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https://api.github.com/repos/huggingface/datasets/issues/6010
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https://github.com/huggingface/datasets/issues/6010
1,793,838,152
I_kwDODunzps5q68xI
6,010
Improve `Dataset`'s string representation
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2023-07-07T16:38:03
2023-07-07T17:00:51
null
CONTRIBUTOR
null
Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.
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PR_kwDODunzps5U1mus
6,009
Fix cast for dictionaries with no keys
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-07-06T18:48:14
2023-07-07T14:13:00
2023-07-07T14:01:13
CONTRIBUTOR
null
Fix #5677
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https://api.github.com/repos/huggingface/datasets/issues/6008
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1,789,869,344
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6,008
Dataset.from_generator consistently freezes at ~1000 rows
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[ "By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Arra...
2023-07-05T16:06:48
2023-07-06T16:32:02
null
NONE
null
### Describe the bug Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset. I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset. Let me know if you have ideas how to resolve it! ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np def gen(): for row in range(10000): yield {"i": np.random.rand(512, 512, 3)} Dataset.from_generator(gen) # -> 90% of the time gets stuck around 1000 rows ``` ### Expected behavior Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 12.0.1 - Pandas version: 1.5.1
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1,789,782,693
I_kwDODunzps5qreql
6,007
Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset
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[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.", "I am afraid int32 is not the reason for this error.\r\n\r\nI have submitt...
2023-07-05T15:16:50
2023-07-07T10:46:12
null
CONTRIBUTOR
null
### Describe the bug When load a large dataset with the following code ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ``` We encountered the error: "OverflowError: Python int too large to convert to C long" The error look something like: ``` OverflowError: Python int too large to convert to C long During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) <ipython-input-7-0ed8700e662d> in <module> ----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/') /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs) 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, -> 1751 use_auth_token=use_auth_token, 1752 ) 1753 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs) 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 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 791 try: 792 # Prepare split will record examples associated to the split --> 793 self._prepare_split(split_generator, **prepare_split_kwargs) 794 except OSError as e: 795 raise OSError( /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) 1219 writer.write(example, key) 1220 finally: -> 1221 num_examples, num_bytes = writer.finalize() 1222 1223 split_generator.split_info.num_examples = num_examples /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 536 # Re-intializing to empty list for next batch 537 self.hkey_record = [] --> 538 self.write_examples_on_file() 539 if self.pa_writer is None: 540 if self.schema: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 407 # Since current_examples contains (example, key) tuples 408 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 409 self.write_batch(batch_examples=batch_examples) 410 self.current_examples = [] 411 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 506 col_try_type = try_features[col] if try_features is not None and col in try_features else None 507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 508 arrays.append(pa.array(typed_sequence)) 509 inferred_features[col] = typed_sequence.get_inferred_type() 510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 180 else: 181 trying_cast_to_python_objects = True --> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) 183 # use smaller integer precisions if possible 184 if self.trying_int_optimization: /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() OverflowError: Python int too large to convert to C long ``` However, that dataset can be loaded in a streaming manner: ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True) for i in dataset: pass # it work well ``` Another issue is reported in our dataset hub: https://huggingface.co/datasets/liwu/MNBVC/discussions/2 ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ### Expected behavior the dataset can be safely loaded ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9 - Python version: 3.6.8 - PyArrow version: 6.0.1 - Pandas version: 1.1.5
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6,006
NotADirectoryError when loading gigawords
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[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]
2023-07-05T06:23:41
2023-07-05T06:31:02
2023-07-05T06:31:01
NONE
null
### Describe the bug got `NotADirectoryError` whtn loading gigawords dataset ### Steps to reproduce the bug When running ``` import datasets datasets.load_dataset('gigaword') ``` Got the following exception: ```bash Traceback (most recent call last): [0/1862] File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single for key, record in generator: File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b 64efb424b6/gigaword.py", line 115, in _generate_examples with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s: File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper return function(*args, use_auth_token=use_auth_token, **kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope n return open(main_hop, mode, *args, **kwargs) NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e 89780c4be7599794a4f559048ec/org_data/train.src.txt' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "gigaword.py", line 38, in <module> main() File "gigaword.py", line 35, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data dataset = self.load_dataset() File "gigaword.py", line 29, in load_dataset return datasets.load_dataset('gigaword') File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Download and process the dataset successfully ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10 - Python version: 3.8.0 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3
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6,005
Drop Python 3.7 support
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-07-04T15:02:37
2023-07-06T15:32:41
2023-07-06T15:22:43
CONTRIBUTOR
null
`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :). (Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)
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Misc improvements
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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_a...
2023-07-03T18:29:14
2023-07-06T17:04:11
2023-07-06T16:55:25
CONTRIBUTOR
null
Contains the following improvements: * fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section * updates `Makefile` to also run the style checks on `utils` and `setup.py` * deletes a test for GH-hosted datasets (no longer supported) * deletes `convert_dataset.sh` (outdated) * aligns `utils/release.py` with `transformers` (the current version is outdated)
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6,003
interleave_datasets & DataCollatorForLanguageModeling having a conflict ?
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2023-07-03T17:15:31
2023-07-03T17:15:31
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### Describe the bug Hi everyone :) I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`: - `tokenize()` runs fine - `group_text()` runs fine Everytime, on step 19, I get ```pytb File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens inputs[indices_random] = random_words[indices_random] RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source. ``` I tried: - training without interleave on dataset 1, it runs - training without interleave on dataset 2, it runs - training without `.to_iterable_dataset()`, it hangs then crash - training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps. I might have coded something wrong, but I don't get what ### Steps to reproduce the bug I have this function: ```py def build_dataset(path: str, percent: str): dataset = load_dataset( "text", data_files={"train": [path]}, split=f"train[{percent}]" ) dataset = dataset.map( lambda examples: tokenize(examples["text"]), batched=True, num_proc=num_proc, ) dataset = dataset.map( group_texts, batched=True, num_proc=num_proc, desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}", remove_columns=["text"] ) print(len(dataset)) return dataset.to_iterable_dataset() ``` I hardcoded group_text: ```py def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // 512) * 512 # Split by chunks of max_len. result = { k: [t[i: i + 512] for i in range(0, total_length, 512)] for k, t in concatenated_examples.items() } # result = {k: [el for el in elements if el] for k, elements in result.items()} return result ``` And then I build datasets using the following code: ```py train1 = build_dataset("d1.txt", ":95%") train2 = build_dataset("d2.txt", ":95%") dev1 = build_dataset("d1.txt", "95%:") dev2 = build_dataset("d2.txt", "95%:") ``` and finally I run ```py train_dataset = interleave_datasets( [train1, train2], probabilities=[0.8, 0.2], seed=42 ) eval_dataset = interleave_datasets( [dev1, dev2], probabilities=[0.8, 0.2], seed=42 ) ``` Then I run the training part which remains mostly untouched: > CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16 ### Expected behavior The model should then train normally, but fails every time at the same step (19). printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well] ### Environment info transformers[torch] 4.30.2 Ubuntu A100 0 CUDA 12 Driver Version: 525.116.04
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6,002
Add KLUE-MRC metrics
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[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]
2023-07-03T12:11:10
2023-07-03T15:34:17
null
NONE
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## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416
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6,001
Align `column_names` type check with type hint in `sort`
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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | rea...
2023-06-30T13:15:50
2023-06-30T14:18:32
2023-06-30T14:11:24
CONTRIBUTOR
null
Fix #5998
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