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https://github.com/huggingface/datasets/issues/3909
Error loading file audio when downloading the Common Voice dataset directly from the Hub
Thanks! And sorry for posting this problem in what turned on to be an unrelated thread. I rewrote the code, and the model works. The WER is 0.137 however, so I'm not sure if I have missed a step. I will look further into that at a later point. The transcriptions look good through manual inspection. The rewritten code: ```python from datasets import load_dataset, load_metric from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor, Wav2Vec2Processor librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset wer = load_metric("wer") model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to("cuda") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr", do_upper_case=True) def map_to_pred(batch): audio = batch["audio"] features = processor(audio["array"], sampling_rate=audio["sampling_rate"], padding=True, return_tensors="pt") input_features = features.input_features.to("cuda") attention_mask = features.attention_mask.to("cuda") gen_tokens = model.generate(input_features=input_features, attention_mask=attention_mask) batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True) return batch result = librispeech_eval.map(map_to_pred)#, batched=True, batch_size=8) print("WER:", wer.compute(predictions=result["transcription"], references=result["text"])) ```
## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0
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Error loading file audio when downloading the Common Voice dataset directly from the Hub ## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0 Thanks! And sorry for posting this problem in what turned on to be an unrelated thread. I rewrote the code, and the model works. The WER is 0.137 however, so I'm not sure if I have missed a step. I will look further into that at a later point. The transcriptions look good through manual inspection. The rewritten code: ```python from datasets import load_dataset, load_metric from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor, Wav2Vec2Processor librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset wer = load_metric("wer") model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to("cuda") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr", do_upper_case=True) def map_to_pred(batch): audio = batch["audio"] features = processor(audio["array"], sampling_rate=audio["sampling_rate"], padding=True, return_tensors="pt") input_features = features.input_features.to("cuda") attention_mask = features.attention_mask.to("cuda") gen_tokens = model.generate(input_features=input_features, attention_mask=attention_mask) batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True) return batch result = librispeech_eval.map(map_to_pred)#, batched=True, batch_size=8) print("WER:", wer.compute(predictions=result["transcription"], references=result["text"])) ```
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https://github.com/huggingface/datasets/issues/3909
Error loading file audio when downloading the Common Voice dataset directly from the Hub
I think the issue comes from the fact that you set `batched=False` while `map_to_pred` still returns a list of strings for "transcription". You can fix it by adding `[0]` at the end of this line to get the string: ```python batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)[0] ```
## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0
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Error loading file audio when downloading the Common Voice dataset directly from the Hub ## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0 I think the issue comes from the fact that you set `batched=False` while `map_to_pred` still returns a list of strings for "transcription". You can fix it by adding `[0]` at the end of this line to get the string: ```python batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)[0] ```
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https://github.com/huggingface/datasets/issues/3909
Error loading file audio when downloading the Common Voice dataset directly from the Hub
We no longer use `torchaudio` for decoding MP3 files, and the problem with model cards has been addressed, so I'm closing this issue.
## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0
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Error loading file audio when downloading the Common Voice dataset directly from the Hub ## Describe the bug When loading the Common_Voice dataset, by downloading it directly from the Hugging Face hub, some files can not be opened. ## Steps to reproduce the bug ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "it", split="test") #test_dataset = load_dataset('csv', data_files = {'test': '/workspace/Dataset/Common_Voice/cv-corpus80/it/test.csv'}) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) ``` ## Expected results The common voice dataset downloaded and correctly loaded whit the use of the hugging face datasets library. ## Actual results The error is: ```python 0ex [00:00, ?ex/s] --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-48-ef87f4129e6e> in <module> 7 return batch 8 ----> 9 test_dataset = test_dataset.map(speech_file_to_array_fn) /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 2107 2108 if num_proc is None or num_proc == 1: -> 2109 return self._map_single( 2110 function=function, 2111 with_indices=with_indices, /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 516 self: "Dataset" = kwargs.pop("self") 517 # apply actual function --> 518 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 519 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 520 for dataset in datasets: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs) 483 } 484 # apply actual function --> 485 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs) 486 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 487 # re-apply format to the output /opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs) 411 # Call actual function 412 --> 413 out = func(self, *args, **kwargs) 414 415 # Update fingerprint of in-place transforms + update in-place history of transforms /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in _map_single(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only) 2465 if not batched: 2466 for i, example in enumerate(pbar): -> 2467 example = apply_function_on_filtered_inputs(example, i, offset=offset) 2468 if update_data: 2469 if i == 0: /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset) 2372 if with_rank: 2373 additional_args += (rank,) -> 2374 processed_inputs = function(*fn_args, *additional_args, **fn_kwargs) 2375 if update_data is None: 2376 # Check if the function returns updated examples /opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py in decorated(item, *args, **kwargs) 2067 ) 2068 # Use the LazyDict internally, while mapping the function -> 2069 result = f(decorated_item, *args, **kwargs) 2070 # Return a standard dict 2071 return result.data if isinstance(result, LazyDict) else result <ipython-input-48-ef87f4129e6e> in speech_file_to_array_fn(batch) 3 def speech_file_to_array_fn(batch): 4 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() ----> 5 speech_array, sampling_rate = torchaudio.load(batch["path"]) 6 batch["speech"] = resampler(speech_array).squeeze().numpy() 7 return batch /opt/conda/lib/python3.8/site-packages/torchaudio/backend/sox_io_backend.py in load(filepath, frame_offset, num_frames, normalize, channels_first, format) 150 filepath, frame_offset, num_frames, normalize, channels_first, format) 151 filepath = os.fspath(filepath) --> 152 return torch.ops.torchaudio.sox_io_load_audio_file( 153 filepath, frame_offset, num_frames, normalize, channels_first, format) 154 RuntimeError: Error loading audio file: failed to open file common_voice_it_17415776.mp3 ``` ## Environment info - `datasets` version: 1.18.4 - Platform: Linux-5.4.0-x86_64-with-glibc2.10 - Python version: 3.8.5 - PyArrow version: 7.0.0 We no longer use `torchaudio` for decoding MP3 files, and the problem with model cards has been addressed, so I'm closing this issue.
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https://github.com/huggingface/datasets/issues/3906
NonMatchingChecksumError on Spider dataset
Hi @kolk, thanks for reporting. Indeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change: - #3787 We just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4 Please, feel free to update your local `datasets` version, so that you get the fix: ```shell pip install -U datasets ```
## Describe the bug Failure to generate dataset ```spider``` because of checksums error for dataset source files. ## Steps to reproduce the bug ``` from datasets import load_dataset spider = load_dataset("spider") ``` ## Expected results Checksums should match for files from url ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ## Actual results ``` >>> load_dataset("spider") load_dataset("spider") Downloading and preparing dataset spider/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to /home/user/.cache/huggingface/datasets/spider/spider/1.0.0/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7... Traceback (most recent call last): File "/home/user/py3_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-5-d4cb54197348>", line 1, in <module> load_dataset("spider") File "/home/user/py3_env/lib/python3.8/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ``` ## Environment info datasets version: 1.18.3 Platform: Ubuntu 20 LTS Python version: 3.8.10 PyArrow version: 6.0.1
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NonMatchingChecksumError on Spider dataset ## Describe the bug Failure to generate dataset ```spider``` because of checksums error for dataset source files. ## Steps to reproduce the bug ``` from datasets import load_dataset spider = load_dataset("spider") ``` ## Expected results Checksums should match for files from url ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ## Actual results ``` >>> load_dataset("spider") load_dataset("spider") Downloading and preparing dataset spider/spider (download: 95.12 MiB, generated: 5.17 MiB, post-processed: Unknown size, total: 100.29 MiB) to /home/user/.cache/huggingface/datasets/spider/spider/1.0.0/79778ebea87c59b19411f1eb3eda317e9dd5f7788a556d837ef25c3ae6e5e8b7... Traceback (most recent call last): File "/home/user/py3_env/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3441, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-5-d4cb54197348>", line 1, in <module> load_dataset("spider") File "/home/user/py3_env/lib/python3.8/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/home/user/py3_env/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0'] ``` ## Environment info datasets version: 1.18.3 Platform: Ubuntu 20 LTS Python version: 3.8.10 PyArrow version: 6.0.1 Hi @kolk, thanks for reporting. Indeed, Google Drive service recently changed their service and we had to add a fix to our library to cope with that change: - #3787 We just made patch release last week: 1.18.4 https://github.com/huggingface/datasets/releases/tag/1.18.4 Please, feel free to update your local `datasets` version, so that you get the fix: ```shell pip install -U datasets ```
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https://github.com/huggingface/datasets/issues/3904
CONLL2003 Dataset not available
Thanks for reporting, @omarespejel. I'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https://data.deepai.org/conll2003.zip Might it be due to a temporary problem in the data owner site (https://data.deepai.org/) that is fixed now? Could you please try loading the dataset again and tell if the problem persists?
## Describe the bug [CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip' ![image](https://user-images.githubusercontent.com/4755430/158084483-ff83631c-5154-4823-892d-577bf1166db0.png) ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("conll2003") ``` ## Expected results Download the conll2003 dataset. ## Actual results Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)`
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CONLL2003 Dataset not available ## Describe the bug [CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip' ![image](https://user-images.githubusercontent.com/4755430/158084483-ff83631c-5154-4823-892d-577bf1166db0.png) ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("conll2003") ``` ## Expected results Download the conll2003 dataset. ## Actual results Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)` Thanks for reporting, @omarespejel. I'm sorry but I can't reproduce the issue: the loading of the dataset works perfecto for me and I can reach the data URL: https://data.deepai.org/conll2003.zip Might it be due to a temporary problem in the data owner site (https://data.deepai.org/) that is fixed now? Could you please try loading the dataset again and tell if the problem persists?
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https://github.com/huggingface/datasets/issues/3904
CONLL2003 Dataset not available
I am getting the same issue. I use google colab with CPU. The code I used is exactly the same as described above. ``` from datasets import load_dataset dataset = load_dataset("conll2003") ``` The produced error: ![image](https://github.com/huggingface/datasets/assets/9371628/d87f7fb0-ef58-4755-abb5-f8f92c51fe02) Note: This error is different from what was initially described in this thread. This is because I use CPU. When I use GPU I reproduce the same initial error of the thread. Moreover, I receive the following warning: ``` WARNING:urllib3.connection:Certificate did not match expected hostname: data.deepai.org. Certificate: {'subject': ((('commonName', '*.b-cdn.net'),),), 'issuer': ((('countryName', 'GB'),), (('stateOrProvinceName', 'Greater Manchester'),), (('localityName', 'Salford'),), (('organizationName', 'Sectigo Limited'),), (('commonName', 'Sectigo RSA Domain Validation Secure Server CA'),)), 'version': 3, 'serialNumber': 'DDED48B13E1EA03983E833AB2C35EF07', 'notBefore': 'Nov 7 00:00:00 2022 GMT', 'notAfter': 'Nov 11 23:59:59 2023 GMT', 'subjectAltName': (('DNS', '*.b-cdn.net'), ('DNS', 'b-cdn.net')), 'OCSP': ('http://ocsp.sectigo.com/',), 'caIssuers': ('http://crt.sectigo.com/SectigoRSADomainValidationSecureServerCA.crt',)} Downloading and preparing dataset conll2003/conll2003 to /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98... WARNING:urllib3.connection:Certificate did not match expected hostname: data.deepai.org. Certificate: {'subject': ((('commonName', '*.b-cdn.net'),),), 'issuer': ((('countryName', 'GB'),), (('stateOrProvinceName', 'Greater Manchester'),), (('localityName', 'Salford'),), (('organizationName', 'Sectigo Limited'),), (('commonName', 'Sectigo RSA Domain Validation Secure Server CA'),)), 'version': 3, 'serialNumber': 'DDED48B13E1EA03983E833AB2C35EF07', 'notBefore': 'Nov 7 00:00:00 2022 GMT', 'notAfter': 'Nov 11 23:59:59 2023 GMT', 'subjectAltName': (('DNS', '*.b-cdn.net'), ('DNS', 'b-cdn.net')), 'OCSP': ('http://ocsp.sectigo.com/',), 'caIssuers': ('http://crt.sectigo.com/SectigoRSADomainValidationSecureServerCA.crt',)} ```
## Describe the bug [CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip' ![image](https://user-images.githubusercontent.com/4755430/158084483-ff83631c-5154-4823-892d-577bf1166db0.png) ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("conll2003") ``` ## Expected results Download the conll2003 dataset. ## Actual results Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)`
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CONLL2003 Dataset not available ## Describe the bug [CONLL2003](https://huggingface.co/datasets/conll2003) Dataset can no longer reach 'https://data.deepai.org/conll2003.zip' ![image](https://user-images.githubusercontent.com/4755430/158084483-ff83631c-5154-4823-892d-577bf1166db0.png) ## Steps to reproduce the bug ```python from datasets import load_dataset datasets = load_dataset("conll2003") ``` ## Expected results Download the conll2003 dataset. ## Actual results Error: `ConnectionError: Couldn't reach https://data.deepai.org/conll2003.zip (error 502)` I am getting the same issue. I use google colab with CPU. The code I used is exactly the same as described above. ``` from datasets import load_dataset dataset = load_dataset("conll2003") ``` The produced error: ![image](https://github.com/huggingface/datasets/assets/9371628/d87f7fb0-ef58-4755-abb5-f8f92c51fe02) Note: This error is different from what was initially described in this thread. This is because I use CPU. When I use GPU I reproduce the same initial error of the thread. Moreover, I receive the following warning: ``` WARNING:urllib3.connection:Certificate did not match expected hostname: data.deepai.org. Certificate: {'subject': ((('commonName', '*.b-cdn.net'),),), 'issuer': ((('countryName', 'GB'),), (('stateOrProvinceName', 'Greater Manchester'),), (('localityName', 'Salford'),), (('organizationName', 'Sectigo Limited'),), (('commonName', 'Sectigo RSA Domain Validation Secure Server CA'),)), 'version': 3, 'serialNumber': 'DDED48B13E1EA03983E833AB2C35EF07', 'notBefore': 'Nov 7 00:00:00 2022 GMT', 'notAfter': 'Nov 11 23:59:59 2023 GMT', 'subjectAltName': (('DNS', '*.b-cdn.net'), ('DNS', 'b-cdn.net')), 'OCSP': ('http://ocsp.sectigo.com/',), 'caIssuers': ('http://crt.sectigo.com/SectigoRSADomainValidationSecureServerCA.crt',)} Downloading and preparing dataset conll2003/conll2003 to /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98... WARNING:urllib3.connection:Certificate did not match expected hostname: data.deepai.org. Certificate: {'subject': ((('commonName', '*.b-cdn.net'),),), 'issuer': ((('countryName', 'GB'),), (('stateOrProvinceName', 'Greater Manchester'),), (('localityName', 'Salford'),), (('organizationName', 'Sectigo Limited'),), (('commonName', 'Sectigo RSA Domain Validation Secure Server CA'),)), 'version': 3, 'serialNumber': 'DDED48B13E1EA03983E833AB2C35EF07', 'notBefore': 'Nov 7 00:00:00 2022 GMT', 'notAfter': 'Nov 11 23:59:59 2023 GMT', 'subjectAltName': (('DNS', '*.b-cdn.net'), ('DNS', 'b-cdn.net')), 'OCSP': ('http://ocsp.sectigo.com/',), 'caIssuers': ('http://crt.sectigo.com/SectigoRSADomainValidationSecureServerCA.crt',)} ```
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https://github.com/huggingface/datasets/issues/3902
Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'
Update: `"python3 -c "from from datasets import Dataset, DatasetDict"` works, but not if I import without the `python3 -c`
## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0
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Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' ## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0 Update: `"python3 -c "from from datasets import Dataset, DatasetDict"` works, but not if I import without the `python3 -c`
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https://github.com/huggingface/datasets/issues/3902
Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'
Hi @arunasank, thanks for reporting. It seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`). In order to fix this, you should update `fsspec` from within the "problematic" Python virtual env: ``` pip install -U "fsspec[http]>=2021.05.0"
## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0
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Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' ## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0 Hi @arunasank, thanks for reporting. It seems that this can be caused because you are using an old version of `fsspec`: the reason why it works if you run `python3` seems to be that `python3` runs in a Python virtual env (with an updated version of `fsspec`); whereas the error arises when you run the import from other Python virtual env (with an old version of `fsspec`). In order to fix this, you should update `fsspec` from within the "problematic" Python virtual env: ``` pip install -U "fsspec[http]>=2021.05.0"
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https://github.com/huggingface/datasets/issues/3902
Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'
from lightgbm import LGBMModel,LGBMClassifier, plot_importance after importing lib getting (partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) error, can help me
## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0
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Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' ## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0 from lightgbm import LGBMModel,LGBMClassifier, plot_importance after importing lib getting (partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) error, can help me
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https://github.com/huggingface/datasets/issues/3902
Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils'
@deepakmahtha I think you are not using `datasets`: this is the GitHub repository of Hugging Face Datasets. If you are using `lightgbm`, you should report the issue to their repository instead. Anyway, we have proposed a possible fix just in a comment above: to update fsspec. https://github.com/huggingface/datasets/issues/3902#issuecomment-1066517824
## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0
895
47
Can't import datasets: partially initialized module 'fsspec' has no attribute 'utils' ## Describe the bug Unable to import datasets ## Steps to reproduce the bug ```python from datasets import Dataset, DatasetDict ``` ## Expected results The import works without errors ## Actual results ``` AttributeError Traceback (most recent call last) <ipython-input-37-c8cfcbe62127> in <module> 11 # from tqdm import tqdm 12 # import torch ---> 13 from datasets import Dataset 14 # from transformers import Trainer, TrainingArguments, AutoModel, AutoTokenizer, AutoModelForMaskedLM, DataCollatorForLanguageModeling 15 # from sentence_transformers import SentenceTransformer ~/.local/lib/python3.8/site-packages/datasets/__init__.py in <module> 31 ) 32 ---> 33 from .arrow_dataset import Dataset, concatenate_datasets 34 from .arrow_reader import ArrowReader, ReadInstruction 35 from .arrow_writer import ArrowWriter ~/.local/lib/python3.8/site-packages/datasets/arrow_dataset.py in <module> 46 ) 47 ---> 48 import fsspec 49 import numpy as np 50 import pandas as pd ~/.local/lib/python3.8/site-packages/fsspec/__init__.py in <module> 10 from . import _version, caching 11 from .callbacks import Callback ---> 12 from .core import get_fs_token_paths, open, open_files, open_local 13 from .exceptions import FSTimeoutError 14 from .mapping import FSMap, get_mapper ~/.local/lib/python3.8/site-packages/fsspec/core.py in <module> 16 caches, 17 ) ---> 18 from .compression import compr 19 from .registry import filesystem, get_filesystem_class 20 from .utils import ( ~/.local/lib/python3.8/site-packages/fsspec/compression.py in <module> 68 69 ---> 70 register_compression("zip", unzip, "zip") 71 register_compression("bz2", BZ2File, "bz2") 72 ~/.local/lib/python3.8/site-packages/fsspec/compression.py in register_compression(name, callback, extensions, force) 44 45 for ext in extensions: ---> 46 if ext in fsspec.utils.compressions and not force: 47 raise ValueError( 48 "Duplicate compression file extension: %s (%s)" % (ext, name) AttributeError: partially initialized module 'fsspec' has no attribute 'utils' (most likely due to a circular import) ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4 - Platform: Jupyter notebook - Python version: 3.8.10 - PyArrow version: 7.0.0 @deepakmahtha I think you are not using `datasets`: this is the GitHub repository of Hugging Face Datasets. If you are using `lightgbm`, you should report the issue to their repository instead. Anyway, we have proposed a possible fix just in a comment above: to update fsspec. https://github.com/huggingface/datasets/issues/3902#issuecomment-1066517824
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-2.230431318283081 ]
https://github.com/huggingface/datasets/issues/3901
Dataset viewer issue for IndicParaphrase- the preview doesn't show
It seems to have been fixed: <img width="1534" alt="Capture d’écran 2022-04-12 à 14 10 07" src="https://user-images.githubusercontent.com/1676121/162959599-6b7fef7c-8411-4e03-8f00-90040a658079.png">
## Dataset viewer issue for '*IndicParaphrase*' **Link:** *[IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase/viewer/hi/validation)* *The preview of the dataset doesn't come up. The error on the console is: Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/hi_IndicParaphrase_v1.0.tar'* Am I the one who added this dataset ? Yes
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Dataset viewer issue for IndicParaphrase- the preview doesn't show ## Dataset viewer issue for '*IndicParaphrase*' **Link:** *[IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase/viewer/hi/validation)* *The preview of the dataset doesn't come up. The error on the console is: Status code: 400 Exception: FileNotFoundError Message: [Errno 2] No such file or directory: '/home/hf/datasets-preview-backend/hi_IndicParaphrase_v1.0.tar'* Am I the one who added this dataset ? Yes It seems to have been fixed: <img width="1534" alt="Capture d’écran 2022-04-12 à 14 10 07" src="https://user-images.githubusercontent.com/1676121/162959599-6b7fef7c-8411-4e03-8f00-90040a658079.png">
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https://github.com/huggingface/datasets/issues/3896
Missing google file for `multi_news` dataset
`datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`. When loading in streaming mode, the fix is indeed on https://github.com/huggingface/datasets/pull/3843 which will be merged soon :)
## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No
897
29
Missing google file for `multi_news` dataset ## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No `datasets` 1.18.4 fixes the issue when you load the dataset with `load_dataset`. When loading in streaming mode, the fix is indeed on https://github.com/huggingface/datasets/pull/3843 which will be merged soon :)
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https://github.com/huggingface/datasets/issues/3896
Missing google file for `multi_news` dataset
That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release... Once merged, that will fix this issue.
## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No
897
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Missing google file for `multi_news` dataset ## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No That is. The PR #3843 was just opened a bit later we had made our 1.18.4 patch release... Once merged, that will fix this issue.
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https://github.com/huggingface/datasets/issues/3896
Missing google file for `multi_news` dataset
OK. Should fix the viewer for 50 datasets <img width="148" alt="Capture d’écran 2022-03-14 à 11 51 02" src="https://user-images.githubusercontent.com/1676121/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png">
## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No
897
18
Missing google file for `multi_news` dataset ## Dataset viewer issue for '*multi_news*' **Link:** https://huggingface.co/datasets/multi_news ``` Server error Status code: 400 Exception: FileNotFoundError Message: https://drive.google.com/uc?export=download&id=1vRY2wM6rlOZrf9exGTm5pXj5ExlVwJ0C/multi-news-original/train.src ``` Am I the one who added this dataset ? No OK. Should fix the viewer for 50 datasets <img width="148" alt="Capture d’écran 2022-03-14 à 11 51 02" src="https://user-images.githubusercontent.com/1676121/158157853-6c544a47-2d6d-4ac4-964a-6f10951ec36b.png">
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https://github.com/huggingface/datasets/issues/3889
Cannot load beans dataset (Couldn't reach the dataset)
Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)
## Describe the bug The beans dataset is unavailable to download. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('beans') ``` ## Expected results The dataset would be downloaded with no issue. ## Actual results ``` ConnectionError: Couldn't reach https://storage.googleapis.com/ibeans/train.zip (error 403) ``` [It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https://storage.googleapis.com/ibeans/train.zip ) ## Environment info Google Colab
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Cannot load beans dataset (Couldn't reach the dataset) ## Describe the bug The beans dataset is unavailable to download. ## Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset('beans') ``` ## Expected results The dataset would be downloaded with no issue. ## Actual results ``` ConnectionError: Couldn't reach https://storage.googleapis.com/ibeans/train.zip (error 403) ``` [It looks like the billing of this project has been disabled because it is associated with a delinquent account.](https://storage.googleapis.com/ibeans/train.zip ) ## Environment info Google Colab Hi ! A pull request is open to fix the dataset, we'll release a patch soon with a new release of `datasets` :)
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https://github.com/huggingface/datasets/issues/3888
IterableDataset columns and feature types
@lhoestq so in order to address what’s not completed in this issue, do you think it makes sense to add a param `features` to `IterableDataset.map` so that the output features right after the `map` are defined there?
Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova
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IterableDataset columns and feature types Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova @lhoestq so in order to address what’s not completed in this issue, do you think it makes sense to add a param `features` to `IterableDataset.map` so that the output features right after the `map` are defined there?
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https://github.com/huggingface/datasets/issues/3888
IterableDataset columns and feature types
@lhoestq cool then if you agree I can work on that! I’ll also update the docs accordingly once done, thanks!
Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova
899
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IterableDataset columns and feature types Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova @lhoestq cool then if you agree I can work on that! I’ll also update the docs accordingly once done, thanks!
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https://github.com/huggingface/datasets/issues/3888
IterableDataset columns and feature types
I've already started with a PR as a draft @lhoestq, should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so? Thanks!
Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova
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IterableDataset columns and feature types Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova I've already started with a PR as a draft @lhoestq, should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so? Thanks!
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https://github.com/huggingface/datasets/issues/3888
IterableDataset columns and feature types
> should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so? Right now one can use `ds = ds._resolve_features()` do to so. It can be used after `map` or `load_dataset` if the features are not known. Maybe we can make this method public ?
Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova
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IterableDataset columns and feature types Right now, an IterableDataset (e.g. when streaming a dataset) doesn't require to know the list of columns it contains, nor their types: `my_iterable_dataset.features` may be `None` However it's often interesting to know the column types and types. This helps knowing what's inside your dataset without having to manually check a few examples, and this is useful to prepare a processing pipeline or to train models. Here are a few cases that lead to `features` being `None`: 1. when loading a dataset with `load_dataset` on CSV, JSON Lines, etc. files: type inference is only done when iterating over the dataset 2. when calling `map`, because we don't know in advance what's the output of the user's function passed to `map` 3. when calling `rename_columns`, `remove_columns`, etc. because they rely on `map` Things we can consider, for each point above: 1.a infer the type automatically from the first samples on the dataset using prefetching, when the dataset builder doesn't provide the `features` 2.a allow the user to specify the `features` as an argument to `map` (this would be consistent with the non-streaming API) 2.b prefetch the first output value to infer the type 3.a don't rely on `map` directly and reuse the previous `features` and rename/remove the corresponding ones The thing is that prefetching can take a few seconds, while the operations above are instantaneous since no data are downloaded. Therefore I'm not sure whether this solution may be worth it. Maybe prefetching could also be done when explicitly asked by the user cc @mariosasko @albertvillanova > should we also try to look for a way to explicitly request pre-fetching right after a map operation is applied, so that the features are inferred if the user says explicitly so? Right now one can use `ds = ds._resolve_features()` do to so. It can be used after `map` or `load_dataset` if the features are not known. Maybe we can make this method public ?
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
Hi @INF800, Please note that the `imagefolder` feature enhancement was just recently merged to our master branch (https://github.com/huggingface/datasets/commit/207be676bffe9d164740a41a883af6125edef135), but has not yet been released. We are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI. In the meantime, you can incorporate that feature if you install our library from our GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then: ```python In [1]: from datasets import load_dataset ds = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip", split="train") Using custom data configuration default-7eb4e80d960deb18 Downloading and preparing dataset image_folder/default to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 690.19it/s] Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 852.85it/s] Dataset image_folder downloaded and prepared to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data. In [2]: ds Out[2]: Dataset({ features: ['image', 'label'], num_rows: 25000 }) ```
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
901
140
How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` Hi @INF800, Please note that the `imagefolder` feature enhancement was just recently merged to our master branch (https://github.com/huggingface/datasets/commit/207be676bffe9d164740a41a883af6125edef135), but has not yet been released. We are planning to make the 2.0 release of our library in the coming days and then that feature will be available by updating your `datasets` library from PyPI. In the meantime, you can incorporate that feature if you install our library from our GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then: ```python In [1]: from datasets import load_dataset ds = load_dataset("imagefolder", data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip", split="train") Using custom data configuration default-7eb4e80d960deb18 Downloading and preparing dataset image_folder/default to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60... Downloading data files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 690.19it/s] Extracting data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 852.85it/s] Dataset image_folder downloaded and prepared to .../.cache/huggingface/datasets/image_folder/default-7eb4e80d960deb18/0.0.0/8de8dc6d68ce3c81cc102b93cc82ede27162b5d30cd003094f935942c8294f60. Subsequent calls will reuse this data. In [2]: ds Out[2]: Dataset({ features: ['image', 'label'], num_rows: 25000 }) ```
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
901
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How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` Hey @albertvillanova. Does this load entire dataset in memory? Because I am facing huge trouble with loading very big datasets (OOM errors)
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
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How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` Can you provide the error stack trace? The loader only stores the `data_files` dict, which can get big after globbing. Then, the OOM error would mean you don't have enough memory to keep all the paths to the image files. You can circumvent this by generating an archive and loading the dataset from there. Maybe we can optimize the globbing part in our data files resolution at some point, cc @lhoestq for visibility.
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
Hey, memory error is resolved. It was fluke. But there is another issue. Currently `load_dataset("imagefolder", data_dir="./path/to/train",)` takes only `train` as arg to `split` parameter. I am creating vaildation dataset using ``` ds_valid = datasets.DatasetDict(valid=load_dataset("imagefolder", data_dir="./path/to/valid",)['train']) ```
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
901
36
How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` Hey, memory error is resolved. It was fluke. But there is another issue. Currently `load_dataset("imagefolder", data_dir="./path/to/train",)` takes only `train` as arg to `split` parameter. I am creating vaildation dataset using ``` ds_valid = datasets.DatasetDict(valid=load_dataset("imagefolder", data_dir="./path/to/valid",)['train']) ```
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
`data_dir="path/to/folder"` is a shorthand syntax fox `data_files={"train": "path/to/folder/**"}`, so use `data_files` in that case instead: ```python ds = load_dataset("imagefolder", data_files={"train": "path/to/train/**", "test": "path/to/test/**", "valid": "path/to/valid/**"}) ```
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
901
26
How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` `data_dir="path/to/folder"` is a shorthand syntax fox `data_files={"train": "path/to/folder/**"}`, so use `data_files` in that case instead: ```python ds = load_dataset("imagefolder", data_files={"train": "path/to/train/**", "test": "path/to/test/**", "valid": "path/to/valid/**"}) ```
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
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How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` And there was another issue. I loaded black and white images (jpeg file). Using load dataset. It reads it as PIL jpeg data format. But instead of converting it into 3 channel tensor, input to collator function is coming as a single channel tensor.
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https://github.com/huggingface/datasets/issues/3881
How to use Image folder
We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself: ```python def to_rgb(batch): batch["image"] = [img.convert("RGB") for img in batch["image"]] return batch ds_rgb = ds.map(to_rgb, batched=True) ``` Please use our Forum for questions of this kind in the future.
Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ```
901
47
How to use Image folder Ran this code ``` load_dataset("imagefolder", data_dir="./my-dataset") ``` `https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py` missing ``` --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) /tmp/ipykernel_33/1648737256.py in <module> ----> 1 load_dataset("imagefolder", data_dir="./my-dataset") /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1684 revision=revision, 1685 use_auth_token=use_auth_token, -> 1686 **config_kwargs, 1687 ) 1688 /opt/conda/lib/python3.7/site-packages/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, use_auth_token, script_version, **config_kwargs) 1511 download_config.use_auth_token = use_auth_token 1512 dataset_module = dataset_module_factory( -> 1513 path, revision=revision, download_config=download_config, download_mode=download_mode, data_files=data_files 1514 ) 1515 /opt/conda/lib/python3.7/site-packages/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, force_local_path, dynamic_modules_path, data_files, **download_kwargs) 1200 f"Couldn't find a dataset script at {relative_to_absolute_path(combined_path)} or any data file in the same directory. " 1201 f"Couldn't find '{path}' on the Hugging Face Hub either: {type(e1).__name__}: {e1}" -> 1202 ) from None 1203 raise e1 from None 1204 else: FileNotFoundError: Couldn't find a dataset script at /kaggle/working/imagefolder/imagefolder.py or any data file in the same directory. Couldn't find 'imagefolder' on the Hugging Face Hub either: FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/imagefolder/imagefolder.py ``` We don't apply any additional preprocessing on top of `PIL.Image.open(image_file)`, so you need to do the conversion yourself: ```python def to_rgb(batch): batch["image"] = [img.convert("RGB") for img in batch["image"]] return batch ds_rgb = ds.map(to_rgb, batched=True) ``` Please use our Forum for questions of this kind in the future.
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https://github.com/huggingface/datasets/issues/3872
HTTP error 504 Server Error: Gateway Time-out
yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?
I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue.
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HTTP error 504 Server Error: Gateway Time-out I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue. yes but is there any way you could try pushing with `git` command line directly instead of `push_to_hub`?
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https://github.com/huggingface/datasets/issues/3872
HTTP error 504 Server Error: Gateway Time-out
Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command line
I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue.
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HTTP error 504 Server Error: Gateway Time-out I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue. Okay. I didnt saved the dataset to my local machine. So, I processed the dataset and pushed it directly to the hub. I think I should try saving those dataset to my local machine by `save_to_disk` and then push it with git command line
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https://github.com/huggingface/datasets/issues/3872
HTTP error 504 Server Error: Gateway Time-out
`push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors. Regarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download/clone the repository locally by yourself and use `load_from_disk`.
I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue.
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HTTP error 504 Server Error: Gateway Time-out I am trying to push a large dataset(450000+) records with the help of `push_to_hub()` While pushing, it gives some error like this. ``` Traceback (most recent call last): File "data_split_speech.py", line 159, in <module> data_new_2.push_to_hub("user-name/dataset-name",private=True) File "/opt/conda/lib/python3.8/site-packages/datasets/dataset_dict.py", line 951, in push_to_hub repo_id, split, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3556, in _push_parquet_shards_to_hub api.upload_file( File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1017, in upload_file raise err File "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py", line 1008, in upload_file r.raise_for_status() File "/opt/conda/lib/python3.8/site-packages/requests/models.py", line 953, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 504 Server Error: Gateway Time-out for url: https://huggingface.co/api/datasets/user-name/dataset-name/upload/main/data/train2-00041-of-00064.parquet ``` Can anyone help me to resolve this issue. `push_to_hub` is the preferred way of uploading a dataset to the Hub, which can then be reloaded with `load_dataset`. Feel free to try again and see if the server is working as expected now. Maybe we can add a retry mechanism in the meantime to workaround 504 errors. Regarding `save_to_disk`, this must only be used for local serialization (because it's uncompressed and compatible with memory-mapping). If you upload a dataset saved with `save_to_disk` to the Hub, then to reload it you will have to download/clone the repository locally by yourself and use `load_from_disk`.
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https://github.com/huggingface/datasets/issues/3869
Making the Hub the place for datasets in Portuguese
Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https://huggingface.co/docs/datasets/share. That would have the additional benefit that the dataset would live under the NILC organization. @lhoestq correct me if I'm wrong please 😄
Let's make Hugging Face Datasets the central hub for datasets in Portuguese :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community. What are some datasets in Portuguese worth integrating into the Hugging Face hub? Special thanks to @augusnunes for his collaboration on identifying the first ones: - [NILC - USP](http://www.nilc.icmc.usp.br/nilc/index.php/tools-and-resources). Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). cc @osanseviero
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Making the Hub the place for datasets in Portuguese Let's make Hugging Face Datasets the central hub for datasets in Portuguese :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the Portuguese speaking community. What are some datasets in Portuguese worth integrating into the Hugging Face hub? Special thanks to @augusnunes for his collaboration on identifying the first ones: - [NILC - USP](http://www.nilc.icmc.usp.br/nilc/index.php/tools-and-resources). Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). cc @osanseviero Hi @omarespejel! I think the philosophy for `datasets` issues is to create concrete issues with proposals to add a specific, individual dataset rather than umbrella issues for things such as datasets for a language, since we could end up with hundreds of issues (one per language). I see NILC - USP has many datasets, I would suggest to either create an issue for their datasets, or even better, we are trying to push to upload datasets as community datasets instead of adding them to the core library as guided in https://huggingface.co/docs/datasets/share. That would have the additional benefit that the dataset would live under the NILC organization. @lhoestq correct me if I'm wrong please 😄
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https://github.com/huggingface/datasets/issues/3861
big_patent cased version
To follow up on this: the cased and uncased versions actually contain different content, and the cased one is easier since it contains a Summary of the Invention in the input. See the paper describing the issue here: https://aclanthology.org/2022.gem-1.34/
Hi! I am interested in working with the big_patent dataset. In Tensorflow, there are a number of versions of the dataset: - 1.0.0 : lower cased tokenized words - 2.0.0 : Update to use cased raw strings - 2.1.2 (default): Fix update to cased raw strings. The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version?
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big_patent cased version Hi! I am interested in working with the big_patent dataset. In Tensorflow, there are a number of versions of the dataset: - 1.0.0 : lower cased tokenized words - 2.0.0 : Update to use cased raw strings - 2.1.2 (default): Fix update to cased raw strings. The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version? To follow up on this: the cased and uncased versions actually contain different content, and the cased one is easier since it contains a Summary of the Invention in the input. See the paper describing the issue here: https://aclanthology.org/2022.gem-1.34/
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https://github.com/huggingface/datasets/issues/3861
big_patent cased version
Thanks for proposing the addition of the cased version of this dataset and for pinging again recently. I have just merged a PR that adds the cased version: https://huggingface.co/datasets/big_patent/discussions/3 The cased version (2.1.2) is the default one: ```python ds = load_dataset("big_patent", "all") ``` To use the 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ``` Closed by: https://huggingface.co/datasets/big_patent/discussions/3
Hi! I am interested in working with the big_patent dataset. In Tensorflow, there are a number of versions of the dataset: - 1.0.0 : lower cased tokenized words - 2.0.0 : Update to use cased raw strings - 2.1.2 (default): Fix update to cased raw strings. The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version?
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big_patent cased version Hi! I am interested in working with the big_patent dataset. In Tensorflow, there are a number of versions of the dataset: - 1.0.0 : lower cased tokenized words - 2.0.0 : Update to use cased raw strings - 2.1.2 (default): Fix update to cased raw strings. The version in the huggingface `datasets` library is the 1.0.0. I would be very interested in using the 2.1.2 cased version (used more, recently, for example in the Pegasus paper), but it does not seem to be supported (I tried using the `revision` parameter in `load_datasets`). Is there a way to already load it, or would it be possible to add that version? Thanks for proposing the addition of the cased version of this dataset and for pinging again recently. I have just merged a PR that adds the cased version: https://huggingface.co/datasets/big_patent/discussions/3 The cased version (2.1.2) is the default one: ```python ds = load_dataset("big_patent", "all") ``` To use the 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`: ```python ds = load_dataset("big_patent", codes="all", version="1.0.0") ``` Closed by: https://huggingface.co/datasets/big_patent/discussions/3
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https://github.com/huggingface/datasets/issues/3859
Unable to dowload big_patent (FileNotFoundError)
Hi @slvcsl, thanks for reporting. Yesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4. https://pypi.org/project/datasets/#history Please, feel free to update `datasets` library to the latest version: ```shell pip install -U datasets ``` And then you should force redownload of the data file to update your local cache: ```python ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") ``` - Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We already fixed it. See: - #3787
## Describe the bug I am trying to download some splits of the big_patent dataset, using the following code: `ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") ` However, this leads to a FileNotFoundError. FileNotFoundError Traceback (most recent call last) [<ipython-input-3-8d8a745706a9>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset ----> 2 ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") 8 frames [/usr/local/lib/python3.7/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, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1705 ignore_verifications=ignore_verifications, 1706 try_from_hf_gcs=try_from_hf_gcs, -> 1707 use_auth_token=use_auth_token, 1708 ) 1709 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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) 593 if not downloaded_from_gcs: 594 self._download_and_prepare( --> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 596 ) 597 # Sync info [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 659 split_dict = SplitDict(dataset_name=self.name) 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 662 663 # Checksums verification [/root/.cache/huggingface/modules/datasets_modules/datasets/big_patent/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c/big_patent.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 123 split_types = ["train", "val", "test"] 124 extract_paths = dl_manager.extract( --> 125 {k: os.path.join(dl_path, "bigPatentData", k + ".tar.gz") for k in split_types} 126 ) 127 extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types} [/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py](https://localhost:8080/#) in extract(self, path_or_paths, num_proc) 282 download_config.extract_compressed_file = True 283 extracted_paths = map_nested( --> 284 partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False 285 ) 286 path_or_paths = NestedDataStructure(path_or_paths) [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm) 260 mapped = [ 261 _single_map_nested((function, obj, types, None, True)) --> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm) 263 ] 264 else: [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <listcomp>(.0) 260 mapped = [ 261 _single_map_nested((function, obj, types, None, True)) --> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm) 263 ] 264 else: [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _single_map_nested(args) 194 # Singleton first to spare some computation 195 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 196 return function(data_struct) 197 198 # Reduce logging to keep things readable in multiprocessing with tqdm [/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py](https://localhost:8080/#) in cached_path(url_or_filename, download_config, **download_kwargs) 314 elif is_local_path(url_or_filename): 315 # File, but it doesn't exist. --> 316 raise FileNotFoundError(f"Local file {url_or_filename} doesn't exist") 317 else: 318 # Something unknown FileNotFoundError: Local file /root/.cache/huggingface/datasets/downloads/extracted/ad068abb3e11f9f2f5440b62e37eb2b03ee515df9de1637c55cd1793b68668b2/bigPatentData/train.tar.gz doesn't exist I have tried this in a number of machines, including on Colab, so I think this is not environment dependent. How do I load the bigPatent dataset?
905
115
Unable to dowload big_patent (FileNotFoundError) ## Describe the bug I am trying to download some splits of the big_patent dataset, using the following code: `ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") ` However, this leads to a FileNotFoundError. FileNotFoundError Traceback (most recent call last) [<ipython-input-3-8d8a745706a9>](https://localhost:8080/#) in <module>() 1 from datasets import load_dataset ----> 2 ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") 8 frames [/usr/local/lib/python3.7/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, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1705 ignore_verifications=ignore_verifications, 1706 try_from_hf_gcs=try_from_hf_gcs, -> 1707 use_auth_token=use_auth_token, 1708 ) 1709 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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) 593 if not downloaded_from_gcs: 594 self._download_and_prepare( --> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 596 ) 597 # Sync info [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 659 split_dict = SplitDict(dataset_name=self.name) 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 662 663 # Checksums verification [/root/.cache/huggingface/modules/datasets_modules/datasets/big_patent/bdefa7c0b39fba8bba1c6331b70b738e30d63c8ad4567f983ce315a5fef6131c/big_patent.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 123 split_types = ["train", "val", "test"] 124 extract_paths = dl_manager.extract( --> 125 {k: os.path.join(dl_path, "bigPatentData", k + ".tar.gz") for k in split_types} 126 ) 127 extract_paths = {k: os.path.join(extract_paths[k], k) for k in split_types} [/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py](https://localhost:8080/#) in extract(self, path_or_paths, num_proc) 282 download_config.extract_compressed_file = True 283 extracted_paths = map_nested( --> 284 partial(cached_path, download_config=download_config), path_or_paths, num_proc=num_proc, disable_tqdm=False 285 ) 286 path_or_paths = NestedDataStructure(path_or_paths) [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm) 260 mapped = [ 261 _single_map_nested((function, obj, types, None, True)) --> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm) 263 ] 264 else: [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <listcomp>(.0) 260 mapped = [ 261 _single_map_nested((function, obj, types, None, True)) --> 262 for obj in utils.tqdm(iterable, disable=disable_tqdm) 263 ] 264 else: [/usr/local/lib/python3.7/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in _single_map_nested(args) 194 # Singleton first to spare some computation 195 if not isinstance(data_struct, dict) and not isinstance(data_struct, types): --> 196 return function(data_struct) 197 198 # Reduce logging to keep things readable in multiprocessing with tqdm [/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py](https://localhost:8080/#) in cached_path(url_or_filename, download_config, **download_kwargs) 314 elif is_local_path(url_or_filename): 315 # File, but it doesn't exist. --> 316 raise FileNotFoundError(f"Local file {url_or_filename} doesn't exist") 317 else: 318 # Something unknown FileNotFoundError: Local file /root/.cache/huggingface/datasets/downloads/extracted/ad068abb3e11f9f2f5440b62e37eb2b03ee515df9de1637c55cd1793b68668b2/bigPatentData/train.tar.gz doesn't exist I have tried this in a number of machines, including on Colab, so I think this is not environment dependent. How do I load the bigPatent dataset? Hi @slvcsl, thanks for reporting. Yesterday we just made a patch release of our `datasets` library that fixes this issue: version 1.18.4. https://pypi.org/project/datasets/#history Please, feel free to update `datasets` library to the latest version: ```shell pip install -U datasets ``` And then you should force redownload of the data file to update your local cache: ```python ds = load_dataset("big_patent", "g", split="validation", download_mode="force_redownload") ``` - Note that before the fix, you just downloaded and cached the Google Drive virus scan warning page, instead of the data file This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We already fixed it. See: - #3787
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1.0828711986541748, -2.171844005584717 ]
https://github.com/huggingface/datasets/issues/3857
Order of dataset changes due to glob.glob.
I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution. Note that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`
## Describe the bug After discussion with @lhoestq, just want to mention here that `glob.glob(...)` should always be used in combination with `sorted(...)` to make sure the list of files returned by `glob.glob(...)` doesn't change depending on the OS system. There are currently multiple datasets that use `glob.glob()` without making use of `sorted(...)` even the streaming download manager (if I'm not mistaken): https://github.com/huggingface/datasets/blob/c14bfeb4af89da14f870de5ddaa584b08aa08eeb/src/datasets/utils/streaming_download_manager.py#L483
906
48
Order of dataset changes due to glob.glob. ## Describe the bug After discussion with @lhoestq, just want to mention here that `glob.glob(...)` should always be used in combination with `sorted(...)` to make sure the list of files returned by `glob.glob(...)` doesn't change depending on the OS system. There are currently multiple datasets that use `glob.glob()` without making use of `sorted(...)` even the streaming download manager (if I'm not mistaken): https://github.com/huggingface/datasets/blob/c14bfeb4af89da14f870de5ddaa584b08aa08eeb/src/datasets/utils/streaming_download_manager.py#L483 I agree using `glob.glob` alone is bad practice because it's not deterministic. Using `sorted` is a nice solution. Note that the `xglob` function you are referring to in the `streaming_download_manager.py` code just extends `glob.glob` for URLs - we don't change its behavior. That's why it has no `sorted()`
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https://github.com/huggingface/datasets/issues/3855
Bad error message when loading private dataset
We raise the error “ FileNotFoundError: can’t find the dataset” mainly to follow best practice in security (otherwise users could be able to guess what private repositories users/orgs may have) We can indeed reformulate this and add the "If this is a private repository,..." part !
## Describe the bug A pretty common behavior of an interaction between the Hub and datasets is the following. An organization adds a dataset in private mode and wants to load it afterward. ```python from transformers import load_dataset ds = load_dataset("NewT5/dummy_data", "dummy") ``` This command then fails with: ```bash FileNotFoundError: Couldn't find a dataset script at /home/patrick/NewT5/dummy_data/dummy_data.py or any data file in the same directory. Couldn't find 'NewT5/dummy_data' on the Hugging Face Hub either: FileNotFoundError: Dataset 'NewT5/dummy_data' doesn't exist on the Hub ``` **even though** the user has access to the website `NewT5/dummy_data` since she/he is part of the org. We need to improve the error message here similar to how @sgugger, @LysandreJik and @julien-c have done it for transformers IMO. ## Steps to reproduce the bug E.g. execute the following code to see the different error messages between `transformes` and `datasets`. 1. Transformers ```python from transformers import BertModel BertModel.from_pretrained("NewT5/dummy_model") ``` The error message is clearer here - it gives: ``` OSError: patrickvonplaten/gpt2-xl is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`. ``` Let's maybe do the same for datasets? The PR was introduced to `transformers` here: https://github.com/huggingface/transformers/pull/15261 ## Expected results Better error message ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4.dev0 - Platform: Linux-5.15.15-76051515-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.1
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Bad error message when loading private dataset ## Describe the bug A pretty common behavior of an interaction between the Hub and datasets is the following. An organization adds a dataset in private mode and wants to load it afterward. ```python from transformers import load_dataset ds = load_dataset("NewT5/dummy_data", "dummy") ``` This command then fails with: ```bash FileNotFoundError: Couldn't find a dataset script at /home/patrick/NewT5/dummy_data/dummy_data.py or any data file in the same directory. Couldn't find 'NewT5/dummy_data' on the Hugging Face Hub either: FileNotFoundError: Dataset 'NewT5/dummy_data' doesn't exist on the Hub ``` **even though** the user has access to the website `NewT5/dummy_data` since she/he is part of the org. We need to improve the error message here similar to how @sgugger, @LysandreJik and @julien-c have done it for transformers IMO. ## Steps to reproduce the bug E.g. execute the following code to see the different error messages between `transformes` and `datasets`. 1. Transformers ```python from transformers import BertModel BertModel.from_pretrained("NewT5/dummy_model") ``` The error message is clearer here - it gives: ``` OSError: patrickvonplaten/gpt2-xl is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models' If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`. ``` Let's maybe do the same for datasets? The PR was introduced to `transformers` here: https://github.com/huggingface/transformers/pull/15261 ## Expected results Better error message ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.4.dev0 - Platform: Linux-5.15.15-76051515-generic-x86_64-with-glibc2.34 - Python version: 3.9.7 - PyArrow version: 6.0.1 We raise the error “ FileNotFoundError: can’t find the dataset” mainly to follow best practice in security (otherwise users could be able to guess what private repositories users/orgs may have) We can indeed reformulate this and add the "If this is a private repository,..." part !
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https://github.com/huggingface/datasets/issues/3854
load only England English dataset from common voice english dataset
Hi @amanjaiswal777, First note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020. Currently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https://huggingface.co/mozilla-foundation For example, to get their latest Common Voice relase (8.0): - Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 - Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: "accent", "age",... - Then you can load their "en" language dataset as usual, besides passing your authentication token (more info on auth token here: https://huggingface.co/docs/hub/security) ```python from datasets import load_dataset ds_en = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True) ``` - Finally, you can filter only the data instances you are interested in (more info on `filter` here: https://huggingface.co/docs/datasets/process#select-and-filter): ```python ds_england_en = ds_en.filter(lambda item: item["accent"] == "England English") ``` Feel free to reopen this issue if you need further assistance.
training_data = load_dataset("common_voice", "en",split='train[:250]+validation[:250]') testing_data = load_dataset("common_voice", "en", split="test[:200]") I'm trying to load only 8% of the English common voice data with accent == "England English." Can somebody assist me with this? **Typical Voice Accent Proportions:** - 24% United States English - 8% England English - 5% India and South Asia (India, Pakistan, Sri Lanka) - 3% Australian English - 3% Canadian English - 2% Scottish English - 1% Irish English - 1% Southern African (South Africa, Zimbabwe, Namibia) - 1% New Zealand English Can we replicate this for Age as well? **Age proportions of the common voice:-** - 24% 19 - 29 - 14% 30 - 39 - 10% 40 - 49 - 6% < 19 - 4% 50 - 59 - 4% 60 - 69 - 1% 70 – 79
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load only England English dataset from common voice english dataset training_data = load_dataset("common_voice", "en",split='train[:250]+validation[:250]') testing_data = load_dataset("common_voice", "en", split="test[:200]") I'm trying to load only 8% of the English common voice data with accent == "England English." Can somebody assist me with this? **Typical Voice Accent Proportions:** - 24% United States English - 8% England English - 5% India and South Asia (India, Pakistan, Sri Lanka) - 3% Australian English - 3% Canadian English - 2% Scottish English - 1% Irish English - 1% Southern African (South Africa, Zimbabwe, Namibia) - 1% New Zealand English Can we replicate this for Age as well? **Age proportions of the common voice:-** - 24% 19 - 29 - 14% 30 - 39 - 10% 40 - 49 - 6% < 19 - 4% 50 - 59 - 4% 60 - 69 - 1% 70 – 79 Hi @amanjaiswal777, First note that the dataset you are trying to load is deprecated: it was the Common Voice dataset release as of Dec 2020. Currently, Common Voice dataset releases are directly hosted on the Hub, under the Mozilla Foundation organization: https://huggingface.co/mozilla-foundation For example, to get their latest Common Voice relase (8.0): - Go to the dataset page and request access permission (Mozilla Foundation requires this for people willing to use their datasets): https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0 - Looking at the dataset card, you can check that data instances have, among other fields, the ones you are interested in: "accent", "age",... - Then you can load their "en" language dataset as usual, besides passing your authentication token (more info on auth token here: https://huggingface.co/docs/hub/security) ```python from datasets import load_dataset ds_en = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token=True) ``` - Finally, you can filter only the data instances you are interested in (more info on `filter` here: https://huggingface.co/docs/datasets/process#select-and-filter): ```python ds_england_en = ds_en.filter(lambda item: item["accent"] == "England English") ``` Feel free to reopen this issue if you need further assistance.
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https://github.com/huggingface/datasets/issues/3851
Load audio dataset error
Hi @lemoner20, thanks for reporting. I'm sorry but I cannot reproduce your problem: ```python In [1]: from datasets import load_dataset, load_metric, Audio ...: raw_datasets = load_dataset("superb", "ks", split="train") ...: print(raw_datasets[0]["audio"]) Downloading builder script: 30.2kB [00:00, 13.0MB/s] Downloading metadata: 38.0kB [00:00, 16.6MB/s] Downloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9... Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.49G/1.49G [00:37<00:00, 39.3MB/s] Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.3M/71.3M [00:01<00:00, 36.1MB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:41<00:00, 20.67s/it] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:28<00:00, 14.24s/it] Dataset superb downloaded and prepared to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data. {'path': '.../.cache/huggingface/datasets/downloads/extracted/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f/_background_noise_/doing_the_dishes.wav', 'array': array([ 0. , 0. , 0. , ..., -0.00592041, -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000} ``` Which version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ```
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Load audio dataset error ## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ``` Hi @lemoner20, thanks for reporting. I'm sorry but I cannot reproduce your problem: ```python In [1]: from datasets import load_dataset, load_metric, Audio ...: raw_datasets = load_dataset("superb", "ks", split="train") ...: print(raw_datasets[0]["audio"]) Downloading builder script: 30.2kB [00:00, 13.0MB/s] Downloading metadata: 38.0kB [00:00, 16.6MB/s] Downloading and preparing dataset superb/ks (download: 1.45 GiB, generated: 9.64 MiB, post-processed: Unknown size, total: 1.46 GiB) to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9... Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.49G/1.49G [00:37<00:00, 39.3MB/s] Downloading data: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71.3M/71.3M [00:01<00:00, 36.1MB/s] Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:41<00:00, 20.67s/it] Extracting data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:28<00:00, 14.24s/it] Dataset superb downloaded and prepared to .../.cache/huggingface/datasets/superb/ks/1.9.0/fc1f59e1fa54262dfb42de99c326a806ef7de1263ece177b59359a1a3354a9c9. Subsequent calls will reuse this data. {'path': '.../.cache/huggingface/datasets/downloads/extracted/8571921d3088b48f58f75b2e514815033e1ffbd06aa63fd4603691ac9f1c119f/_background_noise_/doing_the_dishes.wav', 'array': array([ 0. , 0. , 0. , ..., -0.00592041, -0.00405884, -0.00253296], dtype=float32), 'sampling_rate': 16000} ``` Which version of `datasets` are you using? Could you please fill in the environment info requested in the bug report template? You can run the command `datasets-cli env` and copy-and-paste its output below ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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https://github.com/huggingface/datasets/issues/3851
Load audio dataset error
@albertvillanova Thanks for your reply. The environment info below ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid - Python version: 3.6.12 - PyArrow version: 6.0.1
## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ```
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Load audio dataset error ## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ``` @albertvillanova Thanks for your reply. The environment info below ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-4.19.91-007.ali4000.alios7.x86_64-x86_64-with-debian-buster-sid - Python version: 3.6.12 - PyArrow version: 6.0.1
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https://github.com/huggingface/datasets/issues/3851
Load audio dataset error
Thanks @lemoner20, I cannot reproduce your issue in datasets version 1.18.3 either. Maybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing "force_redownload"? ```python raw_datasets = load_dataset("superb", "ks", split="train", download_mode="force_redownload")
## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ```
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Load audio dataset error ## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ``` Thanks @lemoner20, I cannot reproduce your issue in datasets version 1.18.3 either. Maybe redownloading the data file may work if you had already cached this dataset previously. Could you please try passing "force_redownload"? ```python raw_datasets = load_dataset("superb", "ks", split="train", download_mode="force_redownload")
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https://github.com/huggingface/datasets/issues/3851
Load audio dataset error
@albertvillanova, you can actually reproduce the error if you reach the cell `common_voice_train[0]["path"]` of this [notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=_0kRndSvqaKk). Error gets solved after updating the versions of the libraries used in there.
## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ```
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Load audio dataset error ## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ``` @albertvillanova, you can actually reproduce the error if you reach the cell `common_voice_train[0]["path"]` of this [notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=_0kRndSvqaKk). Error gets solved after updating the versions of the libraries used in there.
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https://github.com/huggingface/datasets/issues/3851
Load audio dataset error
@jvel07, thanks for reporting and finding a solution. Maybe we could tell @patrickvonplaten about the version pinning issue in his notebook.
## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ```
909
21
Load audio dataset error ## Load audio dataset error Hi, when I load audio dataset following https://huggingface.co/docs/datasets/audio_process and https://github.com/huggingface/datasets/tree/master/datasets/superb, ``` from datasets import load_dataset, load_metric, Audio raw_datasets = load_dataset("superb", "ks", split="train") print(raw_datasets[0]["audio"]) ``` following errors occur ``` --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-3f8253239fa0> in <module> ----> 1 raw_datasets[0]["audio"] /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in __getitem__(self, key) 1924 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" 1925 return self._getitem( -> 1926 key, 1927 ) 1928 /usr/lib/python3.6/site-packages/datasets/arrow_dataset.py in _getitem(self, key, decoded, **kwargs) 1909 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 1910 formatted_output = format_table( -> 1911 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 1912 ) 1913 return formatted_output /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_table(table, key, formatter, format_columns, output_all_columns) 530 python_formatter = PythonFormatter(features=None) 531 if format_columns is None: --> 532 return formatter(pa_table, query_type=query_type) 533 elif query_type == "column": 534 if key in format_columns: /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in __call__(self, pa_table, query_type) 279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 280 if query_type == "row": --> 281 return self.format_row(pa_table) 282 elif query_type == "column": 283 return self.format_column(pa_table) /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in format_row(self, pa_table) 310 row = self.python_arrow_extractor().extract_row(pa_table) 311 if self.decoded: --> 312 row = self.python_features_decoder.decode_row(row) 313 return row 314 /usr/lib/python3.6/site-packages/datasets/formatting/formatting.py in decode_row(self, row) 219 220 def decode_row(self, row: dict) -> dict: --> 221 return self.features.decode_example(row) if self.features else row 222 223 def decode_column(self, column: list, column_name: str) -> list: /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_example(self, example) 1320 else value 1321 for column_name, (feature, value) in utils.zip_dict( -> 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) 1324 } /usr/lib/python3.6/site-packages/datasets/features/features.py in <dictcomp>(.0) 1319 if self._column_requires_decoding[column_name] 1320 else value -> 1321 for column_name, (feature, value) in utils.zip_dict( 1322 {key: value for key, value in self.items() if key in example}, example 1323 ) /usr/lib/python3.6/site-packages/datasets/features/features.py in decode_nested_example(schema, obj) 1053 # Object with special decoding: 1054 elif isinstance(schema, (Audio, Image)): -> 1055 return schema.decode_example(obj) if obj is not None else None 1056 return obj 1057 /usr/lib/python3.6/site-packages/datasets/features/audio.py in decode_example(self, value) 100 array, sampling_rate = self._decode_non_mp3_file_like(file) 101 else: --> 102 array, sampling_rate = self._decode_non_mp3_path_like(path) 103 return {"path": path, "array": array, "sampling_rate": sampling_rate} 104 /usr/lib/python3.6/site-packages/datasets/features/audio.py in _decode_non_mp3_path_like(self, path) 143 144 with xopen(path, "rb") as f: --> 145 array, sampling_rate = librosa.load(f, sr=self.sampling_rate, mono=self.mono) 146 return array, sampling_rate 147 /usr/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type) 110 111 y = [] --> 112 with audioread.audio_open(os.path.realpath(path)) as input_file: 113 sr_native = input_file.samplerate 114 n_channels = input_file.channels /usr/lib/python3.6/posixpath.py in realpath(filename) 392 """Return the canonical path of the specified filename, eliminating any 393 symbolic links encountered in the path.""" --> 394 filename = os.fspath(filename) 395 path, ok = _joinrealpath(filename[:0], filename, {}) 396 return abspath(path) TypeError: expected str, bytes or os.PathLike object, not _io.BufferedReader ``` ## Expected results ``` >>> raw_datasets[0]["audio"] {'array': array([-0.0005188 , -0.00109863, 0.00030518, ..., 0.01730347, 0.01623535, 0.01724243]), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/bb3a06b491a64aff422f307cd8116820b4f61d6f32fcadcfc554617e84383cb7/bed/026290a7_nohash_0.wav', 'sampling_rate': 16000} ``` @jvel07, thanks for reporting and finding a solution. Maybe we could tell @patrickvonplaten about the version pinning issue in his notebook.
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https://github.com/huggingface/datasets/issues/3848
NonMatchingChecksumError when checksum is None
Hi @jxmorris12, thanks for reporting. The objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected. The question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md ```shell datasets-cli test <your-dataset-folder> --save_infos --all_configs ``` On the other hand, you should take into account that the generation of this file is NOT mandatory for personal/community datasets (we only require it for "canonical" datasets, i.e., datasets added to our library GitHub repository: https://github.com/huggingface/datasets/tree/master/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed. Finally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`: ```python load_dataset(..., ignore_verifications=True) ```
I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.
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NonMatchingChecksumError when checksum is None I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. Hi @jxmorris12, thanks for reporting. The objective of `verify_checksums` is to check that both checksums are equal. Therefore if one is None and the other is non-None, they are not equal, and the function accordingly raises a NonMatchingChecksumError. That behavior is expected. The question is: how did you generate the expected checksum? Normally, it should not be None. To properly generate it (it is contained in the `dataset_infos.json` file), you should have runned: https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md ```shell datasets-cli test <your-dataset-folder> --save_infos --all_configs ``` On the other hand, you should take into account that the generation of this file is NOT mandatory for personal/community datasets (we only require it for "canonical" datasets, i.e., datasets added to our library GitHub repository: https://github.com/huggingface/datasets/tree/master/datasets). Therefore, other option would be just to delete the `dataset_infos.json` file. If that file is not present, the function `verify_checksums` is not executed. Finally, you can circumvent the `verify_checksums` function by passing `ignore_verifications=True` to `load_dataset`: ```python load_dataset(..., ignore_verifications=True) ```
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https://github.com/huggingface/datasets/issues/3848
NonMatchingChecksumError when checksum is None
Thanks @albertvillanova! That's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file. Just a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.
I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.
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NonMatchingChecksumError when checksum is None I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. Thanks @albertvillanova! That's fine. I did run that command when I was adding a new dataset. Maybe because the command crashed in the middle, the checksum wasn't stored properly. I don't know where the bug is happening. But either (i) `verify_checksums` should properly handle this edge case, where the passed checksum is None or (ii) the `datasets-cli test` shouldn't generate a corrupted dataset_infos.json file. Just a more high-level thing, I was trying to follow the instructions for adding a dataset in the CONTRIBUTING.md, so if running that command isn't even necessary, that should probably be mentioned in the document, right? But that's somewhat of a moot point, since something isn't working quite right internally if I was able to get into this corrupted state in the first place, just by following those instructions.
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https://github.com/huggingface/datasets/issues/3848
NonMatchingChecksumError when checksum is None
Hi @jxmorris12, Definitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). While we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with: ```shell datasets-cli test <your-dataset-folder> --save_infos --all_configs ``` Also note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.
I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.
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NonMatchingChecksumError when checksum is None I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. Hi @jxmorris12, Definitely, your `dataset_infos.json` was corrupted (and wrongly contains expected None checksum). While we further investigate how this can happen and fix it, feel free to delete your `dataset_infos.json` file and recreate it with: ```shell datasets-cli test <your-dataset-folder> --save_infos --all_configs ``` Also note that `verify_checksum` is working as expected: if it receives a None and and a non-None checksums as input pair, it must raise an exception: they are not equal. That is not a bug.
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https://github.com/huggingface/datasets/issues/3848
NonMatchingChecksumError when checksum is None
At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated... In order to add a dataset, I think the most updated instructions are in our official documentation pages: https://huggingface.co/docs/datasets/share
I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.
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NonMatchingChecksumError when checksum is None I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. At a higher level, also note that we are preparing the release of `datasets` version 2.0, and some docs are being updated... In order to add a dataset, I think the most updated instructions are in our official documentation pages: https://huggingface.co/docs/datasets/share
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https://github.com/huggingface/datasets/issues/3848
NonMatchingChecksumError when checksum is None
Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file: - #3892 The fix will be accessible once this PR merged. And we are planning to do our 2.0 release today. We are also working on updating all our docs for our release today.
I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug.
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NonMatchingChecksumError when checksum is None I ran into the following error when adding a new dataset: ```bash expected_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': None, 'num_bytes': 40662}} recorded_checksums = {'https://adversarialglue.github.io/dataset/dev.zip': {'checksum': 'efb4cbd3aa4a87bfaffc310ae951981cc0a36c6c71c6425dd74e5b55f2f325c9', 'num_bytes': 40662}} verification_name = 'dataset source files' def verify_checksums(expected_checksums: Optional[dict], recorded_checksums: dict, verification_name=None): if expected_checksums is None: logger.info("Unable to verify checksums.") return if len(set(expected_checksums) - set(recorded_checksums)) > 0: raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums))) if len(set(recorded_checksums) - set(expected_checksums)) > 0: raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums))) bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] for_verification_name = " for " + verification_name if verification_name is not None else "" if len(bad_urls) > 0: error_msg = "Checksums didn't match" + for_verification_name + ":\n" > raise NonMatchingChecksumError(error_msg + str(bad_urls)) E datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: E ['https://adversarialglue.github.io/dataset/dev.zip'] src/datasets/utils/info_utils.py:40: NonMatchingChecksumError ``` ## Expected results The dataset downloads correctly, and there is no error. ## Actual results Datasets library is looking for a checksum of None, and it gets a non-None checksum, and throws an error. This is clearly a bug. Hi @jxmorris12, we have discovered the bug why `None` checksums wrongly appeared when generating the `dataset_infos.json` file: - #3892 The fix will be accessible once this PR merged. And we are planning to do our 2.0 release today. We are also working on updating all our docs for our release today.
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
<s>I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching. To fix this we can try making the order of the splits deterministic for map.</s>
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 <s>I think this is because the tokenizer is stateful and because the order in which the splits are processed is not deterministic. Because of that, the hash of the tokenizer may change for certain splits, which causes issues with caching. To fix this we can try making the order of the splits deterministic for map.</s>
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split. Therefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer). This is a bit trickier to fix, we can explore fixing this next week maybe
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Actually this is not because of the order of the splits, but most likely because the tokenizer used to process the second split is in a state that has been modified by the first split. Therefore after reloading the first split from the cache, then the second split can't be reloaded since the tokenizer hasn't seen the first split (and therefore is considered a different tokenizer). This is a bit trickier to fix, we can explore fixing this next week maybe
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Sorry didn't have the bandwidth to take care of this yet - will re-assign when I'm diving into it again !
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Sorry didn't have the bandwidth to take care of this yet - will re-assign when I'm diving into it again !
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
I had this issue with `run_speech_recognition_ctc.py` for wa2vec2.0 fine-tuning. I made a small change and the hash for the function (which includes tokenisation) is now the same before and after pre-porocessing. With the hash being the same, the caching works as intended. Before: ``` def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) ``` After: ``` def prepare_dataset(batch, feature_extractor, tokenizer): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch pd = lambda batch: prepare_dataset(batch, feature_extractor, tokenizer) with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( pd, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) ```
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 I had this issue with `run_speech_recognition_ctc.py` for wa2vec2.0 fine-tuning. I made a small change and the hash for the function (which includes tokenisation) is now the same before and after pre-porocessing. With the hash being the same, the caching works as intended. Before: ``` def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) ``` After: ``` def prepare_dataset(batch, feature_extractor, tokenizer): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch pd = lambda batch: prepare_dataset(batch, feature_extractor, tokenizer) with training_args.main_process_first(desc="dataset map preprocessing"): vectorized_datasets = raw_datasets.map( pd, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=num_workers, desc="preprocess datasets", ) ```
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Not sure why the second one would work and not the first one - they're basically the same with respect to hashing. In both cases the function is hashed recursively, and therefore the feature_extractor and the tokenizer are hashed the same way. With which tokenizer or feature extractor are you experiencing this behavior ? Do you also experience this ? > Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Not sure why the second one would work and not the first one - they're basically the same with respect to hashing. In both cases the function is hashed recursively, and therefore the feature_extractor and the tokenizer are hashed the same way. With which tokenizer or feature extractor are you experiencing this behavior ? Do you also experience this ? > Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache.
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Thanks ! Hopefully this can be useful to others, and also to better understand and improve hashing/caching
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Thanks ! Hopefully this can be useful to others, and also to better understand and improve hashing/caching
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
`tokenizer.save_pretrained(training_args.output_dir)` produces a different tokenizer hash when loaded on restart of the script. When I was debugging before I was terminating the script prior to this command, then rerunning. I compared the tokenizer items on the first and second runs, there are two different items: 1st: ``` ('_additional_special_tokens', [AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True)]) ... ('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7f4d6d0ddb38>) ``` 2nd: ``` ('_additional_special_tokens', [AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True)]) ... ('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7efc23dcce80>) ``` On every run of this the special tokens are being added on, and the hash is different on the `tokens_trie`. The increase in the special tokens category could be cleaned, but not sure about the hash for the `tokens_trie`. What might work is that the call for the tokenizer encoding can be translated into a function that strips any unnecessary information out, but that's a guess.
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 `tokenizer.save_pretrained(training_args.output_dir)` produces a different tokenizer hash when loaded on restart of the script. When I was debugging before I was terminating the script prior to this command, then rerunning. I compared the tokenizer items on the first and second runs, there are two different items: 1st: ``` ('_additional_special_tokens', [AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True)]) ... ('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7f4d6d0ddb38>) ``` 2nd: ``` ('_additional_special_tokens', [AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("<s>", rstrip=False, lstrip=False, single_word=False, normalized=True), AddedToken("</s>", rstrip=False, lstrip=False, single_word=False, normalized=True)]) ... ('tokens_trie', <transformers.tokenization_utils.Trie object at 0x7efc23dcce80>) ``` On every run of this the special tokens are being added on, and the hash is different on the `tokens_trie`. The increase in the special tokens category could be cleaned, but not sure about the hash for the `tokens_trie`. What might work is that the call for the tokenizer encoding can be translated into a function that strips any unnecessary information out, but that's a guess.
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Thanks for investigating ! Does that mean that `save_pretrained`() produces non-deterministic tokenizers on disk ? Or is it `from_pretrained()` which is not deterministic given the same files on disk ? I think one way to fix this would be to make save/from_pretrained deterministic, or make the pickling of `transformers.tokenization_utils.Trie` objects deterministic (this could be implemented in `transformers`, but maybe let's discuss in an issue in `transformers` before opening a PR)
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Thanks for investigating ! Does that mean that `save_pretrained`() produces non-deterministic tokenizers on disk ? Or is it `from_pretrained()` which is not deterministic given the same files on disk ? I think one way to fix this would be to make save/from_pretrained deterministic, or make the pickling of `transformers.tokenization_utils.Trie` objects deterministic (this could be implemented in `transformers`, but maybe let's discuss in an issue in `transformers` before opening a PR)
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Late to the party but everything should be deterministic (afaik at least). But `Trie` is a simple class object, so afaik it's hash function is linked to its `id(self)` so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Late to the party but everything should be deterministic (afaik at least). But `Trie` is a simple class object, so afaik it's hash function is linked to its `id(self)` so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ?
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
> But Trie is a simple class object, so afaik it's hash function is linked to its id(self) so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ? We're computing the hash of the pickle dump of the class so it should be fine, as long as the pickle dump is deterministic
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 > But Trie is a simple class object, so afaik it's hash function is linked to its id(self) so basically where it's stored in memory, so super highly non deterministic. Could that be the issue ? We're computing the hash of the pickle dump of the class so it should be fine, as long as the pickle dump is deterministic
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
I've ported wav2vec2.0 fine-tuning into Optimum-Graphcore which is where I found the issue. The majority of the script was copied from the Transformers version to keep it similar, [here is the tokenizer loading section from the source](https://github.com/huggingface/transformers/blob/f0982682bd6fd0b438dda79ec45f3a8fac83a985/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L531). In the last comment I have two loaded tokenizers, one from run 'N' of the script and one from 'N+1'. I think what's happening is that when you add special tokens (e.g. PAD and UNK) another AddedToken object is appended when tokenizer is saved regardless of whether special tokens are there already. If there is a AddedTokens cleanup at load/save this could solve the issue, but then is Trie going to cause hash to be different? I'm not sure.
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 I've ported wav2vec2.0 fine-tuning into Optimum-Graphcore which is where I found the issue. The majority of the script was copied from the Transformers version to keep it similar, [here is the tokenizer loading section from the source](https://github.com/huggingface/transformers/blob/f0982682bd6fd0b438dda79ec45f3a8fac83a985/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py#L531). In the last comment I have two loaded tokenizers, one from run 'N' of the script and one from 'N+1'. I think what's happening is that when you add special tokens (e.g. PAD and UNK) another AddedToken object is appended when tokenizer is saved regardless of whether special tokens are there already. If there is a AddedTokens cleanup at load/save this could solve the issue, but then is Trie going to cause hash to be different? I'm not sure.
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Which Python version are you using ? The trie is basically a big dict of dics, so deterministic nature depends on python version: https://stackoverflow.com/questions/2053021/is-the-order-of-a-python-dictionary-guaranteed-over-iterations Maybe the investigation is actually not finding the right culprit though (the memory id is changed, but `datasets` is not using that to compare, so maybe we need to be looking within `datasets` so see where the comparison fails)
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Which Python version are you using ? The trie is basically a big dict of dics, so deterministic nature depends on python version: https://stackoverflow.com/questions/2053021/is-the-order-of-a-python-dictionary-guaranteed-over-iterations Maybe the investigation is actually not finding the right culprit though (the memory id is changed, but `datasets` is not using that to compare, so maybe we need to be looking within `datasets` so see where the comparison fails)
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https://github.com/huggingface/datasets/issues/3847
Datasets' cache not re-used
Similar issue found on `BartTokenizer`. You can bypass the bug by loading a fresh new tokenizer everytime. ``` dataset = dataset.map(lambda x: tokenize_func(x, BartTokenizer.from_pretrained(xxx)), num_proc=num_proc, desc='Tokenize') ```
## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1
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Datasets' cache not re-used ## Describe the bug For most tokenizers I have tested (e.g. the RoBERTa tokenizer), the data preprocessing cache are not fully reused in the first few runs, although their `.arrow` cache files are in the cache directory. ## Steps to reproduce the bug Here is a reproducer. The GPT2 tokenizer works perfectly with caching, but not the RoBERTa tokenizer in this example. ```python from datasets import load_dataset from transformers import AutoTokenizer raw_datasets = load_dataset("wikitext", "wikitext-2-raw-v1") # tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer = AutoTokenizer.from_pretrained("roberta-base") text_column_name = "text" column_names = raw_datasets["train"].column_names def tokenize_function(examples): return tokenizer(examples[text_column_name], return_special_tokens_mask=True) tokenized_datasets = raw_datasets.map( tokenize_function, batched=True, remove_columns=column_names, load_from_cache_file=True, desc="Running tokenizer on every text in dataset", ) ``` ## Expected results No tokenization would be required after the 1st run. Everything should be loaded from the cache. ## Actual results Tokenization for some subsets are repeated at the 2nd and 3rd run. Starting from the 4th run, everything are loaded from cache. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Ubuntu 18.04.6 LTS - Python version: 3.6.9 - PyArrow version: 6.0.1 Similar issue found on `BartTokenizer`. You can bypass the bug by loading a fresh new tokenizer everytime. ``` dataset = dataset.map(lambda x: tokenize_func(x, BartTokenizer.from_pretrained(xxx)), num_proc=num_proc, desc='Tokenize') ```
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https://github.com/huggingface/datasets/issues/3841
Pyright reportPrivateImportUsage when `from datasets import load_dataset`
Hi! This issue stems from `datasets` having `py.typed` defined (see https://github.com/microsoft/pyright/discussions/3764#discussioncomment-3282142) - to avoid it, we would either have to remove `py.typed` (added to be compliant with PEP-561) or export the names with `__all__`/`from .submodule import name as name`. Transformers is fine as it no longer has `py.typed` (removed in https://github.com/huggingface/transformers/pull/18485) WDYT @lhoestq @albertvillanova @polinaeterna @sgugger's point makes sense - we should either be "properly typed" (have py.typed + mypy tests) or drop `py.typed` as Transformers did (I like this option better). (cc @Wauplin since `huggingface_hub` has the same issue.)
## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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Pyright reportPrivateImportUsage when `from datasets import load_dataset` ## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi! This issue stems from `datasets` having `py.typed` defined (see https://github.com/microsoft/pyright/discussions/3764#discussioncomment-3282142) - to avoid it, we would either have to remove `py.typed` (added to be compliant with PEP-561) or export the names with `__all__`/`from .submodule import name as name`. Transformers is fine as it no longer has `py.typed` (removed in https://github.com/huggingface/transformers/pull/18485) WDYT @lhoestq @albertvillanova @polinaeterna @sgugger's point makes sense - we should either be "properly typed" (have py.typed + mypy tests) or drop `py.typed` as Transformers did (I like this option better). (cc @Wauplin since `huggingface_hub` has the same issue.)
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https://github.com/huggingface/datasets/issues/3841
Pyright reportPrivateImportUsage when `from datasets import load_dataset`
> (cc @Wauplin since huggingface_hub has the same issue.) Hmm maybe we have the same issue but I haven't been able to reproduce something similar to `"load_dataset" is not exported from module "datasets"` message (using VSCode+Pylance -that is powered by Pyright). `huggingface_hub` contains a `py.typed` file but the package itself is actually typed. We are running `mypy` in our CI tests since ~3 months and so far it seems to be ok. But happy to change if it causes some issues with linters. Also the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block.
## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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Pyright reportPrivateImportUsage when `from datasets import load_dataset` ## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 > (cc @Wauplin since huggingface_hub has the same issue.) Hmm maybe we have the same issue but I haven't been able to reproduce something similar to `"load_dataset" is not exported from module "datasets"` message (using VSCode+Pylance -that is powered by Pyright). `huggingface_hub` contains a `py.typed` file but the package itself is actually typed. We are running `mypy` in our CI tests since ~3 months and so far it seems to be ok. But happy to change if it causes some issues with linters. Also the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block.
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https://github.com/huggingface/datasets/issues/3841
Pyright reportPrivateImportUsage when `from datasets import load_dataset`
@Wauplin I only get the error if I use Pyright's CLI tool or the Pyright extension (not sure why, but Pylance also doesn't report this issue on my machine) > Also the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block. I tried to fix the issue with `TYPE_CHECKING`, but it still fails if `py.typed` is present.
## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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Pyright reportPrivateImportUsage when `from datasets import load_dataset` ## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 @Wauplin I only get the error if I use Pyright's CLI tool or the Pyright extension (not sure why, but Pylance also doesn't report this issue on my machine) > Also the top-level [`__init__.py`](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/__init__.py) is quite different in `hfh` than `datasets` (at first glance). We have a section at the bottom to import all high level methods/classes in a `if TYPE_CHECKING` block. I tried to fix the issue with `TYPE_CHECKING`, but it still fails if `py.typed` is present.
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https://github.com/huggingface/datasets/issues/3841
Pyright reportPrivateImportUsage when `from datasets import load_dataset`
@mariosasko thank for the tip. I have been able to reproduce the issue as well. I would be up for including a (huge) static `__all__` variable in the `__init__.py` (since the file is already generated automatically in `hfh`) but honestly I don't think it's worth the hassle. I'll delete the `py.typed` file in `huggingface_hub` to be consistent between HF libraries. I opened a PR here: https://github.com/huggingface/huggingface_hub/pull/1329
## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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Pyright reportPrivateImportUsage when `from datasets import load_dataset` ## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 @mariosasko thank for the tip. I have been able to reproduce the issue as well. I would be up for including a (huge) static `__all__` variable in the `__init__.py` (since the file is already generated automatically in `hfh`) but honestly I don't think it's worth the hassle. I'll delete the `py.typed` file in `huggingface_hub` to be consistent between HF libraries. I opened a PR here: https://github.com/huggingface/huggingface_hub/pull/1329
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-2.1605889797210693 ]
https://github.com/huggingface/datasets/issues/3841
Pyright reportPrivateImportUsage when `from datasets import load_dataset`
I am getting this error in google colab today: ![image](https://user-images.githubusercontent.com/3464445/219883967-c7193a23-0388-4ba3-b00c-a53883fb6512.png) The code runs just fine too.
## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0
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Pyright reportPrivateImportUsage when `from datasets import load_dataset` ## Describe the bug Pyright complains about module not exported. ## Steps to reproduce the bug Use an editor/IDE with Pyright Language server with default configuration: ```python from datasets import load_dataset ``` ## Expected results No complain from Pyright ## Actual results Pyright complain below: ``` `load_dataset` is not exported from module "datasets" Import from "datasets.load" instead [reportPrivateImportUsage] ``` Importing from `datasets.load` does indeed solves the problem but I believe importing directly from top level `datasets` is the intended usage per the documentation. ## Environment info - `datasets` version: 1.18.3 - Platform: macOS-12.2.1-arm64-arm-64bit - Python version: 3.9.10 - PyArrow version: 7.0.0 I am getting this error in google colab today: ![image](https://user-images.githubusercontent.com/3464445/219883967-c7193a23-0388-4ba3-b00c-a53883fb6512.png) The code runs just fine too.
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-2.234018325805664 ]
https://github.com/huggingface/datasets/issues/3832
Making Hugging Face the place to go for Graph NNs datasets
It will be indeed really great to add support to GNN datasets. Big :+1: for this initiative.
Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero
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Making Hugging Face the place to go for Graph NNs datasets Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero It will be indeed really great to add support to GNN datasets. Big :+1: for this initiative.
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https://github.com/huggingface/datasets/issues/3832
Making Hugging Face the place to go for Graph NNs datasets
@napoles-uach identifies the [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression). Added to the Tasks in the initial issue.
Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero
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Making Hugging Face the place to go for Graph NNs datasets Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero @napoles-uach identifies the [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression). Added to the Tasks in the initial issue.
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https://github.com/huggingface/datasets/issues/3832
Making Hugging Face the place to go for Graph NNs datasets
Great initiative! Let's keep this issue for these 3 datasets, but moving forward maybe let's create a new issue per dataset :rocket: great work @napoles-uach and @omarespejel!
Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero
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Making Hugging Face the place to go for Graph NNs datasets Let's make Hugging Face Datasets the central hub for GNN datasets :) **Motivation**. Datasets are currently quite scattered and an open-source central point such as the Hugging Face Hub would be ideal to support the growth of the GNN field. What are some datasets worth integrating into the Hugging Face hub? Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Special thanks to @napoles-uach for his collaboration on identifying the first ones: - [ ] [SNAP-Stanford OGB Datasets](https://github.com/snap-stanford/ogb). - [ ] [SNAP-Stanford Pretrained GNNs Chemistry and Biology Datasets](https://github.com/snap-stanford/pretrain-gnns). - [ ] [TUDatasets](https://chrsmrrs.github.io/datasets/) (A collection of benchmark datasets for graph classification and regression) cc @osanseviero Great initiative! Let's keep this issue for these 3 datasets, but moving forward maybe let's create a new issue per dataset :rocket: great work @napoles-uach and @omarespejel!
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https://github.com/huggingface/datasets/issues/3831
when using to_tf_dataset with shuffle is true, not all completed batches are made
Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.
## Describe the bug when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch ## Steps to reproduce the bug this is the sample code below https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing ## Expected results regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16. ## Actual results 4 batches ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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when using to_tf_dataset with shuffle is true, not all completed batches are made ## Describe the bug when converting a dataset to tf_dataset by using to_tf_dataset with shuffle true, the remainder is not converted to one batch ## Steps to reproduce the bug this is the sample code below https://colab.research.google.com/drive/1_oRXWsR38ElO1EYF9ayFoCU7Ou1AAej4?usp=sharing ## Expected results regardless of shuffle is true or not, 67 rows dataset should be 5 batches when batch size is 16. ## Actual results 4 batches ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1 Hi @greenned, this is expected behaviour for `to_tf_dataset`. By default, we drop the smaller 'remainder' batch during training (i.e. when `shuffle=True`). If you really want to keep that batch, you can set `drop_remainder=False` when calling `to_tf_dataset()`.
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https://github.com/huggingface/datasets/issues/3830
Got error when load cnn_dailymail dataset
Was able to reproduce the issue on Colab; full logs below. ``` --------------------------------------------------------------------------- NotADirectoryError Traceback (most recent call last) [<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>() 1 import datasets 2 ----> 3 train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") 5 frames [/usr/local/lib/python3.7/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, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1705 ignore_verifications=ignore_verifications, 1706 try_from_hf_gcs=try_from_hf_gcs, -> 1707 use_auth_token=use_auth_token, 1708 ) 1709 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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) 593 if not downloaded_from_gcs: 594 self._download_and_prepare( --> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 596 ) 597 # Sync info [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 659 split_dict = SplitDict(dataset_name=self.name) 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 662 663 # Checksums verification [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 253 def _split_generators(self, dl_manager): 254 dl_paths = dl_manager.download_and_extract(_DL_URLS) --> 255 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN) 256 # Generate shared vocabulary 257 [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _subset_filenames(dl_paths, split) 154 else: 155 logger.fatal("Unsupported split: %s", split) --> 156 cnn = _find_files(dl_paths, "cnn", urls) 157 dm = _find_files(dl_paths, "dm", urls) 158 return cnn + dm [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _find_files(dl_paths, publisher, url_dict) 133 else: 134 logger.fatal("Unsupported publisher: %s", publisher) --> 135 files = sorted(os.listdir(top_dir)) 136 137 ret_files = [] NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ```
When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ```
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Got error when load cnn_dailymail dataset When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ``` Was able to reproduce the issue on Colab; full logs below. ``` --------------------------------------------------------------------------- NotADirectoryError Traceback (most recent call last) [<ipython-input-2-39967739ba7f>](https://localhost:8080/#) in <module>() 1 import datasets 2 ----> 3 train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") 5 frames [/usr/local/lib/python3.7/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, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, script_version, **config_kwargs) 1705 ignore_verifications=ignore_verifications, 1706 try_from_hf_gcs=try_from_hf_gcs, -> 1707 use_auth_token=use_auth_token, 1708 ) 1709 [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) 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) 593 if not downloaded_from_gcs: 594 self._download_and_prepare( --> 595 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs 596 ) 597 # Sync info [/usr/local/lib/python3.7/dist-packages/datasets/builder.py](https://localhost:8080/#) in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs) 659 split_dict = SplitDict(dataset_name=self.name) 660 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) --> 661 split_generators = self._split_generators(dl_manager, **split_generators_kwargs) 662 663 # Checksums verification [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _split_generators(self, dl_manager) 253 def _split_generators(self, dl_manager): 254 dl_paths = dl_manager.download_and_extract(_DL_URLS) --> 255 train_files = _subset_filenames(dl_paths, datasets.Split.TRAIN) 256 # Generate shared vocabulary 257 [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _subset_filenames(dl_paths, split) 154 else: 155 logger.fatal("Unsupported split: %s", split) --> 156 cnn = _find_files(dl_paths, "cnn", urls) 157 dm = _find_files(dl_paths, "dm", urls) 158 return cnn + dm [/root/.cache/huggingface/modules/datasets_modules/datasets/cnn_dailymail/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234/cnn_dailymail.py](https://localhost:8080/#) in _find_files(dl_paths, publisher, url_dict) 133 else: 134 logger.fatal("Unsupported publisher: %s", publisher) --> 135 files = sorted(os.listdir(top_dir)) 136 137 ret_files = [] NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' ```
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https://github.com/huggingface/datasets/issues/3830
Got error when load cnn_dailymail dataset
Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 We are planning to make a patch release today (indeed, we were planning to do it last Friday). In the meantime, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ``` CC: @lhoestq
When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ```
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Got error when load cnn_dailymail dataset When using datasets.load_dataset method to load cnn_dailymail dataset, got error as below: - windows os: FileNotFoundError: [WinError 3] 系统找不到指定的路径。: 'D:\\SourceCode\\DataScience\\HuggingFace\\Data\\downloads\\1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b\\cnn\\stories' - google colab: NotADirectoryError: [Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/1bc05d24fa6dda2468e83a73cf6dc207226e01e3c48a507ea716dc0421da583b/cnn/stories' The code is to load dataset: windows os: ``` from datasets import load_dataset dataset = load_dataset("cnn_dailymail", "3.0.0", cache_dir="D:\\SourceCode\\DataScience\\HuggingFace\\Data") ``` google colab: ``` import datasets train_data = datasets.load_dataset("cnn_dailymail", "3.0.0", split="train") ``` Hi @jon-tow, thanks for reporting. And hi @dynamicwebpaige, thanks for your investigation. This issue was already reported - #3784 and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 We are planning to make a patch release today (indeed, we were planning to do it last Friday). In the meantime, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ``` CC: @lhoestq
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https://github.com/huggingface/datasets/issues/3829
[📄 Docs] Create a `datasets` performance guide.
Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily. I think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging/optimizing them
## Brief Overview Downloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https://github.com/huggingface/datasets/issues/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments. ## Feature Request Could we create a performance guide for using `datasets`, similar to: * [Better performance with the `tf.data` API](https://github.com/huggingface/datasets/issues/3735) * [Analyze `tf.data` performance with the TF Profiler](https://www.tensorflow.org/guide/data_performance_analysis) This performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below). ![image](https://user-images.githubusercontent.com/3712347/156859152-a3cb9565-3ec6-4d39-8e77-56d0a75a4954.png) ## Related Issues * [wiki_dpr pre-processing performance #1670](https://github.com/huggingface/datasets/issues/1670) * [Adjusting chunk size for streaming datasets #3499](https://github.com/huggingface/datasets/issues/3499) * [how large datasets are handled under the hood #1004](https://github.com/huggingface/datasets/issues/1004) * [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https://github.com/huggingface/datasets/issues/1830) * [Best way to batch a large dataset? #315](https://github.com/huggingface/datasets/issues/315) * [Saving processed dataset running infinitely #1911](https://github.com/huggingface/datasets/issues/1911)
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[📄 Docs] Create a `datasets` performance guide. ## Brief Overview Downloading, saving, and preprocessing large datasets from the `datasets` library can often result in [performance bottlenecks](https://github.com/huggingface/datasets/issues/3735). These performance snags can be challenging to identify and to debug, especially for users who are less experienced with building deep learning experiments. ## Feature Request Could we create a performance guide for using `datasets`, similar to: * [Better performance with the `tf.data` API](https://github.com/huggingface/datasets/issues/3735) * [Analyze `tf.data` performance with the TF Profiler](https://www.tensorflow.org/guide/data_performance_analysis) This performance guide should detail practical options for improving performance with `datasets`, and enumerate any common best practices. It should also show how to use tools like the PyTorch Profiler or the TF Profiler to identify any performance bottlenecks (example below). ![image](https://user-images.githubusercontent.com/3712347/156859152-a3cb9565-3ec6-4d39-8e77-56d0a75a4954.png) ## Related Issues * [wiki_dpr pre-processing performance #1670](https://github.com/huggingface/datasets/issues/1670) * [Adjusting chunk size for streaming datasets #3499](https://github.com/huggingface/datasets/issues/3499) * [how large datasets are handled under the hood #1004](https://github.com/huggingface/datasets/issues/1004) * [using map on loaded Tokenizer 10x - 100x slower than default Tokenizer? #1830](https://github.com/huggingface/datasets/issues/1830) * [Best way to batch a large dataset? #315](https://github.com/huggingface/datasets/issues/315) * [Saving processed dataset running infinitely #1911](https://github.com/huggingface/datasets/issues/1911) Hi ! Yes this is definitely something we'll explore, since optimizing processing pipelines can be challenging and because performance is key here: we want anyone to be able to play with large-scale datasets more easily. I think we'll start by documenting the performance of the dataset transforms we provide, and then we can have some tools to help debugging/optimizing them
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https://github.com/huggingface/datasets/issues/3828
The Pile's _FEATURE spec seems to be incorrect
Hi @dlwh, thanks for reporting. Please note, that the source data files for "all" config are different from the other configurations. The "all" config contains the official Pile data files, from https://mystic.the-eye.eu/public/AI/pile/ All data examples contain a "meta" dict with a single "pile_set_name" key: ```python In [1]: from datasets import load_dataset ds = load_dataset("the_pile", "all", split="train", streaming=True) item = next(iter(ds)) Downloading builder script: 9.09kB [00:00, 4.42MB/s] In [3]: item["meta"] Out[3]: {'pile_set_name': 'Pile-CC'} ``` On the other hand, all the other subset configs data files come from the Pile preliminary components directory: https://mystic.the-eye.eu/public/AI/pile_preliminary_components/ For theses components, the "meta" field may have different keys depending on the subset: "id", "language", "pmid",... Because of that, if we had kept the `dict` data format for the "meta" field, we would have an error when trying to concatenate different subsets, whose "meta" keys are not identical. In order to avoid that, the "meta" field is cast to `str` in all these cases, so that there is no incompatibility in their "meta" data type when concatenating. You can check, for example, that for "pubmed_central" the "meta" field is cast to `str`: ```python In [4]: from datasets import load_dataset ds = load_dataset("the_pile", "pubmed_central", split="train", streaming=True) item = next(iter(ds)) In [5]: item["meta"] Out[5]: "{'id': 'PMC6071596'}" ``` Feel free to reopen this issue if you have further questions.
## Describe the bug If you look at https://huggingface.co/datasets/the_pile/blob/main/the_pile.py: For "all" * the pile_set_name is never set for data * there's actually an id field inside of "meta" For subcorpora pubmed_central and hacker_news: * the meta is specified to be a string, but it's actually a dict with an id field inside. ## Steps to reproduce the bug ## Expected results Feature spec should match the data I'd think? ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version:
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The Pile's _FEATURE spec seems to be incorrect ## Describe the bug If you look at https://huggingface.co/datasets/the_pile/blob/main/the_pile.py: For "all" * the pile_set_name is never set for data * there's actually an id field inside of "meta" For subcorpora pubmed_central and hacker_news: * the meta is specified to be a string, but it's actually a dict with an id field inside. ## Steps to reproduce the bug ## Expected results Feature spec should match the data I'd think? ## Actual results Specify the actual results or traceback. ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: - Platform: - Python version: - PyArrow version: Hi @dlwh, thanks for reporting. Please note, that the source data files for "all" config are different from the other configurations. The "all" config contains the official Pile data files, from https://mystic.the-eye.eu/public/AI/pile/ All data examples contain a "meta" dict with a single "pile_set_name" key: ```python In [1]: from datasets import load_dataset ds = load_dataset("the_pile", "all", split="train", streaming=True) item = next(iter(ds)) Downloading builder script: 9.09kB [00:00, 4.42MB/s] In [3]: item["meta"] Out[3]: {'pile_set_name': 'Pile-CC'} ``` On the other hand, all the other subset configs data files come from the Pile preliminary components directory: https://mystic.the-eye.eu/public/AI/pile_preliminary_components/ For theses components, the "meta" field may have different keys depending on the subset: "id", "language", "pmid",... Because of that, if we had kept the `dict` data format for the "meta" field, we would have an error when trying to concatenate different subsets, whose "meta" keys are not identical. In order to avoid that, the "meta" field is cast to `str` in all these cases, so that there is no incompatibility in their "meta" data type when concatenating. You can check, for example, that for "pubmed_central" the "meta" field is cast to `str`: ```python In [4]: from datasets import load_dataset ds = load_dataset("the_pile", "pubmed_central", split="train", streaming=True) item = next(iter(ds)) In [5]: item["meta"] Out[5]: "{'id': 'PMC6071596'}" ``` Feel free to reopen this issue if you have further questions.
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https://github.com/huggingface/datasets/issues/3823
500 internal server error when trying to open a dataset composed of Zarr stores
Hi @jacobbieker, thanks for reporting! I have transferred this issue to our Hub team and they are investigating it. I keep you informed.
## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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500 internal server error when trying to open a dataset composed of Zarr stores ## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @jacobbieker, thanks for reporting! I have transferred this issue to our Hub team and they are investigating it. I keep you informed.
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https://github.com/huggingface/datasets/issues/3823
500 internal server error when trying to open a dataset composed of Zarr stores
Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https://github.com/github/git-sizer on it: ``` Processing blobs: 147448 Processing trees: 27 Processing commits: 4 Matching commits to trees: 4 Processing annotated tags: 0 Processing references: 3 | Name | Value | Level of concern | | ---------------------------- | --------- | ------------------------------ | | Biggest objects | | | | * Trees | | | | * Maximum entries [1] | 167 k | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! | | | | | | Biggest checkouts | | | | * Number of files [2] | 189 k | *** | [1] aa057d2667c34c70c6146efc631f5c9917ff326e (refs/heads/main:2016.zarr/unknown) [2] 6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs/heads/main^{tree}) ``` You can check https://github.com/github/git-sizer for more information on how to avoid such pathological structures.
## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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500 internal server error when trying to open a dataset composed of Zarr stores ## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @jacobbieker, we are investigating this issue on our side and we'll see if we can fix it, but please note that your repo is considered problematic for git. Here are the results of running https://github.com/github/git-sizer on it: ``` Processing blobs: 147448 Processing trees: 27 Processing commits: 4 Matching commits to trees: 4 Processing annotated tags: 0 Processing references: 3 | Name | Value | Level of concern | | ---------------------------- | --------- | ------------------------------ | | Biggest objects | | | | * Trees | | | | * Maximum entries [1] | 167 k | !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! | | | | | | Biggest checkouts | | | | * Number of files [2] | 189 k | *** | [1] aa057d2667c34c70c6146efc631f5c9917ff326e (refs/heads/main:2016.zarr/unknown) [2] 6897b7bf6440fdd16b2c39d08085a669e7eaa59d (refs/heads/main^{tree}) ``` You can check https://github.com/github/git-sizer for more information on how to avoid such pathological structures.
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https://github.com/huggingface/datasets/issues/3823
500 internal server error when trying to open a dataset composed of Zarr stores
Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.
## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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500 internal server error when trying to open a dataset composed of Zarr stores ## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi, thanks for getting back to me so quick! And yeah, I figured that was probably the problem. I was going to try to delete the repo, but couldn't through the website, so if that's the easiest way to solve it, I can regenerate the dataset in a different format with less tiny files, and you guys can delete the repo as it is. Zarr just saves everything as lots of small files to make chunks easy to load, which is why I was preferring that format, but maybne that just doesn't work well for HF datasets.
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https://github.com/huggingface/datasets/issues/3823
500 internal server error when trying to open a dataset composed of Zarr stores
Hi @jacobbieker, For future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues... On the meantime, they have made a fix and your dataset is working: https://huggingface.co/datasets/openclimatefix/mrms
## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0
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500 internal server error when trying to open a dataset composed of Zarr stores ## Describe the bug The dataset [openclimatefix/mrms](https://huggingface.co/datasets/openclimatefix/mrms) gives a 500 server error when trying to open it on the website, or through code. The dataset doesn't have a loading script yet, and I did push two [xarray](https://docs.xarray.dev/en/stable/) Zarr stores of data there recentlyish. The Zarr stores are composed of lots of small files, which I am guessing is probably the problem, as we have another [OCF dataset](https://huggingface.co/datasets/openclimatefix/eumetsat_uk_hrv) using xarray and Zarr, but with the Zarr stored on GCP public datasets instead of directly in HF datasets, and that one opens fine. In general, we were hoping to use HF datasets to release some more public geospatial datasets as benchmarks, which are commonly stored as Zarr stores as they can be compressed well and deal with the multi-dimensional data and coordinates fairly easily compared to other formats, but with this error, I'm assuming we should try a different format? For context, we are trying to have complete public model+data reimplementations of some SOTA weather and solar nowcasting models, like [MetNet, MetNet-2,](https://github.com/openclimatefix/metnet) [DGMR](https://github.com/openclimatefix/skillful_nowcasting), and [others](https://github.com/openclimatefix/graph_weather), which all have large, complex datasets. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("openclimatefix/mrms") ``` ## Expected results The dataset should be downloaded or open up ## Actual results A 500 internal server error ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: Linux-5.15.25-1-MANJARO-x86_64-with-glibc2.35 - Python version: 3.9.10 - PyArrow version: 7.0.0 Hi @jacobbieker, For future use cases, our Hub team is still pondering whether to limit the maximum number of files per repo to avoid technical issues... On the meantime, they have made a fix and your dataset is working: https://huggingface.co/datasets/openclimatefix/mrms
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https://github.com/huggingface/datasets/issues/3822
Add Biwi Kinect Head Pose Database
Official dataset location : https://icu.ee.ethz.ch/research/datsets.html In the "Biwi Kinect Head Pose Database" section, I do not find any information regarding "Downloading the dataset." . Do we mail the authors regarding this ? I found the dataset on Kaggle : [Link](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database) , but since 🤗 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. Any inputs on how we could proceed ? Thank you. [ Need your inputs here, @lhoestq or @mariosasko ]
## Adding a Dataset - **Name:** Biwi Kinect Head Pose Database - **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles. - **Data:** [*link to the Github repository or current dataset location*](https://icu.ee.ethz.ch/research/datsets.html) - **Motivation:** Useful pose estimation dataset Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add Biwi Kinect Head Pose Database ## Adding a Dataset - **Name:** Biwi Kinect Head Pose Database - **Description:** Over 15K images of 20 people recorded with a Kinect while turning their heads around freely. For each frame, depth and rgb images are provided, together with ground in the form of the 3D location of the head and its rotation angles. - **Data:** [*link to the Github repository or current dataset location*](https://icu.ee.ethz.ch/research/datsets.html) - **Motivation:** Useful pose estimation dataset Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Official dataset location : https://icu.ee.ethz.ch/research/datsets.html In the "Biwi Kinect Head Pose Database" section, I do not find any information regarding "Downloading the dataset." . Do we mail the authors regarding this ? I found the dataset on Kaggle : [Link](https://www.kaggle.com/kmader/biwi-kinect-head-pose-database) , but since 🤗 does not host any of the datasets, this would require the user to provide their Kaggle username and API key to download. Any inputs on how we could proceed ? Thank you. [ Need your inputs here, @lhoestq or @mariosasko ]
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https://github.com/huggingface/datasets/issues/3820
`pubmed_qa` checksum mismatch
Hi @jon-tow, thanks for reporting. This issue was already reported and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 We are planning to make a patch release today. In the meantime, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ```
## Describe the bug Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets try: datasets.load_dataset("pubmed_qa", "pqa_labeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_unlabeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_artificial") except Exception as e: print(e) ``` ## Expected results Successful download. ## Actual results Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: macOS - Python version: 3.8.1 - PyArrow version: 3.0.0
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`pubmed_qa` checksum mismatch ## Describe the bug Loading [`pubmed_qa`](https://huggingface.co/datasets/pubmed_qa) results in a mismatched checksum error. ## Steps to reproduce the bug ```python # Sample code to reproduce the bug import datasets try: datasets.load_dataset("pubmed_qa", "pqa_labeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_unlabeled") except Exception as e: print(e) try: datasets.load_dataset("pubmed_qa", "pqa_artificial") except Exception as e: print(e) ``` ## Expected results Successful download. ## Actual results Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.9/site-packages/datasets/load.py", line 1702, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 594, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.9/site-packages/datasets/builder.py", line 665, in _download_and_prepare verify_checksums( File "/usr/local/lib/python3.9/site-packages/datasets/utils/info_utils.py", line 40, in verify_checksums raise NonMatchingChecksumError(error_msg + str(bad_urls)) datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=1RsGLINVce-0GsDkCLDuLZmoLuzfmoCuQ', 'https://drive.google.com/uc?export=download&id=15v1x6aQDlZymaHGP7cZJZZYFfeJt2NdS'] ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3 - Platform: macOS - Python version: 3.8.1 - PyArrow version: 3.0.0 Hi @jon-tow, thanks for reporting. This issue was already reported and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 We are planning to make a patch release today. In the meantime, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/3818
Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI
Hi, thanks for reporting! We can add a `sources: datasets.Value("string")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?
**Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI **Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py). Hi, thanks for reporting! We can add a `sources: datasets.Value("string")` feature to the `Features` dict in the `SARI` script to fix this. Would you be interested in submitting a PR?
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https://github.com/huggingface/datasets/issues/3818
Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI
Hi Mario, Thanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric: ``` features=datasets.Features( { "sources": datasets.Value("string", id="sequence"), "predictions": datasets.Value("string", id="sequence"), "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"), } ), ``` But that only avoids a failure in `encode_batch` in the `add_batch` method: ``` batch = {"predictions": predictions, "references": references} batch = self.info.features.encode_batch(batch) ``` The real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param: ``` def add_batch(self, *, predictions=None, references=None): def add(self, *, prediction=None, reference=None): def compute(self, *, predictions=None, references=None, **kwargs) ``` And then, it fails: `TypeError: add_batch() got an unexpected keyword argument sources` I need this for adding any metric based on SARI or alike, not only for sari.py :) Let me know if I understood correctly the proposed solution.
**Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI **Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py). Hi Mario, Thanks for your message. I did try to add `sources` into the `Features` dict using a script for the metric: ``` features=datasets.Features( { "sources": datasets.Value("string", id="sequence"), "predictions": datasets.Value("string", id="sequence"), "references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"), } ), ``` But that only avoids a failure in `encode_batch` in the `add_batch` method: ``` batch = {"predictions": predictions, "references": references} batch = self.info.features.encode_batch(batch) ``` The real problem is that `add_batch()`, `add()` and `compute()` does not receive a `sources` param: ``` def add_batch(self, *, predictions=None, references=None): def add(self, *, prediction=None, reference=None): def compute(self, *, predictions=None, references=None, **kwargs) ``` And then, it fails: `TypeError: add_batch() got an unexpected keyword argument sources` I need this for adding any metric based on SARI or alike, not only for sari.py :) Let me know if I understood correctly the proposed solution.
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https://github.com/huggingface/datasets/issues/3818
Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI
The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR.
**Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py).
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Support for "sources" parameter in the add() and add_batch() methods in datasets.metric - SARI **Is your feature request related to a problem? Please describe.** The methods `add_batch` and `add` from the `Metric` [class](https://github.com/huggingface/datasets/blob/1675ad6a958435b675a849eafa8a7f10fe0f43bc/src/datasets/metric.py) does not work with [SARI](https://github.com/huggingface/datasets/blob/master/metrics/sari/sari.py) metric. This metric not only relies on the predictions and references, but also in the input. For example, when the `add_batch` method is used, then the `compute()` method fails: ``` metric = load_metric("sari") metric.add_batch( predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) metric.compute() > TypeError: _compute() missing 1 required positional argument: 'sources' ``` Therefore, the `compute() `method can only be used standalone: ``` metric = load_metric("sari") result = metric.compute( sources=["About 95 species are currently accepted ."], predictions=["About 95 you now get in ."], references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]) > {'sari': 26.953601953601954} ``` **Describe the solution you'd like** Support for an additional parameter `sources` in the `add_batch` and `add` of the `Metric` class. ``` add_batch(*, sources=None, predictions=None, references=None, **kwargs) add(*, sources=None, predictions=None, references=None, **kwargs) compute() ``` **Describe alternatives you've considered** I've tried to override the `add_batch` and `add`, however, these are highly dependent to the `Metric` class. We could also write a simple function that compute the scores of a sentences list, but then we lose the functionality from the original [add](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add) and [add_batch method](https://huggingface.co/docs/datasets/_modules/datasets/metric.html#Metric.add_batch). **Additional context** These methods are used in the transformers [pytorch examples](https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization_no_trainer.py). The `Metric` class has been modified recently to support this use-case, but the `add_batch` + `compute` pattern still doesn't work correctly. I'll open a PR.
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-2.1934309005737305 ]
https://github.com/huggingface/datasets/issues/3813
Add MetaShift dataset
I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.
## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add MetaShift dataset ## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). I would like to take this up and give it a shot. Any image specific - dataset guidelines to keep in mind ? Thank you.
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https://github.com/huggingface/datasets/issues/3813
Add MetaShift dataset
I've started working on adding this dataset. I require some inputs on the following : Ref for the initial draft [here](https://github.com/dnaveenr/datasets/blob/add_metashift_dataset/datasets/metashift/metashift.py) 1. The dataset does not have a typical - train/test/val split. What do we do for the _split_generators() function ? How do we go about this ? 2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : Code : https://github.com/Weixin-Liang/MetaShift/blob/main/dataset/generate_full_MetaShift.py Info : https://metashift.readthedocs.io/en/latest/sub_pages/download_MetaShift.html#generate-the-full-metashift-dataset Can I just copy over the required functions into the metashift.py to generate the dataset ? 3. How do we complete the _generate_examples for this dataset ? The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here. Inputs, suggestions would be helpful. Thank you.
## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add MetaShift dataset ## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). I've started working on adding this dataset. I require some inputs on the following : Ref for the initial draft [here](https://github.com/dnaveenr/datasets/blob/add_metashift_dataset/datasets/metashift/metashift.py) 1. The dataset does not have a typical - train/test/val split. What do we do for the _split_generators() function ? How do we go about this ? 2. This dataset builds on the Visual Genome dataset, using a metadata file. The dataset is generated using generate_full_MetaShift.py script. By default, the authors choose to generate the dataset only for a SELECTED_CLASSES. The following script is used : Code : https://github.com/Weixin-Liang/MetaShift/blob/main/dataset/generate_full_MetaShift.py Info : https://metashift.readthedocs.io/en/latest/sub_pages/download_MetaShift.html#generate-the-full-metashift-dataset Can I just copy over the required functions into the metashift.py to generate the dataset ? 3. How do we complete the _generate_examples for this dataset ? The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here. Inputs, suggestions would be helpful. Thank you.
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-2.232809066772461 ]
https://github.com/huggingface/datasets/issues/3813
Add MetaShift dataset
Hi ! Thanks for adding this dataset :) Let me answer your questions: 1. in this case you can put everything in the "train" split 2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible 3. see point 2 haha > The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here. Yup ! We can also define a `selected_classes` parameter such that users can do ```python load_dataset("metashift", selected_classes=["cat", "dog", ...]) ```
## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add MetaShift dataset ## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Hi ! Thanks for adding this dataset :) Let me answer your questions: 1. in this case you can put everything in the "train" split 2. Yes you can copy the script (provided you also include the MIT license of the code in the file header for example). Though we ideally try to not create new directories nor files when generating dataset, so if possible this script should be adapted to not create the file structure they mentioned, but instead yield the images one by one in `_generate_examples`. Let me know if you think this is feasible 3. see point 2 haha > The user has the ability to use default selected classes, get the complete dataset or add more specific additional classes. I think config would be a good option here. Yup ! We can also define a `selected_classes` parameter such that users can do ```python load_dataset("metashift", selected_classes=["cat", "dog", ...]) ```
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-2.18141770362854 ]
https://github.com/huggingface/datasets/issues/3813
Add MetaShift dataset
Great. This is helpful. Thanks @lhoestq . Regarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.
## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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Add MetaShift dataset ## Adding a Dataset - **Name:** MetaShift - **Description:** collection of 12,868 sets of natural images across 410 classes- - **Paper:** https://arxiv.org/abs/2202.06523v1 - **Data:** https://github.com/weixin-liang/metashift Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md). Great. This is helpful. Thanks @lhoestq . Regarding Point 2, I'll try using yield instead of creating the directories and see if its feasible. selected_classes config sounds good.
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https://github.com/huggingface/datasets/issues/3809
Checksums didn't match for datasets on Google Drive
Hi @muelletm, thanks for reporting. This issue was already reported and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 Until our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ```
## Describe the bug Datasets hosted on Google Drive do not seem to work right now. Loading them fails with a checksum error. ## Steps to reproduce the bug ```python from datasets import load_dataset for dataset in ["head_qa", "yelp_review_full"]: try: load_dataset(dataset) except Exception as exception: print("Error", dataset, exception) ``` Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4). ## Expected results The datasets should be loaded. ## Actual results ``` Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9... Error head_qa Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t'] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Error yelp_review_full Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1
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Checksums didn't match for datasets on Google Drive ## Describe the bug Datasets hosted on Google Drive do not seem to work right now. Loading them fails with a checksum error. ## Steps to reproduce the bug ```python from datasets import load_dataset for dataset in ["head_qa", "yelp_review_full"]: try: load_dataset(dataset) except Exception as exception: print("Error", dataset, exception) ``` Here is a [colab](https://colab.research.google.com/drive/1wOtHBmL8I65NmUYakzPV5zhVCtHhi7uQ#scrollTo=cDzdCLlk-Bo4). ## Expected results The datasets should be loaded. ## Actual results ``` Downloading and preparing dataset head_qa/es (download: 75.69 MiB, generated: 2.86 MiB, post-processed: Unknown size, total: 78.55 MiB) to /root/.cache/huggingface/datasets/head_qa/es/1.1.0/583ab408e8baf54aab378c93715fadc4d8aa51b393e27c3484a877e2ac0278e9... Error head_qa Checksums didn't match for dataset source files: ['https://drive.google.com/u/0/uc?export=download&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t'] Downloading and preparing dataset yelp_review_full/yelp_review_full (download: 187.06 MiB, generated: 496.94 MiB, post-processed: Unknown size, total: 684.00 MiB) to /root/.cache/huggingface/datasets/yelp_review_full/yelp_review_full/1.0.0/13c31a618ba62568ec8572a222a283dfc29a6517776a3ac5945fb508877dde43... Error yelp_review_full Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0'] ``` ## Environment info - `datasets` version: 1.18.3 - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.12 - PyArrow version: 6.0.1 Hi @muelletm, thanks for reporting. This issue was already reported and its root cause is a change in the Google Drive service. See: - #3786 We have already fixed it. See: - #3787 Until our next `datasets` library release, you can get this fix by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, if you had previously tried to load the data and got the checksum error, you should force the redownload of the data (before the fix, you just downloaded and cached the virus scan warning page, instead of the data file): ```shell load_dataset("...", download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/3808
Pre-Processing Cache Fails when using a Factory pattern
Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export PYTHONHASHSEED=1234`
## Describe the bug If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time. ## Steps to reproduce the bug ```python def preprocess_function_factory(augmentation=None): def preprocess_function(examples): # Tokenize the texts if augmentation: conversions1 = [ augmentation(example) for example in examples[sentence1_key] ] if sentence2_key is None: args = (conversions1,) else: conversions2 = [ augmentation(example) for example in examples[sentence2_key] ] args = (conversions1, conversions2) else: args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer( *args, padding=padding, max_length=max_seq_length, truncation=True ) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [ (label_to_id[l] if l != -1 else -1) for l in examples["label"] ] return result return preprocess_function capitalize = lambda x: x.capitalize() preprocess_function = preprocess_function_factory(augmentation=capitalize) print(hash(preprocess_function)) # This will change on each run raw_datasets = raw_datasets.map( preprocess_function, batched=True, load_from_cache_file=True, desc="Running transformation and tokenizer on dataset", ) ``` ## Expected results Running the code twice will cause the cache to be re-used. ## Actual results Running the code twice causes the whole dataset to be re-processed
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Pre-Processing Cache Fails when using a Factory pattern ## Describe the bug If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time. ## Steps to reproduce the bug ```python def preprocess_function_factory(augmentation=None): def preprocess_function(examples): # Tokenize the texts if augmentation: conversions1 = [ augmentation(example) for example in examples[sentence1_key] ] if sentence2_key is None: args = (conversions1,) else: conversions2 = [ augmentation(example) for example in examples[sentence2_key] ] args = (conversions1, conversions2) else: args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer( *args, padding=padding, max_length=max_seq_length, truncation=True ) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [ (label_to_id[l] if l != -1 else -1) for l in examples["label"] ] return result return preprocess_function capitalize = lambda x: x.capitalize() preprocess_function = preprocess_function_factory(augmentation=capitalize) print(hash(preprocess_function)) # This will change on each run raw_datasets = raw_datasets.map( preprocess_function, batched=True, load_from_cache_file=True, desc="Running transformation and tokenizer on dataset", ) ``` ## Expected results Running the code twice will cause the cache to be re-used. ## Actual results Running the code twice causes the whole dataset to be re-processed Ok - this is still an issue but I believe the root cause is different than I originally thought. I'm now able to get caching to work consistently with the above example as long as I fix the python hash seed `export PYTHONHASHSEED=1234`
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https://github.com/huggingface/datasets/issues/3808
Pre-Processing Cache Fails when using a Factory pattern
Hi! Yes, our hasher should work with decorators. For instance, this dummy example: ```python def f(arg): def f1(ex): return {"a": ex["col1"] + arg} return f1 ``` gives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f("string1")` returns `"408c9059f89dbd6c"` on my machine). Could you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)` This could be related to https://github.com/huggingface/datasets/issues/3638.
## Describe the bug If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time. ## Steps to reproduce the bug ```python def preprocess_function_factory(augmentation=None): def preprocess_function(examples): # Tokenize the texts if augmentation: conversions1 = [ augmentation(example) for example in examples[sentence1_key] ] if sentence2_key is None: args = (conversions1,) else: conversions2 = [ augmentation(example) for example in examples[sentence2_key] ] args = (conversions1, conversions2) else: args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer( *args, padding=padding, max_length=max_seq_length, truncation=True ) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [ (label_to_id[l] if l != -1 else -1) for l in examples["label"] ] return result return preprocess_function capitalize = lambda x: x.capitalize() preprocess_function = preprocess_function_factory(augmentation=capitalize) print(hash(preprocess_function)) # This will change on each run raw_datasets = raw_datasets.map( preprocess_function, batched=True, load_from_cache_file=True, desc="Running transformation and tokenizer on dataset", ) ``` ## Expected results Running the code twice will cause the cache to be re-used. ## Actual results Running the code twice causes the whole dataset to be re-processed
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76
Pre-Processing Cache Fails when using a Factory pattern ## Describe the bug If you utilize a pre-processing function which is created using a factory pattern, the function hash changes on each run (even if the function is identical) and therefore the data will be reproduced each time. ## Steps to reproduce the bug ```python def preprocess_function_factory(augmentation=None): def preprocess_function(examples): # Tokenize the texts if augmentation: conversions1 = [ augmentation(example) for example in examples[sentence1_key] ] if sentence2_key is None: args = (conversions1,) else: conversions2 = [ augmentation(example) for example in examples[sentence2_key] ] args = (conversions1, conversions2) else: args = ( (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) ) result = tokenizer( *args, padding=padding, max_length=max_seq_length, truncation=True ) # Map labels to IDs (not necessary for GLUE tasks) if label_to_id is not None and "label" in examples: result["label"] = [ (label_to_id[l] if l != -1 else -1) for l in examples["label"] ] return result return preprocess_function capitalize = lambda x: x.capitalize() preprocess_function = preprocess_function_factory(augmentation=capitalize) print(hash(preprocess_function)) # This will change on each run raw_datasets = raw_datasets.map( preprocess_function, batched=True, load_from_cache_file=True, desc="Running transformation and tokenizer on dataset", ) ``` ## Expected results Running the code twice will cause the cache to be re-used. ## Actual results Running the code twice causes the whole dataset to be re-processed Hi! Yes, our hasher should work with decorators. For instance, this dummy example: ```python def f(arg): def f1(ex): return {"a": ex["col1"] + arg} return f1 ``` gives the same hash across different Python sessions (`datasets.fingerprint.Hasher.hash(f("string1")` returns `"408c9059f89dbd6c"` on my machine). Could you please make the example self-contained? This way, we can reproduce the bug. Additionally, you can try to find the problematic object yourself by testing their hash with `datasets.fingerprint.Hasher.hash(obj)` This could be related to https://github.com/huggingface/datasets/issues/3638.
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https://github.com/huggingface/datasets/issues/3807
NonMatchingChecksumError in xcopa dataset
Hi @afcruzs-ms, thanks for opening this separate issue for your problem. The root problem in the other issue (#3792) was a change in the service of Google Drive. But in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one. Let me look at it...
## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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NonMatchingChecksumError in xcopa dataset ## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version: Hi @afcruzs-ms, thanks for opening this separate issue for your problem. The root problem in the other issue (#3792) was a change in the service of Google Drive. But in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one. Let me look at it...
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https://github.com/huggingface/datasets/issues/3807
NonMatchingChecksumError in xcopa dataset
@afcruzs-ms, I'm not able to reproduce the issue you reported: ```python In [1]: from datasets import load_dataset ...: dataset = load_dataset("xcopa", "it") Downloading builder script: 5.21kB [00:00, 2.75MB/s] Downloading metadata: 28.6kB [00:00, 14.5MB/s] Downloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6... Downloading data: 642kB [00:00, 5.42MB/s] Dataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s] In [2]: dataset Out[2]: DatasetDict({ test: Dataset({ features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'], num_rows: 500 }) validation: Dataset({ features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'], num_rows: 100 }) }) ``` Maybe you have some issue with your cached data... Could you please try to force the redownload of the data? ```python dataset = load_dataset("xcopa", "it", download_mode="force_redownload") ```
## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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NonMatchingChecksumError in xcopa dataset ## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version: @afcruzs-ms, I'm not able to reproduce the issue you reported: ```python In [1]: from datasets import load_dataset ...: dataset = load_dataset("xcopa", "it") Downloading builder script: 5.21kB [00:00, 2.75MB/s] Downloading metadata: 28.6kB [00:00, 14.5MB/s] Downloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6... Downloading data: 642kB [00:00, 5.42MB/s] Dataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s] In [2]: dataset Out[2]: DatasetDict({ test: Dataset({ features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'], num_rows: 500 }) validation: Dataset({ features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'], num_rows: 100 }) }) ``` Maybe you have some issue with your cached data... Could you please try to force the redownload of the data? ```python dataset = load_dataset("xcopa", "it", download_mode="force_redownload") ```
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https://github.com/huggingface/datasets/issues/3807
NonMatchingChecksumError in xcopa dataset
unfortunately, i am having a similar problem with the irc_disentaglement dataset :/ my code: ``` from datasets import load_dataset dataset = load_dataset("irc_disentangle", download_mode="force_redownload") ``` however, it produces the same error as @afcruzs-ms ``` [38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0: [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls)) [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info("All the checksums matched successfully" + for_verification_name) NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master'] ``` I attempted to use the `ignore_verifications' as such: ``` ds = datasets.load_dataset('irc_disentangle', download_mode="force_redownload", ignore_verifications=True) ``` ``` Downloading builder script: 12.0kB [00:00, 5.92MB/s] Downloading metadata: 7.58kB [00:00, 3.48MB/s] No config specified, defaulting to: irc_disentangle/ubuntu Downloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5... Downloading data: 118MB [00:09, 12.1MB/s] Dataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data. 100%|██████████| 3/3 [00:00<00:00, 675.38it/s] ``` but, this returns an empty set? ``` DatasetDict({ train: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) test: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) validation: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) }) ``` not sure what else to try at this point? Thanks in advanced🤗
## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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NonMatchingChecksumError in xcopa dataset ## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version: unfortunately, i am having a similar problem with the irc_disentaglement dataset :/ my code: ``` from datasets import load_dataset dataset = load_dataset("irc_disentangle", download_mode="force_redownload") ``` however, it produces the same error as @afcruzs-ms ``` [38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0: [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls)) [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info("All the checksums matched successfully" + for_verification_name) NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master'] ``` I attempted to use the `ignore_verifications' as such: ``` ds = datasets.load_dataset('irc_disentangle', download_mode="force_redownload", ignore_verifications=True) ``` ``` Downloading builder script: 12.0kB [00:00, 5.92MB/s] Downloading metadata: 7.58kB [00:00, 3.48MB/s] No config specified, defaulting to: irc_disentangle/ubuntu Downloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5... Downloading data: 118MB [00:09, 12.1MB/s] Dataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data. 100%|██████████| 3/3 [00:00<00:00, 675.38it/s] ``` but, this returns an empty set? ``` DatasetDict({ train: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) test: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) validation: Dataset({ features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'], num_rows: 0 }) }) ``` not sure what else to try at this point? Thanks in advanced🤗
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https://github.com/huggingface/datasets/issues/3807
NonMatchingChecksumError in xcopa dataset
Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it: - #4376
## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version:
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NonMatchingChecksumError in xcopa dataset ## Describe the bug Loading the xcopa dataset doesn't work, it fails due to a mismatch in the checksum. ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("xcopa", "it") ``` ## Expected results The dataset should be loaded correctly. ## Actual results Fails with: ```python in verify_checksums(expected_checksums, recorded_checksums, verification_name) 38 if len(bad_urls) > 0: 39 error_msg = "Checksums didn't match" + for_verification_name + ":\n" ---> 40 raise NonMatchingChecksumError(error_msg + str(bad_urls)) 41 logger.info("All the checksums matched successfully" + for_verification_name) 42 NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://github.com/cambridgeltl/xcopa/archive/master.zip'] ``` ## Environment info <!-- You can run the command `datasets-cli env` and copy-and-paste its output below. --> - `datasets` version: 1.18.3, and 1.18.4.dev0 - Platform: - Python version: 3.8 - PyArrow version: Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it: - #4376
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https://github.com/huggingface/datasets/issues/3804
Text builder with custom separator line boundaries
Hi ! Interresting :) Could you give more details on what kind of separators you would like to use instead ?
**Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries.
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Text builder with custom separator line boundaries **Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries. Hi ! Interresting :) Could you give more details on what kind of separators you would like to use instead ?
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https://github.com/huggingface/datasets/issues/3804
Text builder with custom separator line boundaries
Ok I see, maybe there can be a `sep` parameter to allow users to specify what line/paragraph separator they'd like to use
**Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries.
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Text builder with custom separator line boundaries **Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries. Ok I see, maybe there can be a `sep` parameter to allow users to specify what line/paragraph separator they'd like to use
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https://github.com/huggingface/datasets/issues/3804
Text builder with custom separator line boundaries
Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset: - #3704 Let me make a PR proposal.
**Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries.
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Text builder with custom separator line boundaries **Is your feature request related to a problem? Please describe.** The current [Text](https://github.com/huggingface/datasets/blob/207be676bffe9d164740a41a883af6125edef135/src/datasets/packaged_modules/text/text.py#L23) builder implementation splits texts with `splitlines()` which splits the text on several line boundaries. Not all of them are always wanted. **Describe the solution you'd like** ```python if self.config.sample_by == "line": batch_idx = 0 while True: batch = f.read(self.config.chunksize) if not batch: break batch += f.readline() # finish current line if self.config.custom_newline is None: batch = batch.splitlines(keepends=self.config.keep_linebreaks) else: batch = batch.split(self.config.custom_newline)[:-1] pa_table = pa.Table.from_arrays([pa.array(batch)], schema=schema) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield (file_idx, batch_idx), pa_table batch_idx += 1 ``` **A clear and concise description of what you want to happen.** Creating the dataset rows with a subset of the `splitlines()` line boundaries. Thanks for requesting this enhancement. We have recently found a somehow related issue with another dataset: - #3704 Let me make a PR proposal.
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https://github.com/huggingface/datasets/issues/3792
Checksums didn't match for dataset source
Same issue with `dataset = load_dataset("dbpedia_14")` ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']
## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Checksums didn't match for dataset source ## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No Same issue with `dataset = load_dataset("dbpedia_14")` ``` NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']
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https://github.com/huggingface/datasets/issues/3792
Checksums didn't match for dataset source
I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such: `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", ignore_verifications=True)` `dataset = load_dataset("dbpedia_14", ignore_verifications=True)` This will, most probably, skip the verifications and integrity checks listed [here](https://huggingface.co/docs/datasets/loading_datasets.html#integrity-verifications)
## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Checksums didn't match for dataset source ## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No I think this is a side-effect of #3787. The checksums won't match because the URLs have changed. @rafikg @Y0mingZhang, while this is fixed, maybe you can load the datasets as such: `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", ignore_verifications=True)` `dataset = load_dataset("dbpedia_14", ignore_verifications=True)` This will, most probably, skip the verifications and integrity checks listed [here](https://huggingface.co/docs/datasets/loading_datasets.html#integrity-verifications)
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-2.094294548034668 ]
https://github.com/huggingface/datasets/issues/3792
Checksums didn't match for dataset source
Hi! Installing the `datasets` package from master (`pip install git+https://github.com/huggingface/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset("dbpedia_14", download_mode="force_redownload")`) should fix the issue.
## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Checksums didn't match for dataset source ## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No Hi! Installing the `datasets` package from master (`pip install git+https://github.com/huggingface/datasets.git`) and then redownloading the datasets with `download_mode` set to `force_redownload` (e.g. `dataset = load_dataset("dbpedia_14", download_mode="force_redownload")`) should fix the issue.
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-2.0948872566223145 ]
https://github.com/huggingface/datasets/issues/3792
Checksums didn't match for dataset source
Hi @rafikg and @Y0mingZhang, thanks for reporting. Indeed it seems that Google Drive changed their way to access their data files. We have recently handled that change: - #3787 but it will be accessible to users only in our next release of the `datasets` version. - Note that our latest release (version 1.18.3) was made before this fix: https://github.com/huggingface/datasets/releases/tag/1.18.3 In the meantime, as @mariosasko explained, you can incorporate this "fix" by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, you should force the redownload of the data (before the fix, you are just downloading/caching the virus scan warning page, instead of the data file): ```shell data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", download_mode="force_redownload")
## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Checksums didn't match for dataset source ## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No Hi @rafikg and @Y0mingZhang, thanks for reporting. Indeed it seems that Google Drive changed their way to access their data files. We have recently handled that change: - #3787 but it will be accessible to users only in our next release of the `datasets` version. - Note that our latest release (version 1.18.3) was made before this fix: https://github.com/huggingface/datasets/releases/tag/1.18.3 In the meantime, as @mariosasko explained, you can incorporate this "fix" by installing our library from the GitHub master branch: ```shell pip install git+https://github.com/huggingface/datasets#egg=datasets ``` Then, you should force the redownload of the data (before the fix, you are just downloading/caching the virus scan warning page, instead of the data file): ```shell data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", download_mode="force_redownload")
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-1.8331695795059204, 1.0427368879318237, -2.123701810836792 ]
https://github.com/huggingface/datasets/issues/3792
Checksums didn't match for dataset source
@albertvillanova by running: ``` pip install git+https://github.com/huggingface/datasets#egg=datasets data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", download_mode="force_redownload", ignore_verifications=True) ``` I had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code both `locally and on google colab`: ``` """Yields examples.""" with open(filepath, "rb") as f: data = pickle.load(f) for id_, row in enumerate(data.items()): yield id_, {"url": row[0], "article": self._process_article(row[1])} ```
## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No
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Checksums didn't match for dataset source ## Dataset viewer issue for 'wiki_lingua*' **Link:** *link to the dataset viewer page* `data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]") ` *short description of the issue* ``` [NonMatchingChecksumError: Checksums didn't match for dataset source files: ['https://drive.google.com/uc?export=download&id=11wMGqNVSwwk6zUnDaJEgm3qT71kAHeff']]() ``` Am I the one who added this dataset ? No @albertvillanova by running: ``` pip install git+https://github.com/huggingface/datasets#egg=datasets data = datasets.load_dataset("wiki_lingua", name=language, split="train[:2000]", download_mode="force_redownload", ignore_verifications=True) ``` I had a pickle error **UnpicklingError: invalid load key, '<'** in this part of code both `locally and on google colab`: ``` """Yields examples.""" with open(filepath, "rb") as f: data = pickle.load(f) for id_, row in enumerate(data.items()): yield id_, {"url": row[0], "article": self._process_article(row[1])} ```
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