWA26hjg2ZCRhjpEQtDL70FSBN?_encoding=UTF8&%2AVersion%2A=1&%2Aentries%2A=0&mgh=1)
2. Run the following code snippet
```python
from nlp import load_dataset
# AmazonPhotos.zip is in the root of the folder
dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
```
3. Here is the stack trace
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-4-26786bb5fa93> in <module>
----> 1 dataset = load_dataset("xtreme", "PAN-X.fr", data_dir='./')
/usr/local/lib/python3.6/dist-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
464 split_dict = SplitDict(dataset_name=self.name)
465 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 466 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
467 # Checksums verification
468 if verify_infos:
/usr/local/lib/python3.6/dist-packages/nlp/datasets/xtreme/b8c2ed3583a7a7ac60b503576dfed3271ac86757628897e945bd329c43b8a746/xtreme.py in _split_generators(self, dl_manager)
725 panx_dl_dir = dl_manager.extract(panx_path)
726 lang = self.config.name.split(".")[1]
--> 727 lang_folder = dl_manager.extract(os.path.join(panx_dl_dir, lang + ".tar.gz"))
728 return [
729 nlp.SplitGenerator(
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in extract(self, path_or_paths)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
170 return tuple(mapped)
171 # Singleton
--> 172 return function(data_struct)
173
174
/usr/local/lib/python3.6/dist-packages/nlp/utils/download_manager.py in <lambda>(path)
196 """
197 return map_nested(
--> 198 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
199 )
200
/usr/local/lib/python3.6/dist-packages/nlp/utils/file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
203 elif urlparse(url_or_filename).scheme == "":
204 # File, but it doesn't exist.
--> 205 raise FileNotFoundError("Local file {} doesn't exist".format(url_or_filename))
206 else:
207 # Something unknown
FileNotFoundError: Local file /root/.cache/huggingface/datasets/9b8c4f1578e45cb2539332c79738beb3b54afbcd842b079cabfd79e3ed6704f6/fr.tar.gz doesn't exist
```
## OS and hardware
```
- `nlp` version: 0.3.0
- Platform: Linux-4.15.0-72-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (True)
- Tensorflow version (GPU?): 2.1.0 (True)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
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| 2,215
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Add datasets SLR35 and SLR36 to OpenSLR
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[
"Hi @lhoestq,\r\nCould you please help me, I got this error message in all \"ci/circleci: run_dataset_script_tests_pyarrow*\" tests:\r\n```\r\n...\r\n \"\"\"Wrapper classes for various types of tokenization.\"\"\"\r\n \r\n from bleurt.lib import bert_tokenization\r\n import tensorflow.compat.v1 as tf\r\n> import sentencepiece as spm\r\nE ModuleNotFoundError: No module named 'sentencepiece'\r\n...\r\n```\r\nI am not sure why I do get it. Thanks.\r\n",
"Hi ! This issue appeared on master since the last update of `BLEURT`.\r\nI'm working on a fix. You can ignore this issue for this PR",
"> Hi ! This issue appeared on master since the last update of `BLEURT`.\r\n> I'm working on a fix. You can ignore this issue for this PR\r\n\r\nThanks for the info",
"Merging since the CI is fixed on master"
] | 2021-04-13T08:24:07Z
| 2021-04-13T14:05:14Z
| 2021-04-13T14:05:14Z
|
CONTRIBUTOR
| null | 0
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I would like to add [SLR35](https://openslr.org/35/) (18GB) and [SLR36](https://openslr.org/36/) (22GB) which are Large Javanese and Sundanese ASR training data set collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
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JSONDecodeError on JSON with multiple lines
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[
"Hi !\r\n\r\nThe `json` dataset script does support this format. For example loading a dataset with this format works on my side:\r\n```json\r\n{\"key1\":11, \"key2\":12, \"key3\":13}\r\n{\"key1\":21, \"key2\":22, \"key3\":23}\r\n```\r\n\r\nCan you show the full stacktrace please ? Also which version of datasets and pyarrow are you using ?\r\n\r\n",
"Hi Quentin!\r\n\r\nI apologize for bothering you. There was some issue with my pyarrow version as far as I understand. I don't remember the exact version I was using as I didn't check it.\r\n\r\nI repeated it with `datasets 1.2.1` and `pyarrow 2.0.0` and it worked.\r\n\r\nClosing this issue. Again, sorry for the bother.\r\n\r\nThanks,\r\nGunjan"
] | 2021-01-27T00:19:22Z
| 2021-01-31T08:47:18Z
| 2021-01-31T08:47:18Z
|
CONTRIBUTOR
| null | null | null |
Hello :),
I have been trying to load data using a JSON file. Based on the [docs](https://huggingface.co/docs/datasets/loading_datasets.html#json-files), the following format is supported:
```json
{"key1":11, "key2":12, "key3":13}
{"key1":21, "key2":22, "key3":23}
```
But, when I try loading a dataset with the same format, I get a JSONDecodeError : `JSONDecodeError: Extra data: line 2 column 1 (char 7142)`. Now, this is expected when using `json` to load a JSON file. But I was wondering if there are any special arguments to pass when using `load_dataset` as the docs suggest that this format is supported.
When I convert the JSON file to a list of dictionaries format, I get AttributeError: `AttributeError: 'list' object has no attribute 'keys'`. So, I can't convert them to list of dictionaries either.
Please let me know :)
Thanks,
Gunjan
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4947). All of your documentation changes will be reflected on that endpoint."
] | 2022-09-07T17:14:49Z
| 2023-09-24T10:05:38Z
| 2022-09-08T09:13:10Z
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Image Data loaded Twice
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[] | 2023-12-13T13:11:42Z
| 2023-12-13T13:11:42Z
| null |
NONE
| null | null | null |
### Describe the bug
When I learn from https://huggingface.co/docs/datasets/image_load and try to load image data from a folder. I noticed that the image was read twice in the returned data. As you can see in the attached image, there are only four images in the train folder, but reading brings up eight images
### Steps to reproduce the bug
from datasets import Dataset, load_dataset
dataset = load_dataset("imagefolder", data_dir="data/", drop_labels=False)
# print(dataset["train"][0]["image"] == dataset["train"][1]["image"])
print(dataset)
print(dataset["train"]["image"])
print(len(dataset["train"]["image"]))
### Expected behavior
DatasetDict({
train: Dataset({
features: ['image', 'label'],
num_rows: 8
})
})
[<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D1CA8B0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D2452E0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245310>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2453A0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245460>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2877x2129 at 0x1BD1D245430>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D2454F0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=4208x3120 at 0x1BD1D245550>]
8
### Environment info
- `datasets` version: 2.14.5
- Platform: Windows-10-10.0.22621-SP0
- Python version: 3.9.17
- Huggingface_hub version: 0.19.4
- PyArrow version: 13.0.0
- Pandas version: 2.0.3
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Dataset joelito/mc4_legal does not work with multiple files
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[
"Thanks for reporting @JoelNiklaus.\r\n\r\nPlease note that since we moved all dataset loading scripts to the Hub, the issues and pull requests relative to specific datasets are directly handled on the Hub, in their Community tab. I'm transferring this issue there: https://huggingface.co/datasets/joelito/mc4_legal/discussions\r\n\r\nI am also having a look at the bug in your script.",
"Issue transferred to: https://huggingface.co/datasets/joelito/mc4_legal/discussions/1"
] | 2022-11-28T00:16:16Z
| 2022-11-28T07:22:42Z
| 2022-11-28T07:22:42Z
|
CONTRIBUTOR
| null | null | null |
### Describe the bug
The dataset https://huggingface.co/datasets/joelito/mc4_legal works for languages like bg with a single data file, but not for languages with multiple files like de. It shows zero rows for the de dataset.
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main) [1]> python test_mc4_legal.py (debug)
Found cached dataset mc4_legal (/Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/de/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f)
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 0
})
joelniklaus@Joels-MacBook-Pro ~/N/P/C/L/p/m/mc4_legal (main)> python test_mc4_legal.py (debug)
Downloading and preparing dataset mc4_legal/bg to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f...
Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1240.55it/s]
Dataset mc4_legal downloaded and prepared to /Users/joelniklaus/.cache/huggingface/datasets/mc4_legal/bg/0.0.0/fb6952a097180f8c936e2a7605525ff670354a344fc1a2c70107684d3f7cb02f. Subsequent calls will reuse this data.
Dataset({
features: ['index', 'url', 'timestamp', 'matches', 'text'],
num_rows: 204
})
### Steps to reproduce the bug
import datasets
from datasets import load_dataset, get_dataset_config_names
language = "bg"
test = load_dataset("joelito/mc4_legal", language, split='train')
### Expected behavior
It should display the correct number of rows for the de dataset which should be a large number (thousands or more).
### Environment info
Package Version
------------------------ --------------
absl-py 1.3.0
aiohttp 3.8.1
aiosignal 1.2.0
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
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[] | 2022-08-10T05:12:34Z
| 2022-08-12T14:17:57Z
| 2022-08-12T14:17:57Z
|
MEMBER
| null | null | null |
## Describe the bug
Our loading script for OPUS ParaCrawl loads its 7.1 version. Current existing version is 9.
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Issue: Dataset download error
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[
"Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.",
"It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and then update the dataset_infos.json file with\r\n```\r\ndatasets-cli test ./datasets/iwslt2017 --all_configs --save_infos --ignore_verifications\r\n```",
"Is this a command to update my local files or fix the file Github repo in general? (I am not so familiar with the datasets-cli command here)\r\n\r\nI also took a brief look at the **Sharing your dataset** section, looks like I could fix that locally and push it to the repo? I guess we are \"canonical\" category?",
"This command will update your local file. Then you can open a Pull Request to push your fix to the github repo :)\r\nAnd yes you are right, it is a \"canonical\" dataset, i.e. a dataset script defined in this github repo (as opposed to dataset repositories of users on the huggingface hub)",
"Hi, thanks for the answer. \r\n\r\nI gave a try to the problem today. But I encountered an upload error: \r\n\r\n```\r\ngit push -u origin fix_link_iwslt\r\nEnter passphrase for key '/home2/xuhuizh/.ssh/id_rsa': \r\nERROR: Permission to huggingface/datasets.git denied to XuhuiZhou.\r\nfatal: Could not read from remote repository.\r\n\r\nPlease make sure you have the correct access rights\r\nand the repository exists.\r\n```\r\n\r\nAny insight here? \r\n\r\nBy the way, when I run the datasets-cli command, it shows the following error, but does not seem to be the error coming from `iwslt.py`\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home2/xuhuizh/anaconda3/envs/UMT/bin/datasets-cli\", line 33, in <module>\r\n sys.exit(load_entry_point('datasets', 'console_scripts', 'datasets-cli')())\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/datasets_cli.py\", line 35, in main\r\n service.run()\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/commands/test.py\", line 141, in run\r\n try_from_hf_gcs=False,\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 579, in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/builder.py\", line 639, in _download_and_prepare\r\n self.info.download_checksums, dl_manager.get_recorded_sizes_checksums(), \"dataset source files\"\r\n File \"/home2/xuhuizh/projects/datasets/src/datasets/utils/info_utils.py\", line 32, in verify_checksums\r\n raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))\r\ndatasets.utils.info_utils.ExpectedMoreDownloadedFiles: {'https://wit3.fbk.eu/archive/2017-01-trnmted//texts/DeEnItNlRo/DeEnItNlRo/DeEnItNlRo-DeEnItNlRo.tgz'}\r\n```",
"Hi ! To create a PR on this repo your must fork it and create a branch on your fork. See how to fork the repo [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#start-by-preparing-your-environment).\r\nAnd to make the command work without the `ExpectedMoreDownloadedFiles` error, you just need to use the `--ignore_verifications` flag.",
"Hi @XuhuiZhou,\r\n\r\nAs @lhoestq has well explained, you need to fork HF's repository, create a feature branch in your fork, push your changes to it and then open a Pull Request to HF's upstream repository. This is so because at HuggingFace Datasets we follow a development model called \"Fork and Pull Model\". You can find more information here:\r\n- [Understanding the GitHub flow](https://guides.github.com/introduction/flow/)\r\n- [Forking Projects](https://guides.github.com/activities/forking/)\r\n\r\nAlternatively, if you find all these steps too complicated, you can use the GitHub official command line tool: [GitHub CLI](https://cli.github.com/). Once installed, in order to create a Pull Request, you only need to use this command:\r\n```shell\r\ngh pr create --web\r\n```\r\nThis utility will automatically create the fork, push your changes and open a Pull Request, under the hood."
] | 2021-03-18T06:36:06Z
| 2021-03-22T11:52:31Z
| null |
NONE
| null | null | null |
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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Hi
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[] | 2020-12-02T23:47:14Z
| 2020-12-03T16:42:41Z
| 2020-12-03T16:42:41Z
|
NONE
| null | null | null |
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
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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laion2B-en failed to load on Windows with PrefetchVirtualMemory failed
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[
"~~You will see this error if the cache dir filepath contains relative `..` paths. Use `os.path.realpath(_CACHE_DIR)` before passing it to the `load_dataset` function.~~",
"This is a real issue and not related to paths.",
"Based on the StackOverflow answer, this causes the error to go away:\r\n```diff\r\ndiff --git a/table.py b/table.py\r\n--- a/table.py\t\r\n+++ b/table.py\t(date 1701824849806)\r\n@@ -47,7 +47,7 @@\r\n \r\n \r\n def _memory_mapped_record_batch_reader_from_file(filename: str) -> pa.RecordBatchStreamReader:\r\n- memory_mapped_stream = pa.memory_map(filename)\r\n+ memory_mapped_stream = pa.memory_map(filename, \"r+\")\r\n return pa.ipc.open_stream(memory_mapped_stream)\r\n```\r\nBut now loading the dataset goes very, very slowly, which is unexpected.",
"I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...",
"Hi! \r\n\r\nInstead of generating one (potentially large) Arrow file, we shard the generated data into 500 MB shards because memory-mapping large Arrow files can be problematic on some systems. Maybe deleting the dataset's cache and increasing the shard size (controlled with the `datasets.config.MAX_SHARD_SIZE` variable; e.g. to \"4GB\") can fix the issue for you.\r\n\r\n> I don't really comprehend what it is that `datasets` gave me when it downloaded the laion2B-en dataset, because nothing can seemingly read these 1024 .arrow files it is retrieving. Not `polars`, not `pyarrow`, it's not an `ipc` file, it's not a `parquet` file...\r\n\r\nOur `.arrow` files are in the [Arrow streaming format](https://arrow.apache.org/docs/python/ipc.html#using-streams). To load them as a `polars` DataFrame, do the following:\r\n```python\r\ndf = pl.from_arrow(Dataset.from_from(path_to_arrow_file).data.table)\r\n```\r\n\r\nWe plan to switch to the IPC version eventually.\r\n",
"Hmm, I have a feeling this works fine on Linux, and is a real bug for however `datasets` is doing the sharding on Windows. I will follow up, but I think this is a real bug."
] | 2023-12-06T00:07:34Z
| 2023-12-06T23:26:23Z
| null |
NONE
| null | null | null |
### Describe the bug
I have downloaded laion2B-en, and I'm receiving the following error trying to load it:
```
Resolving data files: 100%|██████████| 128/128 [00:00<00:00, 1173.79it/s]
Traceback (most recent call last):
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 31, in <module>
count = compute_frequencies()
^^^^^^^^^^^^^^^^^^^^^
File "D:\Art-Workspace\src\artworkspace\tokeneval\compute_frequencies.py", line 17, in compute_frequencies
laion2b_dataset = load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\load.py", line 2165, in load_dataset
ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1187, in as_dataset
datasets = map_nested(
^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\utils\py_utils.py", line 456, in map_nested
return function(data_struct)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1217, in _build_single_dataset
ds = self._as_dataset(
^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\builder.py", line 1291, in _as_dataset
dataset_kwargs = ArrowReader(cache_dir, self.info).read(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 244, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 265, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 200, in _read_files
pa_table: Table = self._get_table_from_filename(f_dict, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 336, in _get_table_from_filename
table = ArrowReader.read_table(filename, in_memory=in_memory)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\arrow_reader.py", line 357, in read_table
return table_cls.from_file(filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 1059, in from_file
table = _memory_mapped_arrow_table_from_file(filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\bberman\Documents\Art-Workspace\venv\Lib\site-packages\datasets\table.py", line 66, in _memory_mapped_arrow_table_from_file
pa_table = opened_stream.read_all()
^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow\ipc.pxi", line 757, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow\error.pxi", line 91, in pyarrow.lib.check_status
OSError: [WinError 8] PrefetchVirtualMemory failed. Detail: [Windows error 8] Not enough memory resources are available to process this command.
```
This error is probably a red herring: https://stackoverflow.com/questions/50263929/numpy-memmap-returns-not-enough-memory-while-there-are-plenty-available In other words, the issue is related to asking for a memory mapping of length N > M the length of the file on Windows. This gracefully succeeds on Linux.
I have 1024 arrow files in my cache instead of 128 like in the repository for it. Probably related. I don't know why `datasets` reorganized/rewrote the dataset in my cache to be 1024 slices instead of the original 128.
### Steps to reproduce the bug
```
# as a huggingface developer, you may already have laion2B-en somewhere
_CACHE_DIR = "."
from datasets import load_dataset
load_dataset("laion/laion2B-en", split="train", cache_dir=_CACHE_DIR, keep_in_memory=False)
```
### Expected behavior
This should correctly load as a memory mapped Arrow dataset.
### Environment info
- `datasets` version: 2.15.0
- Platform: Windows-10-10.0.20348-SP0 (this is windows 2022)
- Python version: 3.11.4
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.2
- `fsspec` version: 2023.10.0
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cast_column function not working with map function in streaming mode for Audio features
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[
"Hi! This is probably due to the fact that `IterableDataset.map` sets `features` to `None` before mapping examples. We can fix the issue by passing the old `features` dict to the map generator and performing encoding/decoding there (before calling the map transform function)."
] | 2021-12-30T14:52:01Z
| 2022-01-18T19:54:07Z
| 2022-01-18T19:54:07Z
|
NONE
| null | null | null |
## Describe the bug
I am trying to use Audio class for loading audio features using custom dataset. I am able to cast 'audio' feature into 'Audio' format with cast_column function. On using map function, I am not getting 'Audio' casted feature but getting path of audio file only.
I am getting features of 'audio' of string type with load_dataset call. After using cast_column 'audio' feature is converted into 'Audio' type. But in map function I am not able to get Audio type for audio feature & getting string type data containing path of file only. So I am not able to use processor in encode function.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset, Audio
from transformers import Wav2Vec2Processor
def encode(batch, processor):
print("Audio: ",batch['audio'])
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
return batch
def print_ds(ds):
iterator = iter(ds)
for d in iterator:
print("Data: ",d)
break
processor = Wav2Vec2Processor.from_pretrained(pretrained_model_path)
dataset = load_dataset("custom_dataset.py","train",data_files={'train':'train_path.txt'},
data_dir="data", streaming=True, split="train")
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
print("Features: ",dataset.features)
print_ds(dataset)
dataset = dataset.map(lambda x: encode(x,processor))
print("Features: ",dataset.features)
print_ds(dataset)
```
## Expected results
map function not printing Audio type features be used with processor function and getting error in processor call due to this.
## Actual results
# after load_dataset call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Value(dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': 'data/0116_003.wav'}
# after cast_column call
Features: {'sentence': Value(dtype='string', id=None), 'audio': Audio(sampling_rate=16000, mono=True, _storage_dtype='string', id=None)}
Data: {'sentence': 'और अपने पेट को माँ की स्वादिष्ट गरमगरम जलेबियाँ हड़पते\n', 'audio': {'path': 'data/0116_003.wav', 'array': array([ 1.2662281e-06, 1.0264218e-06, -1.3615092e-06, ...,
1.3017889e-02, 1.0085563e-02, 4.8155054e-03], dtype=float32), 'sampling_rate': 16000}}
# after map call
Features: None
Audio: data/0116_003.wav
Traceback (most recent call last):
File "demo2.py", line 36, in <module>
print_ds(dataset)
File "demo2.py", line 11, in print_ds
for d in iterator:
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 341, in __iter__
for key, example in self._iter():
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 338, in _iter
yield from ex_iterable
File "/opt/conda/lib/python3.7/site-packages/datasets/iterable_dataset.py", line 192, in __iter__
yield key, self.function(example)
File "demo2.py", line 32, in <lambda>
dataset = dataset.map(lambda x: batch_encode(x,processor))
File "demo2.py", line 6, in batch_encode
batch["input_values"] = processor(batch["audio"]['array'], sampling_rate=16000).input_values
TypeError: string indices must be integers
## Environment info
- `datasets` version: 1.17.0
- Platform: Linux-4.14.243 with-debian-bullseye-sid
- Python version: 3.7.9
- PyArrow version: 6.0.1
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Add Portuguese Hate Speech dataset
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[
"@lhoestq done! (The failing tests don't seem to be related)",
"merging since the CI is fixed on master"
] | 2020-12-09T18:48:16Z
| 2020-12-14T18:06:42Z
| 2020-12-14T16:22:20Z
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Binary Portuguese Hate Speech dataset from [this paper](https://www.aclweb.org/anthology/W19-3510/).
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Fix flaky test again for s3 serialization
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Following https://github.com/huggingface/datasets/pull/3388 that wasn't enough (see CI error [here](https://app.circleci.com/pipelines/github/huggingface/datasets/9080/workflows/b971fb27-ff20-4220-9416-c19acdfdf6f4/jobs/55985))
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Name json file "squad.json" instead of "squad.py.json"
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issues with downloading datasets for wmt16 and wmt19
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[
"I found `UNv1.0.en-ru.tar.gz` here: https://conferences.unite.un.org/uncorpus/en/downloadoverview, so it can be reconstructed with:\r\n```\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.00\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.01\r\nwget -c https://stuncorpusprod.blob.core.windows.net/corpusfiles/UNv1.0.en-ru.tar.gz.02\r\ncat UNv1.0.en-ru.tar.gz.0* > UNv1.0.en-ru.tar.gz\r\n```\r\nit has other languages as well, in case https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/ is gone",
"Further, `nlp.load_dataset('wmt19', 'ru-en')` has only the `train` and `val` datasets. `test` is missing.\r\n\r\nFixed locally for summarization needs, by running:\r\n```\r\npip install sacrebleu\r\nsacrebleu -t wmt19 -l ru-en --echo src > test.source\r\nsacrebleu -t wmt19 -l ru-en --echo ref > test.target\r\n```\r\nh/t @sshleifer ",
"Fixed in https://github.com/huggingface/datasets/pull/1912"
] | 2020-08-10T17:32:51Z
| 2022-10-04T17:46:59Z
| 2022-10-04T17:46:58Z
|
CONTRIBUTOR
| null | null | null |
I have encountered multiple issues while trying to:
```
import nlp
dataset = nlp.load_dataset('wmt16', 'ru-en')
metric = nlp.load_metric('wmt16')
```
1. I had to do `pip install -e ".[dev]" ` on master, currently released nlp didn't work (sorry, didn't save the error) - I went back to the released version and now it worked. So it must have been some outdated dependencies that `pip install -e ".[dev]" ` fixed.
2. it was downloading at 60kbs - almost 5 hours to get the dataset. It was downloading all pairs and not just the one I asked for.
I tried the same code with `wmt19` in parallel and it took a few secs to download and it only fetched data for the requested pair. (but it failed too, see below)
3. my machine has crushed and when I retried I got:
```
Traceback (most recent call last):
File "./download.py", line 9, in <module>
dataset = nlp.load_dataset('wmt16', 'ru-en')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 549, in load_dataset
download_config=download_config, download_mode=download_mode, ignore_verifications=ignore_verifications,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 449, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/stas/anaconda3/envs/main/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/builder.py", line 422, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/stas/anaconda3/envs/main/lib/python3.7/os.py", line 221, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/stas/.cache/huggingface/datasets/wmt16/ru-en/1.0.0/4d8269cdd971ed26984a9c0e4a158e0c7afc8135fac8fb8ee43ceecf38fd422d.incomplete'
```
it can't handle resumes. but neither allows a new start. Had to delete it manually.
4. and finally when it downloaded the dataset, it then failed to fetch the metrics:
```
Traceback (most recent call last):
File "./download.py", line 15, in <module>
metric = nlp.load_metric('wmt16')
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 442, in load_metric
module_path, hash = prepare_module(path, download_config=download_config, dataset=False)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/load.py", line 258, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/mnt/nvme1/code/huggingface/nlp-master/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/metrics/wmt16/wmt16.py
```
5. If I run the same code with `wmt19`, it fails too:
```
ConnectionError: Couldn't reach https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
```
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Add Food-101
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| 2021-08-20T14:31:33Z
| 2021-08-19T12:48:06Z
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CONTRIBUTOR
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Adds image classification dataset [Food-101](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/).
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News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
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"These ranges seem to be valid English. Closing."
] | 2021-05-24T10:03:34Z
| 2022-10-05T17:13:49Z
| 2022-10-05T17:13:49Z
|
NONE
| null | null | null |
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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MDExOlB1bGxSZXF1ZXN0NjMwMDQxNzc4
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Fix incorrect version specification for the pyarrow package
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| 2021-05-05T10:09:16Z
| 2021-05-05T09:21:58Z
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This PR addresses the bug in the pyarrow version specification, which is detailed in #2316 .
Simply, I put a comma between the version bounds.
Fix #2316.
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Remove config names as yaml keys
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[
"I included the change from https://github.com/huggingface/datasets/pull/4302 directly in this PR, this way the datasets will be updated right away in the CI (the CI is only triggered when a dataset card is changed)",
"_The documentation is not available anymore as the PR was closed or merged._",
"Alright it's ready now :)\r\n\r\nHere is an example for the `ade_corpus_v2` dataset card. Notice the new `configs` key:\r\n\r\nhttps://github.com/huggingface/datasets/blob/76d9a141740a03f6836feb251f6059894b8d8046/datasets/ade_corpus_v2/README.md#L1-L78\r\n\r\nCI failures are only related to dataset cards missing some content."
] | 2022-05-18T13:59:24Z
| 2022-05-20T09:35:26Z
| 2022-05-20T09:27:19Z
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Many datasets have dots in their config names. However it causes issues with the YAML tags of the dataset cards since we can't have dots in YAML keys.
I fix this, I removed the tags separations per config name completely, and have a single flat YAML for all configurations. Dataset search doesn't use this info anyway. I removed all the config names used as YAML keys, and I moved them in under a new `config:` key.
This is related to https://github.com/huggingface/datasets/pull/2362 (internal https://github.com/huggingface/moon-landing/issues/946).
Also removing the dots in the YAML keys would allow us to do as in https://github.com/huggingface/datasets/pull/4302 which removes a hack that replaces all the dots by underscores in the YAML tags.
I also added a test in the CI that checks that all the YAML tags to make sure that:
- they can be parsed using a YAML parser
- they contain only valid YAML tags like languages or task_ids
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Cannot import load_dataset on Colab
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"I'm facing the same issue on Colab today too.\r\n\r\n```\r\nModuleNotFoundError Traceback (most recent call last)\r\n<ipython-input-4-5833ac0f5437> in <module>()\r\n 3 \r\n 4 from ray import tune\r\n----> 5 from datasets import DatasetDict, Dataset\r\n 6 from datasets import load_dataset, load_metric\r\n 7 from dataclasses import dataclass\r\n\r\n7 frames\r\n/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()\r\n 25 import posixpath\r\n 26 import requests\r\n---> 27 from tqdm.contrib.concurrent import thread_map\r\n 28 \r\n 29 from .. import __version__, config, utils\r\n\r\nModuleNotFoundError: No module named 'tqdm.contrib.concurrent'\r\n\r\n---------------------------------------------------------------------------\r\nNOTE: If your import is failing due to a missing package, you can\r\nmanually install dependencies using either !pip or !apt.\r\n\r\nTo view examples of installing some common dependencies, click the\r\n\"Open Examples\" button below.\r\n---------------------------------------------------------------------------\r\n```",
"@phosseini \r\nI think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\nFor now I just downgraded to 1.9.0 and it is working fine.",
"> @phosseini\r\n> I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )\r\n> For now I just downgraded to 1.9.0 and it is working fine.\r\n\r\nSame here, downgraded to 1.9.0 for now and works fine.",
"Hi, \r\n\r\nupdating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:\r\n```\r\n!pip install tqdm --upgrade\r\n```",
"Hi @bayartsogt-ya and @phosseini, thanks for reporting.\r\n\r\nWe are fixing this critical issue and making an urgent patch release of the `datasets` library today.\r\n\r\nIn the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library: \r\n```\r\n!pip install -U tqdm\r\n```"
] | 2021-07-21T15:52:51Z
| 2021-07-22T07:26:25Z
| 2021-07-22T07:09:07Z
|
NONE
| null | null | null |
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
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Adapt image datasets
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[
"This PR can be merged after #3163 is merged (this PR is pretty big because I was working on the forked branch).\r\n\r\n@lhoestq @albertvillanova Could you please take a look at the changes in `src/datasets/utils/streaming_download_manager.py`? These changes were required to support streaming of the `cats_vs_dogs` and the `beans` datasets.",
"The CI failures are due to the missing fields in the README files.",
"and thanks for adding support for Path.name and Path.parent for streaming :)"
] | 2021-12-01T19:52:01Z
| 2021-12-09T18:37:42Z
| 2021-12-09T18:37:41Z
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This PR:
* adapts the ImageClassification template to use the new Image feature
* adapts the following datasets to use the new Image feature:
* beans (+ fixes streaming)
* cast_vs_dogs (+ fixes streaming)
* cifar10
* cifar100
* fashion_mnist
* mnist
* head_qa
cc @nateraw
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Support Audio feature in streaming mode
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| 2021-11-12T14:13:05Z
| 2021-11-12T14:13:04Z
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Fix #3132.
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"_The documentation is not available anymore as the PR was closed or merged._",
"Finally I'm done updating the dependencies ^^'\r\n\r\ncc @sashavor can you review my changes in the metric card please ?",
"Looks good to me! Just fixed a tiny typo :wink: ",
"Thanks !"
] | 2022-03-16T15:56:47Z
| 2022-03-22T15:10:12Z
| 2022-03-22T15:05:30Z
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The COMET metric has been broken for a while since big breaking changes happened. We did not catch them in the CI because the slow test mocks the download_model function that was changed.
This PR fixes the metric, updates the download_model mock and updates the doctest.
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Add SEDE dataset
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"Thanks @albertvillanova for your great suggestions! I just pushed a new commit with the necessary fixes. For some reason, the test `test_metric_common` failed for `meteor` metric, which doesn't have any connection to this PR, so I'm trying to rebase and see if it helps.",
"Hi @Hazoom,\r\n\r\nYou were right: the non-passing test had nothing to do with this PR.\r\n\r\nUnfortunately, you did a git rebase (instead of a git merge), which is not recommended once you have already opened a pull request because you mess up your pull request history. You can see that your pull request now contains:\r\n- your commits repeated two times\r\n- and commits which are not yours from the master branch\r\n\r\nIf you would like to clean your pull request, please make:\r\n```\r\ngit reset --hard 587b93a\r\ngit fetch upstream master\r\ngit merge upstream/master\r\ngit push --force origin sede\r\n```",
"> Hi @Hazoom,\r\n> \r\n> You were right: the non-passing test had nothing to do with this PR.\r\n> \r\n> Unfortunately, you did a git rebase (instead of a git merge), which is not recommended once you have already opened a pull request because you mess up your pull request history. You can see that your pull request now contains:\r\n> \r\n> * your commits repeated two times\r\n> * and commits which are not yours from the master branch\r\n> \r\n> If you would like to clean your pull request, please make:\r\n> \r\n> ```\r\n> git reset --hard 587b93a\r\n> git fetch upstream master\r\n> git merge upstream/master\r\n> git push --force origin sede\r\n> ```\r\n\r\nThanks @albertvillanova ",
"> Nice! Just one final request before approving your pull request:\r\n> \r\n> As you have updated the \"QuerySetId\" field data type, the size of the dataset is smaller now. You should regenerate the metadata. Please run:\r\n> \r\n> ```\r\n> rm datasets/sede/dataset_infos.json\r\n> datasets-cli test datasets/sede --save_infos --all_configs\r\n> ```\r\n\r\n@albertvillanova Good catch, just fixed it."
] | 2021-09-19T13:11:24Z
| 2021-09-24T10:39:55Z
| 2021-09-24T10:39:54Z
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This PR adds the SEDE dataset for the task of realistic Text-to-SQL, following the instructions of how to add a database and a dataset card.
Please see our paper for more details: https://arxiv.org/abs/2106.05006
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Add DatasetDict.to_pandas
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"The current implementation is what I had in mind, i.e. concatenate all splits by default.\r\n\r\nHowever, I think most tabular datasets would come as a single split. So for that usecase, it wouldn't change UX if we raise when there are more than one splits.\r\n\r\nAnd for multiple splits, the user either passes a list, or they can pass `splits=\"all\"` to have all splits concatenated.",
"I think it's better to raise an error in cases when there are multiple splits but no split is specified so that users know for sure with which data they are working. I imagine a case when a user loads a dataset that they don't know much about (like what splits it has), and if they get a concatenation of everything, it might lead to incorrect processing or interpretations and it would be hard to notice it.\r\n(\"explicit is better than implicit\")",
"I just changed to raise an error if there are multiple splits. The error shows an example of how to choose a split to convert.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5312). All of your documentation changes will be reflected on that endpoint.",
"Thanks for the review, I've updated the type hint and added a line to raise an error on bad splits :)",
"Merging https://github.com/huggingface/datasets/pull/5301 would eliminate the need for this PR, no?\r\n\r\nIn the meantime, I find the current API cleaner.",
"This solution is simpler than https://github.com/huggingface/datasets/pull/5301 and covers most cases for tabular datasets, so I'm in favor of merging this one and put https://github.com/huggingface/datasets/pull/5301 on stand by",
"Let me know if it sounds good to you @mariosasko @albertvillanova :)",
"I'm still not convinced. If `DatasetDict` needs this method and there is no other way, then IMO it would make more sense to return a dictionary with the splits converted to `pd.DataFrame`. ",
"@mariosasko the issue we're dealing with is that in tabular scenarios, we often don't have splits in the dataset, and imposing that concept to people dealing with the library hampers adoption.",
"@adrinjalali This PR proposes a solution inconsistent with the existing API (in other words, a solution that clutters our API 🙂). Moreover, our library primarily focuses on larger-than-RAM datasets, and tabular datasets don't (directly) fall into this group.\r\n\r\nInstead of the temporary \"fix\" proposed here, it makes much more sense to align `load_dataset` with both tabular and DL workflows \"in a consistent way\", so I suggest we continue our discussion from https://github.com/huggingface/datasets/issues/5189 to have this resolved by version 3.0.",
"closing this one for now"
] | 2022-11-29T16:30:02Z
| 2023-09-24T10:06:19Z
| 2023-01-25T17:33:42Z
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From discussions in https://github.com/huggingface/datasets/issues/5189, for tabular data it doesn't really make sense to have to do
```python
df = load_dataset(...)["train"].to_pandas()
```
because many datasets are not split.
In this PR I added `to_pandas` to `DatasetDict` which returns the DataFrame:
If there's only one split, you don't need to specify the split name:
```python
df = load_dataset(...).to_pandas()
```
EDIT: and if a dataset has multiple splits:
```python
df = load_dataset(...).to_pandas(splits=["train", "test"])
# or
df = load_dataset(...).to_pandas(splits="all")
# raises an error because you need to select the split(s) to convert
load_dataset(...).to_pandas()
```
I do have one question though @merveenoyan @adrinjalali @mariosasko:
Should we raise an error if there are multiple splits and ask the user to choose one explicitly ?
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wikipedia.py generator that extracts XML doesn't release memory
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"Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?",
"OK! I'll send it later."
] | 2021-03-11T12:51:24Z
| 2021-03-22T08:33:52Z
| 2021-03-22T08:33:52Z
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CONTRIBUTOR
| null | null | null |
I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.
I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502
`root.clear()` intend to clear memory, but it doesn't.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494
I replaced them with `elem.clear()`, then it seems to work correctly.
here is the notebook to reproduce it.
https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb
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MDExOlB1bGxSZXF1ZXN0NTQ2MTY4MTk3
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Add missing homepage in some dataset cards
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In some dataset cards the homepage field in the `Dataset Description` section was missing/empty
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ConnectionError and SSLError
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[
"Hi ! You can download the `oscar.py` file from this repository at `/datasets/oscar/oscar.py`.\r\n\r\nThen you can load the dataset by passing the local path to `oscar.py` to `load_dataset`:\r\n```python\r\nload_dataset(\"path/to/oscar.py\", \"unshuffled_deduplicated_it\")\r\n```",
"it works,but another error occurs.\r\n```\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (SSLError(MaxRetryError(\"HTTPSConnectionPool(host='s3.amazonaws.com', port=443): Max retries exceeded with url: /datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))\")))\r\n```\r\nI can access `https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/it/it_sha256.txt` and `https://aws.amazon.com/cn/s3/` directly, so why it reports a SSLError, should I need tomodify the host file？",
"Could it be an issue with your python environment or your version of OpenSSL ?",
"you are so wise!\r\nit report [ConnectionError] in python 3.9.7\r\nand works well in python 3.8.12\r\n\r\nI need you help again: how can I specify the path for download files?\r\nthe data is too large and my C hardware is not enough",
"Cool ! And you can specify the path for download files with to the `cache_dir` parameter:\r\n```python\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('oscar', 'unshuffled_deduplicated_it', cache_dir='path/to/directory')",
"It takes me some days to download data completely, Despise sometimes it occurs again, change py version is feasible way to avoid this ConnectionEror.\r\nparameter `cache_dir` works well, thanks for your kindness again!"
] | 2022-03-20T06:45:37Z
| 2022-03-30T08:13:32Z
| 2022-03-30T08:13:32Z
|
NONE
| null | null | null |
code
```
from datasets import load_dataset
dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
```
bug report
```
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_29788/2615425180.py in <module>
----> 1 dataset = load_dataset('oscar', 'unshuffled_deduplicated_it')
D:\DataScience\PythonSet\IDES\anaconda\lib\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, revision, use_auth_token, task, streaming, **config_kwargs)
1658
1659 # Create a dataset builder
-> 1660 builder_instance = load_dataset_builder(
1661 path=path,
1662 name=name,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, **config_kwargs)
1484 download_config = download_config.copy() if download_config else DownloadConfig()
1485 download_config.use_auth_token = use_auth_token
-> 1486 dataset_module = dataset_module_factory(
1487 path,
1488 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1236 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}"
1237 ) from None
-> 1238 raise e1 from None
1239 else:
1240 raise FileNotFoundError(
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_dir, data_files, **download_kwargs)
1173 if path.count("/") == 0: # even though the dataset is on the Hub, we get it from GitHub for now
1174 # TODO(QL): use a Hub dataset module factory instead of GitHub
-> 1175 return GithubDatasetModuleFactory(
1176 path,
1177 revision=revision,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in get_module(self)
531 revision = self.revision
532 try:
--> 533 local_path = self.download_loading_script(revision)
534 except FileNotFoundError:
535 if revision is not None or os.getenv("HF_SCRIPTS_VERSION", None) is not None:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\load.py in download_loading_script(self, revision)
511 if download_config.download_desc is None:
512 download_config.download_desc = "Downloading builder script"
--> 513 return cached_path(file_path, download_config=download_config)
514
515 def download_dataset_infos_file(self, revision: Optional[str]) -> str:
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
232 if is_remote_url(url_or_filename):
233 # URL, so get it from the cache (downloading if necessary)
--> 234 output_path = get_from_cache(
235 url_or_filename,
236 cache_dir=cache_dir,
D:\DataScience\PythonSet\IDES\anaconda\lib\site-packages\datasets\utils\file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token, ignore_url_params, download_desc)
580 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
581 if head_error is not None:
--> 582 raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
583 elif response is not None:
584 raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.0.0/datasets/oscar/oscar.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.0.0/datasets/oscar/oscar.py (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1129)')))")))
```
It may be caused by Caused by SSLError(in China?) because it works well on google colab.
So how can I download this dataset manually?
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a
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[] | 2022-08-14T15:01:16Z
| 2022-08-14T15:09:59Z
| 2022-08-14T15:09:59Z
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NONE
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| 853,181,564
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MDU6SXNzdWU4NTMxODE1NjQ=
| 2,190
|
News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs
|
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[
"Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```",
"Hello @albertvillanova, \r\n\r\nThanks for the suggestion. I didn't know you could do that. however, it didn't resolve the issue\r\n\r\n\r\n"
] | 2021-04-08T07:53:43Z
| 2021-05-24T10:03:55Z
| 2021-05-24T10:03:55Z
|
NONE
| null | null | null |
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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|
[speedup] Use indices mappings instead of deepcopy for all the samples reordering methods
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[
"Ok I fixed `concatenate_datasets` and added tests\r\nFeel free to merge if it's good for you @thomwolf ",
"Ok, adding some benchmarks for map/filters and then I'll merge",
"Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n```\r\n/__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\nand PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n(supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\nprotect its data or make it writeable before converting it to a tensor. This type of warning will be\r\nsuppressed for the rest of this program.\r\n(Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n532\r\n return torch.tensor(x, **format_kwargs)\r\n```",
"> Warning from pytorch that we should maybe consider at some point @lhoestq:\r\n> \r\n> ```\r\n> /__w/nlp/nlp/src/nlp/arrow_dataset.py:648: UserWarning: The given NumPy array is not writeable,\r\n> and PyTorch does not support non-writeable tensors. This means you can write to the underlying\r\n> (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to\r\n> protect its data or make it writeable before converting it to a tensor. This type of warning will be\r\n> suppressed for the rest of this program.\r\n> (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\r\n> 532\r\n> return torch.tensor(x, **format_kwargs)\r\n> ```\r\n\r\nNot sure why we have that, it's probably linked to zero copy from arrow to numpy"
] | 2020-08-18T17:36:02Z
| 2020-08-28T08:41:51Z
| 2020-08-28T08:41:50Z
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Use an indices mapping instead of rewriting the dataset for all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`).
Added a `flatten_indices` method which copy the dataset to a new table to remove the indices mapping with tests.
All the samples re-ordering/selection methods should be a lot faster. The downside is that iterating on very large batch of the dataset might be a little slower when we have changed the order of the samples since with in these case we use `pyarrow.Table.take` instead of `pyarrow.Table.slice`. There is no free lunch but the speed of iterating over the dataset is rarely the bottleneck.
*Backward breaking change*: the `cache_file_name` argument in all the samples re-ordering/selection methods (`select`, `sort`, `shuffle`, `shard`, `train_test_split`) is now called `indices_cache_file_name` on purpose to make it explicit to the user that this caching file is used for caching the indices mapping and not the dataset itself.
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PR_kwDODunzps4-hwsl
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Fix minor typo in error message for missing imports
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"_The documentation is not available anymore as the PR was closed or merged._"
] | 2022-09-07T17:20:51Z
| 2022-09-08T14:59:31Z
| 2022-09-08T14:57:15Z
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Add loading variable number of columns for different splits
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[
"Hi! Indeed the column is missing, but you shouldn't get an error? Have you made some modifications (locally) to the loading script? I've opened a PR to add the missing columns to the script. "
] | 2022-05-31T13:40:16Z
| 2022-06-03T16:25:25Z
| 2022-06-03T16:25:25Z
|
NONE
| null | null | null |
**Is your feature request related to a problem? Please describe.**
The original dataset `blended_skill_talk` consists of different sets of columns for the different splits: (test/valid) splits have additional data column `label_candidates` that the (train) doesn't have.
When loading such data, an exception occurs at table.py:cast_table_to_schema, because of mismatched columns.
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Don't keep the dummy data folder or dataset_infos.json when resolving data files
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"Hi @lhoestq I am new to huggingface datasets, I would like to work on this issue!\r\n",
"Thanks for the help :) \r\n\r\nAs mentioned in the PR, excluding files named \"dummy_data.zip\" is actually more general than excluding the files inside a \"dummy\" folder. I just did the change in the PR, I think we can merge it now"
] | 2021-09-07T14:09:04Z
| 2021-09-29T09:05:38Z
| 2021-09-29T09:05:38Z
|
MEMBER
| null | null | null |
When there's no dataset script, all the data files of a folder or a repository on the Hub are loaded as data files.
There are already a few exceptions:
- files starting with "." are ignored
- the dataset card "README.md" is ignored
- any file named "config.json" is ignored (currently it isn't used anywhere, but it could be used in the future to define splits or configs for example, but not 100% sure)
However any data files in a folder named "dummy" should be ignored as well as they should only be used to test the dataset.
Same for "dataset_infos.json" which should only be used to get the `dataset.info`
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Multidimensional arrays in a Dataset
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[
"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n ],\r\n 'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset)\r\n```\r\n\r\nThis will work but to use it with the torch formatter you must specify the `Array2D` feature type in order to tell the shape:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])\r\n ],\r\n 'input_ids': [1, 2, 3, 4]\r\n}\r\ndataset = Dataset.from_dict(dataset, features=Features({\r\n \"bbox\": Array2D(shape=(3, 4), dtype=\"int64\"),\r\n \"input_ids\": Value(\"int64\")\r\n}))\r\ndataset.set_format(\"torch\")\r\nprint(dataset[0]['bbox'])\r\n# tensor([[1, 2, 3, 4],\r\n# [1, 2, 3, 4],\r\n# [1, 2, 3, 4]])\r\n```\r\nIf you don't specify the `Array2D` feature type, then the inferred type will be Sequence(Sequence(Value(\"int64\"))) and therefore the torch formatter will return list of tensors",
"Thanks for the explanation. \r\nWith my original DataFrame, I did\r\n```\r\ndataset = dataset.to_dict(\"list\")\r\n```\r\nand then the rest of the transformation from dictionary works just fine."
] | 2021-03-18T16:29:14Z
| 2021-03-25T12:46:53Z
| 2021-03-25T12:46:53Z
|
NONE
| null | null | null |
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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Support dill 0.3.6
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"_The documentation is not available anymore as the PR was closed or merged._",
"I think it hasn't been merged ? https://github.com/uqfoundation/dill/pull/501\r\n\r\nThough I can see that the CI is green because it uses dill 0.3.1.1 - we should probably fix the dill version in both CIs:\r\n- use 0.3.1.1 for the CI with the minimum requirements\r\n- use latest for the CI with the latest requirements",
"I have noticed our CI uses `dill-0.3.1.1`, so not really testing dill 0.3.6...",
"The dill version in our CI is due to `apache-beam`...",
"I've tested locally: we need a specific fix for 0.3.6 (different from the previous ones)...",
"I think we can force the version of dill to be whatever we want in the CI - no matter what beam says. The alternative would be to run beam tests separately but it's more work",
"@lhoestq I tried the easiest solution: force dill==0.3.6 ignoring the requirement of apache-beam. But it doesn't work:\r\n- For example, for `tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet`:\r\n```\r\n @dill.dill.register(dill.dill.ModuleType)\r\n def save_module(pickler, obj):\r\n if dill.dill.is_dill(pickler) and obj is pickler._main:\r\n return old_save_module(pickler, obj)\r\n else:\r\n> dill.dill.log.info('M2: %s' % obj)\r\nE AttributeError: module 'dill._dill' has no attribute 'log'\r\n\r\nvenv/lib/python3.9/site-packages/apache_beam/internal/dill_pickler.py:170: AttributeError\r\n```\r\n - Apache Beam registers some dill functions (`save_module`) which are incompatible with dill 0.3.6 (in 0.3.6 'dill._dill' has no attribute 'log' but 'logger')\r\n - This has an impact in CI tests using either Apache Beam or `multiprocess` (even without using Apache Beam!):\r\n```\r\nFAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_beam.py::BeamBuilderTest::test_nested_features - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_as_dataset - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'\r\nFAILED tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet - AttributeError: module 'dill._dill' has no attribute 'log'\r\n```\r\n\r\nI guess we should implement the other option: run beam tests separately.\r\n\r\nI'm opening another PR for the CI refactoring.",
"Ah crap >< maybe only install apache_beam for the \"minimum requirements\" CI",
"@lhoestq if we install apache-beam only in the \"minimum requirements\" CI, then this other PR should be merged first:\r\n- #5168 \r\n\r\nOtherwise, our CI for \"latest\" will fail because it will try to run the beam tests (because PyTorch is installed but indeed apache-beam is not installed).",
"One of the test is failing because we set \r\n```python\r\n# google colab doesn't allow to pickle loggers\r\n# so we want to make sure each tests passes without pickling the logger\r\ndef reduce_ex(self):\r\n raise pickle.PicklingError()\r\n\r\ndatasets.arrow_dataset.logger.__reduce_ex__ = reduce_ex\r\n```\r\nin `test_arrow_dataset.py` to avoid pickling the logger because it used to fail on google colab.\r\n\r\nNow pickling the logger seems to be working on google colab again - so you can remove it, and it should fix some tests",
"For the other 2 errors:\r\n- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock\r\n- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock\r\n\r\nI have implemented a pickable MagicMock."
] | 2022-10-26T08:24:59Z
| 2022-10-28T05:41:05Z
| 2022-10-28T05:38:14Z
|
MEMBER
| null | 0
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This PR:
- ~~Unpins dill to allow installing dill>=0.3.6~~
- ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~
- Pins dill<0.3.7 to allow latest dill 0.3.6
- Implements a fix for dill `save_function` for dill 0.3.6
- Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6
- Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0)
Fix #5162.
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Support case-insensitive Hub dataset name in load_dataset
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[
"Closing as case-insensitivity should be only for URL redirection on the Hub. In the APIs, we will only support the canonical name (https://github.com/huggingface/moon-landing/pull/2399#issuecomment-1382085611)"
] | 2023-01-13T13:07:07Z
| 2023-01-13T20:12:32Z
| 2023-01-13T20:12:32Z
|
CONTRIBUTOR
| null | null | null |
### Feature request
The dataset name on the Hub is case-insensitive (see https://github.com/huggingface/moon-landing/pull/2399, internal issue), i.e., https://huggingface.co/datasets/GLUE redirects to https://huggingface.co/datasets/glue.
Ideally, we could load the glue dataset using the following:
```
from datasets import load_dataset
load_dataset('GLUE', 'cola')
```
It breaks because the loading script `GLUE.py` does not exist (`glue.py` should be selected instead).
Minor additional comment: in other cases without a loading script, we can load the dataset, but the automatically generated config name depends on the casing:
- `load_dataset('severo/danish-wit')` generates the config name `severo--danish-wit-e6fda5b070deb133`, while
- `load_dataset('severo/danish-WIT')` generates the config name `severo--danish-WIT-e6fda5b070deb133`
### Motivation
To follow the same UX on the Hub and in the datasets library.
### Your contribution
...
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Add video feature
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"@NielsRogge @rwightman may have additional requirements regarding this feature.\r\n\r\nWhen adding a new (decodable) type, the hardest part is choosing the right decoding library. What I mean by \"right\" here is that it has all the features we need and is easy to install (with GPU support?).\r\n\r\nSome candidates/options:\r\n* [`decord`](https://github.com/dmlc/decord): no longer [maintained](https://github.com/dmlc/decord/issues/214), not trivial to install with GPU support\r\n* [`pyAV`](https://github.com/PyAV-Org/PyAV): used for CPU decoding in `torchvision`, GPU decoding not supported if I'm not mistaken, otherwise the best candidate probably\r\n* [`video_reader`](https://github.com/pytorch/vision/blob/de350bc01ad2193ea2888f0ce8a6a346d3cba5a9/torchvision/csrc/io/video_reader/video_reader.cpp): used for GPU decoding in `torchvision`, depends on `torch'\r\n* OpenCV: uses `ffmpeg` for video decoding under the hood\r\n* ...\r\n\r\nAnd the last resort is building our own library, which is the most flexible solution but also requires the most work.\r\n\r\nPS: I'm adding a link to an article that compares various video decoding libraries: https://towardsdatascience.com/lightning-fast-video-reading-in-python-c1438771c4e6",
"@mariosasko is GPU decoding a hard requirement here? Do we really need it? (I don't know)\r\n\r\nSomething to consider with `decord` is that it doesn't (AFAIK) support writing videos, so you'd still need something else for that. also I've noticed [issues](https://github.com/dmlc/decord/issues/242) with decord's ability to decode stereo audio streams along side the video (which you don't run into with PyAV).\r\n\r\n---\r\n\r\nI think PyAV should be able to do the job just fine to start. If we write the video io utilities as their own functions, we can hot swap them later if we find/write a different solution that's faster/better.",
"Video is still a bit of a mess, but I'd say pyAV is likely the best approach (or supporting all three via pytorchvideo, but that adds a middle man dependency).\r\n\r\nBeing able to decode on the GPU, into memory that could be passed off to a Tensor in whatever framework is being used would be the dream, I don't think there is any interop of that nature working right now. Number of decoder instances per GPU is limited so it's not clear if balancing load btw GPU decoders and CPUs would be needed in say large scale video training.\r\n\r\nAny of these solutions is less than ideal due to the nature of video, having a simple Python interface video / start -> end results in lots of extra memory (you need to decode whole range of the clips into a buffer before using anything). Any scalable video system would be streaming on the fly (issuing frames via callbacks as soon as the stream is far enough along to have re-ordered the frames and synced audio+video+other metadata (sensors, CC, etc).\r\n\r\n",
"For standalone usage, decoding on GPU could be ideal but isn't async processing of inputs on CPUs while letting the accelerator busy for training the de-facto? Of course, I am aware of other advanced mechanisms such as CPU offloading, but I think my point is conveyed. ",
"Here's a minimal implementation of the helper functions we'd need from PyAV, a lot of which I borrowed from `pytorchvideo`, stripping out the `torch` specific stuff:\r\n\r\n[](https://colab.research.google.com/gist/nateraw/c327cb6ff6b074e6ddc8068d19c0367d/pyav-io.ipynb)\r\n \r\nIt's not too much code...@mariosasko we could probably just maintain these helper fns within the `datasets` library, right? ",
"Also wanted to note I added a PR for video classification in `transformers` here, which uses `decord`. It's still open...should we make a decision now to align the libraries we are using between `datasets` and `transformers`? (CC @Narsil )\r\n\r\nhttps://github.com/huggingface/transformers/pull/20151",
"Fully agree on at least trying to unite things.\r\n\r\nMaking clear function boundaries to help us change dependency if needed seems like a good idea since there doesn't seem to be a clear winner.\r\n\r\nI also happen to like directly calling ffmpeg. For some reason it was a lot faster than pyav. "
] | 2022-11-10T17:36:11Z
| 2022-12-02T15:13:15Z
| null |
CONTRIBUTOR
| null | null | null |
### Feature request
Add a `Video` feature to the library so folks can include videos in their datasets.
### Motivation
Being able to load Video data would be quite helpful. However, there are some challenges when it comes to videos:
1. Videos, unlike images, can end up being extremely large files
2. Often times when training video models, you need to do some very specific sampling. Videos might end up needing to be broken down into X number of clips used for training/inference
3. Videos have an additional audio stream, which must be accounted for
4. The feature needs to be able to encode/decode videos (with right video settings) from bytes.
### Your contribution
I did work on this a while back in [this (now closed) PR](https://github.com/huggingface/datasets/pull/4532). It used a library I made called [encoded_video](https://github.com/nateraw/encoded-video), which is basically the utils from [pytorchvideo](https://github.com/facebookresearch/pytorchvideo), but without the `torch` dep. It included the ability to read/write from bytes, as we need to do here. We don't want to be using a sketchy library that I made as a dependency in this repo, though.
Would love to use this issue as a place to:
- brainstorm ideas on how to do this right
- list ways/examples to work around it for now
CC @sayakpaul @mariosasko @fcakyon
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Add yoruba text
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"closing since #1379 got merged"
] | 2020-12-12T16:29:30Z
| 2020-12-13T18:37:58Z
| 2020-12-13T18:37:58Z
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Adding Yoruba text C3
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[style/quality] Moving to isort 5.0.0 + style/quality on datasets and metrics
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"Ready for review @lhoestq, just updated a few 156 files here"
] | 2020-09-09T15:47:21Z
| 2020-09-10T10:05:04Z
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Move the repo to isort 5.0.0.
Also start testing style/quality on datasets and metrics.
Specific rule: we allow F401 (unused imports) in metrics to be able to add imports to detect early on missing dependencies.
Maybe we could add this in datasets but while cleaning this I've seen many example of really unused imports in dataset so maybe it's better to have it as a line-by-line nova instead of a general rule like in metrics.
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add one field "example_id", but I can't see it in the "comput_loss" function
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"Hi ! Your function looks fine, I used to map `squad` locally and it indeed added the `example_id` field correctly.\r\n\r\nHowever I think that in the `compute_loss` method only a subset of the fields are available: the model inputs. Since `example_id` is not a model input (it's not passed as a parameter to the model), the data loader doesn't need to return it by default.\r\n\r\nHowever you can disable this behavior by setting `remove_unused_columns` to `False` to your training arguments. In this case in `compute_loss` you will get the full item with all the fields.\r\n\r\nNote that since the model doesn't take `example_id` as input, you will have to remove it from the inputs when `model(**inputs)` is called",
"Hi, I have set **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**, but the field doesn't been contained yet.\r\n```\r\ndef main():\r\n argp = HfArgumentParser(TrainingArguments)\r\n # The HfArgumentParser object collects command-line arguments into an object (and provides default values for unspecified arguments).\r\n # In particular, TrainingArguments has several keys that you'll need/want to specify (when you call run.py from the command line):\r\n # --do_train\r\n # When included, this argument tells the script to train a model.\r\n # See docstrings for \"--task\" and \"--dataset\" for how the training dataset is selected.\r\n # --do_eval\r\n # When included, this argument tells the script to evaluate the trained/loaded model on the validation split of the selected dataset.\r\n # --per_device_train_batch_size <int, default=8>\r\n # This is the training batch size.\r\n # If you're running on GPU, you should try to make this as large as you can without getting CUDA out-of-memory errors.\r\n # For reference, with --max_length=128 and the default ELECTRA-small model, a batch size of 32 should fit in 4gb of GPU memory.\r\n # --num_train_epochs <float, default=3.0>\r\n # How many passes to do through the training data.\r\n # --output_dir <path>\r\n # Where to put the trained model checkpoint(s) and any eval predictions.\r\n # *This argument is required*.\r\n\r\n argp.add_argument('--model', type=str,\r\n default='google/electra-small-discriminator',\r\n help=\"\"\"This argument specifies the base model to fine-tune.\r\n This should either be a HuggingFace model ID (see https://huggingface.co/models)\r\n or a path to a saved model checkpoint (a folder containing config.json and pytorch_model.bin).\"\"\")\r\n argp.add_argument('--task', type=str, choices=['nli', 'qa'], required=True,\r\n help=\"\"\"This argument specifies which task to train/evaluate on.\r\n Pass \"nli\" for natural language inference or \"qa\" for question answering.\r\n By default, \"nli\" will use the SNLI dataset, and \"qa\" will use the SQuAD dataset.\"\"\")\r\n argp.add_argument('--dataset', type=str, default=None,\r\n help=\"\"\"This argument overrides the default dataset used for the specified task.\"\"\")\r\n argp.add_argument('--max_length', type=int, default=128,\r\n help=\"\"\"This argument limits the maximum sequence length used during training/evaluation.\r\n Shorter sequence lengths need less memory and computation time, but some examples may end up getting truncated.\"\"\")\r\n argp.add_argument('--max_train_samples', type=int, default=None,\r\n help='Limit the number of examples to train on.')\r\n argp.add_argument('--max_eval_samples', type=int, default=None,\r\n help='Limit the number of examples to evaluate on.')\r\n\r\n argp.remove_unused_columns = False\r\n training_args, args = argp.parse_args_into_dataclasses()\r\n args.remove_unused_columns=False\r\n training_args.remove_unused_columns=False\r\n```\r\n\r\n\r\n```\r\n**************** train_dataset: Dataset({\r\n features: ['id', 'title', 'context', 'question', 'answers'],\r\n num_rows: 87599\r\n})\r\n\r\n\r\n**************** train_dataset_featurized: Dataset({\r\n features: ['attention_mask', 'end_positions', 'input_ids', 'start_positions', 'token_type_ids'],\r\n num_rows: 87714\r\n})\r\n```",
"Hi, I print the value, all are set to False, but don't work.\r\n```\r\n********************* training_args: TrainingArguments(\r\n_n_gpu=1,\r\nadafactor=False,\r\nadam_beta1=0.9,\r\nadam_beta2=0.999,\r\nadam_epsilon=1e-08,\r\ndataloader_drop_last=False,\r\ndataloader_num_workers=0,\r\ndataloader_pin_memory=True,\r\nddp_find_unused_parameters=None,\r\ndebug=[],\r\ndeepspeed=None,\r\ndisable_tqdm=False,\r\ndo_eval=False,\r\ndo_predict=False,\r\ndo_train=True,\r\neval_accumulation_steps=None,\r\neval_steps=None,\r\nevaluation_strategy=IntervalStrategy.NO,\r\nfp16=False,\r\nfp16_backend=auto,\r\nfp16_full_eval=False,\r\nfp16_opt_level=O1,\r\ngradient_accumulation_steps=1,\r\ngreater_is_better=None,\r\ngroup_by_length=False,\r\nignore_data_skip=False,\r\nlabel_names=None,\r\nlabel_smoothing_factor=0.0,\r\nlearning_rate=5e-05,\r\nlength_column_name=length,\r\nload_best_model_at_end=False,\r\nlocal_rank=-1,\r\nlog_level=-1,\r\nlog_level_replica=-1,\r\nlog_on_each_node=True,\r\nlogging_dir=./re_trained_model/runs/Dec01_14-15-08_399b9290604c,\r\nlogging_first_step=False,\r\nlogging_steps=500,\r\nlogging_strategy=IntervalStrategy.STEPS,\r\nlr_scheduler_type=SchedulerType.LINEAR,\r\nmax_grad_norm=1.0,\r\nmax_steps=-1,\r\nmetric_for_best_model=None,\r\nmp_parameters=,\r\nno_cuda=False,\r\nnum_train_epochs=3.0,\r\noutput_dir=./re_trained_model,\r\noverwrite_output_dir=False,\r\npast_index=-1,\r\nper_device_eval_batch_size=8,\r\nper_device_train_batch_size=8,\r\nprediction_loss_only=False,\r\npush_to_hub=False,\r\npush_to_hub_model_id=re_trained_model,\r\npush_to_hub_organization=None,\r\npush_to_hub_token=None,\r\nremove_unused_columns=False,\r\nreport_to=['tensorboard'],\r\nresume_from_checkpoint=None,\r\nrun_name=./re_trained_model,\r\nsave_on_each_node=False,\r\nsave_steps=500,\r\nsave_strategy=IntervalStrategy.STEPS,\r\nsave_total_limit=None,\r\nseed=42,\r\nsharded_ddp=[],\r\nskip_memory_metrics=True,\r\ntpu_metrics_debug=False,\r\ntpu_num_cores=None,\r\nuse_legacy_prediction_loop=False,\r\nwarmup_ratio=0.0,\r\nwarmup_steps=0,\r\nweight_decay=0.0,\r\n)\r\n```\r\n```\r\n********************* args: Namespace(dataset='squad', max_eval_samples=None, max_length=128, max_train_samples=None, model='google/electra-small-discriminator', remove_unused_columns=False, task='qa')\r\n2021-12-01 14:15:10,048 - WARNING - datasets.builder - Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\r\nSome weights of the model checkpoint at google/electra-small-discriminator were not used when initializing ElectraForQuestionAnswering: ['discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias']\r\n- This IS expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\r\n- This IS NOT expected if you are initializing ElectraForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\r\nSome weights of ElectraForQuestionAnswering were not initialized from the model checkpoint at google/electra-small-discriminator and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\r\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\r\nPreprocessing data... (this takes a little bit, should only happen once per dataset)\r\n```",
"Hmmm, it might be because the default data collator removes all the fields with `string` type:\r\n\r\nhttps://github.com/huggingface/transformers/blob/4c0dd199c8305903564c2edeae23d294edd4b321/src/transformers/data/data_collator.py#L107-L112\r\n\r\nI guess you also need a custom data collator that doesn't remove them.",
"can you give a tutorial about how to do this?",
"I overwrite **get_train_dataloader**, and remove **_remove_unused_columns**, but it doesn't work.\r\n\r\n```\r\n def get_train_dataloader(self) -> DataLoader:\r\n \"\"\"\r\n Returns the training :class:`~torch.utils.data.DataLoader`.\r\n\r\n Will use no sampler if :obj:`self.train_dataset` does not implement :obj:`__len__`, a random sampler (adapted\r\n to distributed training if necessary) otherwise.\r\n\r\n Subclass and override this method if you want to inject some custom behavior.\r\n \"\"\"\r\n if self.train_dataset is None:\r\n raise ValueError(\"Trainer: training requires a train_dataset.\")\r\n\r\n train_dataset = self.train_dataset\r\n # if is_datasets_available() and isinstance(train_dataset, datasets.Dataset):\r\n # train_dataset = self._remove_unused_columns(train_dataset, description=\"training\")\r\n\r\n if isinstance(train_dataset, torch.utils.data.IterableDataset):\r\n if self.args.world_size > 1:\r\n train_dataset = IterableDatasetShard(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_processes=self.args.world_size,\r\n process_index=self.args.process_index,\r\n )\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n collate_fn=self.data_collator,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n\r\n train_sampler = self._get_train_sampler()\r\n\r\n return DataLoader(\r\n train_dataset,\r\n batch_size=self.args.train_batch_size,\r\n sampler=train_sampler,\r\n collate_fn=self.data_collator,\r\n drop_last=self.args.dataloader_drop_last,\r\n num_workers=self.args.dataloader_num_workers,\r\n pin_memory=self.args.dataloader_pin_memory,\r\n )\r\n```",
"Hi, it works now, thank you.\r\n1. **args.remove_unused_columns=False** and **training_args.remove_unused_columns=False**\r\n2. overwrite **get_train_dataloader**, and remove **_remove_unused_columns**\r\n3. add new fields, and can be got in **inputs**. "
] | 2021-12-01T09:35:09Z
| 2021-12-01T16:02:39Z
| 2021-12-01T16:02:39Z
|
NONE
| null | null | null |
Hi, I add one field **example_id**, but I can't see it in the **comput_loss** function, how can I do this? below is the information of inputs
```
*********************** inputs: {'attention_mask': tensor([[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
...,
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0],
[1, 1, 1, ..., 0, 0, 0]], device='cuda:0'), 'end_positions': tensor([ 25, 97, 93, 44, 25, 112, 109, 134], device='cuda:0'), 'input_ids': tensor([[ 101, 2054, 2390, ..., 0, 0, 0],
[ 101, 2054, 2515, ..., 0, 0, 0],
[ 101, 2054, 2106, ..., 0, 0, 0],
...,
[ 101, 2339, 2001, ..., 0, 0, 0],
[ 101, 2054, 2515, ..., 0, 0, 0],
[ 101, 2054, 2003, ..., 0, 0, 0]], device='cuda:0'), 'start_positions': tensor([ 20, 90, 89, 41, 25, 96, 106, 132], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
...,
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 0, 0, 0]], device='cuda:0')}
```
```
# This function preprocesses a question answering dataset, tokenizing the question and context text
# and finding the right offsets for the answer spans in the tokenized context (to use as labels).
# Adapted from https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_qa.py
def prepare_train_dataset_qa(examples, tokenizer, max_seq_length=None):
questions = [q.lstrip() for q in examples["question"]]
max_seq_length = tokenizer.model_max_length
# tokenize both questions and the corresponding context
# if the context length is longer than max_length, we split it to several
# chunks of max_length
tokenized_examples = tokenizer(
questions,
examples["context"],
truncation="only_second",
max_length=max_seq_length,
stride=min(max_seq_length // 2, 128),
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length"
)
# Since one example might give us several features if it has a long context,
# we need a map from a feature to its corresponding example.
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# The offset mappings will give us a map from token to character position
# in the original context. This will help us compute the start_positions
# and end_positions to get the final answer string.
offset_mapping = tokenized_examples.pop("offset_mapping")
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
tokenized_examples["example_id"] = []
for i, offsets in enumerate(offset_mapping):
input_ids = tokenized_examples["input_ids"][i]
# We will label features not containing the answer the index of the CLS token.
cls_index = input_ids.index(tokenizer.cls_token_id)
sequence_ids = tokenized_examples.sequence_ids(i)
# from the feature idx to sample idx
sample_index = sample_mapping[i]
# get the answer for a feature
answers = examples["answers"][sample_index]
tokenized_examples["example_id"].append(examples["id"][sample_index])
if len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Start/end character index of the answer in the text.
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# Start token index of the current span in the text.
token_start_index = 0
while sequence_ids[token_start_index] != 1:
token_start_index += 1
# End token index of the current span in the text.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != 1:
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (offsets[token_start_index][0] <= start_char and
offsets[token_end_index][1] >= end_char):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while token_start_index < len(offsets) and \
offsets[token_start_index][0] <= start_char:
token_start_index += 1
tokenized_examples["start_positions"].append(
token_start_index - 1)
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(token_end_index + 1)
return tokenized_examples
```
_Originally posted by @yanllearnn in https://github.com/huggingface/datasets/issues/3333#issuecomment-983457161_
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|
Modify `is_remote_filesystem` to return True for FUSE-mounted paths
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[
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5885). All of your documentation changes will be reflected on that endpoint.",
"@lhoestq would you or another maintainer be able to review please? :)",
"Why you do need to support FUSE mounted paths ?\r\n\r\n`datasets` uses data that live on disk for fast lookups - FUSE mounted disks would lead to poor performance and I wouldn't recomment using it.",
"Fuse is commonly used to mount remote file systems (e.g. S3, DBFS) as a local directory. Since it's slower than using an actual local device, it's better to treat it as remote to reduce latency.",
"I think people would be confused if they don't have the same dataset behavior depending on the disk type.\r\n\r\nIf they want to use a remote bucket they should use the remote URI instead, e.g. `s3://...`. Advancements on this are tracked at #5281 "
] | 2023-05-23T01:04:54Z
| 2023-05-25T08:50:48Z
| null |
CONTRIBUTOR
| null | 0
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Add ReFreSD dataset
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[
"Cool dataset! Replying in-line:\r\n\r\n> This PR adds the **ReFreSD dataset**.\r\n> The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.\r\n> \r\n> Need feedback on:\r\n> \r\n> * I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.\r\n\r\nyou can use `--match_text_files` in the dummy data generation:\r\n`python datasets-cli dummy_data datasets/refresd --auto_generate --match_text_files \"REFreSD_rationale\"`\r\n\r\n> * The feature names.\r\n> \r\n> * I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.\r\n\r\nIt would actually be even better to use the `Translation` feature here to replace best:\r\n`\"sentence_pair\": datasets.Translation(languages=['en', 'fr']),`\r\n\r\nThen during `_generate_examples` this filed should look like\"\r\n`{\"sentence_pair\": {\"fr\": french, \"en\": english}}`\r\n\r\n> * There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.\r\nLooks good!\r\n\r\n> * The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.\r\n\r\nHaving the feature declared as `\"rationale_en\": datasets.Sequence(datasets.Value(\"int32\"))` should work\r\n\r\n> \r\n> Thanks in advance\r\n\r\nHope that helps you out! Don't forget to `make style`, rebase from master, and run all the tests before pushing again! You will also need to add a `README.md` as described in the guide:\r\nhttps://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md#tag-the-dataset-and-write-the-dataset-card",
"Thanks a lot for the answer, that does help a lot !\r\nI opened a PR for a License in the original repo so I was waiting for that for the model card. If there is no news on Monday, I'll add it without License. ",
"Looks good! It looks like it might need a rebase to pass the tests. Once you do that, should be good to go!"
] | 2020-12-04T20:45:11Z
| 2020-12-16T16:01:18Z
| 2020-12-16T16:01:18Z
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This PR adds the **ReFreSD dataset**.
The original data is hosted [on this github repo](https://github.com/Elbria/xling-SemDiv) and we use the `REFreSD_rationale` to expose all the data.
Need feedback on:
- I couldn't generate the dummy data. The file we download is a tsv file, but without extension, I suppose this is the problem. I'm sure there is a simple trick to make this work.
- The feature names.
- I don't know if it's better to stick to the classic `sentence1`, `sentence2` or to `sentence_en`, `sentence_fr` to be more explicit.
- There is a binary label (called `label`, no problem here), and a 3-class label called `#3_labels` in the original tsv. I changed it to `all_labels` but I'm sure there is better.
- The rationales are lists of integers, extracted as a string at first. I wonder what's the best way to treat them, any idea? Also, I couldn't manage to make a `Sequence` of `int8` but I'm sure I've missed something simple.
Thanks in advance
|
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| 2,149
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Telugu subset missing for xtreme tatoeba dataset
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[
"Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this",
"Fixed in #2180"
] | 2021-03-30T15:26:34Z
| 2022-10-05T13:28:30Z
| 2022-10-05T13:28:30Z
|
CONTRIBUTOR
| null | null | null |
from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.
but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
lang3_dict = {
'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn',
'deu':'de', 'ell':'el', 'spa':'es', 'est':'et',
'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr',
'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id',
'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka',
'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr',
'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw',
'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here
'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh',
'eng':'en',
}
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I_kwDODunzps5Xta6g
| 5,324
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Fix docstrings and types in documentation that appears on the website
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[
"I agree we have a mess with docstrings...",
"Ok, I believe we've cleaned up most of the old syntax we were using for the user-facing docs! There are still a couple of `:obj:`'s and `:class:` floating around in the docstrings we don't expose that I'll track down :)"
] | 2022-12-01T15:34:53Z
| 2022-12-13T19:03:55Z
| null |
CONTRIBUTOR
| null | null | null |
While I was working on https://github.com/huggingface/datasets/pull/5313 I've noticed that we have a mess in how we annotate types and format args and return values in the code. And some of it is displayed in the [Reference section](https://huggingface.co/docs/datasets/package_reference/builder_classes) of the documentation on the website.
Would be nice someday, maybe before releasing datasets 3.0.0, to unify it......
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"I addressed your comments about the docstrings and the output validation :)",
"I updated the bleurt mocking method and bleurt test is passing now.\r\nI also ran the slow tests and they are passing for bleurt.",
"Thanks @lhoestq and @albertvillanova !"
] | 2021-04-07T09:30:50Z
| 2021-04-20T14:20:44Z
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The `cast` operation on a pyarrow Table may create new arrays in memory.
This is an issue since users expect memory mapped datasets to not fill up the RAM.
To fix that I used `map` to write a new arrow file on disk when cast is used.
To make things more convenient I introduced the `arrow` formatting of a dataset, to make it return pyarrow tables instead of python dicts. This way one can use pyarrow transforms directly when using `map`.
edit: we'll use the same mechanism for `filter`
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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... 😅 \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."
] | 2021-06-03T00:27:30Z
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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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Fix typo in logging docs
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"_The documentation is not available anymore as the PR was closed or merged._",
"> This PR fixes #4271.\r\n\r\nThings have not changed when searching \"tqdm\" in the Dataset document. The second result still performs as \"Enable\".",
"Hi @jiangwy99, the fix will appear on the `main` version of the docs:\r\n\r\n\r\n",
"Fixed now, thanks."
] | 2022-05-03T20:47:57Z
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This PR fixes #4271.
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MEMBER
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After `datasets` 1.13.3 release, this does not appear in Zenodo releases: https://zenodo.org/record/5570305
TODO:
- [x] Contact Zenodo support
- [x] Check it is fixed
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[
"`from_generator` expects a generator function, not a generator object, so this should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(rows)\r\n\r\nfor r in ds:\r\n print(r)\r\n```",
"@mariosasko your code was incomplete, so I tried to fix it:\r\n\r\n```py\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\nclient = bigquery.Client()\r\n\r\ndef gen():\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nThe error is also present in this case:\r\n\r\n```\r\n_pickle.PicklingError: Pickling client objects is explicitly not supported.\r\nClients have non-trivial state that is local and unpickleable.\r\n```\r\n\r\nI think it doesn't matter if the generator is an object or a function. The problem is that the generator is referencing an object that is not pickable (the client in this case). ",
"It does matter: this function expects a generator function, as stated in the docs.\r\n\r\nThis should work:\r\n```python\r\nfrom datasets import Dataset\r\nfrom google.cloud import bigquery\r\n\r\ndef gen():\r\n client = bigquery.Client()\r\n # Perform a query.\r\n QUERY = (\r\n 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '\r\n 'WHERE state = \"TX\" '\r\n 'LIMIT 100')\r\n query_job = client.query(QUERY) # API request\r\n yield from query_job.result() # Waits for query to finish\r\n\r\nds = Dataset.from_generator(gen)\r\n\r\nfor r in ds:\r\n print(r)\r\n```\r\n\r\nWe could allow passing non-picklable objects and use a random hash for the generated arrow file. In that case, the caching mechanism would not work, meaning repeated calls with the same set of arguments would generate new datasets instead of reusing the cached version, but this behavior is still better than raising an error.",
"Thank you @mariosasko . Your last code is working indeed. Curiously, the important detail here was to wrap the client instantiation within the generator itself. If the line `client = bigquery.Client()` is moved outside, then the error is back.\r\n\r\nI see now also your point in regard to the generator being a generator function. We can close the issue if you want."
] | 2023-04-14T13:50:59Z
| 2023-04-17T12:20:43Z
| 2023-04-17T12:20:43Z
|
NONE
| null | null | null |
### Describe the bug
Creating a dataset from a generator using `Dataset.from_generator()` fails if the generator is the [Google Big Query Python client](https://cloud.google.com/python/docs/reference/bigquery/latest). The problem is that the Big Query client is not pickable. And the function `create_config_id` tries to get a hash of the generator by pickling it. So the following error is generated:
```
_pickle.PicklingError: Pickling client objects is explicitly not supported.
Clients have non-trivial state that is local and unpickleable.
```
### Steps to reproduce the bug
1. Install the big query client and datasets `pip install google-cloud-bigquery datasets`
2. Run the following code:
```py
from datasets import Dataset
from google.cloud import bigquery
client = bigquery.Client()
# Perform a query.
QUERY = (
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
'WHERE state = "TX" '
'LIMIT 100')
query_job = client.query(QUERY) # API request
rows = query_job.result() # Waits for query to finish
ds = Dataset.from_generator(rows)
for r in ds:
print(r)
```
### Expected behavior
Two options:
1. Ignore the pickle errors when computing the hash
2. Provide a scape hutch so that we can avoid calculating the hash for the generator. For example, allowing to provide a hash from the user.
### Environment info
python 3.9
google-cloud-bigquery 3.9.0
datasets 2.11.0
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"We no longer host datasets in this repo. You should use the HF Hub instead."
] | 2023-07-11T17:25:49Z
| 2023-07-20T10:11:41Z
| 2023-07-20T10:11:41Z
|
NONE
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