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I_kwDODunzps51lqeb

`Dataset.from_generator` should not try to download from HF GCS

closed

CONTRIBUTOR

### Describe the bug When using [`Dataset.from_generator`](https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/arrow_dataset.py#L1072) with `streaming=False`, the internal logic will call [`download_and_prepare`](https://github.com/huggingface/datasets/blob/main/src/datasets/io/generator.py#L47) which will attempt to download from HF GCS which is redundant, because user has already provided the generator from which the data should be drawn. If someone attempts to call `Dataset.from_generator` from an environment that doesn't have external internet access (for example internal production machine) and doesn't set `HF_DATASETS_OFFLINE=1`, this will result in process being stuck at building connection. ### Steps to reproduce the bug ```python import datasets def gen(): for _ in range(100): yield {"text": "dummy text"} dataset = datasets.Dataset.from_generator(gen) ``` A minimum example executed on any environment that doesn't have access to HF GCS can result in the error ### Expected behavior `try_from_hf_gcs` should be set to False here https://github.com/huggingface/datasets/blob/c9c1166e1cf81d38534020f9c167b326585339e5/src/datasets/io/generator.py#L51 ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-3.10.0-1160.90.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.17.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3

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I_kwDODunzps51i3W1

TensorDataset format does not work with Trainer from transformers

closed

NONE

### Describe the bug The model was built to do fine tunning on BERT model for relation extraction. trainer.train() returns an error message ```TypeError: vars() argument must have __dict__ attribute``` when it has `train_dataset` generated from `torch.utils.data.TensorDataset` However, in the document, the required data format is `torch.utils.data.TensorDataset`.  Transformers trainer is supposed to accept the train_dataset in the format of torch.utils.data.TensorDataset, but it returns error message *"TypeError: vars() argument must have __dict__ attribute"* ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-30-5df728c929a2> in <cell line: 1>() ----> 1 trainer.train() 2 trainer.evaluate(test_dataset) 9 frames /usr/local/lib/python3.10/dist-packages/transformers/data/data_collator.py in <listcomp>(.0) 107 108 if not isinstance(features[0], Mapping): --> 109 features = [vars(f) for f in features] 110 first = features[0] 111 batch = {} TypeError: vars() argument must have __dict__ attribute ``` ### Steps to reproduce the bug Create train_dataset using `torch.utils.data.TensorDataset`, for instance, ```train_dataset = torch.utils.data.TensorDataset(train_input_ids, train_attention_masks, train_labels)``` Feed this `train_dataset` to your trainer and run trainer.train ``` trainer = Trainer(model, training_args, train_dataset=train_dataset, eval_dataset=dev_dataset, compute_metrics=compute_metrics, ) ``` ### Expected behavior Trainer should start training ### Environment info It is running on Google Colab - `datasets` version: 2.14.6 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.17.3 - PyArrow version: 9.0.0 - Pandas version: 1.5.3

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I_kwDODunzps51hzC8

Multi process map did not load cache file correctly

closed

NONE

### Describe the bug When I was training model on Multiple GPUs by DDP, the dataset is tokenized multiple times after main process.   Code is modified from [run_clm.py](https://github.com/huggingface/transformers/blob/7d8ff3629b2725ec43ace99c1a6e87ac1978d433/examples/pytorch/language-modeling/run_clm.py#L484) ### Steps to reproduce the bug ``` block_size = data_args.block_size IGNORE_INDEX = -100 Ignore_Input = False def tokenize_function(examples): sources = [] targets = [] for instruction, inputs, output in zip(examples['instruction'], examples['input'], examples['output']): source = instruction + inputs target = f"{output}{tokenizer.eos_token}" sources.append(source) targets.append(target) tokenized_sources = tokenizer(sources, return_attention_mask=False) tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False ) all_input_ids = [] all_labels = [] for s, t in zip(tokenized_sources['input_ids'], tokenized_targets['input_ids']): if len(s) > block_size and Ignore_Input == False: # print(s) continue input_ids = torch.LongTensor(s + t)[:block_size] if Ignore_Input: labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:block_size] else: labels = input_ids assert len(input_ids) == len(labels) all_input_ids.append(input_ids) all_labels.append(labels) results = { 'input_ids': all_input_ids, 'labels': all_labels, } return results with training_args.main_process_first(desc="dataset map tokenization ", local=False): # print('local_rank',training_args.local_rank) if not data_args.streaming: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on dataset ", ) else: tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, desc="Running tokenizer on dataset " ) ``` ### Expected behavior This code should only tokenize the dataset in the main process, and the other processes load the dataset after waiting ### Environment info transformers == 4.34.1 datasets == 2.14.5

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https://github.com/huggingface/datasets/pull/6368

PR_kwDODunzps5eRZwQ

Fix python formatting for complex types in `format_table`

closed

COLLABORATOR

Fix #6366

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PR_kwDODunzps5eQy1D

Fix time measuring snippet in docs

closed

COLLABORATOR

Fix https://discuss.huggingface.co/t/attributeerror-enter/60509

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I_kwDODunzps51bxJy

with_format() function returns bytes instead of PIL images even when image column is not part of "columns"

closed

NONE

### Describe the bug When using the with_format() function on a dataset containing images, even if the image column is not part of the columns provided in the function, its type will be changed to bytes. Here is a minimal reproduction of the bug: https://colab.research.google.com/drive/1hyaOspgyhB41oiR1-tXE3k_gJCdJUQCf?usp=sharing ### Steps to reproduce the bug 1. Load the image dataset 2. apply with_format(columns=["text"]) 3. Check the type of images in the "image" column before and after applying with_format ### Expected behavior The type should stay the same, but it does not ### Environment info datasets==2.14.6

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I_kwDODunzps51bfTo

Parquet size grows exponential for categorical data

closed

NONE

### Describe the bug It seems that when saving a data frame with a categorical column inside the size can grow exponentially. This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data. ### Steps to reproduce the bug To reproduce the bug, it is enough to run this script: ``` import pandas as pd import os if __name__ == "__main__": for n in [10, 1e2, 1e3, 1e4, 1e5]: for n_col in [1, 10, 100, 1000, 10000]: input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))]) input.iloc[0:100].to_parquet("a.parquet") for col in input.columns: input[col] = input[col].astype("category") input.iloc[0:100].to_parquet("b.parquet") a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024) b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024) print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}") ``` That produces this output: <img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15"> ### Expected behavior In my opinion either: 1. The two file should have (almost) the same size 2. There should be warning telling the user that such difference in size is possible ### Environment info Python 3.8.18 pandas==2.0.3 numpy==1.24.4

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I_kwDODunzps51XqHq

ArrowNotImplementedError: Unsupported cast from string to list using function cast_list

closed

NONE

Hi, I am trying to load a local csv dataset(similar to explodinggradients_fiqa) using load_dataset. When I try to pass features, I am facing the mentioned issue. CSV Data sample(golden_dataset.csv): Question | Context | answer | groundtruth "what is abc?" | "abc is this and that" | "abc is this " | "abc is this and that" ``` import csv # built it based on https://huggingface.co/datasets/explodinggradients/fiqa/viewer/ragas_eval?row=0 mydict = [ {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]}, {'question' : "what is abc?", 'contexts': ["abc is this and that"], 'answer': "abc is this " , 'groundtruth': ["abc is this and that"]} ] fields = ['question', 'contexts', 'answer', 'ground_truths'] with open('golden_dataset.csv', 'w', newline='\n') as file: writer = csv.DictWriter(file, fieldnames = fields) writer.writeheader() for row in mydict: writer.writerow(row) ``` Retrieved dataset: DatasetDict({ train: Dataset({ features: ['question', 'contexts', 'answer', 'ground_truths'], num_rows: 1 }) }) Code to reproduce issue: ``` from datasets import load_dataset, Features, Sequence, Value encode_features = Features( { "question": Value(dtype='string', id=0), "contexts": Sequence(feature=Value(dtype='string', id=1)), "answer": Value(dtype='string', id=2), "ground_truths": Sequence(feature=Value(dtype='string',id=3)), } ) eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) ``` Error trace: ``` --------------------------------------------------------------------------- ArrowNotImplementedError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1925, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1924 _time = time.time() -> 1925 for _, table in generator: 1926 if max_shard_size is not None and writer._num_bytes > max_shard_size: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:192, in Csv._generate_tables(self, files) 189 # Uncomment for debugging (will print the Arrow table size and elements) 190 # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") 191 # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) --> 192 yield (file_idx, batch_idx), self._cast_table(pa_table) 193 except ValueError as e: File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/packaged_modules/csv/csv.py:167, in Csv._cast_table(self, pa_table) 165 if all(not require_storage_cast(feature) for feature in self.config.features.values()): 166 # cheaper cast --> 167 pa_table = pa.Table.from_arrays([pa_table[field.name] for field in schema], schema=schema) 168 else: 169 # more expensive cast; allows str <-> int/float or str to Audio for example File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:3781, in pyarrow.lib.Table.from_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:1449, in pyarrow.lib._sanitize_arrays() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/array.pxi:354, in pyarrow.lib.asarray() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/table.pxi:551, in pyarrow.lib.ChunkedArray.cast() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/compute.py:400, in cast(arr, target_type, safe, options, memory_pool) 399 options = CastOptions.safe(target_type) --> 400 return call_function("cast", [arr], options, memory_pool) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:572, in pyarrow._compute.call_function() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/_compute.pyx:367, in pyarrow._compute.Function.call() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/pyarrow/error.pxi:121, in pyarrow.lib.check_status() ArrowNotImplementedError: Unsupported cast from string to list using function cast_list The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[57], line 1 ----> 1 eval_dataset = load_dataset('csv', data_files='/golden_dataset.csv', features = encode_features ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2153, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2150 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2152 # Download and prepare data -> 2153 builder_instance.download_and_prepare( 2154 download_config=download_config, 2155 download_mode=download_mode, 2156 verification_mode=verification_mode, 2157 try_from_hf_gcs=try_from_hf_gcs, 2158 num_proc=num_proc, 2159 storage_options=storage_options, 2160 ) 2162 # Build dataset for splits 2163 keep_in_memory = ( 2164 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2165 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_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) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 **prepare_split_kwargs, 958 **download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, **prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` Environment Info: datasets version: 2.14.5 Python version: 3.10.8 PyArrow version: 12.0.1 Pandas version: 2.0.3 I have also tried to load dataset first and then use cast_column, or save_to_disk and load_from_disk.

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I_kwDODunzps51WuWN

dataset.transform() hangs indefinitely while finetuning the stable diffusion XL

closed

NONE

### Describe the bug Multi-GPU fine-tuning the stable diffusion X by following https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/README_sdxl.md hangs indefinitely. ### Steps to reproduce the bug accelerate launch train_text_to_image_sdxl.py --pretrained_model_name_or_path=$MODEL_NAME --pretrained_vae_model_name_or_path=$VAE_NAME --dataset_name=$DATASET_NAME --enable_xformers_memory_efficient_attention --resolution=512 --center_crop --random_flip --proportion_empty_prompts=0.2 --train_batch_size=1 --gradient_accumulation_steps=4 --gradient_checkpointing --max_train_steps=10000 --use_8bit_adam --learning_rate=1e-06 --lr_scheduler="constant" --lr_warmup_steps=0 --mixed_precision="fp16" --report_to="wandb" --validation_prompt="a cute Sundar Pichai creature" --validation_epochs 5 --checkpointing_steps=5000 --output_dir="sdxl-pokemon-model" ### Expected behavior It should start the training as it does for the single GPU training. I opened the issue in diffusers **https://github.com/huggingface/diffusers/issues/5534 but it does seem to be an issue with the Pokemon dataset. I added some debug prints ``` print("==========HERE3=============") with accelerator.main_process_first(): print(accelerator.is_main_process) print("===========Here3.1===========") if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) print("===========Here3.2===========") # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) print("==========HERE4=============") Corresponding Output Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 1 Local process index: 1 Device: cuda:1 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 2 Local process index: 2 Device: cuda:2 Mixed precision type: fp16 10/25/2023 21:18:04 - INFO - main - Distributed environment: MULTI_GPU Backend: nccl Num processes: 3 Process index: 0 Local process index: 0 Device: cuda:0 Mixed precision type: fp16 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. {‘variance_type’, ‘clip_sample_range’, ‘thresholding’, ‘dynamic_thresholding_ratio’} was not found in config. Values will be initialized to default values. {‘attention_type’, ‘reverse_transformer_layers_per_block’, ‘dropout’} was not found in config. Values will be initialized to default values. ==========HERE1============= ==========HERE1============= ==========HERE1============= ==========HERE2============= ==========HERE2============= ==========HERE2============= ==========HERE3============= True ===========Here3.1=========== ===========Here3.2=========== ==========HERE3============= ==========HERE3========= ``` ### Environment info _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_kmp_llvm conda-forge absl-py 2.0.0 pypi_0 pypi accelerate 0.24.0 pypi_0 pypi aiohttp 3.8.6 pypi_0 pypi aiosignal 1.3.1 pypi_0 pypi appdirs 1.4.4 pyh9f0ad1d_0 conda-forge async-timeout 4.0.3 pypi_0 pypi attrs 23.1.0 pypi_0 pypi bitsandbytes 0.41.1 pypi_0 pypi blas 1.0 mkl blessings 1.7 py39h06a4308_1002 brotli-python 1.0.9 py39h6a678d5_7 bzip2 1.0.8 h7b6447c_0 ca-certificates 2023.08.22 h06a4308_0 cachetools 5.3.2 pypi_0 pypi certifi 2023.7.22 py39h06a4308_0 cffi 1.15.1 py39h5eee18b_3 charset-normalizer 2.0.4 pyhd3eb1b0_0 click 8.1.7 unix_pyh707e725_0 conda-forge cryptography 41.0.3 py39hdda0065_0 cuda-cudart 11.7.99 0 nvidia cuda-cupti 11.7.101 0 nvidia cuda-libraries 11.7.1 0 nvidia cuda-nvrtc 11.7.99 0 nvidia cuda-nvtx 11.7.91 0 nvidia cuda-runtime 11.7.1 0 nvidia datasets 2.14.6 pypi_0 pypi diffusers 0.22.0.dev0 pypi_0 pypi dill 0.3.7 pypi_0 pypi docker-pycreds 0.4.0 py_0 conda-forge ffmpeg 4.3 hf484d3e_0 pytorch filelock 3.12.4 pypi_0 pypi freetype 2.12.1 h4a9f257_0 frozenlist 1.4.0 pypi_0 pypi fsspec 2023.10.0 pypi_0 pypi ftfy 6.1.1 pypi_0 pypi giflib 5.2.1 h5eee18b_3 gitdb 4.0.11 pyhd8ed1ab_0 conda-forge gitpython 3.1.40 pyhd8ed1ab_0 conda-forge gmp 6.2.1 h295c915_3 gnutls 3.6.15 he1e5248_0 google-auth 2.23.3 pypi_0 pypi google-auth-oauthlib 1.1.0 pypi_0 pypi gpustat 0.6.0 pyhd3eb1b0_1 grpcio 1.59.0 pypi_0 pypi huggingface-hub 0.17.3 pypi_0 pypi idna 3.4 py39h06a4308_0 importlib-metadata 6.8.0 pypi_0 pypi intel-openmp 2023.1.0 hdb19cb5_46305 jinja2 3.1.2 pypi_0 pypi jpeg 9e h5eee18b_1 lame 3.100 h7b6447c_0 lcms2 2.12 h3be6417_0 ld_impl_linux-64 2.38 h1181459_1 lerc 3.0 h295c915_0 libcublas 11.10.3.66 0 nvidia libcufft 10.7.2.124 h4fbf590_0 nvidia libcufile 1.8.0.34 0 nvidia libcurand 10.3.4.52 0 nvidia libcusolver 11.4.0.1 0 nvidia libcusparse 11.7.4.91 0 nvidia libdeflate 1.17 h5eee18b_1 libffi 3.4.4 h6a678d5_0 libgcc-ng 13.2.0 h807b86a_2 conda-forge libgfortran-ng 13.2.0 h69a702a_2 conda-forge libgfortran5 13.2.0 ha4646dd_2 conda-forge libiconv 1.16 h7f8727e_2 libidn2 2.3.4 h5eee18b_0 libnpp 11.7.4.75 0 nvidia libnvjpeg 11.8.0.2 0 nvidia libpng 1.6.39 h5eee18b_0 libprotobuf 3.20.3 he621ea3_0 libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge libtasn1 4.19.0 h5eee18b_0 libtiff 4.5.1 h6a678d5_0 libunistring 0.9.10 h27cfd23_0 libwebp 1.3.2 h11a3e52_0 libwebp-base 1.3.2 h5eee18b_0 llvm-openmp 14.0.6 h9e868ea_0 lz4-c 1.9.4 h6a678d5_0 markdown 3.5 pypi_0 pypi markupsafe 2.1.3 pypi_0 pypi mkl 2023.1.0 h213fc3f_46343 mkl-service 2.4.0 py39h5eee18b_1 mkl_fft 1.3.8 py39h5eee18b_0 mkl_random 1.2.4 py39hdb19cb5_0 multidict 6.0.4 pypi_0 pypi multiprocess 0.70.15 pypi_0 pypi ncurses 6.4 h6a678d5_0 nettle 3.7.3 hbbd107a_1 numpy 1.26.0 py39h5f9d8c6_0 numpy-base 1.26.0 py39hb5e798b_0 nvidia-ml 7.352.0 pyhd3eb1b0_0 oauthlib 3.2.2 pypi_0 pypi openh264 2.1.1 h4ff587b_0 openjpeg 2.4.0 h3ad879b_0 openssl 3.0.11 h7f8727e_2 packaging 23.2 pypi_0 pypi pandas 2.1.1 pypi_0 pypi pathtools 0.1.2 py_1 conda-forge pillow 10.0.1 py39ha6cbd5a_0 pip 23.3 py39h06a4308_0 protobuf 4.23.4 pypi_0 pypi psutil 5.9.6 pypi_0 pypi pyarrow 13.0.0 pypi_0 pypi pyasn1 0.5.0 pypi_0 pypi pyasn1-modules 0.3.0 pypi_0 pypi pycparser 2.21 pyhd3eb1b0_0 pyopenssl 23.2.0 py39h06a4308_0 pysocks 1.7.1 py39h06a4308_0 python 3.9.18 h955ad1f_0 python-dateutil 2.8.2 pypi_0 pypi python_abi 3.9 2_cp39 conda-forge pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch pytorch-cuda 11.7 h778d358_5 pytorch pytorch-mutex 1.0 cuda pytorch pytz 2023.3.post1 pypi_0 pypi pyyaml 6.0.1 pypi_0 pypi readline 8.2 h5eee18b_0 regex 2023.10.3 pypi_0 pypi requests 2.31.0 py39h06a4308_0 requests-oauthlib 1.3.1 pypi_0 pypi rsa 4.9 pypi_0 pypi safetensors 0.4.0 pypi_0 pypi scipy 1.11.3 py39h5f9d8c6_0 sentry-sdk 1.32.0 pyhd8ed1ab_0 conda-forge setproctitle 1.1.10 py39h3811e60_1004 conda-forge setuptools 68.0.0 py39h06a4308_0 six 1.16.0 pyh6c4a22f_0 conda-forge smmap 5.0.0 pyhd8ed1ab_0 conda-forge sqlite 3.41.2 h5eee18b_0 tbb 2021.8.0 hdb19cb5_0 tensorboard 2.15.0 pypi_0 pypi tensorboard-data-server 0.7.2 pypi_0 pypi tk 8.6.12 h1ccaba5_0 tokenizers 0.14.1 pypi_0 pypi torchaudio 0.13.1 py39_cu117 pytorch torchtriton 2.1.0 py39 pytorch torchvision 0.14.1 py39_cu117 pytorch tqdm 4.66.1 pypi_0 pypi transformers 4.34.1 pypi_0 pypi typing_extensions 4.7.1 py39h06a4308_0 tzdata 2023.3 pypi_0 pypi urllib3 1.26.18 py39h06a4308_0 wandb 0.15.12 pyhd8ed1ab_0 conda-forge wcwidth 0.2.8 pypi_0 pypi werkzeug 3.0.1 pypi_0 pypi wheel 0.41.2 py39h06a4308_0 xformers 0.0.22.post7 py39_cu11.7.1_pyt1.13.1 xformers xxhash 3.4.1 pypi_0 pypi xz 5.4.2 h5eee18b_0 yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 pypi_0 pypi zipp 3.17.0 pypi_0 pypi zlib 1.2.13 h5eee18b_0 zstd 1.5.5 hc292b87_0

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PR_kwDODunzps5d_MxD

Simplify filesystem logic

closed

COLLABORATOR

Simplifies the existing filesystem logic (e.g., to avoid unnecessary if-else as mentioned in https://github.com/huggingface/datasets/pull/6098#issue-1827655071)

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I_kwDODunzps51Kcn2

Add support for `Sequence(Audio/Image)` feature in `push_to_hub`

closed

CONTRIBUTOR

### Feature request Allow for `Sequence` of `Image` (or `Audio`) to be embedded inside the shards. ### Motivation Currently, thanks to #3685, when `embed_external_files` is set to True (which is the default) in `push_to_hub`, features of type `Image` and `Audio` are embedded inside the arrow/parquet shards, instead of only storing paths to the files. I've noticed that this behavior does not extend to `Sequence` of `Image`, when working with a [dataset of timelapse images](https://huggingface.co/datasets/1aurent/Human-Embryo-Timelapse). ### Your contribution I'll submit a PR if I find a way to add this feature

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I_kwDODunzps51JUwX

Stuck in "Resolving data files..."

open

NONE

### Describe the bug I have an image dataset with 300k images, the size of image is 768 * 768. When I run `dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train')` in second time, it takes 50 minutes to finish "Resolving data files" part, what's going on in this part? From my understand, after Arrow files been created in the first run, the second run should not take time longer than one or two minutes. ### Steps to reproduce the bug # Run following code two times dataset = load_dataset("imagefolder", data_dir="/path/to/img_dir", split='train') ### Expected behavior Fast dataset building ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Huggingface_hub version: 0.17.3 - PyArrow version: 10.0.1 - Pandas version: 1.5.3

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I_kwDODunzps51H75D

Mounting datasets cache fails due to absolute paths.

closed

NONE

### Describe the bug Creating a datasets cache and mounting this into, for example, a docker container, renders the data unreadable due to absolute paths written into the cache. ### Steps to reproduce the bug 1. Create a datasets cache by downloading some data 2. Mount the dataset folder into a docker container or remote system. 3. (Edit) Set `HF_HOME` or `HF_DATASET_CACHE` to point to the mounted cache. 4. Attempt to access the data from within the docker container. 5. An error is thrown saying no file exists at \<absolute path to original cache location\> ### Expected behavior The data is loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.16.4 - PyArrow version: 13.0.0 - Pandas version: 2.0.3

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I_kwDODunzps51Gj2r

Allow passing a multiprocessing context to functions that support `num_proc`

open

CONTRIBUTOR

### Feature request Allow specifying [a multiprocessing context](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods) to functions that support `num_proc` or use multiprocessing pools. For example, the following could be done: ```python dataset = dataset.map(_func, num_proc=2, mp_context=multiprocess.get_context("spawn")) ``` Or at least the multiprocessing start method ("fork", "spawn", "fork_server" or `None`): ```python dataset = dataset.map(_func, num_proc=2, mp_start_method="spawn") ``` Another option could be passing the `pool` as an argument. ### Motivation By default, `multiprocess` (the `multiprocessing`-fork library that this repo uses) uses the "fork" start method for multiprocessing pools (for the default context). It could be changed by using `set_start_method`. However, this conditions the multiprocessing start method from all processing in a Python program that uses the default context, because [you can't call that function more than once](https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods:~:text=set_start_method()%20should%20not%20be%20used%20more%20than%20once%20in%20the%20program.). My proposal is to allow using a different multiprocessing context, not to condition the whole Python program. One reason to change the start method is that "fork" (the default) makes child processes likely deadlock if thread pools were created before (and also this is not supported by POSIX). For example, this happens when using PyTorch because OpenMP threads are used for CPU intra-op parallelism, which is enabled by default (e.g., for context see [`torch.set_num_threads`](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html)). This can also be fixed by setting `torch.set_num_threads(1)` (or similarly by other methods) but this conditions the whole Python program as it can only be set once to guarantee its behavior (similarly to). There are noticeable performance differences when setting this number to 1 even when using GPU(s). Using, e.g., a "spawn" start method would solve this issue. For more context, see: * https://discuss.huggingface.co/t/dataset-map-stuck-with-torch-set-num-threads-set-to-2-or-larger/37984 * https://discuss.huggingface.co/t/using-num-proc-1-in-dataset-map-hangs/44310 ### Your contribution I'd be happy to review a PR that makes such a change. And if you really don't have the bandwidth for it, I'd consider creating one.

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PR_kwDODunzps5d5Jri

Add `fsspec` version to the `datasets-cli env` command output

closed

COLLABORATOR

... to make debugging issues easier, as `fsspec`'s releases often introduce breaking changes.

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PR_kwDODunzps5d5B2B

More hub centric docs

closed

MEMBER

Let's have more hub-centric documentation in the datasets docs Tutorials - Add “Configure the dataset viewer” page - Change order: - Overview - and more focused on the Hub rather than the library - Then all the hub related things - and mention how to read/write with other tools like pandas - Then all the datasets lib related things in a subsection Also: - Rename “know your dataset” page to “Explore your dataset” - Remove “Evaluate Predictions” page since it's 'evaluate' stuff (or move to legacy section ?) TODO: - [ ] write the “Configure the dataset viewer” page

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I_kwDODunzps51CGC8

`IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader`

open

NONE

### Describe the bug Looks like `IterableDataset.from_spark` does not support multiple workers in pytorch `Dataloader` if I'm not missing anything. Also, returns not consistent error messages, which probably depend on the nondeterministic order of worker executions Some exampes I've encountered: ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/instrumentation_utils.py", line 54, in wrapper logger.log_failure( File "/databricks/spark/python/pyspark/databricks/usage_logger.py", line 70, in log_failure self.logger.recordFunctionCallFailureEvent( File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(*a, **kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'c' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(*args, **kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 98, in _invoke_function return Column(jf(*args)) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1322, in __call__ return_value = get_return_value( File "/databricks/spark/python/pyspark/errors/exceptions/captured.py", line 188, in deco return f(*a, **kw) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/protocol.py", line 342, in get_return_value return OUTPUT_CONVERTER[type](answer[2:], gateway_client) KeyError: 'm' ``` ``` File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 79, in __iter__ yield from self.generate_examples_fn() File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-68c05436-3512-41c4-88ca-5630012b70d1/lib/python3.10/site-packages/datasets/packaged_modules/spark/spark.py", line 49, in generate_fn df_with_partition_id = df.select("*", pyspark.sql.functions.spark_partition_id().alias("part_id")) File "/databricks/spark/python/pyspark/sql/utils.py", line 162, in wrapped return f(*args, **kwargs) File "/databricks/spark/python/pyspark/sql/functions.py", line 4893, in spark_partition_id return _invoke_function("spark_partition_id") File "/databricks/spark/python/pyspark/sql/functions.py", line 97, in _invoke_function jf = _get_jvm_function(name, SparkContext._active_spark_context) File "/databricks/spark/python/pyspark/sql/functions.py", line 88, in _get_jvm_function return getattr(sc._jvm.functions, name) File "/databricks/spark/python/lib/py4j-0.10.9.7-src.zip/py4j/java_gateway.py", line 1725, in __getattr__ raise Py4JError(message) py4j.protocol.Py4JError: functions does not exist in the JVM ``` ### Steps to reproduce the bug ```python import pandas as pd import numpy as np batch_size = 16 pdf = pd.DataFrame({ key: np.random.rand(16*100) for key in ['feature', 'target'] }) test_df = spark.createDataFrame(pdf) from datasets import IterableDataset from torch.utils.data import DataLoader ids = IterableDataset.from_spark(test_df) for batch in DataLoader(ids, batch_size=16, num_workers=4): for k, b in batch.items(): print(k, b.shape, sep='\t') print('\n') ``` ### Expected behavior For `num_workers` equal to 0 or 1 works fine as expected: ``` feature torch.Size([16]) target torch.Size([16]) feature torch.Size([16]) target torch.Size([16]) .... ``` Expected to support workers >1. ### Environment info Databricks 13.3 LTS ML runtime - Spark 3.4.1 pyspark==3.4.1 py4j==0.10.9.7 datasets==2.13.1 and also tested with datasets==2.14.6

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I_kwDODunzps50-5uy

load_dataset save_to_disk load_from_disk error

closed

NONE

### Describe the bug datasets version： 2.10.1 I `load_dataset `and `save_to_disk` sucessfully on windows10( **and I `load_from_disk(/LLM/data/wiki)` succcesfully on windows10**), and I copy the dataset `/LLM/data/wiki` into a ubuntu system, but when I `load_from_disk(/LLM/data/wiki)` on ubuntu, something weird happens: ``` load_from_disk('/LLM/data/wiki') File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/load.py", line 1874, in load_from_disk return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/dataset_dict.py", line 1309, in load_from_disk dataset_dict[k] = Dataset.load_from_disk( File "/usr/local/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1543, in load_from_disk fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 610, in get_fs_token_paths chain = _un_chain(urlpath0, storage_options or {}) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/core.py", line 325, in _un_chain cls = get_filesystem_class(protocol) File "/usr/local/miniconda3/lib/python3.8/site-packages/fsspec/registry.py", line 232, in get_filesystem_class raise ValueError(f"Protocol not known: {protocol}") ValueError: Protocol not known: /LLM/data/wiki ``` It seems that something went wrong on the arrow file? How can I solve this , since currently I can not save_to_disk on ubuntu system ### Steps to reproduce the bug datasets version： 2.10.1 ### Expected behavior datasets version： 2.10.1 ### Environment info datasets version： 2.10.1

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I_kwDODunzps509kL5

Error loading wikitext data raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.")

closed

NONE

I was trying to load the wiki dataset, but i got this error traindata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='train') File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/load.py", line 1804, in load_dataset ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) File "/home/aelkordy/.conda/envs/prune_llm/lib/python3.9/site-packages/datasets/builder.py", line 1108, in as_dataset raise NotImplementedError(f"Loading a dataset cached in a {type(self._fs).__name__} is not supported.") NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.

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PR_kwDODunzps5dyMvh

Fix use_dataset.mdx

closed

CONTRIBUTOR

The current example isn't working because it can't find `labels` inside the Dataset object. So I've added an extra step to the process. Tested and working in Colab.

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I_kwDODunzps5077-T

Different objects are returned from calls that should be returning the same kind of object.

open

NONE

### Describe the bug 1. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir, split='train[:1%]') 2. dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=training_args.cache_dir) The only difference I would expect these calls to have is the size of the dataset. But, while 2. returns a dictionary with "train" key in it, 1. returns a dataset WITHOUT any initial "train" keyword. Both calls are to be used within exactly the same context. They should return identically structured datasets of different size. ### Steps to reproduce the bug See above. ### Expected behavior Expect both calls to return the same structured Dataset structure but with different number of elements, i.e. call 1. should have 1% of the data of the call 2.0 ### Environment info Ubuntu 20.04 gcc 9.x.x. It is really irrelevant.

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I_kwDODunzps506SIZ

Can't load ds = load_dataset("imdb")

closed

NONE

### Describe the bug I did `from datasets import load_dataset, load_metric` and then `ds = load_dataset("imdb")` and it gave me the error: ExpectedMoreDownloadedFiles: {'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz'} I tried doing `ds = load_dataset("imdb",download_mode="force_redownload")` as well as reinstalling dataset. I still face this problem. ### Steps to reproduce the bug 1. from datasets import load_dataset, load_metric 2. ds = load_dataset("imdb") ### Expected behavior It should load and give me this when I run `ds` DatasetDict({ train: Dataset({ features: ['text', 'label'], num_rows: 25000 }) test: Dataset({ features: ['text', 'label'], num_rows: 25000 }) unsupervised: Dataset({ features: ['text', 'label'], num_rows: 50000 }) }) ### Environment info - `datasets` version: 2.14.6 - Platform: Linux-5.4.0-164-generic-x86_64-with-glibc2.17 - Python version: 3.8.18 - Huggingface_hub version: 0.16.2 - PyArrow version: 13.0.0 - Pandas version: 2.0.2

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I_kwDODunzps505pUY

Parquet stream-conversion fails to embed images/audio files from gated repos

open

COLLABORATOR

it seems to be an issue with datasets not passing the token to embed_table_storage when generating a dataset See https://github.com/huggingface/datasets-server/issues/2010

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I_kwDODunzps50xAqj

Incorrect example code in 'Create a dataset' docs

closed

NONE

### Describe the bug On [this](https://huggingface.co/docs/datasets/create_dataset) page, the example code for loading in images and audio is incorrect. Currently, examples are: ``` python from datasets import ImageFolder dataset = load_dataset("imagefolder", data_dir="/path/to/pokemon") ``` and ``` python from datasets import AudioFolder dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I'm pretty sure the imports are wrong and should be: ``` python from datasets import load_dataset dataset = load_dataset("audiofolder", data_dir="/path/to/folder") ``` I am happy to update this if this is right but just wanted to check before making any changes. ### Steps to reproduce the bug Go to https://huggingface.co/docs/datasets/create_dataset ### Expected behavior N/A ### Environment info N/A

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PR_kwDODunzps5dnZM_

Fix UnboundLocalError if preprocessing returns an empty list

closed

CONTRIBUTOR

If this tokenization function is used with IterableDatasets and no sample is as big as the context length, `input_batch` will be an empty list. ``` def tokenize(batch, tokenizer, context_length): outputs = tokenizer( batch["text"], truncation=True, max_length=context_length, return_overflowing_tokens=True, return_length=True ) input_batch = [] for length, input_ids in zip(outputs["length"], outputs["input_ids"]): if length == context_length: input_batch.append(input_ids) return {"input_ids": input_batch} dataset.map(tokenize, batched=True, batch_size=batch_size, fn_kwargs={"context_length": context_length, "tokenizer": tokenizer}, remove_columns=dataset.column_names) ``` This will throw the following error: UnboundLocalError: local variable 'batch_idx' referenced before assignment, because the for loop was not executed a single time ``` for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example current_idx += batch_idx + 1 ``` Some of the possible solutions ``` for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example try: current_idx += batch_idx + 1 except: current_idx += 1 ``` or ``` batch_idx = 0 for batch_idx, example in enumerate(_batch_to_examples(transformed_batch)): yield new_key, example current_idx += batch_idx + 1 ```

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I_kwDODunzps50sEBe

support squad structure datasets using a YAML parameter

open

NONE

### Feature request Since the squad structure is widely used, I think it could be beneficial to support it using a YAML parameter. could you implement automatic data loading of squad-like data using squad JSON format, to read it from JSON files and view it in the correct squad structure. The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers ### Motivation Dataset repo requires arbitrary Python code execution ### Your contribution The dataset structure should be like this: https://huggingface.co/datasets/squad Columns:id,title,context,question,answers train and dev sets in squad structure JSON files

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PR_kwDODunzps5diyd5

set dev version

closed

MEMBER

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PR_kwDODunzps5dipeb

Remove unused argument in `_get_data_files_patterns`

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PR_kwDODunzps5dijxt

Release: 2.14.6

closed

MEMBER

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PR_kwDODunzps5dhGpW

Release 2.14.5

closed

MEMBER

(wrong release number - I was continuing the 2.14 branch but 2.14.5 was released from `main`)

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PR_kwDODunzps5dhFfg

minor release step improvement

closed

MEMBER

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PR_kwDODunzps5dg_sb

pin fsspec before it switches to glob.glob

closed

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PR_kwDODunzps5dg9Uu

Pin supported upper version of fsspec

closed

MEMBER

Pin upper version of `fsspec` to avoid disruptions introduced by breaking changes (and the need of urgent patch releases with hotfixes) on each release on their side. See: - #6331 - #6210 - #5731 - #5617 - #5447 I propose that we explicitly test, introduce fixes and support each new `fsspec` version release. CC: @LysandreJik

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PR_kwDODunzps5dgy0w

unpin-fsspec

closed

MEMBER

Close #6333.

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PR_kwDODunzps5dggIV

Support fsspec 2023.10.0

closed

MEMBER

Fix #6333.

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PR_kwDODunzps5dgbpR

datasets.filesystems: fix is_remote_filesystems

closed

CONTRIBUTOR

Close #6330, close #6333. `fsspec.implementations.LocalFilesystem.protocol` was changed from `str` "file" to `tuple[str,...]` ("file", "local") in `fsspec>=2023.10.0` This commit supports both styles.

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I_kwDODunzps50oRe3

Support fsspec 2023.10.0

closed

MEMBER

Once root issue is fixed, remove temporary pin of fsspec < 2023.10.0 introduced by: - #6331 Related to issue: - #6330 As @ZachNagengast suggested, the issue might be related to: - https://github.com/fsspec/filesystem_spec/pull/1381

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PR_kwDODunzps5dgW3w

Replace deprecated license_file in setup.cfg

closed

MEMBER

Replace deprecated license_file in `setup.cfg`. See: https://github.com/huggingface/datasets/actions/runs/6610930650/job/17953825724?pr=6331 ``` /tmp/pip-build-env-a51hls20/overlay/lib/python3.8/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg` !! ******************************************************************************** The license_file parameter is deprecated, use license_files instead. By 2023-Oct-30, you need to update your project and remove deprecated calls or your builds will no longer be supported. See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details. ******************************************************************************** !! ```

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PR_kwDODunzps5dgRQt

Temporarily pin fsspec < 2023.10.0

closed

MEMBER

Temporarily pin fsspec < 2023.10.0 until permanent solution is found. Hot fix #6330. See: https://github.com/huggingface/datasets/actions/runs/6610904287/job/17953774987 ``` ... ERROR tests/test_iterable_dataset.py::test_iterable_dataset_from_file - NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported. = 373 failed, 2055 passed, 17 skipped, 8 warnings, 6 errors in 228.14s (0:03:48) = ```

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I_kwDODunzps50lwEu

Latest fsspec==2023.10.0 issue with streaming datasets

closed

CONTRIBUTOR

### Describe the bug Loading a streaming dataset with this version of fsspec fails with the following error: `NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet.` I suspect the issue is with this PR https://github.com/fsspec/filesystem_spec/pull/1381 ### Steps to reproduce the bug 1. Upgrade fsspec to version `2023.10.0` 2. Attempt to load a streaming dataset e.g. `load_dataset("laion/gpt4v-emotion-dataset", split="train", streaming=True)` 3. Observe the following exception: ``` File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/load.py", line 2146, in load_dataset return builder_instance.as_streaming_dataset(split=split) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/hostedtoolcache/Python/3.11.6/x64/lib/python3.11/site-packages/datasets/builder.py", line 1318, in as_streaming_dataset raise NotImplementedError( NotImplementedError: Loading a streaming dataset cached in a LocalFileSystem is not supported yet. ``` ### Expected behavior Should stream the dataset as normal. ### Environment info datasets@main fsspec==2023.10.0

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I_kwDODunzps50lAZk

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

closed

NONE

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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I_kwDODunzps50lAXw

شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی

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NONE

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I_kwDODunzps50jh2j

FileNotFoundError when trying to load the downloaded dataset with `load_dataset(..., streaming=True)`

closed

CONTRIBUTOR

### Describe the bug Hi, I'm trying to load the dataset `togethercomputer/RedPajama-Data-1T-Sample` with `load_dataset` in streaming mode, i.e., `streaming=True`, but `FileNotFoundError` occurs. ### Steps to reproduce the bug I've downloaded the dataset and save it to the cache dir in advance. My hope is loading the files in offline environment and without taking too much hours to prepross the entire data before running into the training process. So I try the following code to load the files streamingly ```py dataset = load_dataset('togethercomputer/RedPajama-Data-1T-Sample', streaming=True) print(next(iter(dataset['train']))) ``` Sadly, it raises the following: ``` FileNotFoundError: [Errno 2] No such file or directory: 'CURRENT_CODE_PATH/arxiv_sample.jsonl' ``` I've noticed that the dataset can be properly found in the begining ``` Using the latest cached version of the module from /root/.cache/huggingface/modules/datasets_modules/datasets/togethercomputer--RedPajama-Data-1T-Sample/6ea3bc8ec2e84ec6d2df1930942e9028ace8c5b9d9143823cf911c50bbd92039 (last modified on Sat Oct 21 20:12:57 2023) since it couldn't be found locally at togethercomputer/RedPajama-Data-1T-Sample., or remotely on the Hugging Face Hub. ``` But it seems that the paths couldn't be properly parsed when loading iteratively. How should I fix this error. I've tried specifying `data_files` or `data_dir` as `.../arxiv_sample.jsonl` but none of them works. Thanks. ### Expected behavior Properly load the dataset. ### Environment info `datasets==2.14.5`

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PR_kwDODunzps5dcSRa

Create battery_analysis.py

closed

NONE

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PR_kwDODunzps5dcSM3

Create battery_analysis.py

closed

NONE

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I_kwDODunzps50iN2f

Conversion to Arrow fails due to wrong type heuristic

closed

NONE

### Describe the bug I have a list of dictionaries with valid/JSON-serializable values. One key is the denominator for a paragraph. In 99.9% of cases its a number, but there are some occurences of '1a', '2b' and so on. If trying to convert this list to a dataset with `Dataset.from_list()`, I always get `ArrowInvalid: Could not convert '1' with type str: tried to convert to int64`, presumably because pyarrow tries to convert the keys to integers. Is there any way to circumvent this and fix dtypes? I didn't find anything in the documentation. ### Steps to reproduce the bug * create a list of dicts with one key being a string of an integer for the first few thousand occurences and try to convert to dataset. ### Expected behavior There shouldn't be an error (e.g. some flag to turn off automatic str to numeric conversion). ### Environment info - `datasets` version: 2.14.5 - Platform: Linux-5.15.0-84-generic-x86_64-with-glibc2.35 - Python version: 3.9.18 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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I_kwDODunzps50e21c

Loading dataset from large GCS bucket very slow since 2.14

open

NONE

### Describe the bug Since updating to >2.14 we have very slow access to our parquet files on GCS when loading a dataset (>30 min vs 3s). Our GCS bucket has many objects and resolving globs is very slow. I could track down the problem to this change: https://github.com/huggingface/datasets/blame/bade7af74437347a760830466eb74f7a8ce0d799/src/datasets/data_files.py#L348 The underlying implementation with gcsfs is really slow. Could you go back to the old way if we are simply giving the parquet files and no glob pattern? Thank you. ### Steps to reproduce the bug Load a dataset from a GCS bucket that has many files. ### Expected behavior Used to be fast (3s) in 2.13 ### Environment info datasets==2.14.5

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PR_kwDODunzps5dT5vG

Fix regex `get_data_files` formatting for base paths

closed

CONTRIBUTOR

With this pr https://github.com/huggingface/datasets/pull/6309, it is formatting the entire base path into regex, which results in the undesired formatting error `doesn't match the pattern` because of the line in `glob_pattern_to_regex`: `.replace("//", "/")`: - Input: `hf://datasets/...` - Output: `hf:/datasets/...` This fix will only convert the `split_pattern` to regex and keep the `base_path` unchanged. cc @albertvillanova hopefully this still works with your implementation

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PR_kwDODunzps5dS3Mc

Fix typos

closed

CONTRIBUTOR

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I_kwDODunzps50YpdM

Dataset slice splits can't load training and validation at the same time

closed

NONE

### Describe the bug According to the [documentation](https://huggingface.co/docs/datasets/v2.14.5/loading#slice-splits) is should be possible to run the following command: `train_test_ds = datasets.load_dataset("bookcorpus", split="train+test")` to load the train and test sets from the dataset. However executing the equivalent code: `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` only yields the following output: > Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 54175 > }) Where loading the dataset without the split argument yields: > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > validation: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 6798 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) Thus, the API seems to be broken in this regard. This is a bit annoying since I want to be able to use the split argument with `split="train[:10%]+test[:10%]"` to have smaller dataset to work with when validating my model is working correctly. ### Steps to reproduce the bug `speech_commands_v1 = load_dataset("superb", "ks", split="train+test")` ### Expected behavior > DatasetDict({ > train: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 51094 > }) > test: Dataset({ > features: ['file', 'audio', 'label'], > num_rows: 3081 > }) > }) ### Environment info ``` import datasets print(datasets.__version__) ``` > 2.14.5 ``` import sys print(sys.version) ``` > 3.9.17 (main, Jul 5 2023, 20:41:20) > [GCC 11.2.0]

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I_kwDODunzps50WrVV

Datasets.map is severely broken

open

NONE

### Describe the bug Regardless of how many cores I used, I have 16 or 32 threads, map slows down to a crawl at around 80% done, lingers maybe until 97% extremely slowly and NEVER finishes the job. It just hangs. After watching this for 27 hours I control-C out of it. Until the end one process appears to be doing something, but it never ends. I saw some comments about fast tokenizers using Rust and all and tried different variations. NOTHING works. ### Steps to reproduce the bug Running it without breaking the dataset into parts results in the same behavior. The loop was an attempt to see if this was a RAM issue. for idx in range(100): dataset = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]') dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False, num_proc=1, remove_columns=["text", "meta"]) dataset.save_to_disk(training_args.cache_dir + f"/training_data_{idx}") ### Expected behavior I expect map to run at more or less the same speed it starts with and FINISH its processing. ### Environment info Python 3.8, same with 3.10 makes no difference. Ubuntu 20.04,

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PR_kwDODunzps5dRC9V

Deterministic set hash

closed

MEMBER

Sort the items in a set according to their `datasets.fingerprint.Hasher.hash` hash to get a deterministic hash of sets. This is useful to get deterministic hashes of tokenizers that use a trie based on python sets. reported in https://github.com/huggingface/datasets/issues/3847

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I_kwDODunzps50WKHk

sentiment140 dataset unavailable

closed

NONE

### Describe the bug loading the dataset using load_dataset("sentiment140") returns the following error ConnectionError: Couldn't reach http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip (error 403) ### Steps to reproduce the bug Run the following code (version should not matter). ``` from datasets import load_dataset data = load_dataset("sentiment140") ``` ### Expected behavior The dataset should be loaded just like any other. The main issue is that it is no longer hosted by stanford. It is still available from a [Google Drive Link](https://docs.google.com/file/d/0B04GJPshIjmPRnZManQwWEdTZjg/edit). ### Environment info - `datasets` version: 2.14.5 - Platform: Windows-10-10.0.19045-SP0 - Python version: 3.10.8 - Huggingface_hub version: 0.17.3 - PyArrow version: 13.0.0 - Pandas version: 2.1.1

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PR_kwDODunzps5dQGpg

Fix loading Hub datasets with CSV metadata file

closed

MEMBER

Currently, the reading of the metadata file infers the file extension (.jsonl or .csv) from the passed filename. However, downloaded files from the Hub don't have file extension. For example: - the original file: `hf://datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5916a4-16977085077831/metadata.jsonl` - corresponds to the downloaded path: `/tmp/pytest-of-username/pytest-46/cache/datasets/downloads/9f5374dbb470f711f6b89d66a5eec1f19cc96324b26bcbebe29138bda6cb20e6`, which does not have extension In the case where the metadata file does not have an extension, the reader assumes it is a JSONL file, thus the reported error when trying to read a CSV file as a JSONL one: `ArrowInvalid: JSON parse error: Invalid value. in row 0` This behavior was introduced by: - #4837 This PR extracts the metadata file extension from the original filename (instead of the downloaded one) and passes it as a parameter to the read_metadata function. Fix #6315.

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I_kwDODunzps50Vh3z

Hub datasets with CSV metadata raise ArrowInvalid: JSON parse error: Invalid value. in row 0

closed

MEMBER

When trying to load a Hub dataset that contains a CSV metadata file, it raises an `ArrowInvalid` error: ``` E pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0 pyarrow/error.pxi:100: ArrowInvalid ``` See: https://huggingface.co/datasets/lukarape/public_small_papers/discussions/1

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PR_kwDODunzps5dPo25

Support creating new branch in push_to_hub

closed

NONE

This adds support for creating a new branch when pushing a dataset to the hub. Tested both methods locally and branches are created.

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PR_kwDODunzps5dPGmL

Fix commit message formatting in multi-commit uploads

closed

MEMBER

Currently, the commit message keeps on adding: - `Upload dataset (part 00000-of-00002)` - `Upload dataset (part 00000-of-00002) (part 00001-of-00002)` Introduced in https://github.com/huggingface/datasets/pull/6269 This PR fixes this issue to have - `Upload dataset (part 00000-of-00002)` - `Upload dataset (part 00001-of-00002)`

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PR_kwDODunzps5dKWDF

docs: resolving namespace conflict, refactored variable

closed

CONTRIBUTOR

In docs of about_arrow.md, in the below example code  The variable name 'time' was being used in a way that could potentially lead to a namespace conflict with Python's built-in 'time' module. It is not a good convention and can lead to unintended variable shadowing for any user re-using the example code. To ensure code clarity, and prevent potential naming conflicts renamed the variable 'time' to 'elapsed_time' in the example code.

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I_kwDODunzps50MAih

cast_column to Sequence with length=4 occur exception raise in datasets/table.py:2146

closed

NONE

### Describe the bug i load a dataset from local csv file which has 187383612 examples, then use `map` to generate new columns for test. here is my code : ``` import os from datasets import load_dataset from datasets.features import Sequence, Value def add_new_path(example): example["ais_bbox"] = [100,100,200,200] example["ais_image_path"] = os.path.join("images", example["image_path"]) if example["image_path"] else "" return example ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1749/") hf_ds = ais_dataset.map(add_new_path, batched=False, num_proc=32) ds = hf_ds.cast_column("ais_bbox", Sequence(Value("int32"), length=4)) ``` and the `cast_column` raise an exception ``` Casting the dataset: 3%|███▉ ... File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2110, in cast_column return self.cast(features) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2055, in cast dataset = dataset.map( File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3097, in map for rank, done, content in Dataset._map_single(**dataset_kwargs): File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3474, in _map_single batch = apply_function_on_filtered_inputs( File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3353, in apply_function_on_filtered_inputs processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2329, in table_cast return cast_table_to_schema(table, schema) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2288, in cast_table_to_schema arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2288, in <listcomp> arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()] File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks]) File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2145, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type list<item: int64> to Sequence(feature=Value(dtype='int32', id=None), length=4, id=None) ``` i check the source code and make debug info: in datasets/table.py:2092 ``` 2091 if feature.length > -1: 2092 if feature.length * len(array) == len(array.values): 2093 return pa.FixedSizeListArray.from_arrays(_c(array.values, feature.feature), feature.length) 2094 print(len(array)) 2095 print(len(array.values)) ``` my feature.length is 4. but feature.length * len(array) == len(array.values) is false. print(len(array)) is 262 print(len(array.values)) is 4000 then I use "for item in array" to print each item then get 262 * [100,100,200,200] and use "for item in array.values" to print each item and get 4000 int32 which are 1000 * [100,100,200,200] i'm wondering the `chunk` in each `array.chunks`, the "chunk.values" may get all the chunks's value rather than single chunk? but i check the pyarrow's doc seems chunk.values is chunk's value not all. ### Steps to reproduce the bug code provided above. ### Expected behavior feature.length * len(array) == len(array.values) should be true. and there should not has Exception. ### Environment info python3.9 x86_64 datasets: 2.14.4 pyarrow: 13.0.0 or 10.0.0

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PR_kwDODunzps5dBPnY

Add return_file_name in load_dataset

closed

NONE

Proposition to fix #5806. Added an optional parameter `return_file_name` in the dataset builder config. When set to `True`, the function will include the file name corresponding to the sample in the returned output. There is a difference between arrow-based and folder-based datasets to return the file name: - for arrow-based: a column is concatenated after the table is cast. - for folder-based: `dataset.info.features` has the entry `file_name` and the original file name is passed to the `sample_metadata` dictionary. The difference in behavior might be a concern, also I do not know whether the `file_name` should return the original file path or the downloaded one for folder-based datasets. I added some tests for the datasets that already had a test file.

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PR_kwDODunzps5c_YcX

Fix get_data_patterns for directories with the word data twice

closed

MEMBER

Before the fix, `get_data_patterns` inferred wrongly the split name for paths with the word "data" twice: - For the URL path: `hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train-00001-of-00009.parquet` (note the org name `piuba-bigdata/` ending with `data/`) - The inferred split name was: `articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train` instead of `train` This PR fixes this issue by passing the `base_path` (`hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357`) to `_get_data_files_patterns` and prepending it to the regex split pattern (`data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].*\\..*`). Fix #6305. Fix https://huggingface.co/datasets/piuba-bigdata/articles_and_comments/discussions/1

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I_kwDODunzps50CfkB

module 'resource' has no attribute 'error'

closed

NONE

### Describe the bug just run import: `from datasets import load_dataset` and then: ``` File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\arrow_dataset.py", line 66, in <module> from .arrow_reader import ArrowReader File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\download\__init__.py", line 10, in <module> from .streaming_download_manager import StreamingDownloadManager File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\download\streaming_download_manager.py", line 21, in <module> from ..filesystems import COMPRESSION_FILESYSTEMS File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\datasets\filesystems\__init__.py", line 8, in <module> import fsspec.asyn File "C:\ProgramData\anaconda3\envs\py310\lib\site-packages\fsspec\asyn.py", line 157, in <module> ResourceEror = resource.error AttributeError: module 'resource' has no attribute 'error' Process finished with exit code 1 ``` and the error codes are: ``` try: import resource except ImportError: resource = None ResourceError = OSError else: ResourceEror = resource.error ``` 1. miss spelling : "ResourceEror " should be "ResourceErorr" 2. module 'resource' has no attribute 'error' ### Steps to reproduce the bug only one step: `from datasets import load_dataset` ### Expected behavior slove error: module 'resource' has no attribute 'error' ### Environment info python=3.10 datasets==2.14.5

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PR_kwDODunzps5c9s0j

Fix typo in code example in docs

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I_kwDODunzps50AyY8

pyinstaller : OSError: could not get source code

closed

NONE

### Describe the bug I ran a package with pyinstaller and got the following error： ### Steps to reproduce the bug ``` ... File "datasets\__init__.py", line 52, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\inspect.py", line 30, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\load.py", line 58, in <module> File "<frozen importlib._bootstrap>", line 1027, in _find_and_load File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 688, in _load_unlocked File "PyInstaller\loader\pyimod02_importers.py", line 499, in exec_module File "datasets\packaged_modules\__init__.py", line 31, in <module> File "inspect.py", line 1147, in getsource File "inspect.py", line 1129, in getsourcelines File "inspect.py", line 958, in findsource OSError: could not get source code ``` ### Expected behavior I have looked up the relevant information, but I can't find a suitable reason ### Environment info ```python python 3.10 datasets 2.14.4 pyinstaller 5.6.2 ```

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I_kwDODunzps5z_cUg

Cannot load dataset with `2.14.5`: `FileNotFound` error

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NONE

### Describe the bug I'm trying to load [piuba-bigdata/articles_and_comments] and I'm stumbling with this error on `2.14.5`. However, this works on `2.10.0`. ### Steps to reproduce the bug [Colab link](https://colab.research.google.com/drive/1SAftFMQnFE708ikRnJJHIXZV7R5IBOCE#scrollTo=r2R2ipCCDmsg) ```python Downloading readme: 100% 1.19k/1.19k [00:00<00:00, 30.9kB/s] --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) [<ipython-input-2-807c3583d297>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 load_dataset("piuba-bigdata/articles_and_comments", split="train") 2 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs) 2127 2128 # Create a dataset builder -> 2129 builder_instance = load_dataset_builder( 2130 path=path, 2131 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, **config_kwargs) 1813 download_config = download_config.copy() if download_config else DownloadConfig() 1814 download_config.storage_options.update(storage_options) -> 1815 dataset_module = dataset_module_factory( 1816 path, 1817 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, **download_kwargs) 1506 raise e1 from None 1507 if isinstance(e1, FileNotFoundError): -> 1508 raise FileNotFoundError( 1509 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1510 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" FileNotFoundError: Couldn't find a dataset script at /content/piuba-bigdata/articles_and_comments/articles_and_comments.py or any data file in the same directory. Couldn't find 'piuba-bigdata/articles_and_comments' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in piuba-bigdata/articles_and_comments. ``` ### Expected behavior It should load normally. ### Environment info ``` - `datasets` version: 2.14.5 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.18.0 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 ```

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PR_kwDODunzps5c7-4q

Update README.md

closed

CONTRIBUTOR

Fixed typos in ReadMe and added punctuation marks Tensorflow --> TensorFlow

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