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PR_kwDODunzps5F1r2k

feat: depth estimation dataset guide.

open

2022-12-20T05:32:11Z

2022-12-20T05:42:02Z

CONTRIBUTOR

This PR adds a guide for prepping datasets for depth estimation. PR to add documentation images is up here: https://huggingface.co/datasets/huggingface/documentation-images/discussions/22

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I_kwDODunzps5Zo4CU

The dataset "the_pile", subset "enron_emails" , load_dataset() failure

closed

2022-12-20T02:19:13Z

2022-12-20T07:52:54Z

2022-12-20T07:52:54Z

NONE

### Describe the bug When run "datasets.load_dataset("the_pile","enron_emails")" failure  ### Steps to reproduce the bug Run below code in python cli: >>> import datasets >>> datasets.load_dataset("the_pile","enron_emails") ### Expected behavior Load dataset "the_pile", "enron_emails" successfully. ### Environment info Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.7.1 - Platform: Linux-5.15.0-53-generic-x86_64-with-glibc2.35 - Python version: 3.10.6 - PyArrow version: 10.0.0 - Pandas version: 1.4.3

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PR_kwDODunzps5Fz5lw

Add a parallel implementation of to_tf_dataset()

open

2022-12-19T19:40:27Z

2022-12-19T19:45:06Z

MEMBER

Hey all! Here's a first draft of the PR to add a multiprocessing implementation for `to_tf_dataset()`. It worked in some quick testing for me, but obviously I need to do some much more rigorous testing/benchmarking, and add some proper library tests. The core idea is that we do everything using `multiprocessing` and `numpy`, and just wrap a `tf.data.Dataset` around the output. We could also rewrite the existing single-threaded implementation based on this code, which might simplify it a bit. Checklist: - [X] Add initial draft - [x] Check that it works regardless of whether the `collate_fn` or dataset returns `tf` or `np` arrays - [ ] Check that it works with `tf.string` return data - [ ] Check indices are correctly reshuffled each epoch - [ ] Check `fit()` with multiple epochs works fine and that the progress bar is correct - [ ] Check there are no memory leaks or zombie processes - [ ] Benchmark performance - [ ] Tweak params for dataset inference - can we speed things up there a bit? - [ ] Add tests to the library - [ ] Add a PR to `transformers` to expose the `num_workers` argument via `prepare_tf_dataset`

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PR_kwDODunzps5FxWkM

set dev version

closed

2022-12-19T10:56:56Z

2022-12-19T11:01:55Z

2022-12-19T10:57:16Z

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PR_kwDODunzps5FxUbG

Release: 2.8.0

closed

2022-12-19T10:48:26Z

2022-12-19T10:55:43Z

2022-12-19T10:53:15Z

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I_kwDODunzps5ZhMMx

Using too many threads results in: Got disconnected from remote data host. Retrying in 5sec

open

2022-12-18T11:38:58Z

2022-12-19T16:33:31Z

CONTRIBUTOR

### Describe the bug `streaming_download_manager` seems to disconnect if too many runs access the same underlying dataset 🧐 The code works fine for me if I have ~100 runs in parallel, but disconnects once scaling to 200. Possibly related: - https://github.com/huggingface/datasets/pull/3100 - https://github.com/huggingface/datasets/pull/3050 ### Steps to reproduce the bug Running ```python c4 = datasets.load_dataset("c4", "en", split="train", streaming=True).skip(args.start).take(args.end-args.start) df = pd.DataFrame(c4, index=None) ``` with different start & end arguments on 200 CPUs in parallel yields: ``` WARNING:datasets.load:Using the latest cached version of the module from /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/df532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01 (last modified on Mon Dec 12 10:45:02 2022) since it couldn't be found locally at c4. WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [1/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [2/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [3/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [4/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [5/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [6/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [7/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [8/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [9/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [10/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [11/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [12/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [13/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [14/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [15/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [16/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [17/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [18/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [19/20] WARNING:datasets.download.streaming_download_manager:Got disconnected from remote data host. Retrying in 5sec [20/20] ╭───────────────────── Traceback (most recent call last) ──────────────────────╮ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/dec-2022-tasky/inference │ │ _c4.py:68 in <module> │ │ │ │ 65 │ model.eval() │ │ 66 │ │ │ 67 │ c4 = datasets.load_dataset("c4", "en", split="train", streaming=Tru │ │ ❱ 68 │ df = pd.DataFrame(c4, index=None) │ │ 69 │ texts = df["text"].to_list() │ │ 70 │ preds = batch_inference(texts, batch_size=args.batch_size) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/site-packages/pandas/core/frame.p │ │ y:684 in __init__ │ │ │ │ 681 │ │ # For data is list-like, or Iterable (will consume into list │ │ 682 │ │ elif is_list_like(data): │ │ 683 │ │ │ if not isinstance(data, (abc.Sequence, ExtensionArray)): │ │ ❱ 684 │ │ │ │ data = list(data) │ │ 685 │ │ │ if len(data) > 0: │ │ 686 │ │ │ │ if is_dataclass(data[0]): │ │ 687 │ │ │ │ │ data = dataclasses_to_dicts(data) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:751 in __iter__ │ │ │ │ 748 │ │ yield from ex_iterable.shard_data_sources(shard_idx) │ │ 749 │ │ │ 750 │ def __iter__(self): │ │ ❱ 751 │ │ for key, example in self._iter(): │ │ 752 │ │ │ if self.features: │ │ 753 │ │ │ │ # `IterableDataset` automatically fills missing colum │ │ 754 │ │ │ │ # This is done with `_apply_feature_types`. │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:741 in _iter │ │ │ │ 738 │ │ │ ex_iterable = self._ex_iterable.shuffle_data_sources(self │ │ 739 │ │ else: │ │ 740 │ │ │ ex_iterable = self._ex_iterable │ │ ❱ 741 │ │ yield from ex_iterable │ │ 742 │ │ │ 743 │ def _iter_shard(self, shard_idx: int): │ │ 744 │ │ if self._shuffling: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:617 in __iter__ │ │ │ │ 614 │ │ self.n = n │ │ 615 │ │ │ 616 │ def __iter__(self): │ │ ❱ 617 │ │ yield from islice(self.ex_iterable, self.n) │ │ 618 │ │ │ 619 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 620 │ │ """Doesn't shuffle the wrapped examples iterable since it wou │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:594 in __iter__ │ │ │ │ 591 │ │ │ 592 │ def __iter__(self): │ │ 593 │ │ #ex_iterator = iter(self.ex_iterable) │ │ ❱ 594 │ │ yield from islice(self.ex_iterable, self.n, None) │ │ 595 │ │ #for _ in range(self.n): │ │ 596 │ │ # next(ex_iterator) │ │ 597 │ │ #yield from islice(ex_iterator, self.n, None) │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/iterable_dataset.py:106 in __iter__ │ │ │ │ 103 │ │ self.kwargs = kwargs │ │ 104 │ │ │ 105 │ def __iter__(self): │ │ ❱ 106 │ │ yield from self.generate_examples_fn(**self.kwargs) │ │ 107 │ │ │ 108 │ def shuffle_data_sources(self, generator: np.random.Generator) -> │ │ 109 │ │ return ShardShuffledExamplesIterable(self.generate_examples_f │ │ │ │ /users/muennighoff/.cache/huggingface/modules/datasets_modules/datasets/c4/d │ │ f532b158939272d032cc63ef19cd5b83e9b4d00c922b833e4cb18b2e9869b01/c4.py:89 in │ │ _generate_examples │ │ │ │ 86 │ │ for filepath in filepaths: │ │ 87 │ │ │ logger.info("generating examples from = %s", filepath) │ │ 88 │ │ │ with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8" │ │ ❱ 89 │ │ │ │ for line in f: │ │ 90 │ │ │ │ │ if line: │ │ 91 │ │ │ │ │ │ example = json.loads(line) │ │ 92 │ │ │ │ │ │ yield id_, example │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:313 in read1 │ │ │ │ 310 │ │ │ │ 311 │ │ if size < 0: │ │ 312 │ │ │ size = io.DEFAULT_BUFFER_SIZE │ │ ❱ 313 │ │ return self._buffer.read1(size) │ │ 314 │ │ │ 315 │ def peek(self, n): │ │ 316 │ │ self._check_not_closed() │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/_compression.py:68 in readinto │ │ │ │ 65 │ │ │ 66 │ def readinto(self, b): │ │ 67 │ │ with memoryview(b) as view, view.cast("B") as byte_view: │ │ ❱ 68 │ │ │ data = self.read(len(byte_view)) │ │ 69 │ │ │ byte_view[:len(data)] = data │ │ 70 │ │ return len(data) │ │ 71 │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:493 in read │ │ │ │ 490 │ │ │ │ self._new_member = False │ │ 491 │ │ │ │ │ 492 │ │ │ # Read a chunk of data from the file │ │ ❱ 493 │ │ │ buf = self._fp.read(io.DEFAULT_BUFFER_SIZE) │ │ 494 │ │ │ │ │ 495 │ │ │ uncompress = self._decompressor.decompress(buf, size) │ │ 496 │ │ │ if self._decompressor.unconsumed_tail != b"": │ │ │ │ /opt/cray/pe/python/3.9.12.1/lib/python3.9/gzip.py:96 in read │ │ │ │ 93 │ │ │ read = self._read │ │ 94 │ │ │ self._read = None │ │ 95 │ │ │ return self._buffer[read:] + \ │ │ ❱ 96 │ │ │ │ self.file.read(size-self._length+read) │ │ 97 │ │ │ 98 │ def prepend(self, prepend=b''): │ │ 99 │ │ if self._read is None: │ │ │ │ /pfs/lustrep4/scratch/project_462000119/muennighoff/nov-2022-bettercom/venv/ │ │ lib/python3.9/site-packages/datasets/download/streaming_download_manager.py: │ │ 365 in read_with_retries │ │ │ │ 362 │ │ │ │ ) │ │ 363 │ │ │ │ time.sleep(config.STREAMING_READ_RETRY_INTERVAL) │ │ 364 │ │ else: │ │ ❱ 365 │ │ │ raise ConnectionError("Server Disconnected") │ │ 366 │ │ return out │ │ 367 │ │ │ 368 │ file_obj.read = read_with_retries │ ╰──────────────────────────────────────────────────────────────────────────────╯ ConnectionError: Server Disconnected ``` ### Expected behavior There should be no disconnect I think. ### Environment info ``` datasets=2.7.0 Python 3.9.12 ```

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PR_kwDODunzps5FtRU4

Simplify skipping

closed

2022-12-17T17:23:52Z

2022-12-18T21:43:31Z

2022-12-18T21:40:21Z

CONTRIBUTOR

Was hoping to find a way to speed up the skipping as I'm running into bottlenecks skipping 100M examples on C4 (it takes 12 hours to skip), but didn't find anything better than this small change :( Maybe there's a way to directly skip whole shards to speed it up? 🧐

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PR_kwDODunzps5Fs9w5

Fix xpandas_read_excel

open

2022-12-17T12:58:52Z

2022-12-19T09:33:03Z

MEMBER

This PR fixes `xpandas_read_excel`: - Support passing a path string, besides a file-like object - Support passing `use_auth_token` - First assumes the host server supports HTTP range requests; only if a ValueError is thrown (Cannot seek streaming HTTP file), then if preserves previous behavior (see [#3355](https://github.com/huggingface/datasets/pull/3355)). Fix https://huggingface.co/datasets/bigbio/meqsum/discussions/1 Fix: - https://github.com/bigscience-workshop/biomedical/issues/801 Related to: - #3355

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I_kwDODunzps5ZfRLM

Add a robustness benchmark dataset for vision

open

2022-12-17T12:35:13Z

2022-12-20T06:21:41Z

CONTRIBUTOR

### Name ImageNet-C ### Paper Benchmarking Neural Network Robustness to Common Corruptions and Perturbations ### Data https://github.com/hendrycks/robustness ### Motivation It's a known fact that vision models are brittle when they meet with slightly corrupted and perturbed data. This is also correlated to the robustness aspects of vision models. Researchers use different benchmark datasets to evaluate the robustness aspects of vision models. ImageNet-C is one of them. Having this dataset in 🤗 Datasets would allow researchers to evaluate and study the robustness aspects of vision models. Since the metric associated with these evaluations is top-1 accuracy, researchers should be able to easily take advantage of the evaluation benchmarks on the Hub and perform comprehensive reporting. ImageNet-C is a large dataset. Once it's in, it can act as a reference and we can also reach out to the authors of the other robustness benchmark datasets in vision, such as ObjectNet, WILDS, Metashift, etc. These datasets cater to different aspects. For example, ObjectNet is related to assessing how well a model performs under sub-population shifts. Related thread: https://huggingface.slack.com/archives/C036H4A5U8Z/p1669994598060499

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PR_kwDODunzps5Fqaj-

Distributed support

open

2022-12-16T17:43:47Z

2022-12-16T18:21:30Z

MEMBER

To split your dataset across your training nodes, you can use the new [`datasets.distributed.split_dataset_by_node`]: ```python import os from datasets.distributed import split_dataset_by_node ds = split_dataset_by_node(ds, rank=int(os.environ["RANK"]), world_size=int(os.environ["WORLD_SIZE"])) ``` This works for both map-style datasets and iterable datasets. The dataset is split for the node at rank `rank` in a pool of nodes of size `world_size`. For map-style datasets: Each node is assigned a chunk of data, e.g. rank 0 is given the first chunk of the dataset. For iterable datasets: If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples. This can also be combined with a `torch.utils.data.DataLoader` if you want each node to use multiple workers to load the data. This also supports shuffling. At each epoch, the iterable dataset shards are reshuffled across all the nodes - you just have to call `iterable_ds.set_epoch(epoch_number)`. TODO: - [x] docs for usage in PyTorch - [x] unit tests - [x] integration tests with torch.distributed.launch Related to https://github.com/huggingface/transformers/issues/20770 Close https://github.com/huggingface/datasets/issues/5360

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PR_kwDODunzps5FpZyx

Align remove columns behavior and input dict mutation in `map` with previous behavior

closed

2022-12-16T14:28:47Z

2022-12-16T16:28:08Z

2022-12-16T16:25:12Z

CONTRIBUTOR

Align the `remove_columns` behavior and input dict mutation in `map` with the behavior before https://github.com/huggingface/datasets/pull/5252.

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PR_kwDODunzps5FlevK

Fix remove columns from lazy dict

closed

2022-12-15T22:04:12Z

2022-12-15T22:27:53Z

2022-12-15T22:24:50Z

MEMBER

This was introduced in https://github.com/huggingface/datasets/pull/5252 and causing the transformers CI to break: https://app.circleci.com/pipelines/github/huggingface/transformers/53886/workflows/522faf2e-a053-454c-94f8-a617fde33393/jobs/648597 Basically this code should return a dataset with only one column: ```python from datasets import * ds = Dataset.from_dict({"a": range(5)}) def f(x): x["b"] = x["a"] return x ds = ds.map(f, remove_columns=["a"]) assert ds.column_names == ["b"] ```

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PR_kwDODunzps5FjSFl

ExamplesIterable fixes

closed

2022-12-15T14:23:05Z

2022-12-15T14:44:47Z

2022-12-15T14:41:45Z

MEMBER

fix typing and ExamplesIterable.shard_data_sources

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PR_kwDODunzps5Fi6ZD

fix: image array should support other formats than uint8

open

2022-12-15T13:17:50Z

2022-12-15T13:22:15Z

CONTRIBUTOR

Currently images that are provided as ndarrays, but not in `uint8` format are going to loose data. Namely, for example in a depth image where the data is in float32 format, the type-casting to uint8 will basically make the whole image blank. `PIL.Image.fromarray` [does support mode `F`](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes). although maybe some further metadata could be supplied via the [Image](https://huggingface.co/docs/datasets/v2.7.1/en/package_reference/main_classes#datasets.Image) object.

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PR_kwDODunzps5Fiss1

Support for writing arrow files directly with BeamWriter

open

2022-12-15T12:38:05Z

2022-12-19T17:05:00Z

CONTRIBUTOR

Make it possible to write Arrow files directly with `BeamWriter` rather than converting from Parquet to Arrow, which is sub-optimal, especially for big datasets for which Beam is primarily used.

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I_kwDODunzps5ZTEe1

Dataset.from_generator() crashes on simple example

closed

2022-12-15T10:21:28Z

2022-12-15T11:51:33Z

2022-12-15T11:51:33Z

NONE

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I_kwDODunzps5ZRDrg

Run 'GPT-J' failure due to download dataset fail (' ConnectionError: Couldn't reach http://eaidata.bmk.sh/data/enron_emails.jsonl.zst ' )

closed

2022-12-15T01:23:03Z

2022-12-15T07:45:54Z

2022-12-15T07:45:53Z

NONE

### Describe the bug Run model "GPT-J" with dataset "the_pile" fail. The fail out is as below:  Looks like which is due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst" unreachable . ### Steps to reproduce the bug Steps to reproduce this issue: git clone https://github.com/huggingface/transformers cd transformers python examples/pytorch/language-modeling/run_clm.py --model_name_or_path EleutherAI/gpt-j-6B --dataset_name the_pile --dataset_config_name enron_emails --do_eval --output_dir /tmp/output --overwrite_output_dir ### Expected behavior This issue looks like due to "http://eaidata.bmk.sh/data/enron_emails.jsonl.zst " couldn't be reached. Is there another way to download the dataset "the_pile" ? Is there another way to cache the dataset "the_pile" but not let the hg to download it when runtime ? ### Environment info huggingface_hub version: 0.11.1 Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.35 Python version: 3.9.12 Running in iPython ?: No Running in notebook ?: No Running in Google Colab ?: No Token path ?: /home/taosy/.huggingface/token Has saved token ?: False Configured git credential helpers: FastAI: N/A Tensorflow: N/A Torch: N/A Jinja2: N/A Graphviz: N/A Pydot: N/A

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I_kwDODunzps5ZPMFh

How concatenate `Audio` elements using batch mapping

open

2022-12-14T18:13:55Z

2022-12-15T10:53:28Z

NONE

### Describe the bug I am trying to do concatenate audios in a dataset e.g. `google/fleurs`. ```python print(dataset) # Dataset({ # features: ['path', 'audio'], # num_rows: 24 # }) def mapper_function(batch): # to merge every 3 audio # np.concatnate(audios[i: i+3]) for i in range(i, len(batch), 3) dataset = dataset.map(mapper_function, batch=True, batch_size=24) print(dataset) # Expected output: # Dataset({ # features: ['path', 'audio'], # num_rows: 8 # }) ``` I tried to construct `result={}` dictionary inside the mapper function, I just found it will not work because it needs `byte` also needed :(( I'd appreciate if your share any use cases similar to my problem or any solutions really. Thanks! cc: @lhoestq ### Steps to reproduce the bug 1. load audio dataset 2. try to merge every k audios and return as one ### Expected behavior Merged dataset with a fewer rows. If we merge every 3 rows, then `n // 3` number of examples. ### Environment info - `datasets` version: 2.1.0 - Platform: Linux-5.15.65+-x86_64-with-debian-bullseye-sid - Python version: 3.7.12 - PyArrow version: 8.0.0 - Pandas version: 1.3.5

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I_kwDODunzps5ZOZnp

IterableDataset returns duplicated data using PyTorch DDP

open

2022-12-14T16:06:19Z

2022-12-16T17:47:20Z

MEMBER

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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PR_kwDODunzps5FYHWm

Raise error if ClassLabel names is not python list

open

2022-12-13T23:04:06Z

2022-12-16T14:06:26Z

NONE

Checks type of names provided to ClassLabel to avoid easy and hard to debug errors (closes #5332 - see for discussion)

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PR_kwDODunzps5FYBcq

Fix `fs.open` resource leaks

open

2022-12-13T22:35:51Z

2022-12-15T17:09:22Z

NONE

Invoking `{load,save}_from_dict` results in resource leak warnings, this should fix. Introduces no significant logic changes.

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PR_kwDODunzps5FXNyR

Support torch dataloader without torch formatting

closed

2022-12-13T19:39:24Z

2022-12-15T19:19:29Z

2022-12-15T19:15:54Z

MEMBER

In https://github.com/huggingface/datasets/pull/5084 we make the torch formatting consistent with the map-style datasets formatting: a torch formatted iterable dataset will yield torch tensors. The previous behavior of the torch formatting for iterable dataset was simply to make the iterable dataset inherit from `torch.utils.data.Dataset` to make it work in a torch DataLoader. However ideally an unformatted dataset should also work with a DataLoader. To fix that, `datasets.IterableDataset` should inherit from `torch.utils.data.IterableDataset`. Since we don't want to import torch on startup, I created this PR to dynamically make the `datasets.IterableDataset` class inherit form the torch one when a `datasets.IterableDataset` is instantiated and if PyTorch is available. ```python >>> from datasets import load_dataset >>> ds = load_dataset("c4", "en", streaming=True, split="train") >>> import torch.utils.data >>> isinstance(ds, torch.utils.data.IterableDataset) True >>> dataloader = torch.utils.data.DataLoader(ds, batch_size=32, num_workers=4) >>> for example in dataloader: ...: ... ```

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PR_kwDODunzps5FW-c9

Clean filesystem and logging docstrings

closed

2022-12-13T18:54:09Z

2022-12-14T17:25:58Z

2022-12-14T17:22:16Z

MEMBER

This PR cleans the `Filesystems` and `Logging` docstrings.

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PR_kwDODunzps5FQcYG

Clean up Table class docstrings

closed

2022-12-13T00:29:47Z

2022-12-13T18:17:56Z

2022-12-13T18:14:42Z

MEMBER

This PR cleans up the `Table` class docstrings :)

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I_kwDODunzps5Y8MUt

Consider using "Sequence" instead of "List"

open

2022-12-12T15:39:45Z

2022-12-16T21:02:44Z

NONE

### Feature request Hi, please consider using `Sequence` type annotation instead of `List` in function arguments such as in [`Dataset.from_parquet()`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L1088). It leads to type checking errors, see below. **How to reproduce** ```py list_of_filenames = ["foo.parquet", "bar.parquet"] ds = Dataset.from_parquet(list_of_filenames) ``` **Expected mypy output:** ``` Success: no issues found ``` **Actual mypy output:** ```py test.py:19: error: Argument 1 to "from_parquet" of "Dataset" has incompatible type "List[str]"; expected "Union[Union[str, bytes, PathLike[Any]], List[Union[str, bytes, PathLike[Any]]]]" [arg-type] test.py:19: note: "List" is invariant -- see https://mypy.readthedocs.io/en/stable/common_issues.html#variance test.py:19: note: Consider using "Sequence" instead, which is covariant ``` **Env:** mypy 0.991, Python 3.10.0, datasets 2.7.1

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I_kwDODunzps5Y7Eo0

Support remote file systems for `Audio`

closed

2022-12-12T13:22:13Z

2022-12-12T13:37:14Z

2022-12-12T13:37:14Z

NONE

### Feature request Hi there! It would be super cool if `Audio()`, and potentially other features, could read files from a remote file system. ### Motivation Large amounts of data is often stored in buckets. `load_from_disk` is able to retrieve data from cloud storage but to my knowledge actually copies the datasets across first, so if you're working off a system with smaller disk specs (like a VM), you can run out of space very quickly. ### Your contribution Something like this (for Google Cloud Platform in this instance): ```python from datasets import Dataset, Audio import gcsfs fs = gcsfs.GCSFileSystem() list_of_audio_fp = {'audio': ['1', '2', '3']} ds = Dataset.from_dict(list_of_audio_fp) ds = ds.cast_column("audio", Audio(sampling_rate=16000, fs=fs)) ``` Under the hood: ```python import librosa from io import BytesIO def load_audio(fp, sampling_rate=None, fs=None): if fs is not None: with fs.open(fp, 'rb') as f: arr, sr = librosa.load(BytesIO(f), sr=sampling_rate) else: # Perform existing io operations ``` Written from memory so some things could be wrong.

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I_kwDODunzps5Y279-

__init__() got an unexpected keyword argument 'input_size'

open

2022-12-12T02:52:03Z

2022-12-19T01:38:48Z

NONE

### Describe the bug I try to define a custom configuration with a input_size attribute following the instructions by "Specifying several dataset configurations" in https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html But when I load the dataset, I got an error "__init__() got an unexpected keyword argument 'input_size'" ### Steps to reproduce the bug Following is the code to define the dataset: class CsvConfig(datasets.BuilderConfig): """BuilderConfig for CSV.""" input_size: int = 2048 class MRF(datasets.ArrowBasedBuilder): """Archival MRF data""" BUILDER_CONFIG_CLASS = CsvConfig VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ CsvConfig(name="default", version=VERSION, description="MRF data", input_size=2048), ] ... def _generate_examples(self): input_size = self.config.input_size if input_size > 1000: numin = 10000 else: numin = 15000 Below is the code to load the dataset: reader = load_dataset("default", input_size=1024) ### Expected behavior I hope to pass the "input_size" parameter to MRF datasets, and change "input_size" to any value when loading the datasets. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.31 - Python version: 3.9.12 - PyArrow version: 9.0.0 - Pandas version: 1.5.0

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I_kwDODunzps5Y2aiw

Do we need to implement `_prepare_split`?

closed

2022-12-12T01:38:54Z

2022-12-16T20:07:56Z

2022-12-12T16:48:56Z

NONE

### Describe the bug I'm not sure this is a bug or if it's just missing in the documentation, or i'm not doing something correctly, but I'm subclassing `DatasetBuilder` and getting the following error because on the `DatasetBuilder` class the `_prepare_split` method is abstract (as are the others we are required to implement, hence the genesis of my question): ``` Traceback (most recent call last): File "/home/jason/source/python/prism_machine_learning/examples/create_hf_datasets.py", line 28, in <module> dataset_builder.download_and_prepare() File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 704, in download_and_prepare self._download_and_prepare( File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 793, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/jason/.virtualenvs/pml/lib/python3.8/site-packages/datasets/builder.py", line 1124, in _prepare_split raise NotImplementedError() NotImplementedError ``` ### Steps to reproduce the bug I will share implementation if it turns out that everything should be working (i.e. we only need to implement those 3 methods the docs mention), but I don't want to distract from the original question. ### Expected behavior I just need to know if there are additional methods we need to implement when subclassing `DatasetBuilder` besides what the documentation specifies -> `_info`, `_split_generators` and `_generate_examples` ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-5.4.0-135-generic-x86_64-with-glibc2.2.5 - Python version: 3.8.12 - PyArrow version: 7.0.0 - Pandas version: 1.4.1

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PR_kwDODunzps5E8y2E

Clean up Loading methods docstrings

closed

2022-12-09T22:25:30Z

2022-12-12T17:27:20Z

2022-12-12T17:24:01Z

MEMBER

Clean up for the docstrings in Loading methods!

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PR_kwDODunzps5E8N6G

Clean up remaining Main Classes docstrings

closed

2022-12-09T20:17:15Z

2022-12-12T17:27:17Z

2022-12-12T17:24:13Z

MEMBER

This PR cleans up the remaining docstrings in Main Classes (`IterableDataset`, `IterableDatasetDict`, and `Features`).

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I_kwDODunzps5YoXKK

The data downloaded in the download folder of the cache does not respect `umask`

open

2022-12-09T15:46:27Z

2022-12-09T17:21:26Z

NONE

### Describe the bug For a project on a cluster we are several users to share the same cache for the datasets library. And we have a problem with the permissions on the data downloaded in the cache. Indeed, it seems that the data is downloaded by giving read and write permissions only to the user launching the command (and no permissions to the group). In our case, those permissions don't respect the `umask` of this user, which was `0007`. Traceback: ``` Using custom data configuration default Downloading and preparing dataset text_caps/default to /gpfswork/rech/cnw/commun/datasets/HuggingFaceM4___text_caps/default/1.0.0/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141... Downloading data files: 100%|████████████████████| 3/3 [00:00<00:00, 921.62it/s] --------------------------------------------------------------------------- PermissionError Traceback (most recent call last) Cell In [3], line 1 ----> 1 ds = load_dataset(dataset_name) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/load.py:1746, 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) 1743 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 1745 # Download and prepare data -> 1746 builder_instance.download_and_prepare( 1747 download_config=download_config, 1748 download_mode=download_mode, 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, 1751 use_auth_token=use_auth_token, 1752 ) 1754 # Build dataset for splits 1755 keep_in_memory = ( 1756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 1757 ) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:704, in DatasetBuilder.download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 702 logger.warning("HF google storage unreachable. Downloading and preparing it from source") 703 if not downloaded_from_gcs: --> 704 self._download_and_prepare( 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 706 ) 707 # Sync info 708 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:1227, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos) 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/builder.py:771, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 769 split_dict = SplitDict(dataset_name=self.name) 770 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 771 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 773 # Checksums verification 774 if verify_infos and dl_manager.record_checksums: File /gpfswork/rech/cnw/commun/modules/datasets_modules/datasets/HuggingFaceM4--TextCaps/2b9ad220cd90fcf2bfb454645bc54364711b83d6d39401ffdaf8cc40882e9141/TextCaps.py:125, in TextCapsDataset._split_generators(self, dl_manager) 123 def _split_generators(self, dl_manager): 124 # urls = _URLS[self.config.name] # TODO later --> 125 data_dir = dl_manager.download_and_extract(_URLS) 126 gen_kwargs = { 127 split_name: { 128 f"{dir_name}_path": Path(data_dir[dir_name][split_name]) (...) 133 for split_name in ["train", "val", "test"] 134 } 136 for split_name in ["train", "val", "test"]: File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:431, in DownloadManager.download_and_extract(self, url_or_urls) 415 def download_and_extract(self, url_or_urls): 416 """Download and extract given url_or_urls. 417 418 Is roughly equivalent to: (...) 429 extracted_path(s): `str`, extracted paths of given URL(s). 430 """ --> 431 return self.extract(self.download(url_or_urls)) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:324, in DownloadManager.download(self, url_or_urls) 321 self.downloaded_paths.update(dict(zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()))) 323 start_time = datetime.now() --> 324 self._record_sizes_checksums(url_or_urls, downloaded_path_or_paths) 325 duration = datetime.now() - start_time 326 logger.info(f"Checksum Computation took {duration.total_seconds() // 60} min") File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/download/download_manager.py:229, in DownloadManager._record_sizes_checksums(self, url_or_urls, downloaded_path_or_paths) 226 """Record size/checksum of downloaded files.""" 227 for url, path in zip(url_or_urls.flatten(), downloaded_path_or_paths.flatten()): 228 # call str to support PathLike objects --> 229 self._recorded_sizes_checksums[str(url)] = get_size_checksum_dict( 230 path, record_checksum=self.record_checksums 231 ) File /gpfswork/rech/cnw/commun/conda/lucile-m4_3/lib/python3.8/site-packages/datasets/utils/info_utils.py:82, in get_size_checksum_dict(path, record_checksum) 80 if record_checksum: 81 m = sha256() ---> 82 with open(path, "rb") as f: 83 for chunk in iter(lambda: f.read(1 << 20), b""): 84 m.update(chunk) PermissionError: [Errno 13] Permission denied: '/gpfswork/rech/cnw/commun/datasets/downloads/1e6aa6d23190c30885194fabb193dce3874d902d7636b66315ee8aaa584e80d6' ``` ### Steps to reproduce the bug I think the following will reproduce the bug. Given 2 users belonging to the same group with `umask` set to `0007` - first run with User 1: ```python from datasets import load_dataset ds_name = "HuggingFaceM4/VQAv2" ds = load_dataset(ds_name) ``` - then run with User 2: ```python from datasets import load_dataset ds_name = "HuggingFaceM4/TextCaps" ds = load_dataset(ds_name) ``` ### Expected behavior No `PermissionError` ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-4.18.0-305.65.1.el8_4.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.13 - PyArrow version: 7.0.0 - Pandas version: 1.4.2

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https://github.com/huggingface/datasets/pull/5347

PR_kwDODunzps5E6jb1

Force soundfile to return float32 instead of the default float64

open

2022-12-09T15:10:24Z

2022-12-19T10:07:20Z

NONE

(Fixes issue #5345)

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I_kwDODunzps5YoBB3

[Quick poll] Give your opinion on the future of the Hugging Face Open Source ecosystem!

open

2022-12-09T14:48:02Z

2022-12-09T14:48:02Z

MEMBER

Thanks to all of you, Datasets is just about to pass 15k stars! Since the last survey, a lot has happened: the [diffusers](https://github.com/huggingface/diffusers), [evaluate](https://github.com/huggingface/evaluate) and [skops](https://github.com/skops-dev/skops) libraries were born. `timm` joined the Hugging Face ecosystem. There were 25 new releases of `transformers`, 21 new releases of `datasets`, 13 new releases of `accelerate`. If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: [**hf.co/oss-survey**](https://docs.google.com/forms/d/e/1FAIpQLSf4xFQKtpjr6I_l7OfNofqiR8s-WG6tcNbkchDJJf5gYD72zQ/viewform?usp=sf_link) (please reply in the above feedback form rather than to this thread) Thank you all on behalf of the HuggingFace team! 🤗

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I_kwDODunzps5Ymwj4

Wrong dtype for array in audio features

open

2022-12-09T11:05:11Z

2022-12-16T13:44:46Z

NONE

### Describe the bug When concatenating/interleaving different datasets, I stumble into an error because the features can't be aligned. After some investigation, I understood that the audio arrays had different dtypes, namely `float32` and `float64`. Consequently, the datasets cannot be merged. ### Steps to reproduce the bug For example, for `facebook/voxpopuli` and `mozilla-foundation/common_voice_11_0`: ``` from datasets import load_dataset, interleave_datasets covost = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True) voxpopuli = datasets.load_dataset("facebook/voxpopuli", "nl", split="train", streaming=True) sample_cv, = covost.take(1) sample_vp, = voxpopuli.take(1) assert sample_cv["audio"]["array"].dtype == sample_vp["audio"]["array"].dtype # Fails dataset = interleave_datasets([covost, voxpopuli]) # ValueError: The features can't be aligned because the key audio of features {'audio_id': Value(dtype='string', id=None), 'language': Value(dtype='int64', id=None), 'audio': {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)}, 'normalized_text': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'speaker_id': Value(dtype='string', id=None), 'is_gold_transcript': Value(dtype='bool', id=None), 'accent': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None)} has unexpected type - {'array': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'path': Value(dtype='string', id=None), 'sampling_rate': Value(dtype='int64', id=None)} (expected either Audio(sampling_rate=16000, mono=True, decode=True, id=None) or Value("null"). ``` ### Expected behavior The audio should be loaded to arrays with a unique dtype (I guess `float32`) ### Environment info ``` - `datasets` version: 2.7.1.dev0 - Platform: Linux-4.18.0-425.3.1.el8.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 ```

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PR_kwDODunzps5E2BPN

Clean up Dataset and DatasetDict

closed

2022-12-09T00:02:08Z

2022-12-13T00:56:07Z

2022-12-13T00:53:02Z

MEMBER

This PR cleans up the docstrings for the other half of the methods in `Dataset` and finishes `DatasetDict`.

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I_kwDODunzps5Yh9if

T5 for Q&A produces truncated sentence

closed

2022-12-08T19:48:46Z

2022-12-08T19:57:17Z

2022-12-08T19:57:17Z

NONE

Dear all, I am fine-tuning T5 for Q&A task using the MedQuAD ([GitHub - abachaa/MedQuAD: Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites](https://github.com/abachaa/MedQuAD)) dataset. In the dataset, there are many long answers with thousands of words. I have used pytorch_lightning to train the T5-large model. I have two questions. For example, I set both the max_length, max_input_length, max_output_length to 128. How to deal with those long answers? I just left them as is and the T5Tokenizer can automatically handle. I would assume the tokenizer just truncates an answer at the position of 128th word (or 127th). Is it possible that I manually split an answer into different parts, each part has 128 words; and then all these sub-answers serve as a separate answer to the same question? Another question is that I get incomplete (truncated) answers when using the fine-tuned model in inference, even though the predicted answer is shorter than 128 words. I found a message posted 2 years ago saying that one should add at the end of texts when fine-tuning T5. I followed that but then got a warning message that duplicated were found. I am assuming that this is because the tokenizer truncates an answer text, thus is missing in the truncated answer, such that the end token is not produced in predicted answer. However, I am not sure. Can anybody point out how to address this issue? Any suggestions are highly appreciated. Below is some code snippet. ` import pytorch_lightning as pl from torch.utils.data import DataLoader import torch import numpy as np import time from pathlib import Path from transformers import ( Adafactor, T5ForConditionalGeneration, T5Tokenizer, get_linear_schedule_with_warmup ) from torch.utils.data import RandomSampler from question_answering.utils import * class T5FineTuner(pl.LightningModule): def __init__(self, hyparams): super(T5FineTuner, self).__init__() self.hyparams = hyparams self.model = T5ForConditionalGeneration.from_pretrained(hyparams.model_name_or_path) self.tokenizer = T5Tokenizer.from_pretrained(hyparams.tokenizer_name_or_path) if self.hyparams.freeze_embeds: self.freeze_embeds() if self.hyparams.freeze_encoder: self.freeze_params(self.model.get_encoder()) # assert_all_frozen() self.step_count = 0 self.output_dir = Path(self.hyparams.output_dir) n_observations_per_split = { 'train': self.hyparams.n_train, 'validation': self.hyparams.n_val, 'test': self.hyparams.n_test } self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()} self.em_score_list = [] self.subset_score_list = [] data_folder = r'C:\Datasets\MedQuAD-master' self.train_data, self.val_data, self.test_data = load_medqa_data(data_folder) def freeze_params(self, model): for param in model.parameters(): param.requires_grad = False def freeze_embeds(self): try: self.freeze_params(self.model.model.shared) for d in [self.model.model.encoder, self.model.model.decoder]: self.freeze_params(d.embed_positions) self.freeze_params(d.embed_tokens) except AttributeError: self.freeze_params(self.model.shared) for d in [self.model.encoder, self.model.decoder]: self.freeze_params(d.embed_tokens) def lmap(self, f, x): return list(map(f, x)) def is_logger(self): return self.trainer.proc_rank <= 0 def forward(self, input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, labels=None): return self.model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=labels ) def _step(self, batch): labels = batch['target_ids'] labels[labels[:, :] == self.tokenizer.pad_token_id] = -100 outputs = self( input_ids = batch['source_ids'], attention_mask=batch['source_mask'], labels=labels, decoder_attention_mask=batch['target_mask'] ) loss = outputs[0] return loss def ids_to_clean_text(self, generated_ids): gen_text = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) return self.lmap(str.strip, gen_text) def _generative_step(self, batch): t0 = time.time() generated_ids = self.model.generate( batch["source_ids"], attention_mask=batch["source_mask"], use_cache=True, decoder_attention_mask=batch['target_mask'], max_length=128, num_beams=2, early_stopping=True ) preds = self.ids_to_clean_text(generated_ids) targets = self.ids_to_clean_text(batch["target_ids"]) gen_time = (time.time() - t0) / batch["source_ids"].shape[0] loss = self._step(batch) base_metrics = {'val_loss': loss} summ_len = np.mean(self.lmap(len, generated_ids)) base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=targets) em_score, subset_match_score = calculate_scores(preds, targets) self.em_score_list.append(em_score) self.subset_score_list.append(subset_match_score) em_score = torch.tensor(em_score, dtype=torch.float32) subset_match_score = torch.tensor(subset_match_score, dtype=torch.float32) base_metrics.update(em_score=em_score, subset_match_score=subset_match_score) # rouge_results = self.rouge_metric.compute() # rouge_dict = self.parse_score(rouge_results) return base_metrics def training_step(self, batch, batch_idx): loss = self._step(batch) tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def training_epoch_end(self, outputs): avg_train_loss = torch.stack([x['loss'] for x in outputs]).mean() tensorboard_logs = {'avg_train_loss': avg_train_loss} # return {'avg_train_loss': avg_train_loss, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs} def validation_step(self, batch, batch_idx): return self._generative_step(batch) def validation_epoch_end(self, outputs): avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() tensorboard_logs = {'val_loss': avg_loss} if len(self.em_score_list) <= 2: average_em_score = sum(self.em_score_list) / len(self.em_score_list) average_subset_match_score = sum(self.subset_score_list) / len(self.subset_score_list) else: latest_em_score = self.em_score_list[:-2] latest_subset_score = self.subset_score_list[:-2] average_em_score = sum(latest_em_score) / len(latest_em_score) average_subset_match_score = sum(latest_subset_score) / len(latest_subset_score) average_em_score = torch.tensor(average_em_score, dtype=torch.float32) average_subset_match_score = torch.tensor(average_subset_match_score, dtype=torch.float32) tensorboard_logs.update(em_score=average_em_score, subset_match_score=average_subset_match_score) self.target_gen = [] self.prediction_gen = [] return { 'avg_val_loss': avg_loss, 'em_score': average_em_score, 'subset_match_socre': average_subset_match_score, 'log': tensorboard_logs, 'progress_bar': tensorboard_logs } def configure_optimizers(self): model = self.model no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": self.hyparams.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = Adafactor(optimizer_grouped_parameters, lr=self.hyparams.learning_rate, scale_parameter=False, relative_step=False) self.opt = optimizer return [optimizer] def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure=None, on_tpu=False, using_native_amp=False, using_lbfgs=False): optimizer.step(closure=optimizer_closure) optimizer.zero_grad() self.lr_scheduler.step() def get_tqdm_dict(self): tqdm_dict = {"loss": "{:.3f}".format(self.trainer.avg_loss), "lr": self.lr_scheduler.get_last_lr()[-1]} return tqdm_dict def train_dataloader(self): n_samples = self.n_obs['train'] train_dataset = get_dataset(tokenizer=self.tokenizer, data=self.train_data, num_samples=n_samples, args=self.hyparams) sampler = RandomSampler(train_dataset) dataloader = DataLoader(train_dataset, sampler=sampler, batch_size=self.hyparams.train_batch_size, drop_last=True, num_workers=4) # t_total = ( # (len(dataloader.dataset) // (self.hyparams.train_batch_size * max(1, self.hyparams.n_gpu))) # // self.hyparams.gradient_accumulation_steps # * float(self.hyparams.num_train_epochs) # ) t_total = 100000 scheduler = get_linear_schedule_with_warmup( self.opt, num_warmup_steps=self.hyparams.warmup_steps, num_training_steps=t_total ) self.lr_scheduler = scheduler return dataloader def val_dataloader(self): n_samples = self.n_obs['validation'] validation_dataset = get_dataset(tokenizer=self.tokenizer, data=self.val_data, num_samples=n_samples, args=self.hyparams) sampler = RandomSampler(validation_dataset) return DataLoader(validation_dataset, shuffle=False, batch_size=self.hyparams.eval_batch_size, sampler=sampler, num_workers=4) def test_dataloader(self): n_samples = self.n_obs['test'] test_dataset = get_dataset(tokenizer=self.tokenizer, data=self.test_data, num_samples=n_samples, args=self.hyparams) return DataLoader(test_dataset, batch_size=self.hyparams.eval_batch_size, num_workers=4) def on_save_checkpoint(self, checkpoint): save_path = self.output_dir.joinpath("best_tfmr") self.model.config.save_step = self.step_count self.model.save_pretrained(save_path) self.tokenizer.save_pretrained(save_path) import os import argparse import pytorch_lightning as pl from question_answering.t5_closed_book import T5FineTuner if __name__ == '__main__': args_dict = dict( output_dir="", # path to save the checkpoints model_name_or_path='t5-large', tokenizer_name_or_path='t5-large', max_input_length=128, max_output_length=128, freeze_encoder=False, freeze_embeds=False, learning_rate=1e-5, weight_decay=0.0, adam_epsilon=1e-8, warmup_steps=0, train_batch_size=4, eval_batch_size=4, num_train_epochs=2, gradient_accumulation_steps=10, n_gpu=1, resume_from_checkpoint=None, val_check_interval=0.5, n_val=4000, n_train=-1, n_test=-1, early_stop_callback=False, fp_16=False, opt_level='O1', max_grad_norm=1.0, seed=101, ) args_dict.update({'output_dir': 't5_large_MedQuAD_256', 'num_train_epochs': 100, 'train_batch_size': 16, 'eval_batch_size': 16, 'learning_rate': 1e-3}) args = argparse.Namespace(**args_dict) checkpoint_callback = pl.callbacks.ModelCheckpoint(dirpath=args.output_dir, monitor="em_score", mode="max", save_top_k=1) ## If resuming from checkpoint, add an arg resume_from_checkpoint train_params = dict( accumulate_grad_batches=args.gradient_accumulation_steps, gpus=args.n_gpu, max_epochs=args.num_train_epochs, # early_stop_callback=False, precision=16 if args.fp_16 else 32, # amp_level=args.opt_level, # resume_from_checkpoint=args.resume_from_checkpoint, gradient_clip_val=args.max_grad_norm, checkpoint_callback=checkpoint_callback, val_check_interval=args.val_check_interval, # accelerator='dp' # logger=wandb_logger, # callbacks=[LoggingCallback()], ) model = T5FineTuner(args) trainer = pl.Trainer(**train_params) trainer.fit(model) `

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I_kwDODunzps5YhwcS

Emotion dataset cannot be downloaded

closed

2022-12-08T19:07:09Z

2022-12-09T19:11:51Z

2022-12-09T10:46:11Z

NONE

### Describe the bug The emotion dataset gives a FileNotFoundError. The full error is: `FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1`. It was working yesterday (December 7, 2022), but stopped working today (December 8, 2022). ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("emotion") ``` ### Expected behavior The dataset should load properly. ### Environment info - `datasets` version: 2.7.1 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.9.13 - PyArrow version: 10.0.1 - Pandas version: 1.5.1

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PR_kwDODunzps5Exohx

Remove tasks.json

closed

2022-12-08T11:04:35Z

2022-12-09T12:26:21Z

2022-12-09T12:23:20Z

MEMBER

After discussions in https://github.com/huggingface/datasets/pull/5335 we should remove this file that is not used anymore. We should update https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts instead.

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PR_kwDODunzps5EtWo3

Clean up DatasetInfo and Dataset docstrings

closed

2022-12-08T00:17:53Z

2022-12-08T19:33:14Z

2022-12-08T19:30:10Z

MEMBER

This PR cleans up the docstrings for `DatasetInfo` and about half of the methods in `Dataset`.

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PR_kwDODunzps5EsC8N

Add Video feature, videofolder, and video-classification task

open

2022-12-07T20:48:34Z

2022-12-07T20:54:06Z

CONTRIBUTOR

This PR does the following: - Adds `Video` feature (Resolves #5225 ) - Adds `video-classification` task - Adds `videofolder` packaged module for easy loading of local video classification datasets TODO: - [ ] add tests - [ ] add docs

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I_kwDODunzps5YX2KH

`map()` stops every 1000 steps

closed

2022-12-07T19:09:40Z

2022-12-10T00:39:29Z

2022-12-10T00:39:28Z

NONE

### Describe the bug I am passing the following `prepare_dataset` function to `Dataset.map` (code is inspired from [here](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py#L454)) ```python3 def prepare_dataset(batch): # load and resample audio data from 48 to 16kHz audio = batch["audio"] # compute log-Mel input features from input audio array batch["input_features"] = feature_extractor(audio["array"], sampling_rate=audio["sampling_rate"]).input_features[0] # encode target text to label ids batch["labels"] = tokenizer(batch[text_column]).input_ids return batch ... train_ds = train_ds.map(prepare_dataset) ``` Here is the exact code I am running https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets/blob/main/train.py#L70-L71 It starts using all the cores (I am not sure why because I did not pass `num_proc`) then progress bar stops at every 1k steps. (starts using a single core) then come back to using all the cores again. link to [screen record](https://youtu.be/jPQpQQGp6Gc) Can someone explain this process and maybe provide a way to improve this pipeline? cc: @lhoestq ### Steps to reproduce the bug 1. load the dataset 2. create a Whisper processor 3. create a `prepare_dataset` function 4. pass the function to `dataset.map(prepare_dataset)` ### Expected behavior - Use a single core per a function - not to stop at some point? ### Environment info - `datasets` version: 2.7.1.dev0 - Platform: Linux-5.4.0-109-generic-x86_64-with-glibc2.27 - Python version: 3.8.10 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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I_kwDODunzps5YUNP8

Support webdataset format

open

2022-12-07T11:32:25Z

2022-12-12T14:23:44Z

MEMBER

Webdataset is an efficient format for iterable datasets. It would be nice to support it in `datasets`, as discussed in https://github.com/rom1504/img2dataset/issues/234. In particular it would be awesome to be able to load one using `load_dataset` in streaming mode (either from a local directory, or from a dataset on the Hugging Face Hub). Some datasets on the Hub are already in webdataset format. It terms of implementation, we can have something similar to the Parquet loader. I also think it's fine to have webdataset as an optional dependency.

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PR_kwDODunzps5Egzed

Set `IterableDataset.map` param `batch_size` typing as optional

closed

2022-12-06T17:08:10Z

2022-12-07T14:14:56Z

2022-12-07T14:06:27Z

CONTRIBUTOR

This PR solves #5325 ~Indeed we're using the typing for optional values as `Union[type, None]` as it's similar to how Python 3.10 handles optional values as `type | None`, instead of using `Optional[type]`.~ ~Do we want to start using `Union[type, None]` for type-hinting optional values or just keep on using `Optional`?~ -> Keeping `Optional` still for consistency with the rest of the code in `datasets` Also we now allow `batch_size` to be `None` for `IterableDataset.map` and `IterableDataset.filter`e.g. `MappedExamplesIterable` as `map` is internally instantiating those and propagating the `batch_size` param so if it can be `None` for `map` it should also do so for `MappedExamplesIterable`, as well as for `FilteredExamplesIterable` when calling `IterableDataset.filter`. ## TODOs - [x] Add integration tests - [x] Handle scenario where `batched=True` and `batch_size=None` or `batch_size<=0`

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PR_kwDODunzps5EeHdA

Update tasks.json

closed

2022-12-06T11:37:57Z

2022-12-08T11:05:33Z

2022-12-07T12:46:03Z

CONTRIBUTOR

Context: * https://github.com/huggingface/datasets/issues/5255#issuecomment-1339107195 Cc: @osanseviero

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PR_kwDODunzps5EY9zN

Clean up docstrings

closed

2022-12-05T20:56:08Z

2022-12-09T01:44:25Z

2022-12-09T01:41:44Z

MEMBER

As raised by @polinaeterna in #5324, some of the docstrings are a bit of a mess because it has both Markdown and Sphinx syntax. This PR fixes the docstring for `DatasetBuilder`. I'll start working on cleaning up the rest of the docstrings and removing the old Sphinx syntax (let me know if you prefer one big PR with all the cleaned changes or multiple smaller ones)! 🧼

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PR_kwDODunzps5EXGQ2

fix: 🐛 pass the token to get the list of config names

closed

2022-12-05T16:06:09Z

2022-12-06T08:25:17Z

2022-12-06T08:22:49Z

CONTRIBUTOR

Otherwise, get_dataset_infos doesn't work on gated or private datasets, even with the correct token.

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I_kwDODunzps5YAc0w

Passing numpy array to ClassLabel names causes ValueError

open

2022-12-05T12:59:03Z

2022-12-15T16:40:22Z

NONE

### Describe the bug If a numpy array is passed to the names argument of ClassLabel, creating a dataset with those features causes an error. ### Steps to reproduce the bug https://colab.research.google.com/drive/1cV_es1PWZiEuus17n-2C-w0KEoEZ68IX TLDR: If I define my classes as: ``` my_classes = np.array(['one', 'two', 'three']) ``` Then this errors: ```py features = Features({'value': Value('string'), 'label': ClassLabel(names=my_classes)}) dataset = Dataset.from_list(my_data, features=features) ``` ``` ValueError Traceback (most recent call last) [<ipython-input-8-a8a9d53ec82f>](https://localhost:8080/#) in <module> ----> 1 dataset = Dataset.from_list(my_data, features=features) 11 frames [/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _asdict_inner(obj) 183 for f in fields(obj): 184 value = _asdict_inner(getattr(obj, f.name)) --> 185 if not f.init or value != f.default or f.metadata.get("include_in_asdict_even_if_is_default", False): 186 result[f.name] = value 187 return result ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ``` But this works: ``` features2 = Features({'value': Value('string'), 'label': ClassLabel(names=list(my_classes))}) dataset2 = Dataset.from_list(my_data, features=features2) ``` ### Expected behavior If I provide a numpy array of class names, I would expect either an error that the names list is the wrong type, or for it to be cast internally. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.10 - Python version: 3.8.15 - PyArrow version: 10.0.1 - Pandas version: 1.5.2 Additionally: - Numpy version: 1.23.5

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PR_kwDODunzps5EKDpr

Support for multiple configs in packaged modules via metadata yaml info

open

2022-12-02T16:43:44Z

2022-12-13T11:54:46Z

CONTRIBUTOR

will solve https://github.com/huggingface/datasets/issues/5209 and https://github.com/huggingface/datasets/issues/5151

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PR_kwDODunzps5EGK3y

Clarify imagefolder is for small datasets

closed

2022-12-01T21:47:29Z

2022-12-06T17:20:04Z

2022-12-06T17:16:53Z

MEMBER

Based on feedback from [here](https://github.com/huggingface/datasets/issues/5317#issuecomment-1334108824), this PR adds a note to the `imagefolder` loading and creating docs that `imagefolder` is designed for small scale image datasets.

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PR_kwDODunzps5EFAyT

Fix docs building for main

closed

2022-12-01T17:07:45Z

2022-12-02T16:29:00Z

2022-12-02T16:26:00Z

MEMBER

This PR reverts the triggering event for building documentation introduced by: - #5250 Fix #5326.

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PR_kwDODunzps5EE_3Q

Avoid unwanted behaviour when splits from script and metadata are not matching because of outdated metadata

open

2022-12-01T17:05:23Z

2022-12-16T15:57:10Z

CONTRIBUTOR

will fix #5315

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I_kwDODunzps5Xt1r4

No documentation for main branch is built

closed

2022-12-01T16:50:58Z

2022-12-02T16:26:01Z

2022-12-02T16:26:01Z

MEMBER

Since: - #5250 - Commit: 703b84311f4ead83c7f79639f2dfa739295f0be6 the docs for main branch are no longer built. The change introduced only triggers the docs building for releases.

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I_kwDODunzps5Xtd62

map(...batch_size=None) for IterableDataset

closed

2022-12-01T15:43:42Z

2022-12-07T15:54:43Z

2022-12-07T15:54:42Z

CONTRIBUTOR

### Feature request Dataset.map(...) allows batch_size to be None. It would be nice if IterableDataset did too. ### Motivation Although it may seem a bit of a spurious request given that `IterableDataset` is meant for larger than memory datasets, but there are a couple of reasons why this might be nice. One is that load_dataset(...) can return either IterableDataset or Dataset. mypy will then complain if batch_size=None even if we know it is Dataset. Of course we can do: assert isinstance(d, datasets.DatasetDict) But it is a mild inconvenience. What's more annoying is that whenever we use something like e.g. `combine_datasets(...)`, we end up with the union again, and so have to do the assert again. Another is that we could actually end up with an IterableDataset small enough for memory in normal/correct usage, e.g. by filtering a massive IterableDataset. For practical usages, an alternative to this would be to convert from an iterable dataset to a map-style dataset, but it is not obvious how to do this. ### Your contribution Not this time.

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I_kwDODunzps5Xta6g

Fix docstrings and types in documentation that appears on the website

open

2022-12-01T15:34:53Z

2022-12-13T19:03:55Z

CONTRIBUTOR

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_kwDODunzps5XtZhT

Duplicated Keys in Taskmaster-2 Dataset

closed

2022-12-01T15:31:06Z

2022-12-01T16:26:06Z

2022-12-01T16:26:06Z

NONE

### Describe the bug Loading certain splits () of the taskmaster-2 dataset fails because of a DuplicatedKeysError. This occurs for the following domains: `'hotels', 'movies', 'music', 'sports'`. The domains `'flights', 'food-ordering', 'restaurant-search'` load fine. Output: ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("taskmaster2", "music") ``` Output: ``` --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1532, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1531](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1530) example = self.info.features.encode_example(record) if self.info.features is not None else record -> [1532](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1531) writer.write(example, key) [1533](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1532) num_examples_progress_update += 1 File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:475, in ArrowWriter.write(self, example, key, writer_batch_size) [474](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=473) if self._check_duplicates: --> [475](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=474) self.check_duplicate_keys() [476](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=475) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 During handling of the above exception, another exception occurred: DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1541, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1540](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1539) num_shards = shard_id + 1 -> [1541](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1540) num_examples, num_bytes = writer.finalize() [1542](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1541) writer.close() File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:563, in ArrowWriter.finalize(self, close_stream) [562](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=561) if self._check_duplicates: --> [563](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=562) self.check_duplicate_keys() [564](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=563) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[23], line 1 ----> 1 dataset = load_dataset("taskmaster2", "music") File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py:1741, 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, num_proc, **config_kwargs) [1738](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1737) try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES [1740](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1739) # Download and prepare data -> [1741](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1740) builder_instance.download_and_prepare( [1742](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1741) download_config=download_config, [1743](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1742) download_mode=download_mode, [1744](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1743) ignore_verifications=ignore_verifications, [1745](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1744) try_from_hf_gcs=try_from_hf_gcs, [1746](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1745) use_auth_token=use_auth_token, [1747](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1746) num_proc=num_proc, [1748](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1747) ) [1750](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1749) # Build dataset for splits [1751](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1750) keep_in_memory = ( [1752](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1751) keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) [1753](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1752) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:822, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs) [820](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=819) if num_proc is not None: [821](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=820) prepare_split_kwargs["num_proc"] = num_proc --> [822](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=821) self._download_and_prepare( [823](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=822) dl_manager=dl_manager, [824](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=823) verify_infos=verify_infos, [825](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=824) **prepare_split_kwargs, [826](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=825) **download_and_prepare_kwargs, [827](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=826) ) [828](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=827) # Sync info [829](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=828) self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1555, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs) [1554](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1553) def _download_and_prepare(self, dl_manager, verify_infos, **prepare_splits_kwargs): -> [1555](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1554) super()._download_and_prepare( [1556](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1555) dl_manager, verify_infos, check_duplicate_keys=verify_infos, **prepare_splits_kwargs [1557](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1556) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:913, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) [909](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=908) split_dict.add(split_generator.split_info) [911](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=910) try: [912](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=911) # Prepare split will record examples associated to the split --> [913](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=912) self._prepare_split(split_generator, **prepare_split_kwargs) [914](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=913) except OSError as e: [915](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=914) raise OSError( [916](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=915) "Cannot find data file. " [917](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=916) + (self.manual_download_instructions or "") [918](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=917) + "\nOriginal error:\n" [919](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=918) + str(e) [920](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=919) ) from None File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1396, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) [1394](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1393) gen_kwargs = split_generator.gen_kwargs [1395](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1394) job_id = 0 -> [1396](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1395) for job_id, done, content in self._prepare_split_single( [1397](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1396) {"gen_kwargs": gen_kwargs, "job_id": job_id, **_prepare_split_args} [1398](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1397) ): [1399](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1398) if done: [1400](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1399) result = content File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1550, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1548](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1547) if isinstance(e, SchemaInferenceError) and e.__context__ is not None: [1549](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1548) e = e.__context__ -> [1550](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1549) raise DatasetGenerationError("An error occurred while generating the dataset") from e [1552](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1551) yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Loads the dataset ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 10.0.1 - Pandas version: 1.5.2

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https://github.com/huggingface/datasets/pull/5322

PR_kwDODunzps5EEeQP

Raise error for `.tar` archives in the same way as for `.tar.gz` and `.tgz` in `_get_extraction_protocol`

closed

2022-12-01T15:19:28Z

2022-12-14T16:37:16Z

2022-12-14T16:33:30Z

CONTRIBUTOR

Currently `download_and_extract` doesn't throw an error when it is used with files with `.tar` extension in streaming mode because `_get_extraction_protocol` doesn't do it (like it does for `tar.gz` and `tgz`). `_get_extraction_protocol` returns formatted url as if we support tar protocol but we don't. That means that in dataset scripts `.tar` files would be attempted to load and fail during examples generation (after `download_and_extract` execution). So this PR raises error for `tar` files too.

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https://github.com/huggingface/datasets/pull/5321

PR_kwDODunzps5EEOhE

Fix loading from HF GCP cache

closed

2022-12-01T14:39:06Z

2022-12-01T16:10:09Z

2022-12-01T16:07:02Z

MEMBER

As reported in https://discuss.huggingface.co/t/error-loading-wikipedia-dataset/26599/4 it's not possible to download a cached version of Wikipedia from the HF GCP cache I fixed it and added an integration test (runs in 10sec)

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https://github.com/huggingface/datasets/pull/5320

PR_kwDODunzps5ED_UQ

[Extract] Place the lock file next to the destination directory

closed

2022-12-01T13:55:49Z

2022-12-01T15:36:44Z

2022-12-01T15:33:58Z

MEMBER

Previously it was placed next to the archive to extract, but the archive can be in a read-only directory as noticed in https://github.com/huggingface/datasets/issues/5295 Therefore I moved the lock location to be next to the destination directory, which is required to have write permissions

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https://github.com/huggingface/datasets/pull/5319

PR_kwDODunzps5ECkfc

Fix Text sample_by paragraph

closed

2022-12-01T09:08:09Z

2022-12-01T15:21:44Z

2022-12-01T15:19:00Z

MEMBER

Fix #5316.

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https://github.com/huggingface/datasets/pull/5318

PR_kwDODunzps5EB6RM

Origin/fix missing features error

closed

2022-12-01T06:18:39Z

2022-12-12T19:06:42Z

2022-12-04T05:49:39Z

CONTRIBUTOR

This fixes the problem of when the dataset_load function reads a function with "features" provided but some read batches don't have columns that later show up. For instance, the provided "features" requires columns A,B,C but only columns B,C show. This fixes this by adding the column A with nulls.

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I_kwDODunzps5XpF-U

`ImageFolder` performs poorly with large datasets

open

2022-12-01T00:04:21Z

2022-12-01T21:49:26Z

NONE

### Describe the bug While testing image dataset creation, I'm seeing significant performance bottlenecks with imagefolders when scanning a directory structure with large number of images. ## Setup * Nested directories (5 levels deep) * 3M+ images * 1 `metadata.jsonl` file ## Performance Degradation Point 1 Degradation occurs because [`get_data_files_patterns`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L231-L243) runs the exact same scan for many different types of patterns, and there doesn't seem to be a way to easily limit this. It's controlled by the definition of [`ALL_DEFAULT_PATTERNS`](https://github.com/huggingface/datasets/blob/main/src/datasets/data_files.py#L82-L85). One scan with 3M+ files takes about 10-15 minutes to complete on my setup, so having those extra scans really slows things down – from 10 minutes to 60+. Most of the scans return no matches, but they still take a significant amount of time to complete – hence the poor performance. As a side effect, when this scan is run on 3M+ image files, Python also consumes up to 12 GB of RAM, which is not ideal. ## Performance Degradation Point 2 The second performance bottleneck is in [`PackagedDatasetModuleFactory.get_module`](https://github.com/huggingface/datasets/blob/d7dfbc83d68e87ba002c5eb2555f7a932e59038a/src/datasets/load.py#L707-L711), which calls `DataFilesDict.from_local_or_remote`. It runs for a long time (60min+), consuming significant amounts of RAM – even more than the point 1 above. Based on `iostat -d 2`, it performs **zero** disk operations, which to me suggests that there is a code based bottleneck there that could be sorted out. ### Steps to reproduce the bug ```python from datasets import load_dataset import os import huggingface_hub dataset = load_dataset( 'imagefolder', data_dir='/some/path', # just to spell it out: split=None, drop_labels=True, keep_in_memory=False ) dataset.push_to_hub('account/dataset', private=True) ``` ### Expected behavior While it's certainly possible to write a custom loader to replace `ImageFolder` with, it'd be great if the off-the-shelf `ImageFolder` would by default have a setup that can scale to large datasets. Or perhaps there could be a dedicated loader just for large datasets that trades off flexibility for performance? As in, maybe you have to define explicitly how you want it to work rather than it trying to guess your data structure like `_get_data_files_patterns()` does? ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-4.14.296-222.539.amzn2.x86_64-x86_64-with-glibc2.2.5 - Python version: 3.7.10 - PyArrow version: 10.0.1 - Pandas version: 1.3.5

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I_kwDODunzps5XoC9h

Bug in sample_by="paragraph"

closed

2022-11-30T19:24:13Z

2022-12-01T15:19:02Z

2022-12-01T15:19:02Z

NONE

### Describe the bug I think [this line](https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/text/text.py#L96) is wrong and should be `batch = f.read(self.config.chunksize)`. Otherwise it will never terminate because even when `f` is finished reading, `batch` will still be truthy from the last iteration. ### Steps to reproduce the bug ``` > cat test.txt a b c d e f ```` ```python >>> import datasets >>> datasets.load_dataset("text", data_files={"train":"test.txt"}, sample_by="paragraph") ``` This will go on forever. ### Expected behavior Terminates very quickly. ### Environment info `version = "2.6.1"` but I think the bug is still there on main.

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I_kwDODunzps5XntQt

Adding new splits to a dataset script with existing old splits info in metadata's `dataset_info` fails

open

2022-11-30T18:02:15Z

2022-12-02T07:02:53Z

CONTRIBUTOR

### Describe the bug If you first create a custom dataset with a specific set of splits, generate metadata with `datasets-cli test ... --save_info`, then change your script to include more splits, it fails. That's what happened in https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/discussions/2#6385fd1269634850f8ddff48. ### Steps to reproduce the bug 1. create a dataset with a custom split that returns, for example, only `"train"` split in `_splits_generators'`. specifically, if really want to reproduce, copy `https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py 2. run `datasets-cli test dataset_script.py --save_info --all_configs` - this would generate metadata yaml in `README.md` that would contain info about splits, for example, like this: ``` splits: - name: train num_bytes: 2973286 num_examples: 19747 ``` 3. make changes to your script so that it returns another set of splits, for example, `"train"` and `"test"` (uncomment [these lines](https://huggingface.co/datasets/mrdbourke/food_vision_199_classes/blob/main/food_vision_199_classes.py#L271)) 4. run `load_dataset` and get the following error: ```python Traceback (most recent call last): File "/home/daniel/code/pytorch/env/bin/datasets-cli", line 8, in <module> sys.exit(main()) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/datasets_cli.py", line 39, in main service.run() File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/commands/test.py", line 141, in run builder.download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 822, in download_and_prepare self._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1555, in _download_and_prepare super()._download_and_prepare( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 913, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/builder.py", line 1356, in _prepare_split split_info = self.info.splits[split_generator.name] File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/splits.py", line 525, in __getitem__ instructions = make_file_instructions( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 111, in make_file_instructions name2filenames = { File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/arrow_reader.py", line 112, in <dictcomp> info.name: filenames_for_dataset_split( File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 78, in filenames_for_dataset_split prefix = filename_prefix_for_split(dataset_name, split) File "/home/daniel/code/pytorch/env/lib/python3.8/site-packages/datasets/naming.py", line 57, in filename_prefix_for_split if os.path.basename(name) != name: File "/home/daniel/code/pytorch/env/lib/python3.8/posixpath.py", line 143, in basename p = os.fspath(p) TypeError: expected str, bytes or os.PathLike object, not NoneType ``` 5. bonus: try to regenerate metadata in `README.md` with `datasets-cli` as in step 2 and get the same error. This is because `dataset.info.splits` contains only `"train"` split so when we are doing `self.info.splits[split_generator.name]` it tries to infer smth like `info.splits['train[50%]']` and that's not the case and it fails. ### Expected behavior to be discussed? This can be solved by removing splits information from metadata file first. But I wonder if there is a better way. ### Environment info - Datasets version: 2.7.1 - Python version: 3.8.13

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I_kwDODunzps5XmZ1-

Datasets: classification_report() got an unexpected keyword argument 'suffix'

open

2022-11-30T14:01:03Z

2022-12-01T15:00:46Z

NONE

https://github.com/huggingface/datasets/blob/main/metrics/seqeval/seqeval.py > import datasets predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']] seqeval = datasets.load_metric("seqeval") results = seqeval.compute(predictions=predictions, references=references) print(list(results.keys())) print(results["overall_f1"]) print(results["PER"]["f1"]) It raises the error: > TypeError: classification_report() got an unexpected keyword argument 'suffix' For context, versions on my pip list -v > datasets 1.12.1 seqeval 1.2.2

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PR_kwDODunzps5D6Qfb

Fix description of streaming in the docs

closed

2022-11-29T18:00:28Z

2022-12-01T14:55:30Z

2022-12-01T14:00:34Z

CONTRIBUTOR

We say that "the data is being downloaded progressively" which is not true, it's just streamed, so I fixed it. Probably I missed some other places where it is written? Also changed docstrings for `StreamingDownloadManager`'s `download` and `extract` to reflect the same, as these docstrings are displayed in the documentation cc @lhoestq

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PR_kwDODunzps5D5zxI

Add DatasetDict.to_pandas

open

2022-11-29T16:30:02Z

2022-12-12T18:50:43Z

MEMBER

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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PR_kwDODunzps5D4Mm3

Add `features` param to `IterableDataset.map`

closed

2022-11-29T11:08:34Z

2022-12-06T15:45:02Z

2022-12-06T15:42:04Z

CONTRIBUTOR

## Description As suggested by @lhoestq in #3888, we should be adding the param `features` to `IterableDataset.map` so that the features can be preserved (not turned into `None` as that's the default behavior) whenever the user passes those as param, so as to be consistent with `Dataset.map`, as it provides the `features` param so that those are not inferred by default, but specified by the user, and later validated by `ArrowWriter`. This is internally handled already by the functions relying on `IterableDataset.map` such as `rename_column`, `rename_columns`, and `remove_columns` as described in #5287. ## Usage Example ```python from datasets import load_dataset, Features ds = load_dataset("rotten_tomatoes", split="validation", streaming=True) print(ds.info.features) ds = ds.map( lambda x: {"target": x["label"]}, features=Features( {"target": ds.info.features["label"], "label": ds.info.features["label"], "text": ds.info.features["text"]} ), ) print(ds.info.features) ```

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PR_kwDODunzps5D3rGw

Support xPath for Windows pathnames

closed

2022-11-29T09:20:47Z

2022-11-30T12:00:09Z

2022-11-30T11:57:16Z

MEMBER

This PR implements a string representation of `xPath`, which is valid for local paths (also windows) and remote URLs. Additionally, some `os.path` methods are fixed for remote URLs on Windows machines. Now, on Windows machines: ```python In [2]: str(xPath("C:\\dir\\file.txt")) Out[2]: 'C:\\dir\\file.txt' In [3]: str(xPath("http://domain.com/file.txt")) Out[3]: 'http://domain.com/file.txt' ```

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PR_kwDODunzps5D0g1y

Close stream in `ArrowWriter.finalize` before inference error

closed

2022-11-28T16:59:39Z

2022-12-07T12:55:20Z

2022-12-07T12:52:15Z

CONTRIBUTOR

Ensure the file stream is closed in `ArrowWriter.finalize` before raising the `SchemaInferenceError` to avoid the `PermissionError` on Windows in `incomplete_dir`'s `shutil.rmtree`.

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PR_kwDODunzps5Dz0Tv

Support `topdown` parameter in `xwalk`

closed

2022-11-28T14:42:41Z

2022-12-09T12:58:55Z

2022-12-09T12:55:59Z

CONTRIBUTOR

Add support for the `topdown` parameter in `xwalk` when `fsspec>=2022.11.0` is installed.

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PR_kwDODunzps5Dzj8r

Use correct dataset type in `from_generator` docs

closed

2022-11-28T13:59:10Z

2022-11-28T15:30:37Z

2022-11-28T15:27:26Z

CONTRIBUTOR

Use the correct dataset type in the `from_generator` docs (example with sharding).

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I_kwDODunzps5XYOf_

Can't use custom feature description when loading a dataset

closed

2022-11-28T07:55:44Z

2022-11-28T08:11:45Z

2022-11-28T08:11:44Z

CONTRIBUTOR

### Describe the bug I have created a feature dictionary to describe my datasets' column types, to use when loading the dataset, following [the doc](https://huggingface.co/docs/datasets/main/en/about_dataset_features). It crashes at dataset load. ### Steps to reproduce the bug ```python # Creating features task_list = [f"motif_G{i}" for i in range(19, 53)] features = {t: Sequence(feature=Value(dtype="float64")) for t in task_list} for col_name in ["class_label"]: features[col_name] = Sequence(feature=Value(dtype="int64")) for col_name in ["num_nodes"]: features[col_name] = Value(dtype="int64") for col_name in ["num_bridges", "num_cycles", "avg_shortest_path_len"]: features[col_name] = Sequence(feature=Value(dtype="float64")) for col_name in ["edge_attr", "node_feat", "edge_index"]: features[col_name] = Sequence(feature=Sequence(feature=Value(dtype="int64"))) print(features) dataset = load_dataset(path=f"graphs-datasets/unbalanced-motifs-500K", split="train", features=features) ``` Last line will crash and say 'TypeError: argument of type 'Sequence' is not iterable'. Full stack: ``` Traceback (most recent call last): File "pretrain_tokengt.py", line 131, in <module> main(output_folder = "../workspace/pretraining", File "pretrain_tokengt.py", line 52, in main dataset = load_dataset(path=f"graphs-datasets/{dataset_name}", split="train", features=features) File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1718, in load_dataset builder_instance = load_dataset_builder( File "huggingface_env/lib/python3.8/site-packages/datasets/load.py", line 1514, in load_dataset_builder builder_instance: DatasetBuilder = builder_cls( File "huggingface_env/lib/python3.8/site-packages/datasets/builder.py", line 321, in __init__ info.update(self._info()) File "huggingface_env/lib/python3.8/site-packages/datasets/packaged_modules/json/json.py", line 62, in _info return datasets.DatasetInfo(features=self.config.features) File "<string>", line 20, in __init__ File "huggingface_env/lib/python3.8/site-packages/datasets/info.py", line 155, in __post_init__ self.features = Features.from_dict(self.features) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1599, in from_dict obj = generate_from_dict(dic) File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in generate_from_dict return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1282, in <dictcomp> return {key: generate_from_dict(value) for key, value in obj.items()} File "huggingface_env/lib/python3.8/site-packages/datasets/features/features.py", line 1281, in generate_from_dict if "_type" not in obj or isinstance(obj["_type"], dict): TypeError: argument of type 'Sequence' is not iterable ``` ### Expected behavior For it not to crash. ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.14.0-1054-oem-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 8.0.0 - Pandas version: 1.4.3

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I_kwDODunzps5XW7Sy

Dataset joelito/mc4_legal does not work with multiple files

closed

2022-11-28T00:16:16Z

2022-11-28T07:22:42Z

2022-11-28T07:22:42Z

CONTRIBUTOR

### 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 beautifulsoup4 4.11.1 blinker 1.4 blis 0.7.8 Bottleneck 1.3.4 brotlipy 0.7.0 cachetools 5.2.0 catalogue 2.0.7 certifi 2022.5.18.1 cffi 1.15.1 chardet 4.0.0 charset-normalizer 2.1.0 click 8.0.4 conllu 4.5.2 cryptography 38.0.1 cymem 2.0.6 datasets 2.6.1 dill 0.3.5.1 docker-pycreds 0.4.0 fasttext 0.9.2 fasttext-langdetect 1.0.3 filelock 3.0.12 flatbuffers 20210226132247 frozenlist 1.3.0 fsspec 2022.5.0 gast 0.4.0 gcloud 0.18.3 gitdb 4.0.9 GitPython 3.1.27 google-auth 2.9.0 google-auth-oauthlib 0.4.6 google-pasta 0.2.0 googleapis-common-protos 1.57.0 grpcio 1.47.0 h5py 3.7.0 httplib2 0.21.0 huggingface-hub 0.8.1 idna 3.4 importlib-metadata 4.12.0 Jinja2 3.1.2 joblib 1.0.1 keras 2.9.0 Keras-Preprocessing 1.1.2 langcodes 3.3.0 lxml 4.9.1 Markdown 3.3.7 MarkupSafe 2.1.1 mkl-fft 1.3.1 mkl-random 1.2.2 mkl-service 2.4.0 multidict 6.0.2 multiprocess 0.70.13 murmurhash 1.0.7 numexpr 2.8.1 numpy 1.22.3 oauth2client 4.1.3 oauthlib 3.2.1 opt-einsum 3.3.0 packaging 21.3 pandas 1.4.2 pathtools 0.1.2 pathy 0.6.1 pip 21.1.2 preshed 3.0.6 promise 2.3 protobuf 4.21.9 psutil 5.9.1 pyarrow 8.0.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 pybind11 2.9.2 pycountry 22.3.5 pycparser 2.21 pydantic 1.8.2 PyJWT 2.4.0 pylzma 0.5.0 pyOpenSSL 22.0.0 pyparsing 3.0.4 PySocks 1.7.1 python-dateutil 2.8.2 pytz 2021.3 PyYAML 6.0 regex 2021.4.4 requests 2.28.1 requests-oauthlib 1.3.1 responses 0.18.0 rsa 4.8 sacremoses 0.0.45 scikit-learn 1.1.1 scipy 1.8.1 sentencepiece 0.1.96 sentry-sdk 1.6.0 setproctitle 1.2.3 setuptools 65.5.0 shortuuid 1.0.9 six 1.16.0 smart-open 5.2.1 smmap 5.0.0 soupsieve 2.3.2.post1 spacy 3.3.1 spacy-legacy 3.0.9 spacy-loggers 1.0.2 srsly 2.4.3 tabulate 0.8.9 tensorboard 2.9.1 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.1 tensorflow 2.9.1 tensorflow-estimator 2.9.0 termcolor 2.1.0 thinc 8.0.17 threadpoolctl 3.1.0 tokenizers 0.12.1 torch 1.13.0 tqdm 4.64.0 transformers 4.20.1 typer 0.4.1 typing-extensions 4.3.0 Unidecode 1.3.6 urllib3 1.26.12 wandb 0.12.20 wasabi 0.9.1 web-anno-tsv 0.0.1 Werkzeug 2.1.2 wget 3.2 wheel 0.35.1 wrapt 1.14.1 xxhash 3.0.0 yarl 1.8.1 zipp 3.8.0 Python 3.8.10